Quality control system based on appliance image

Abstract

A method of inspecting a dental appliance for manufacturing defects may include obtaining an image of the dental appliance, identifying an identifier of the dental appliance, determining a digital file associated with the dental appliance from digital files based on the identified identifier, the digital file associated with the dental appliance including a digital model of an intermediate component used in a manufacturing process of the dental appliance, determining an expected characteristic of the dental appliance by digitally manipulating the digital model of the intermediate component used in the manufacturing process of the dental appliance, determining an actual characteristic of the dental appliance from the image of the dental appliance, determining whether a manufacturing defect exists in the dental appliance by comparing the expected characteristic of the dental appliance with the actual characteristic of the dental appliance, and outputting an output associated with the determination of whether a manufacturing defect exists.

Description

[0001] This application is a divisional application of the invention patent application with Chinese application number 201880074124.0 (corresponding to PCT international application number PCT / US2018 / 053564), application date September 28, 2018, and invention name “Quality control system based on orthodontic appliance image”. Technical Field

[0002] Embodiments of the present invention relate to the field of manufacturing customized products, and more particularly, to an image-based quality control system and method for customized manufactured products. Background Art

[0003] For some applications, the shell is formed around the mold to realize the negative side of the mold. The shell is then removed from the mold for further use in various applications. An example application in which the shell is formed around the mold and then used later is corrective dentistry or orthodontic treatment. In such an application, the mold can be a positive mold of the patient's dental arch, and the shell can be an aligner for aligning one or more teeth of the patient. When accessories are used, the mold can also include features related to planned orthodontic accessories and virtual fillings.

[0004] The mold can be formed using casting or a rapid prototyping device. For example, a 3D printer can use additive manufacturing technology (e.g., stereolithography) or subtractive manufacturing technology (e.g., milling) to make the mold. The appliance can then be formed on the mold using a thermoforming device. Once the appliance is formed, it can be trimmed manually or automatically. In some cases, a computer-controlled 4-axis or 5-axis trimmer (e.g., a laser trimmer or a grinder) is used to trim the appliance along the cutting line. The trimmer uses electronic data that identifies the cutting line to trim the appliance. Thereafter, the appliance can be removed from the mold and delivered to the patient. Although much of this process has been automated, further improvements can be made. Summary of the invention

[0005] A first aspect of the present disclosure may include a method, the method comprising: receiving a digital file associated with a plastic shell customized for a patient's dental arch; generating a first image of the plastic shell using one or more imaging devices; determining an inspection plan for the plastic shell based on at least one of first information associated with the first image of the plastic shell or second information associated with the digital file. The inspection plan specifies one or more additional images of the plastic shell to be generated. The method may also include executing the inspection plan to capture one or more additional images of the plastic shell, determining whether one or more defects are included in the plastic shell based at least in part on the one or more additional images, and performing quality control of the plastic shell in response to determining that the plastic shell includes one or more defects.

[0006] The second aspect of the present disclosure can further expand the first aspect of the present disclosure. In the second aspect of the present disclosure, the first image is a top view image of the plastic shell, and determining the inspection scheme includes using at least one of the first information or the second information to determine one or more features of the plastic shell, and determining one or more additional images to be generated based on the one or more features. The one or more features include the precise cutting line of the plastic appliance, the cavity of the plastic appliance associated with the attachment, the angle of the cutting line of the plastic appliance, the distance between the cavities of the plastic appliance associated with the teeth, or at least one of the distances between the cavities of the plastic appliance associated with the attachment. Determining the inspection scheme may also include determining the size of the plastic shell from at least one of the first information or the second information, and determining the settings for generating one or more additional images based on one or more features or at least one feature of the size of the plastic shell. The settings may include at least one of the orientation of one or more imaging devices, the zoom of one or more imaging devices, or the focus of one or more imaging devices. Executing the inspection scheme may include capturing one or more additional images using the settings.

[0007] The third aspect of the present disclosure may further extend the first and / or second aspects of the present disclosure. The third aspect of the present disclosure may include applying the digital file as an input to a model, generating an output by the model that identifies one or more locations of the plastic housing, the one or more locations being identified as high risk areas for one or more defects, and determining one or more additional images based on the one or more locations identified as high risk areas by the output.

[0008] The fourth aspect of the present disclosure may further extend the first to third aspects of the present disclosure. The fourth aspect of the present disclosure may include applying a digital file as an input to a trained machine learning model. The trained machine learning model is trained to identify one or more high-risk areas of one or more defects at one or more locations of a plastic shell. The fourth aspect may also include generating an output by the trained machine learning model, the output identifying one or more locations of the plastic shell identified as high-risk areas for one or more defects, and determining one or more additional images based on the one or more locations identified as high-risk areas by the output.

[0009] The fifth aspect of the present disclosure may further extend the first to fourth aspects of the present disclosure. The fifth aspect of the present disclosure may include applying a digital file as an input to a predictive model. The predictive model performs finite element analysis using the geometry of the plastic shell to calculate one or more values ​​of one or more strains of the plastic shell, and identifies one or more high-risk areas of one or more defects at one or more locations based on one or more values ​​of one or more strains exceeding a threshold at one or more locations of the plastic shell. The fifth aspect may also include: generating an output identifying one or more locations of the plastic shell through the predictive model, the one or more locations being identified as high-risk areas for one or more defects, and determining one or more additional images based on the one or more locations identified as high-risk areas by the output.

[0010] The sixth aspect of the present disclosure may further extend the first to fifth aspects of the present disclosure. The sixth aspect of the present disclosure may include: determining the inspection plan includes applying the digital file to a rule engine using one or more rules, when one or more features are included in the plastic shell at the location of the plastic shell, the one or more rules specify generating one or more additional images of the plastic shell; and executing the inspection plan includes capturing the one or more additional images.

[0011] The seventh aspect of the present disclosure may further extend the sixth aspect of the present disclosure. The seventh aspect of the present disclosure may include generating one or more rules based on at least one of the following: a) historical data including reported defects of a first group of plastic shells and locations of the reported defects on the first group of plastic shells, b) a digital file of a second group of plastic shells with a label indicating whether each of the second group of plastic shells is subject to a defect, or c) a digital file of a third group of plastic shells with a label indicating whether each of the third group of plastic shells includes a possibility of a defect in the plastic shell.

[0012] The eighth aspect of the present disclosure can further expand the first to seventh aspects of the present disclosure. In the eighth aspect of the present disclosure, determining the inspection plan includes obtaining the inspection plan from a memory location. The settings of one or more imaging devices for generating one or more additional images are preset in the inspection plan obtained from the memory location. The settings include at least one of one or more positions of one or more imaging devices for generating one or more additional images, one or more orientations of one or more imaging devices for capturing one or more additional images, one or more focal depths of one or more imaging devices for capturing one or more additional images, or the number of one or more additional images of one or more imaging devices.

[0013] The ninth aspect of the present disclosure can further expand the first to eighth aspects of the present disclosure. In the ninth aspect of the present disclosure, executing the inspection scheme to capture one or more additional images also includes: using a first imaging device among the one or more imaging devices to track the edge of the plastic shell using data from the design file of the plastic shell to capture an image subset of the one or more additional images representing the cutting line of the plastic shell.

[0014] The tenth aspect of the present disclosure can further expand the first to ninth aspects of the present disclosure. In the tenth aspect of the present disclosure, determining whether one or more defects are included in the plastic housing based at least in part on the one or more additional images also includes obtaining a digital model of the plastic housing from a digital file associated with the plastic housing, determining an approximate first characteristic of the plastic housing from the digital model of the plastic housing, determining a second characteristic of the plastic housing from the first image, and comparing the approximate first characteristic with the second characteristic.

[0015] The eleventh aspect of the present disclosure may further extend the first to tenth aspects of the present disclosure. The eleventh aspect of the present disclosure may include executing an inspection scheme to capture one or more additional images of the plastic housing, including configuring the settings of the one or more imaging devices to capture the one or more images based on at least one of the first information or the second information. The settings include at least one of the orientation of the one or more imaging devices, the position of the one or more imaging devices, the zoom of the one or more imaging devices, or the focus depth of the one or more imaging devices.

[0016] The twelfth aspect of the present disclosure can further expand the first to eleventh aspects of the present disclosure. In the twelfth aspect of the present disclosure, the first image of the plastic housing includes at least one of a photographic image, an X-ray image, or a digital image, and the one or more imaging devices include at least one of a camera, a blue laser scanner, a confocal microscope, a stereo image sensor, an X-ray device, or an ultrasound device.

[0017] A thirteenth aspect of the present disclosure may include a method comprising: obtaining one or more images of a first shell customized for a patient's dental arch; using the one or more images to identify an identifier on the first shell; and determining a first digital file associated with the first shell from a set of digital files based on the identifier, and determining an approximate first characteristic of the first shell from the first digital file. The approximate first characteristic is based on manipulation of a digital model of a mold for creating the first shell. The method also includes determining a second characteristic of the first shell from the one or more images, comparing the approximate first characteristic to the second characteristic, and performing quality control on the first shell based on the comparison.

[0018] The fourteenth aspect of the present disclosure can extend the thirteenth aspect of the present disclosure. In the fourteenth aspect of the present disclosure, the digital file includes a digital model of the first shell. In addition, the method also includes generating the digital model of the first shell by performing the following, including: expanding the digital model of the mold into an enlarged digital model, simulating a process of thermoforming a film on the digital model of the mold, calculating a projection of a cutting line onto the enlarged digital model, virtually cutting the enlarged digital model along the cutting line to create a cut enlarged digital model, and selecting an outer surface of the cut enlarged digital model.

[0019] The fifteenth aspect of the present disclosure may extend the thirteenth and / or fourteenth aspects of the present disclosure. In the fifteenth aspect of the present disclosure, the digital file includes a digital model of a mold for creating the first shell. In addition, the method also includes manipulating the digital model of the mold to determine the approximate first characteristic.

[0020] The sixteenth aspect of the present disclosure can expand the thirteenth to fifteenth aspects of the present disclosure. In the sixteenth aspect of the present disclosure, the approximate first characteristic includes an approximate outer surface of the first shell, and the second characteristic includes a first shape of the first shell.

[0021] The seventeenth aspect of the present disclosure can expand the sixteenth aspect of the present disclosure. In the seventeenth aspect of the present disclosure, one or more images include a top view image. In addition, the method further includes: determining a first plane associated with the top view image, calculating a first projection of an approximate outer surface of the first shell onto the first plane, identifying one or more differences between a second shape of the first projection and the first shape based on comparison, and determining whether the one or more differences exceed a first threshold.

[0022] The eighteenth aspect of the present disclosure may extend the seventeenth aspect of the present disclosure. In the eighteenth aspect of the present disclosure, the method further comprises determining whether the first shell is deformed based on whether the difference exceeds a first threshold.

[0023] The nineteenth aspect of the present disclosure can extend the seventeenth aspect to the eighteenth aspect of the present disclosure. In the nineteenth aspect of the present disclosure, identifying one or more differences includes determining one or more regions where the first shape and the second shape do not match, and determining at least one of the thickness of the one or more regions or the area of ​​the one or more regions.

[0024] The twentieth aspect of the present disclosure can extend the seventeenth aspect to the nineteenth aspect of the present disclosure. In the twentieth aspect of the present disclosure, the digital file includes a first digital model of a first shell, the first shell includes an approximate first characteristic, and the first digital model of the first shell has been generated based on the manipulation of the digital model of the mold. The method also includes: determining a static position of the first digital model of the first shell on a flat surface, and calculating a projection of the first digital model with a static position onto a first plane of the top view image.

[0025] The twenty-first aspect of the present disclosure can expand the twentieth aspect of the present disclosure. In the twenty-first aspect of the present disclosure, determining the static position of the first digital model on the flat surface includes: determining the center of mass of the first digital model of the first shell, determining the convex hull of the first digital model, the convex hull including a set of vertices linking the outermost points of the first digital model, and for at least one vertex in the set of vertices, performing the following, including: calculating a line containing the at least one vertex, calculating a projection of the center of mass to a point on the line, determining whether the point on the line is outside the at least one vertex, and in response to determining that the point is not outside the at least one vertex, determining that the at least one vertex is the static position of the first digital model.

[0026] The twenty-second aspect of the present disclosure can expand the twenty to twenty-first aspects of the present disclosure. In the twenty-second aspect of the present disclosure, determining the static position of the first digital model on the flat surface includes: determining the center of mass of the first digital model of the first shell, determining the convex hull of the first digital model, the convex hull including a set of faces linking the outermost points of the first digital model, and for at least one face in the set of faces, performing the following, including: calculating a plane containing the at least one face, calculating the projection of the center of mass to a point on the plane, determining whether the point on the plane is outside at least one face, and in response to determining that the point is not outside at least one face, determining that at least one face is the static position of the first shell.

[0027] The twenty-third aspect of the present disclosure can extend the seventeenth to twenty-second aspects of the present disclosure. In the twenty-third aspect of the present disclosure, the method includes: determining that one or more differences do not exceed a first threshold, generating a modified projection of an approximate outer surface of a first shell by deforming a second shape of a first projection so that a first curvature of the deformed second shape approximately matches a second curvature of the first shape, identifying one or more additional differences between the first curvature of the deformed second shape and the second curvature of the first shape, and determining whether the one or more additional differences exceed a second threshold.

[0028] The twenty-fourth aspect of the present disclosure may extend the twenty-third aspect of the present disclosure. In the twenty-fourth aspect of the present disclosure, the method includes: identifying one or more areas where the second curvature and the first curvature do not match. The one or more areas correspond to the cutting line of the first shell. The method also includes determining whether the cutting line of the first shell will interfere with the fit of the first shell on the patient's dental arch.

[0029] The twenty-fifth aspect of the present disclosure may extend the twenty-third or twenty-fourth aspect of the present disclosure. In the twenty-fifth aspect of the present disclosure, deforming the first shape of the first projection includes: identifying a middle line of the first projection; calculating a projection of a set of lines that intersect perpendicularly with the middle line; identifying points on the set of lines at intersections between each corresponding line of the set of lines and the middle line; and moving the points along the set of lines so that the first curvature of the deformed second shape approximately matches the second curvature of the first shape.

[0030] The twenty-sixth aspect of the present disclosure can expand the seventeenth aspect to the twenty-fifth aspect of the present disclosure. In the twenty-sixth aspect of the present disclosure, the one or more images also include a side view image. In addition, the method also includes: generating a modified projection of the approximate outer surface of the first shell by deforming the second shape of the first projection so that the first curvature of the deformed second shape approximately matches the second curvature of the first shape, determining a second plane associated with the side view image, deforming the approximate outer surface of the first shell according to the deformation of the second shape of the first projection, calculating a second projection of the deformed approximate outer surface of the first shell onto the second plane, determining one or more additional differences between the third shape of the first shell represented in the side view image and the fourth shape represented in the second projection, and determining whether the one or more additional differences exceed a second threshold.

[0031] A twenty-seventh aspect of the present disclosure may include a non-transitory computer-readable medium storing instructions which, when executed by a processing device, causes the processing device to: obtain one or more images of a first shell customized for a patient's dental arch; identify an identifier on the first shell using the one or more images; determine a first digital file associated with the first shell from a set of digital files based on the identifier, determine an approximate first characteristic of the first shell from the first digital file, wherein the approximate first characteristic is based on manipulation of a digital model of a mold used to create the first shell, determine a second characteristic of the first shell from the one or more images, compare the approximate first characteristic to the second characteristic, and perform quality control of the first shell based on the comparison.

[0032] The twenty-eighth aspect of the present disclosure can further expand the twenty-seventh aspect of the present disclosure. In the twenty-eighth aspect of the present disclosure, the digital file includes a digital model of the first shell. In addition, the processing device generates the digital model of the first shell by performing the following operations, including: expanding the digital model of the mold into an enlarged digital model, simulating a process of thermoforming a film on the digital model of the mold, calculating a projection of a cutting line onto the enlarged digital model, virtually cutting the enlarged digital model along the cutting line to create a cut enlarged digital model, and selecting an outer surface of the cut enlarged digital model.

[0033] The twenty-ninth aspect of the present disclosure can further expand the twenty-eighth aspect of the present disclosure. In the twenty-ninth aspect of the present disclosure, the approximate first characteristic includes an approximate outer surface of the first shell, and the second characteristic includes a shape of the first shell.

[0034] A thirtieth aspect of the present disclosure may include a system including a memory storing instructions, and a processing device coupled to the memory. Execution of the instructions causes the processing device to: obtain one or more images of a first shell customized for a patient's dental arch; use the one or more images to identify an identifier on the first shell; determine a first digital file associated with the first shell from a set of digital files based on the identifier, determine an approximate first characteristic of the first shell from the first digital file, wherein the approximate first characteristic is based on manipulation of a digital model of a mold for creating the first shell, determine a second characteristic of the first shell from the one or more images, compare the approximate first characteristic with the second characteristic, and perform quality control on the first shell based on the comparison.

[0035] The thirty-first aspect of the present disclosure can further expand the thirtieth aspect of the present disclosure. In the thirty-first aspect of the present disclosure, one or more images include a top view image, the approximate first characteristic includes the approximate outer surface of the first shell, and the second characteristic includes the first shape of the first shell. In addition, the processing device also determines a first plane associated with the top view image, calculates a first projection of the approximate outer surface of the first shell into the first plane, identifies one or more differences between the second shape of the first projection and the first shape of the first shell based on comparison, and determines whether the one or more differences exceed a first threshold.

[0036] The thirty-second aspect of the present disclosure can further expand the thirty-first aspect of the present disclosure. In the thirty-second aspect of the present disclosure, the processing device also determines that one or more differences do not exceed a first threshold, generates a modified projection of the approximate outer surface of the first shell by deforming the second shape of the first projection so that the first curvature of the deformed second shape approximately matches the second curvature of the first shape, identifies one or more additional differences between the first curvature of the deformed second shape and the second curvature of the first shape, and determines whether the one or more additional differences exceed the second threshold.

[0037] A thirty-third aspect of the present disclosure may include a method for inspecting a dental appliance for manufacturing defects. The method includes obtaining one or more images of a dental appliance, identifying an identifier of the dental appliance, determining a digital file associated with the dental appliance from a set of digital files based on the identified identifier, the digital file associated with the dental appliance including a digital model of an intermediate component used during manufacture of the dental appliance, determining an expected characteristic of the dental appliance by digitally manipulating the digital model of the intermediate component used during manufacture of the dental appliance, determining an actual characteristic of the dental appliance from the one or more images of the dental appliance, determining whether a manufacturing defect exists in the dental appliance by comparing the expected characteristic of the dental appliance with the actual characteristic of the dental appliance, and outputting an output associated with the determination of whether a manufacturing defect exists.

[0038] The thirty-fourth aspect of the present disclosure can further expand the thirty-third aspect of the present disclosure. In the thirty-fourth aspect of the present disclosure, the dental appliance includes a customized orthodontic appliance customized for a specific dental arch and a specific stage of orthodontic treatment of a specific patient, and the intermediate assembly includes a positive mold associated with the patient's specific dental arch and the specific stage of orthodontic treatment.

[0039] The thirty-fifth aspect of the present disclosure may further extend the thirty-third or thirty-fourth aspect of the present disclosure. In the thirty-sixth aspect of the present disclosure, the expected characteristics of the customized orthodontic appliance include the expected contour of the customized orthodontic appliance in the plane, and the actual characteristics of the customized orthodontic appliance include the actual contour of the customized orthodontic appliance in the plane captured by one or more images of the customized orthodontic appliance.

[0040] The thirty-sixth aspect of the present disclosure may further extend the thirty-fifth aspect of the present disclosure. The thirty-sixth aspect of the present disclosure may include comparing expected characteristics of a customized orthodontic appliance with actual characteristics, including comparing an expected profile with an actual profile and determining whether the difference exceeds a threshold.

[0041] The thirty-seventh aspect of the present disclosure can further expand the thirty-fourth to thirty-sixth aspects of the present disclosure. In the thirty-seventh aspect of the present disclosure, the expected characteristics of the customized orthodontic appliance include an expected cutting line for the customized orthodontic appliance. The actual characteristics of the customized orthodontic appliance include an actual cutting line of the customized orthodontic appliance determined from one or more images.

[0042] The thirty-eighth aspect of the present disclosure can further expand the thirty-fourth to thirty-seventh aspects of the present disclosure. In the thirty-eighth aspect of the present disclosure, an identifier of the customized orthodontic appliance is printed on the customized orthodontic appliance, and the identifier of the customized orthodontic appliance is identified by analyzing one or more images of the customized orthodontic appliance.

[0043] The thirty-ninth aspect of the present disclosure may further expand the thirty-fourth to thirty-eighth aspects of the present disclosure. In the thirty-ninth aspect of the present disclosure, identifying an identifier of the customized orthodontic appliance includes receiving a user input of the identifier.

[0044] The 40th aspect of the present disclosure can further expand the 34th to 39th aspects of the present disclosure. In the 40th aspect of the present disclosure, the method further includes: determining an inspection plan for the customized orthodontic appliance based on one or more images of the customized orthodontic appliance obtained or a digital file associated with the customized orthodontic appliance.

[0045] The forty-first aspect of the present disclosure may further extend the forty-first aspect of the present disclosure. In the forty-first aspect of the present disclosure, the examination plan specifies that one or more additional images of the customized orthodontic appliance are to be captured.

[0046] The forty-second aspect of the present disclosure can further expand the fortieth to forty-first aspects of the present disclosure. In the forty-second aspect of the present disclosure, the inspection plan is based on a digital file associated with the customized orthodontic appliance, and the inspection plan is determined by a predictive model that identifies the position of the customized orthodontic appliance that is at a higher risk for one or more defects.

[0047] The 43rd aspect of the present disclosure can further expand the 34th to 42nd aspects of the present disclosure. In the 43rd aspect of the present disclosure, the desired characteristics of the customized orthodontic appliance are determined by digitally manipulating a portion of the surface of the digital model of the mold to approximate the surface of the customized orthodontic appliance.

[0048] The forty-fourth aspect of the present disclosure may further extend the forty-third aspect of the present disclosure. In the forty-fourth aspect of the present disclosure, the surface of the customized orthodontic appliance is approximated by offsetting the portion of the surface of the digital model of the mold by a distance.

[0049] The forty-fifth aspect of the present disclosure can further extend the forty-third to forty-fourth aspects of the present disclosure. In the forty-fifth aspect of the present disclosure, the expected characteristics of the customized orthodontic appliance are determined by virtually projecting a cutting line associated with the customized orthodontic appliance onto an approximate surface of the customized orthodontic appliance.

[0050] The forty-sixth aspect of the present disclosure can further expand the forty-third to forty-fifth aspects of the present disclosure. In the forty-sixth aspect of the present disclosure, the expected characteristics of the customized orthodontic appliance include the expected contour of the customized orthodontic appliance in a plane, and the expected contour of the customized orthodontic appliance is determined by calculating the contour of the approximate surface of the customized orthodontic appliance in the plane.

[0051] The 47th aspect of the present disclosure can further expand the 34th to 46th aspects of the present disclosure. In the 47th aspect of the present disclosure, the expected characteristics of the customized orthodontic appliance are determined by calculating the contour of the digital model of the mold in the plane.

[0052] The forty-eighth aspect of the present disclosure can further expand the forty-seventh aspect of the present disclosure. In the forty-eighth aspect of the present disclosure, the expected characteristics of the customized orthodontic appliance include an expected contour of the customized orthodontic appliance in a plane, and the expected contour of the customized orthodontic appliance is calculated by offsetting the perimeter of the contour of the digital model of the calculated mold in the plane.

[0053] The forty-ninth aspect of the present disclosure may further expand upon the thirty-third to forty-eighth aspects of the present disclosure. In the forty-ninth aspect of the present disclosure, the dental appliance comprises a mandibular advancement feature.

[0054] The fiftieth aspect of the present disclosure may further extend the thirty-third to forty-ninth aspects of the present disclosure. In the fiftieth aspect of the present disclosure, the output includes determining that a defect exists, and the output includes a suggested digital modification to a digital model of an intermediate component used during manufacture of the dental appliance to limit future defects.

[0055] The fifty-first aspect of the present disclosure may further extend the fifty-first aspect of the present disclosure. In the fifty-first aspect of the present disclosure, the suggested digital modification to the digital model of the intermediate component includes at least one of added virtual fill material, correction to the cutting line, and modification to one or more accessories of the intermediate component.

[0056] A fifty-second aspect of the present disclosure may include a method for inspecting a dental appliance for manufacturing defects, the dental appliance being configured to be applied to a dental arch of a patient, the method comprising: obtaining one or more images of the dental appliance, identifying an identifier of the dental appliance, determining a digital file associated with the dental appliance from a set of digital files based on the identified identifier, the digital file associated with the dental appliance comprising a digital model of the dental appliance, generating the digital model of the dental appliance by digitally manipulating the digital model of the patient's dental arch, determining expected characteristics of the dental appliance from the digital model of the dental appliance, determining actual characteristics of the dental appliance from one or more images, determining whether there is a manufacturing defect in the dental appliance by comparing the expected characteristics of the dental appliance with the actual characteristics of the dental appliance, and outputting an output associated with the determination of whether there is a manufacturing defect.

[0057] The fifty-third aspect of the present disclosure can further expand the fifty-second aspect of the present disclosure. In the fifty-third aspect of the present disclosure, the dental appliance includes a customized orthodontic appliance customized for a specific dental arch of a specific patient and a specific stage of orthodontic treatment, and the digital model of the customized orthodontic appliance is generated by manipulating the digital model of the staged dental arch of the patient associated with the specific stage of orthodontic treatment.

[0058] The fifty-fourth aspect of the present disclosure can further expand the fifty-third aspect of the present disclosure. In the fifty-fourth aspect of the present disclosure, the expected characteristics of the customized orthodontic appliance include the expected contour of the customized orthodontic appliance in the plane, and the actual characteristics of the customized orthodontic appliance include the actual contour of the customized orthodontic appliance in the plane captured by one or more images of the customized orthodontic appliance.

[0059] The fifty-fifth aspect of the present disclosure may further extend the fifty-third and / or fifty-fourth aspects of the present disclosure. In the fifty-fifth aspect of the present disclosure, the expected characteristics of the customized orthodontic appliance include an expected cutting line of the customized orthodontic appliance, wherein the actual characteristics of the customized orthodontic appliance include an actual cutting line of the customized orthodontic appliance determined from one or more images.

[0060] The fifty-sixth aspect of the present disclosure can further expand the fifty-third to fifty-fifth aspects of the present disclosure. In the fifty-sixth aspect of the present disclosure, the method further includes: determining an inspection plan for the customized orthodontic appliance based on one or more images of the customized orthodontic appliance obtained or a digital file associated with the customized orthodontic appliance.

[0061] The fifty-seventh aspect of the present disclosure can further expand the fifty-third to fifty-sixth aspects of the present disclosure. In the fifty-seventh aspect of the present disclosure, an identifier of the customized orthodontic appliance is printed on the customized orthodontic appliance, and wherein the identifier of the customized orthodontic appliance is identified by analyzing one or more images of the customized orthodontic appliance or by receiving user input of the identifier.

[0062] The fifty-eighth aspect of the present disclosure may further expand the fifty-second to fifty-seventh aspects of the present disclosure.In the fifty-eighth aspect of the present disclosure, the dental appliance includes a removable palatal expander.

[0063] The fifty-ninth aspect of the present disclosure may further expand the fifty-second to fifty-eighth aspects of the present disclosure. In the fifty-ninth aspect of the present disclosure, the dental appliance includes a removable surgical fixation device.

[0064] The sixtieth aspect of the present disclosure may further expand upon the fifty-second to fifty-ninth aspects of the present disclosure.In the sixtieth aspect of the present disclosure, the dental appliance comprises a removable mandibular advancement feature.

[0065] The sixty-first aspect of the present disclosure may include a method for inspecting a customized orthodontic appliance for manufacturing defects, the customized orthodontic appliance being customized for a specific dental arch of a specific patient and a specific stage of orthodontic treatment, the method comprising: obtaining one or more images of the customized orthodontic appliance, identifying an identifier of the customized orthodontic appliance, determining expected characteristics of the customized orthodontic appliance based on the identifier of the customized orthodontic appliance, determining the expected characteristics of the customized orthodontic appliance by digitally manipulating a digital model of a mold used during manufacture of the customized orthodontic appliance, determining actual characteristics of the customized orthodontic appliance from one or more images, determining whether there are manufacturing defects in the customized orthodontic appliance by comparing the expected characteristics of the customized orthodontic appliance with the actual characteristics of the customized orthodontic appliance, and outputting an output associated with the determination of whether there are manufacturing defects.

[0066] The sixty-second aspect of the present disclosure can further expand the sixty-first aspect of the present disclosure. In the sixty-second aspect of the present disclosure, the expected characteristics of the customized orthodontic appliance include the expected contour of the customized orthodontic appliance in the plane, and the actual characteristics of the customized orthodontic appliance include the actual contour of the customized orthodontic appliance in the plane captured by one or more images of the customized orthodontic appliance.

[0067] The sixty-third aspect of the present disclosure can further expand the sixty-first to sixty-second aspects of the present disclosure. In the sixty-third aspect of the present disclosure, the expected characteristics of the customized orthodontic appliance include an expected cutting line of the customized orthodontic appliance, wherein the actual characteristics of the customized orthodontic appliance include an actual cutting line of the customized orthodontic appliance determined from one or more images.

[0068] The sixty-fourth aspect of the present disclosure can further expand the sixty-first to sixty-third aspects of the present disclosure. In the sixty-fourth aspect of the present disclosure, the method further includes: determining an inspection plan for the customized orthodontic appliance based on one or more images of the customized orthodontic appliance obtained or a digital file associated with the customized orthodontic appliance.

[0069] The sixty-fourth aspect of the present disclosure can further expand the sixty-first to sixty-fourth aspects of the present disclosure. In the sixty-fourth aspect of the present disclosure, an identifier of the customized orthodontic appliance is printed on the customized orthodontic appliance, and wherein the identifier of the customized orthodontic appliance is identified by analyzing one or more images of the customized orthodontic appliance or by receiving user input of the identifier. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The invention is illustrated by way of example and not limitation in the figures of the accompanying drawings.

[0071] Figure 1A-1B A flow chart of a method for performing image-based quality control of a housing using an inspection protocol according to one embodiment is shown.

[0072] Figure 2 An example imaging system including a top view camera and a side view camera is shown according to one embodiment.

[0073] Figure 3A-3B An example top view image and example movement controls and screen paths generated based on the top view image are shown according to one embodiment.

[0074] Figures 4A-4C An example side view composite image of a side of a housing, an edge detected using the side view composite image, and a comparison of the edge to a second edge obtained from a digital model of the housing are shown according to one embodiment.

[0075] Figure 5 A flow chart of a method for determining an inspection plan based on features of a plastic housing according to an embodiment is shown.

[0076] Figure 6 A flow chart of a method for determining to generate one or more additional images based on output from a model according to one embodiment is shown.

[0077] Figure 7 A flow chart of a method for determining an inspection plan using a rule engine according to one embodiment is shown.

[0078] Figures 8A-8B A flow chart of a method for performing image-based quality control for a housing according to one embodiment is shown.

[0079] Figures 9A-9B A flow chart of a method for determining whether the shape of a housing is deformed according to one embodiment is shown.

[0080] Fig.10 A digital model of an appliance is shown projected onto an image of the appliance according to one embodiment.

[0081] Fig.11 A flow chart showing a method for determining a shape difference between a digital model of a housing and an image of the housing according to one embodiment.

[0082] Fig.12 A user interface for image-based quality control of a housing is shown according to one embodiment.

[0083] Figures 13A-13B An example comparison of the contour of a digital model of an appliance with the contour of an image of the appliance to detect deformation is shown according to one embodiment.

[0084] Figures 14A-14C A flow chart illustrating a method of deforming a digital model contour to more closely match an image contour of an appliance to detect cut line changes according to one embodiment.

[0085] Figures 15A-15C An example of deforming a digital model contour to more closely match an image contour of an appliance to detect cut line changes is shown according to one embodiment.

[0086] Fig.16 A flow chart of a method for generating a digital model of a housing according to one embodiment is shown.

[0087] Fig.17 A flow chart illustrating a general method of determining a rest position of a digital model of a housing on a flat surface according to one embodiment.

[0088] Fig.18 A flow chart of a method of using a two-dimensional digital model to determine a stationary position of a digital model of a housing on a flat surface is shown according to one embodiment.

[0089] Figures 19A-19C Example images for determining the resting position of an appliance on a flat surface are shown according to one embodiment.

[0090] Fig. 20 A flow chart of a method for determining a stationary position of a digital model of a housing on a flat surface using a three-dimensional digital model is shown according to one embodiment.

[0091] Fig.21 A block diagram of an example computing device is shown according to one embodiment.

[0092] Fig.22A An example side view image captured without a back screen and without structured light illumination is shown.

[0093] Fig. 22B An example side view image captured using structured light illumination (eg, focused light) without a back screen is shown according to one embodiment.

[0094] Fig.23 An example of crack detection in the contour of an image of an appliance captured using focused light is shown according to one embodiment. DETAILED DESCRIPTION

[0095] Described herein are embodiments of systems, methods and / or computer-readable media covering image-based quality control (IBQC) for customized manufactured products. The customized manufactured product may be a customized medical device. For example, in some embodiments, an image-based quality control system and method may be implemented in the inspection of an orthodontic appliance after manufacture. The quality control of customized manufactured products is particularly difficult, especially in the manufacture of orthodontic appliances, which must be individually customized for each patient. In addition, each appliance in a series of appliances for treating a single patient is unique compared to other appliances in the same series because each appliance is specific to a different treatment phase. Further complicating the problem is that for each treatment phase, each patient receives a pair of appliances, a unique appliance for treating the upper dental arch and a unique appliance for treating the lower dental arch. In some cases, a single treatment may include 50-60 phases for treating complex cases, which means 100-120 appliances uniquely manufactured for a single patient. When appliances are manufactured for patients all over the world, hundreds of thousands of completely unique and customized appliances may need to be manufactured every day. Therefore, quality control of customized manufactured products can be a particularly arduous task. Quality control of manufactured braces can be performed to ensure that the braces are defect-free or that the defects are within a tolerable threshold. The goal of the quality control process can be to detect one or more of the following quality issues: dental arch changes, bends, cut line changes, debris, webbing, trimmed accessories, missing accessories, etc. Typically, technicians manually perform a quality control process to inspect the braces. However, this manual quality control process can be very time consuming and prone to errors due to the inherent subjectivity of the technician. Therefore, embodiments of the present invention can provide a more scalable, automated and / or objective braces quality control process.

[0096] It should be noted that "aligner" and "shell" are used interchangeably herein. As described above, embodiments can detect various quality issues for a given set of appliances. Quality issues can include one or more of the following: dental arch changes, deformations, bending (compression or expansion) of the appliance, changes in cutting lines, debris, sidebands, trimmed attachments, missing attachments, burrs, flaring, power ridge problems, material breaks, short hooks, bubbles, etc. Detecting quality issues can enable the repair of the appliance to eliminate quality issues, prevent the delivery of deformed or substandard appliances, and / or remanufacture of deformed appliances before delivery. In some embodiments, the identification of appliance quality issues can be based on an image of the appliance compared to a digitally generated model of each appliance. In some embodiments, the digital model of each appliance can be included in a digital file associated with the appliance. Optionally, the digital file associated with the manufactured appliance can provide digitally approximated properties of the manufactured appliance (e.g., the outer surface of the appliance, a two-dimensional projection of the outer surface onto a plane, etc.). The characteristics of the digitally generated model of the appliance and / or the digital approximation of the appliance can be based on the manipulation of the digital model of the mold used to create the appliance. In some embodiments, the identification of an appliance quality problem can be based on comparing a first characteristic of the approximation of the appliance (such as determined based on the manipulation of the digital model of the mold used to make the appliance) and a determined second characteristic of the appliance (such as determined from one or more images of the appliance). Some advantages of the disclosed embodiments may include automatic detection of various appliance quality problems and automatic collection of data for statistical analysis. The embodiments may also improve detection results by eliminating human errors (e.g., false positives and false negatives). In addition, the embodiments may reduce the amount of time spent performing quality control, thereby reducing the lead-time of the appliance, which may enable the appliance to be distributed to customers on schedule.

[0097] Various software and / or hardware components may be used to implement the disclosed embodiments. For example, a software component may include computer instructions stored in a tangible, non-transitory computer-readable medium that are executed by one or more processing devices to perform image-based quality control on a customized manufactured housing (e.g., an appliance). The software may set up and calibrate a camera included in the hardware component, use the camera to capture images of the appliance from various angles, generate a digital model of the appliance, perform analysis that compares the digital model of the appliance to an image of the appliance to detect one or more quality issues (e.g., deformation, changes in cut lines, etc.), and classify the appliance based on the analysis results.

[0098] In some embodiments, a digital file associated with a shell that is customized for a patient's dental arch and is being inspected can be received. In some embodiments, the dental appliance includes identification information, such as a customized barcode or part identification number. A first image (e.g., a photographic image, an X-ray image, a digital image) of a plastic shell can be generated using one or more imaging devices (e.g., a camera, a blue laser scanner, a confocal microscope, a stereo image sensor, an X-ray device, an ultrasonic device, etc.). Dental appliance identification information can be captured in the first image and interpreted by the inspection system. Optionally, a technician can also manually enter this information at the inspection site so that the inspection system can obtain a digital file. In another embodiment, a dental appliance sorting system can sort a series of dental appliances in a known order. The inspection system can obtain a dental appliance order from the dental appliance sorting system to know which dental appliances are currently being inspected and the order in which they arrive at the site. Optionally, the dental appliance can arrive at the inspection system in a tray carrying dental appliance identification information (e.g., an RFID tag, a barcode, a serial number, etc.) read by the inspection system. Thereafter, the inspection system may retrieve a digital file associated with the dental appliance based on the dental appliance identification information.

[0099] An inspection recipe for a plastic shell may be determined based on at least one of the first information associated with the first image of the plastic shell or the second information associated with the digital file. The inspection recipe may specify that one or more additional images of the plastic shell (if any) are to be generated, and may specify the settings of one or more imaging devices (e.g., zoom, orientation, focus, etc.). The first information and / or the second information may indicate one or more of the following: the shape of the appliance, the size of the appliance, one or more features of the appliance, a higher risk area for defects, one or more defects (e.g., deformation of the appliance, cracks, etc.), etc., which may be used to determine the additional images to be captured for the inspection recipe. Various defects of the plastic shell may be determined based on the first image and / or one or more additional images. For example, when the first image is a top view image of the plastic appliance, deformation of the plastic shell may be detected, as discussed further below. If the additional image is a side view of the plastic shell, bubbles present on the surface of the plastic shell may be detected and / or inaccurate cutting lines may be detected.

[0100] The inspection plan can be dynamically determined based on the first information and / or the second information, or the inspection plan can be predetermined for the plastic shell and retrieved from a memory location. For example, determining the inspection plan can include using at least one of the first information or the second information to determine one or more features of the plastic shell (e.g., precise cut lines, presence of attachment wells, angles of cut lines, crowding between teeth, crowding between attachment wells, etc.), and determining one or more additional images to be generated based on the one or more features. In addition, the size and / or shape of the plastic shell can be determined from the first information and / or the second information, and the settings of the imaging device used to generate the additional images (e.g., orientation, zoom, focus) can be determined based on the one or more features, sizes, and / or shapes of the plastic shell.

[0101] In some embodiments, in order to determine the inspection plan, the digital file of the plastic shell can be applied as an input to a model (e.g., a predictive model, a machine learning model, etc.) or a rule engine. The model or rule engine can generate an output that identifies one or more locations of the plastic shell, which are identified as high-risk areas for one or more defects. In one example, the model can be a predictive model that performs a numerical simulation on the digital file of the plastic appliance by applying one or more forces on the plastic appliance to simulate the removal process of the plastic appliance from the patient's dental arch. The predictive model can calculate the strain value and the force value applied to cause the strain value. If the strain value or the force value exceeds a threshold at a certain position on the plastic appliance, the position can be determined as a high-risk area for defects. In another example, the model can be a machine learning model that is trained to identify the location of the high-risk area. The machine learning model can be applied to the first image of the appliance and / or the digital file of the appliance, and can generate an output indicating one or more high-risk areas of defects at the position on the appliance. When one or more features are included at one or more positions, the rule engine can use one or more rules that specify that one or more positions are high-risk areas for one or more defects. One or more additional images for the inspection plan can be determined based on the one or more locations identified as high risk areas by the output. Additional details of methods and systems for identifying specific appliance areas for inspection can be found in co-pending U.S. Provisional Application No. 62 / 737,458, filed on September 27, 2018, the entire contents of which are incorporated herein by reference.

[0102] Further determining the inspection scheme may include determining the settings of one or more imaging devices for capturing one or more additional images based on at least one of the first information associated with the first image or the second information associated with the digital file. In one example, the first information and / or the second information may include the size and / or shape of the plastic housing, which may be used to determine the zoom setting and / or the focus setting of the one or more imaging devices. In another example, the first information and / or the second information may include a feature of the plastic housing (e.g., the angle of the cut line), which may be used to determine the orientation of the one or more imaging devices (e.g., the angle at which the image of the feature is generated). In addition, if it is determined that a side view image is to be generated, the settings may include determining the number of images to be generated to be synthesized together to form a composite two-dimensional (2D) image or a three-dimensional (3D) image of the side of the plastic housing as a side panorama of the plastic appliance. The settings of one or more imaging devices may be different to allow the imaging device to capture different images, which may enable the detection of different defects. For example, in order to detect bubbles in a plastic housing, the zoom setting may be configured to a specific micron setting (e.g., 10 microns to 30 microns), while another zoom setting may enable the detection of another type of defect (e.g., burrs).

[0103] The inspection scheme may be executed using the determined settings to capture one or more additional images of the plastic housing. Based on at least one of the first image and / or the one or more additional images, it may be determined whether the plastic housing includes one or more defects. Quality control may be performed on the plastic housing in response to determining that the plastic housing includes one or more defects.

[0104] In some embodiments, detecting defects can include comparing aspects of a digital file of a mold associated with the appliance with aspects of a first image and / or an additional image of the appliance. For example, an approximate first characteristic of the appliance (e.g., an approximate outer surface of the appliance) can be determined from a digital file of a mold associated with the appliance. The approximate first characteristic can be determined based on the manipulation of a digital model used to create a mold of the appliance. Moreover, a second characteristic of the appliance (e.g., a shape of the appliance in the captured image) can be determined from the captured image. The approximate first characteristic and the second characteristic can be compared. The comparison can include calculating a projection of an approximate outer surface of the appliance (e.g., a digital model) into the same plane as the shape of the appliance, and identifying an area between a contour of the approximate outer surface and the shape of the appliance in the image. If the size of the area (e.g., thickness or area) exceeds a threshold, it can be determined that the appliance is deformed. If the size is within the threshold, another comparison can be performed that deforms the curvature of the dental arch in the digital model or the approximate projection toward the curvature of the dental arch of the manufactured appliance in the appliance image. Once deformed, other contours or other approximated features of the digital model can be compared to an image of the appliance or other image of the manufactured appliance to determine if the cut line or other feature of the imaged appliance matches within a threshold. If not, the cut line or other feature can be determined to be defective. Other appliance features that can be analyzed can include debris, webbing, trimmed attachments, and missing attachments, among others. The software can also determine how the manufactured appliance is positioned on a two-dimensional plane and adjust the projection of the digital model or approximated feature and the image of the manufactured appliance accordingly for comparative analysis.

[0105] In some embodiments, a digital model of the appliance can be generated as part of a manufacturing process for the appliance, and the digital model can be received as input. In addition, a user interface can be provided that displays the image-based quality control process (e.g., a digital model of the appliance, an image of the appliance, a comparison of the digital model with the image) and the results (e.g., measurements, classifications).

[0106] The hardware component may include a platform with a fixed table or a rotating table for positioning and image capture of the appliance, a camera setting (e.g., positioning), and / or a lighting system for uniform exposure and image capture using uniform environmental parameters. In addition, the hardware component may also enable the automatic feeding of components (e.g., appliances) into a site where image-based quality control is being performed, and may be sorted at the exit of the site. The image of the appliance may be obtained from one or several projections. Therefore, the hardware component may include a fixed table or a rotating table and one or more cameras with adjustable positioning. Adjusting the configuration of the hardware component may enable the acquisition of appliance images (e.g., top view, side view, diagonal view, etc.) from different angles. In one embodiment, the first camera may be positioned at an angle that enables the capture of a top view image of the appliance being analyzed, and the second camera may be configured to capture one or more side view images or diagonal view images based on the setting. The hardware component may also include a blue laser scanner comprising a camera, a background screen, and an illumination device to obtain an image of the housing. Specific information (e.g., a second characteristic) may be extracted from the image. A blue laser can be applied at an angle to the surface of the appliance, and a blue beam (e.g., having a wavelength of approximately 440-490 nm) can be generated, which is received by a camera to generate an image of the plastic shell. Depth information from the image can be extracted to obtain desired information (e.g., a second characteristic). The hardware can also include a robot-guided camera that uses a design file to guide the camera to generate an image of the appliance being analyzed. The image can be an image of the edge / cut line of the appliance. The hardware can also include an ultrasonic device that emits sound waves to measure the thickness of the appliance. Measuring the thickness of the appliance can enable quality trend analysis and detection of defects related to thickness. The hardware can also include a stereo image sensor to obtain a three-dimensional (3D) image of the plastic shell. The hardware can also include a confocal microscope for obtaining images of the appliance at various depths of focus. The hardware can also include an X-ray device for scanning a clear plastic appliance at various cross-sections and obtaining images of the clear plastic appliance. Specific current and voltage settings can be used to scan cross-sections of a clear plastic appliance and obtain images of the clear plastic appliance.

[0107] Some embodiments are discussed herein with reference to orthodontic appliances (also referred to simply as appliances). However, embodiments also extend to other types of shells formed on molds, such as orthodontic retainers, orthodontic splints, oral inserted sleep appliances (e.g., for minimizing snoring, sleep apnea, etc.) and / or shells for non-dental applications. Other applications can be found when examining 3D printed palatal expanders, removable mandibular repositioning devices, and removable surgical fixtures. Therefore, it should be understood that the embodiments of appliances described herein are also applicable to other types of dental appliances. For example, the principles, features, and methods discussed can be applied to any application or process in which it is useful to perform image-based quality control on any suitable type of custom device (such as eyeglass frames, contact lenses or glass lenses, hearing aids or plugs, artificial knees, prostheses and devices, orthopedic inserts) and protective devices (such as knee pads, mobile cups, or elbows, chin and calf protectors and other similar sports / protective devices).

[0108] In some embodiments, a mold of the patient's dental arch can be made and the shell can be formed on the mold. Fig.21 The manufacturing of the mold can be performed by processing logic of a computing device such as a computing device in the computer system. The processing logic can include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed by a processing device), firmware, or a combination thereof. For example, one or more operations can be performed by a processing device executing a computer-aided drawing (CAD) program or module.

[0109] In order to make the mold, the shape of the patient's dental arch at the treatment stage is determined based on the treatment plan. In the example of orthodontics, a treatment plan can be generated based on an intraoral scan of the dental arch to be modeled. An intraoral scan of the patient's dental arch can be performed to generate a three-dimensional (3D) virtual model (mold) of the patient's dental arch. For example, a full scan of the patient's mandibular and / or maxillary arch can be performed to generate its 3D virtual model. Intraoral scanning can be performed by creating multiple overlapping intraoral images from different scanning stations and then stitching the intraoral images together to provide a composite 3D virtual model. In other applications, a virtual 3D model can also be generated based on a scan of the object to be modeled or based on the use of computer-aided drawing technology (e.g., for designing a virtual 3D mold). Alternatively, an initial negative film (e.g., a dental impression, etc.) can be generated from the actual object to be modeled. The negative film can then be scanned to determine the shape of the positive film to be produced.

[0110] Once the virtual 3D model of the patient's dental arch is generated, the dentist can determine the desired treatment results including the final position and orientation of the patient's teeth. Then, the processing logic can determine multiple treatment stages to progress the teeth from the starting position and orientation to the target final position and orientation. The shape of the final virtual 3D model and each intermediate virtual 3D model can be determined by calculating the progress of tooth movement in the entire orthodontic treatment from the initial tooth placement and orientation to the final corrected tooth placement and orientation. For each treatment stage, a separate virtual 3D model of the patient's dental arch at that treatment stage can be generated. The shape of each virtual 3D model will be different. The original virtual 3D model, the final virtual 3D model, and each intermediate virtual 3D model are unique and customized for the patient.

[0111] Thus, multiple different virtual 3D models can be generated for a single patient. The first virtual 3D model can be a unique model of the patient's dental arch and / or teeth as currently presented by the patient, and the final virtual 3D model can be a model of the patient's dental arch and / or teeth after correction of one or more teeth and / or jaws. Multiple intermediate virtual 3D models can be modeled, each of which can be incrementally different from the previous virtual 3D model.

[0112] Each virtual 3D model of a patient's dental arch can be used to generate a unique, customized physical mold of the dental arch at a particular stage of treatment. The shape of the mold can be based at least in part on the shape of the virtual 3D model at that stage of treatment. The virtual 3D model can be represented in a file such as a computer-aided drawing (CAD) file or a 3D printable file such as a stereolithography (STL) file. The virtual 3D model of the mold can be sent to a third party (e.g., a clinician's office, a laboratory, a manufacturing plant, or other entity). The virtual 3D model can include instructions that will control a manufacturing system or device to produce a mold with a specified geometry.

[0113] A clinician's office, laboratory, manufacturing plant, or other entity can receive a virtual 3D model of the mold, i.e., a digital model that has been created as described above. The entity can input the digital model into a rapid prototyping machine. The rapid prototyping machine then uses the digital model to manufacture the mold. An example of a rapid prototyping machine is a 3D printer. 3D printing includes any layer-based additive manufacturing process. 3D printing can be achieved using additive processes, in which continuous layers of material are formed in a specified shape. 3D printing can be performed using extrusion deposition, granular material bonding, lamination, photopolymerization, continuous liquid interface production (CLIP), or other techniques. 3D printing can also be achieved using subtractive processes (e.g., milling).

[0114] In some cases, stereolithography (SLA), also known as optical manufacturing solid imaging, is used to make SLA molds. In SLA, molds are made by printing thin layers of photocurable materials (e.g., polymer resins) one on top of another. The platform rests in a bath of liquid photopolymer or resin, just below the surface of the bath. A light source (e.g., an ultraviolet laser) tracks a pattern on the platform, curing the photopolymer pointed at by the light source to form the first layer of the mold. The platform is lowered in increments, and the light source tracks a new pattern on the platform to form another layer of the mold at each increment. The process is repeated until the mold is completely manufactured. Once all layers of the mold are formed, the mold can be cleaned and cured.

[0115] Materials such as polyester, copolyester, polycarbonate, polycarbonate, thermoplastic polyurethane, polypropylene, polyethylene, polypropylene and polyethylene copolymers, acrylic, cyclic block copolymers, polyetheretherketone, polyamide, polyethylene terephthalate, polybutylene terephthalate, polyetherimide, polyethersulfone, polytrimethylene terephthalate, styrene block copolymer (SBC), silicone rubber, elastomeric alloy, thermoplastic elastomer (TPE), thermoplastic vulcanizate (TPV) elastomer, polyurethane elastomer, block copolymer elastomer, polyolefin blend elastomer, thermoplastic copolyester elastomer, thermoplastic polyamide elastomer or a combination thereof can be used to directly form the mold. The material used to make the mold can be provided in an uncured form (e.g., as a liquid, resin, powder, etc.) and can be cured (e.g., by photopolymerization, photocuring, gas curing, laser curing, crosslinking, etc.). The properties of the material before curing may be different from the properties of the material after curing.

[0116] An appliance can be formed from each mold and, when applied to the patient's teeth, can provide forces to move the patient's teeth as prescribed by the treatment plan. The shape of each appliance is unique and customized for a specific patient and a specific stage of treatment. In one example, the appliance can be pressure molded or thermoformed on the mold. Each mold can be used to make an appliance that will apply forces to the patient's teeth at a specific stage of orthodontic treatment. Each appliance has a tooth receiving cavity that receives a tooth and resiliently repositions the tooth according to a specific stage of treatment.

[0117] In one embodiment, a sheet of material is press formed or thermoformed on a mold. The sheet can be, for example, a plastic sheet (e.g., an elastic thermoplastic, a sheet of polymer material, etc.). In order to thermoform the housing on the mold, the sheet of material can be heated to a temperature at which the sheet becomes pliable. Pressure can be simultaneously applied to the sheet to form the now pliable sheet around the mold. Once the sheet cools, it will have a shape that conforms to the mold. In one embodiment, a release agent (e.g., a non-stick material) is applied to the mold before forming the housing. This can facilitate the subsequent removal of the mold from the housing.

[0118] Additional information may be added to the appliance. The additional information may be any information related to the appliance. Examples of such additional information include a component number identifier, patient name, patient identifier, case number, sequence identifier (e.g., indicating which appliance a particular liner is in a treatment sequence), date of manufacture, clinician name, logo, etc. For example, after the appliance is thermoformed, the appliance may be laser marked with a component number identifier (e.g., serial number, barcode, etc.). In some embodiments, the system may be configured to read (e.g., optically, magnetically, etc.) an identifier (barcode, serial number, electronic tag, etc.) of a mold to determine the component number identifier associated with the appliance formed thereon. After determining the component number identifier, the system may then mark the appliance with a unique component number identifier. The component number identifier may be computer readable and may associate the appliance with a particular patient, a particular stage in a treatment sequence, an upper shell or a lower shell, a digital model representing a mold from which the appliance was manufactured, and / or a digital file including a virtually generated digital model of the appliance or its approximation properties (e.g., generated by approximating the outer surface of the appliance based on manipulating the digital model of the mold, expanding or scaling projections of the mold in different planes, etc.). In some embodiments, the virtually generated digital model of the appliance or its approximation properties may be compared to properties of the manufactured appliance (e.g., the shape of the appliance) determined from an image of the manufactured appliance for image-based quality control, as described below with reference to FIGS. 1 and 2. Fig. 8A and Figure 8B Described in more detail.

[0119] After the appliance is formed on the mold for the treatment stage, the appliance is then trimmed along the cutting line (also called the trimming line) and the appliance can be removed from the mold. The processing logic determines the cutting line of the appliance. The determination of the cutting line can be based on a virtual 3D model of the dental arch for a specific treatment stage, based on a virtual 3D model of the appliance to be formed on the dental arch, or based on a combination of a virtual 3D model of the dental arch and a virtual 3D model of the appliance. The location and shape of the cutting line are important for the function of the appliance (e.g., the ability of the appliance to apply the desired force to the patient's teeth) and the fit and comfort of the appliance. For shells such as orthodontic braces, orthodontic retainers, and orthodontic splints, the trimming of the shell plays an important role in the efficacy of the intended purpose of the shell (e.g., aligning, retaining, or positioning one or more teeth of the patient) and the fit of the shell on the patient's dental arch. For example, if the shell is trimmed too much, the shell may lose rigidity and the ability of the shell to apply force to the patient's teeth may be impaired.

[0120] On the other hand, if the shell is trimmed too little, some parts of the shell may hit the patient's gums and cause discomfort, swelling and / or other dental problems. In addition, if the shell is trimmed too little in a position, the shell may be too hard in that position. In some embodiments, the cutting line can be a straight line through the appliance at the gum line, below the gum line, or above the gum line. In some embodiments, the cutting line can be a gum cutting line, which represents the interface between the appliance and the patient's gums. In such an embodiment, the cutting line controls the distance between the edge of the appliance and the patient's gum line or gum surface.

[0121] Each patient has a unique dental arch with unique gums. Therefore, the shape and position of the cutting line can be unique and customized for each patient and each treatment stage. For example, the cutting line is customized to be along the gum line (also called the gingival line). In some embodiments, the cutting line can be away from the gum line in some areas and on the gum line in other areas. For example, in some cases, it may be desirable for the cutting line to be away from the gum line (e.g., not contacting the gums), wherein, in the interproximal area between the teeth, the shell will contact the teeth and be on the gum line (e.g., contacting the gums). Therefore, it is important to trim the shell along the predetermined cutting line.

[0122] In some embodiments, the housing can have multiple cutting lines. A first or primary cutting line can control the distance between the edge of the housing and the patient's gum line. Additional cutting lines can be used to cut slots, holes, or other shapes in the housing. For example, additional cutting lines can be used to remove an occlusal surface of the housing, an additional surface of the housing, or a portion of the housing that, when removed, results in the formation of a hook that can be used with an elastic.

[0123] In some embodiments, a gingival cutting line is first determined by defining an initial gingival curve along a line around a tooth (LAT) of the patient's dental arch from a virtual 3D model (also referred to as a digital model) of the patient's dental arch at the treatment stage. The gingival curve may include interproximal areas between adjacent teeth of the patient and interface areas between the teeth and the gingiva. The initially defined gingival curve may be replaced with a modified dynamic curve representing the cutting line.

[0124] Defining an initial gingival curve along the line around the tooth (LAT) can be suitably performed by various conventional processes. For example, such generation of a gingival curve can include any conventional computational orthodontic method or process for identifying a gingival curve. In one example, the initial gingival curve can be generated by using a Hermite-Spline process. Typically, the Hermite form of a cubic polynomial curve segment is determined by constraints on endpoints P1 and P4 and tangent vectors at endpoints R1 and R4. The Hermite curve can be written in the following form:

[0125] Q(s)=(2s 3 -3s 2 +1)P1+(-2s 3 +3s 2 )P4+(s 3 -2s 2 +s)R1+(s 3 -s 2 )R4;s[0,1] (1)

[0126] Equation (1) can be rewritten as:

[0127] Q(s)=F1(s)P1+F2(s)P4+F3(s)R1+F4(s)R4; (2)

[0128] Wherein, equation (2) is the geometric form of the Hermitian spline curve, vectors P1, P4, R1, R4 are geometric coefficients, and the term F is the Hermitian basis function.

[0129] The gingival surface is defined by gingival curves and baselines on all teeth, wherein the baselines are obtained from a digital model of the patient's dental arch. Thus, using a plurality of gingival curves and baselines, a Hermitian surface patch representing the gingival surface can be generated.

[0130] Rather than having a cutting line that causes a sharp point or other narrow area in the interproximal region between the teeth (which may result in weakening of the appliance material during use), the initial gingival curve can be replaced with a cutting line that has been modified from the initial gingival curve. A cutting line can be generated to replace the initial gingival curve by initially obtaining a plurality of sample points from a pair of gingival curve portions located on each side of the interproximal region. The sample points are then converted into a list of points with associated geometric information (e.g., into the Amsterdam Dental Function (ADF) format or other similar data format). The sample points can be appropriately selected near the interior region between the two teeth, but at a sufficient distance from the location where the two teeth meet or reach a point in the interproximal region between the two teeth (or the interval between the two teeth narrows).

[0131] The collection of sample points provides multiple points in space (not in the same plane) that can be used to generate an average plane and a vector perpendicular to the average plane. The sample points associated with the gingival curve portion can then be projected onto the average plane to generate two new curves. In order to minimize the weakening of the area of ​​the braces material in the interproximal region, the modified dynamic curve can be configured with an offset adjustment including a minimum radius setting in the interproximal region to prevent the braces material from being damaged during use. The offset adjustment is also configured to ensure that the resulting cutting line has a sufficient radius in the interproximal region to facilitate sufficient resistance applied to the teeth to cause effective movement, but not a radius that is too small to be easily damaged. For example, sharp points or other narrow portions of the material may generate stress areas that are prone to breakage during use and should therefore be avoided. Therefore, instead of having the cutting line include sharp points or other narrow areas, multiple intersection points and tangent points can be used to generate a cutting line in the interproximal region between adjacent teeth that maintains the structural strength of the braces and prevents sharp points and / or narrow portions that may break. In one embodiment, the cutting line is spaced apart from the gingival surface at areas where the appliance will contact the teeth and is designed to at least partially contact the gingival surface of the patient in one or more interproximal areas between the teeth.

[0132] After determining the cutting line, the appliance can then be cut along the cutting line (or multiple cutting lines) using the markings and / or elements printed in the appliance. In some embodiments, the appliance can be manually cut by a technician using scissors, a drill, a cutting wheel, a scalpel, or any other cutting tool. In another embodiment, the appliance is cut along the cutting line by a computer-controlled trimmer (e.g., a CNC machine or a laser trimmer). The computer-controlled trimmer may include a camera that can identify the cutting line in the appliance. The computer-controlled trimmer can use the image from the camera to determine the location of the cutting line from the markings in the appliance, and can control the angle and position of the trimmer's cutting tool to trim the appliance along the cutting line using the identified markings.

[0133] Additionally or alternatively, the appliance may include coordinate system reference marks that can be used to reference the coordinate system of the trimmer to a predetermined coordinate system of the appliance. The trimmer may receive a digital file with trimming instructions (e.g., indicating the position and angle of a laser or cutting tool of the trimmer so that the trimmer trims the appliance along the cut line). By aligning the coordinate system of the trimmer with the appliance, the accuracy of computer-controlled trimming of the appliance at the cut line can be improved. The coordinate system reference marks may include marks sufficient to identify the origin and the x, y, and z axes.

[0134] Before trimming the appliance, a technician may apply a dye, colored filler, or other material to the appliance to fill in the slight indentation left by one or more elements imprinted in the appliance. The dye, colored filler, etc. may color the slight indentation without coloring the rest of the appliance. This may increase the contrast between the cut line and the rest of the appliance. After the appliance is removed from the mold and trimmed, additional polishing (e.g., polishing of the edges) and / or removal of undesirable artifacts may be performed. After trimming, the appliance may be removed from the mold.

[0135] In embodiments disclosed herein, each appliance or other dental device (e.g., a removable surgical fixture, a removable mandibular repositioning appliance, a removable palatal expander) manufactured can be sent to an image-based quality control (IBQC) site that detects one or more quality issues (e.g., deformation) with the appliance. Alternatively, the appliances marked for quality inspection can be sent to the IBQC site. For example, a digital file of the appliance can be input into a machine learning model, a numerical simulation, a rule engine, and / or other modules to determine if there is an increased chance that any of the appliances will have a defect. The machine learning model, the numerical simulation, the rule engine, and / or other modules can identify a subset of appliances to be inspected using the IBQC system. Optionally, the IBQC system and method can classify the inspected appliances as deformed, potentially deformed, or not deformed, and can also provide recommendations including the results of its analysis (e.g., further inspection required, remanufacturing required, approval, etc.).

[0136] Now turning to the attached figure, Figure 1A A flow chart of a method 100 for performing image-based quality control of a housing (e.g., an orthodontic appliance) according to one embodiment is shown. One or more operations of the method 100 are performed by processing logic of a computing device. The processing logic may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed by a processing device), firmware, or a combination thereof. For example, one or more operations of the method 100 may be performed by an executable program. Fig.21 The image-based quality control module 2150 is executed by the processing device. It should be noted that the method 100 can be performed for each unique appliance or a subset of unique appliances manufactured for each patient's treatment plan.

[0137] At box 102, processing logic can receive a digital file associated with a plastic shell customized for a patient's dental arch. Dental appliance identification information can be captured by a camera and interpreted by an inspection system. Alternatively, a technician can also manually enter this information so that the inspection system can obtain the digital file. In another embodiment, a dental appliance arrangement system can arrange a series of dental appliances in a known order. The inspection system can obtain the dental appliance sequence from the dental device arrangement system to know which dental appliances are currently being inspected and the order in which they arrive at the site. Optionally, the dental appliances can arrive at the inspection system in a tray carrying dental appliance identification information (e.g., RFID tags, bar codes, serial numbers, etc.), which is read by the inspection system. Thereafter, the inspection system can obtain the digital file associated with the dental appliance based on the dental appliance identification information received by the system.

[0138] In some embodiments, the digital file may include a digital model of a plastic shell. In some embodiments, the digital file associated with the plastic shell may include a digital model of a mold for manufacturing the plastic shell. The digital model of the plastic shell can be obtained by manipulating the digital model of the mold and approximating the first characteristics of the plastic shell. For example, in some embodiments, the surface of the mold may be enlarged, expanded, or otherwise offset to approximate the surface (inner and / or outer surface) of the plastic shell. In some cases, the (one or more) surfaces of the mold associated with the patient's teeth and / or accessories and / or virtual fillings are enlarged, expanded, or offset. Optionally, the inner or outer surface of the plastic shell is determined, and other surfaces are approximated based on the thickness of the material used to form the plastic shell. In some cases where a shell is to be formed by thermoforming a sheet of material on a physical mold, the approximate surface may take into account the stretching and thinning of the material on certain parts of the mold.

[0139] At block 104, the processing logic may generate a first image of the plastic housing using one or more imaging devices. The first image may be a top view image, a side view image, or a diagonal image, and may include at least one of a photographic image, an X-ray image, or other digital image (e.g., an ultrasound image). The one or more imaging devices may include at least one of a camera, a blue laser scanner, a confocal microscope, a stereo image sensor, an X-ray device, and / or an ultrasound device.

[0140] In some embodiments, two cameras may be used with specific lighting, backing screens, reflectors, and / or XY stages to capture a first image and / or additional images, as described below with reference to Figure 2 For example, the first image can be captured by a first top-view camera, and the first image can be used to determine the inspection plan, as described below. Figure 3A) can be used to determine the movement control and / or screen path of the backing screen between the front side and the back side of the plastic housing identified in the first image (such as Figure 3B ). The movement control and screen path may be used to position the backing screen when capturing additional images (e.g., side view images) specified in the examination plan. In some embodiments, the digital file may be used to guide one or more imaging devices (e.g., using robot-guided image acquisition) to capture a first image based on a digital model of the plastic appliance included in the digital file by tracing edges and / or cut lines of the plastic shell. Additionally, in some embodiments, the processing logic may analyze the digital file associated with the plastic shell to determine one or more features included in the plastic shell, and configure settings of the one or more imaging devices to capture the first image at a location associated with the one or more features.

[0141] At box 106, the processing logic may determine an inspection plan for the plastic shell based on at least one of the first information associated with the first image of the plastic shell or the second information associated with the digital file. The inspection plan may specify that one or more additional images of the plastic shell are to be generated. For example, the first information associated with the first image of the plastic shell or the second information associated with the digital file may indicate the size and / or shape of the plastic shell, and the processing logic may determine to capture the one or more additional images using a specific zoom setting, focus setting, and / or orientation (e.g., angle, position, etc.) setting. If the first information indicates that the size of the plastic shell is small, the processing logic may determine to capture the additional images with an enlarged image device setting. In addition, in some embodiments, the first information associated with the first image of the plastic shell or the second information associated with the digital file may indicate the presence of certain features (e.g., a precise cut line of the plastic appliance, a cavity of the plastic appliance associated with an attachment, an angle of the cut line, a distance between cavities of the plastic appliance associated with teeth, or a distance between cavities of the plastic appliance associated with an attachment). The processing logic may determine an inspection plan to include one or more additional images of the plastic shell based on the identified features, such as reference Figure 5 Further discussion.

[0142] The processing logic may also determine settings of the imaging devices for capturing one or more additional images. The settings may be based on the size, shape, and / or features identified in the plastic housing. For example, the settings may include at least one of one or more positions of the one or more imaging devices for generating the one or more additional images, one or more orientations of the one or more imaging devices for capturing the one or more additional images, one or more depths of focus of the one or more imaging devices for capturing the one or more additional images, or the number of one or more additional images of the one or more imaging devices.

[0143] Additionally, in some embodiments, the processing logic may determine an inspection plan by applying a digital file associated with the plastic housing as an input to a model (e.g., a trained machine learning model or a numerical simulation), and the model may output one or more locations of the plastic housing that are identified as high risk areas for defects, as described below with reference to Figure 6 Additionally, in some embodiments, the processing logic may determine the inspection plan by applying the digital file associated with the plastic housing to a rule engine using one or more rules, the one or more rules specifying that one or more additional images be captured at the one or more locations of the plastic housing when the one or more features are present at the one or more locations, as described below with reference to Figure 7 As further described.

[0144] In some embodiments, determining the inspection plan may include: Fig.21 The examination plan is obtained from a memory location of the computer system shown. Settings of the one or more imaging devices for generating the one or more additional images may be preset in the examination plan obtained from the memory location. The settings include at least one of one or more positions of the one or more imaging devices for generating the one or more additional images, one or more orientations of the one or more imaging devices for capturing the one or more additional images, one or more focus depths of the one or more imaging devices for capturing the one or more additional images, or the number of the one or more additional images of the one or more imaging devices.

[0145] At box 108, the processing logic may execute the inspection scheme to capture one or more additional images of the plastic housing. In some embodiments, executing the inspection scheme to capture one or more additional images of the plastic housing may include configuring the settings of one or more imaging devices to capture the one or more images based on at least one of the first information associated with the first image or the second information associated with the digital file. The settings may include at least one of the orientation of the one or more imaging devices, the position of the one or more imaging devices, the zoom of the one or more imaging devices, or the focus depth of the one or more imaging devices. In some embodiments, executing the inspection scheme may include: using the imaging device, using data from the design file of the plastic housing or according to the movement control and screen path determined from the first image to track the edge of the plastic housing to capture an image subset of the one or more additional images representing the cut line of the plastic housing.

[0146] At block 110, processing logic may determine whether one or more defects are included in the plastic shell based on the first image and / or one or more additional images. If no defects are included, the method may end. In some embodiments, determining whether a defect exists may include: obtaining a digital file associated with the plastic shell; determining an approximate first characteristic (or expected characteristic) of the plastic shell from the digital file; determining a second characteristic (or actual characteristic) of the manufactured plastic shell from the first image and / or one or more additional images; and comparing the approximate first / expected characteristic to the second / actual characteristic. If the approximate first characteristic and the second characteristic differ by a threshold amount, it may be determined that there is a defect in the plastic shell. In some applications, the digital file associated with the plastic shell includes a digital model of the mold and / or a digital model of the shell. Optionally, determining the expected characteristic of the plastic shell includes manipulating the digital model of the mold, examples of which are provided throughout. For example, the expected characteristic may be determined by manipulating the surface of the digital model of the mold to approximate the outer surface of the manufactured dental appliance. In some cases, the expected characteristic of the dental appliance may be a projection or contour of the expected outer surface of the dental appliance into a plane. In some embodiments, the approximate first characteristic may be a virtual cut line of the digital model of the appliance, and the second characteristic may be an actual cut line from the first image of the plastic shell. It should be noted that "cut line" and "edge" are used interchangeably herein. Figures 4A-4C An example of determining a second characteristic from an additional image of the captured inspection solution and comparing the second characteristic to the approximate first characteristic to determine if any defects exist is shown in FIG.

[0147] At block 110, if it is determined that one or more defects are included in the plastic housing, then at block 112, processing logic may perform quality control on the plastic housing. For example, processing logic may classify the plastic housing as defective and specify one or more remedial actions (e.g., adding filler material, smoothing cut lines, modifying one or more attachments on the mold or attachment cavities of the dental appliance, remanufacturing, etc.) to attempt to eliminate the one or more defects. Examples of quality control operations are described in more detail below.

[0148] Figure 1B A flow chart of a method 120 for performing image-based quality control of a housing (e.g., an orthodontic appliance) according to one embodiment is shown. One or more operations of the method 120 are performed by processing logic of a computing device. The processing logic may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed by a processing device), firmware, or a combination thereof. For example, one or more operations of the method 120 may be performed by an execution Fig.21The image-based quality control module 2150 is executed by the processing device. It should be noted that the method 120 can be performed for each unique appliance or a subset of unique appliances manufactured for each patient's treatment plan.

[0149] At block 122, processing logic may generate a first image of the plastic housing using one or more imaging devices. In some embodiments, the first image may be a top view of the plastic housing. The one or more imaging devices may include a camera, an X-ray device, a blue laser scanner, etc.

[0150] At box 124, the processing logic can determine whether one or more defects are detected in the plastic shell based on the first image. If no defects are detected, the method 120 can end. One or more defects can be detected by comparing the first image of the plastic shell with a digital file including a digital model of the plastic shell. For example, the processing logic can compare the top view image of the first shell with the top view of the digital model of the plastic shell to determine whether the shape of the plastic shell is deformed. If one or more differences between the shape of the plastic shell from the top view image and the shape of the plastic shell from the digital model of the shell exceed a threshold, it can be determined that the plastic shell includes defects. The first image can be applied to a trained machine learning model or a rule engine that is trained to identify high-risk areas for defects, and the rule engine includes rules that certain features at specified locations indicate high-risk areas for defects.

[0151] If one or more defects are detected in the plastic appliance or possible defects (high risk areas for defects) are detected, then at box 126, the processing logic may determine an inspection plan for the plastic shell based at least on the one or more defects. The processing logic may determine one or more additional images to be generated for the inspection plan, as well as the settings of one or more imaging devices for capturing the one or more additional images. For example, if the plastic shell is determined to be deformed based on the top view image, the processing logic may determine to capture one or more side view images of the plastic shell at the deformed position to further analyze (e.g., verify and / or detect other defects) the plastic shell. The determined settings may include magnifying detected defects at certain locations, adjusting the depth of focus to verify defects and / or detect other defects, and the like. Once the inspection plan is determined, the processing logic may execute the inspection plan by capturing one or more additional images using the determined settings.

[0152] At box 128, the processing logic may analyze one or more additional images to verify the detected defects and / or possible defects in the first image, and / or detect one or more new defects and / or possible defects in the plastic housing. In some embodiments, the processing logic may compare the additional images to a similar representation of the plastic appliance in the digital model (e.g., similar zoom settings, focus settings, etc.) to determine any differences and / or verify the defects detected based on the first image. In some embodiments, the processing logic may detect new defects (e.g., cracks) and / or possible defects based on the additional images. As described above, the additional images can be applied to the machine learning model and / or the rules engine.

[0153] At block 130, processing logic may determine whether the defects and / or possible defects detected in the first image are verified, and / or whether any new defects and / or possible defects are detected based on the one or more additional images. If the one or more defects and / or possible defects detected in the first image are not verified, and / or no new defects and / or no possible defects are detected in the one or more additional images, method 120 may end. If the defects and / or possible defects detected in the first image are verified based on the one or more additional images, and / or new defects and / or possible defects are detected based on the one or more additional images, processing logic may perform quality control on the plastic housing at block 132.

[0154] Figure 2 An example imaging system 200 including a top view camera 202 and a side view camera 204 is shown according to one embodiment. The imaging system 200 may be used to extract a cut line of a plastic housing 206 being analyzed to determine if a defect exists by comparing the cut line of the plastic housing with a virtual cut line obtained from a digital model of the plastic housing 206. The plastic housing 206 may be secured in a fixed position by a platform assembly holder 208. According to an embodiment, the top view camera 202 may be configured to obtain a top view image 300 of the transparent plastic housing 206 using a specific lighting setting so that the transparent plastic appliance 206 can be visible in the top view image 300, such as Figure 3A According to an embodiment, the processing logic may obtain an outline 302 of the projection or outline of the plastic housing, such as Figure 3BAs shown. The side view camera 204 can be used to obtain a front view and a rear view of the plastic appliance 206 by rotating around the plastic appliance 206 while the platform assembly holder 208 holds the plastic appliance 206, or by rotating the plastic appliance 206 while the side view camera 204 remains stationary. In some embodiments, the plastic appliance 206 may not be fixed by the platform assembly holder 208, and the plastic appliance 206 may be stationary on the platform while the side view camera 204 captures multiple images around the side of the plastic appliance 206. In some embodiments, the cameras 202 and 204 may be stationary and placed away from the conveyor path. In some embodiments, the imaged plastic housing 206 may be placed on an xyz-θ (4 axes of motion control) platform or table.

[0155] In an embodiment, the imaging system 200 can obtain separated front view and rear view images by using a backing screen 210 without stray light interference from the side that is not currently being inspected. The backing screen 210 can be inserted into the gap between the front (buccal) side and the back (lingual) side of the plastic appliance. The movement control and screen path 304 of the plastic shell 206 are determined by identifying points between the front and back sides of the plastic appliance, which enable the screen path to be generated so that the backing screen 210 does not contact the plastic appliance throughout the screen path. The processing logic can detect the center 306 of the plastic appliance and adjust the movement control and screen path parameters accordingly. In addition, the movement control speed can be high enough to achieve an inspection cycle within a target time period (e.g., 10-20 seconds) for both the front and back sides of the plastic appliance. In an embodiment, the reflector 212 can be used as a deflector to capture images from the back or front side of the plastic appliance when the plastic appliance is held in the platform assembly holder 208. The reflector 212 can be angled at a particular angle (e.g., 45°, 50°, 55°, etc.) and can be used in combination with a light source to enable the capture of images outlining the cutting lines on the anterior (buccal) side and the dorsal (lingual) side of the plastic appliance.

[0156] In some embodiments, the imaging system 200 may not use a back plate to prevent light from the front teeth from interfering with the imaging of the back teeth. In some embodiments, the imaging system 200 may use focused light to obtain the cutting lines of the plastic shell 206. For example, focused light can be used to illuminate only the cutting line currently being inspected (e.g., buccal or lingual) without stray light interference from other cutting lines that are simultaneously present in the field of view of the camera (e.g., to prevent the buccal cutting line and the lingual cutting line from interfering with each other). In such an embodiment, the top view camera 202 can capture a top view image of the plastic shell 206 and extract a top view contour. In some embodiments, the plastic shell 206 can be placed within the field of view of the top view camera 202, and the imaging system 200 can be aligned with the plastic shell 206 to capture the top view image.

[0157] The top view image can be used to determine an inspection plan that includes one or more side view images. Using the top view profile, the profile xy points can be sent to the side view camera 204. In some embodiments, the characteristics of the camera, such as zoom and / or focus depth, can be determined for the inspection plan based on which side of the plastic shell 206 is being captured. For example, when the buccal side is farther away, the focus area of ​​the side view camera 204 can be adjusted to accurately capture the lingual side of the plastic shell 206 without interference. In addition, the top view image can be used to rotate the plastic shell 206 to the appropriate orientation so that the cut line being inspected faces the side view camera 204. In some embodiments, the rotational motion required to rotate the plastic shell 206 can occur simultaneously with the xy motion of the side view camera 204 and may not affect the inspection time.

[0158] Once the inspection plan is determined, imaging of the cut lines can begin. A small cylindrical beam can be moved along the profile using the profile xy points (local structured illumination or SLI) to illuminate only the cut lines above the light path. An xy-rotating stage motion control system or a multi-axis robotic arm can be used to obtain the image. In some embodiments, the plastic housing 206 can be placed on a glass platform and the light column can be illuminated from below the glass platform to avoid total internal reflection directing light to other lines in the field of view.

[0159] Fig.22A An example side view image 2200 captured without a backing screen and without structured light illumination is shown. As depicted, the side view camera captures a cut line 2202 on a first side (e.g., buccal) of the appliance without structured light, which causes a cut line 2204 on an opposite side (e.g., lingual) of the appliance to be illuminated and interfere with proper cut line detection.

[0160] Fig. 22BAn example side view image 2210 captured using structured light illumination (e.g., focused light) without a backing screen is shown in accordance with one embodiment. As depicted, the side view camera captures a cut line 2212 of a first side (e.g., buccal) of an appliance using directed structured light illumination (LSI), which can suppress the cut line on the opposite side (e.g., lingual) and can allow for reliable cut line detection. LSI enables reliable cut line detection in situations where the channel between the sides of the appliance is too narrow and / or too angled to insert a backing screen.

[0161] Fig.23 An example of crack detection in the profile of an image 2300 of an appliance captured using focused light is shown according to one embodiment. As shown, the focused light uses directional structured light illumination to capture a side view image 2300, which produces a high contrast cut line definition of the cut line 2302. The high contrast can be produced by the light reaching the cut line surface at a perpendicular or near perpendicular angle. The captured cut line 2302 can be analyzed using the disclosed techniques and cracks 2304 can be identified. In some embodiments, the focus depth (e.g., 20 microns to 80 microns) can be configured to provide the desired image.

[0162] In embodiments using a backing screen 210 or using focused light without a backing screen, many images of the cut line can be captured and "stitched" or registered together to form a composite image 400. In some embodiments, the composite image 400 can be a panoramic view of each of the front and back sides of the plastic shell. The side view images of the front and back sides can be stitched together to show the cut line in a single plane, which can be similar to the unfolding or spreading of the cut line of a plastic aligner. For example, Figure 4A A side view composite image 400 of the back side of a housing according to one embodiment is depicted. The side view composite image 400 includes three images 402, 404, and 406 stitched together in a linear manner. In the example shown, each of the images 402, 404, and 406 is captured at a pixel resolution of 20 microns. Any suitable number of images (e.g., 20) may be taken to produce the front composite image and the back composite image.

[0163] Figure 4B shows the use of Figure 4A Example edge 408 (eg, second feature) detected in side view composite image 400 of the back side of the housing. Edge 408 may be detected by tracing a cutting line in side view composite image 400 to obtain a line representing edge 408. Figure 4CAn example comparison of an edge 410 detected from a side view synthetic image and a virtual or expected edge (e.g., an approximated first characteristic) 412 of a plastic shell from a digital model of the plastic shell is shown according to one embodiment. In some embodiments, the expected edge 412 is determined by unfolding a virtual cutting line for the dental appliance into a plane for comparison with the edge 410. The edge 410 and the virtual edge 412 can be superimposed, and a difference between the two edges can be determined. If the difference exceeds a threshold, it can be determined that a defect is included in the plastic appliance, and quality control can be performed.

[0164] Figure 5 A flowchart of a method 500 for determining an inspection solution based on features of a plastic housing according to one embodiment is shown. One or more operations of the method 500 are performed by processing logic of a computing device. The processing logic may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed by a processing device), firmware, or a combination thereof. For example, one or more operations of the method 500 may be performed by an execution Fig.21 The method 500 is performed by the processing device of the image-based quality control module 2150. It should be noted that the method 500 can be performed for each unique appliance manufactured for each patient's treatment plan.

[0165] At block 502, processing logic may determine an inspection scheme based on at least one of first information associated with a first image of a plastic shell or second information associated with a digital file associated with the plastic shell. In some embodiments, the first image may be a top view image, a side view image, or a diagonal image of the plastic shell. The first information associated with the first image and / or the second information associated with the digital file may include the size of the plastic shell, the shape of the plastic shell, and / or one or more features of the plastic shell. The one or more features may include at least one of the following: a precise cutting line of the plastic appliance, a cavity of the plastic appliance associated with an attachment, an angle of the cutting line of the plastic appliance, a distance between cavities of the plastic appliance associated with teeth (e.g., tooth crowding), or a distance between cavities of the plastic appliance associated with an attachment (e.g., attachment crowding), a distance between the front and back of the plastic shell, or a thickness of the plastic shell. Block 502 may include performing operations of blocks 504, 506, 508, and 510.

[0166] At box 504, the processing logic may use at least one of the first information or the second information to determine one or more features of the plastic shell. At box 506, the processing logic may determine one or more additional images to be generated for the inspection plan based on the one or more features. As further described below, the determination to generate additional images for the inspection plan can be predetermined for the plastic shell, or dynamically performed using a model and / or rule engine. In one example, if the plastic braces include a precise cut line as a feature, the processing logic may determine to generate additional images at a location associated with the precise cut line. If the angle of the cut line is above a certain threshold angle, the processing logic may determine to generate additional images at a location associated with a cut line with an excessively large angle. If the distance between the teeth is greater than a threshold, the processing logic may determine to generate additional images at the location of the plastic shell associated with those teeth, because the plastic may be thinner and more prone to cracks at that location.

[0167] At box 508, the processing logic may determine the size of the plastic shell from at least one of the first information or the second information. In some embodiments, the processing logic may also determine the shape of the plastic shell from at least one of the first information or the second information. At box 510, the processing logic may determine settings to generate one or more additional images based on at least one of the one or more features, the size of the plastic shell, or the shape of the plastic shell. The settings may include at least one of the orientation of one or more imaging devices, the zoom of one or more imaging devices, or the focus of one or more imaging devices. For example, a plastic shell with a smaller size may result in a larger zoom setting and a greater depth of focus. If there is a cavity associated with an accessory identified by the first information or the second information, the orientation (e.g., positioning and angle) of the camera may be configured to capture a suitable image of the cavity.

[0168] In some embodiments, the settings may be predetermined by default for a first set of additional images generated for each appliance and / or may be dynamically configured for a second set of additional images dynamically determined for an inspection protocol. For example, some defects, such as bubbles on the surface of a plastic housing, may be detected at a particular depth of focus (e.g., 20 microns), and therefore, a predetermined set of images may be configured to be captured on the front and back of the plastic housing to determine if bubbles are present.

[0169] At block 512, processing logic may perform an inspection scheme by capturing one or more additional images using the settings. The one or more additional images may be analyzed to determine whether the plastic housing includes one or more defects. If so, quality control may be performed on the plastic housing.

[0170] Figure 6A flow chart of a method 600 for determining one or more additional images to be generated based on output from a model according to one embodiment is shown. One or more operations of method 600 are performed by processing logic of a computing device. The processing logic may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed by a processing device), firmware, or a combination thereof. For example, one or more operations of method 600 may be performed by an executable program. Fig.21 The method 600 is performed by the processing device of the image-based quality control module 2150. It should be noted that the method 600 can be performed for each unique appliance manufactured for each patient's treatment plan.

[0171] At block 602, processing logic may apply the digital file as an input to a model. The model may allow for targeted inspections by identifying high risk areas for defects in a plastic housing. For example, at block 604, processing logic may generate an output identifying one or more locations of the plastic housing through the model, the one or more locations being identified as high risk areas for one or more defects. At block 606, processing logic may determine one or more additional images to be generated for the inspection plan based on the one or more locations identified as high risk areas by the output.

[0172] In some embodiments, the model may be a machine learning model that is trained to identify one or more high-risk areas of one or more defects at one or more locations of a plastic housing. Processing logic may train the machine learning model to generate a trained machine learning model. The machine learning model may refer to a model artifact created by a training engine using training data (e.g., training inputs and corresponding target outputs). Training may be performed using a set of training data that includes at least one of the following: a) a digital file of a first set of plastic appliances with a label indicating whether each of the first set of plastic appliances has experienced one or more defects, or b) a digital file of a second set of plastic appliances with a label indicating whether each of the second set of plastic appliances includes one or more possible defects. The actual defects of the appliance may be reported by a manufacturing technician, by an automated manufacturing system, and / or by a patient. Such historical data about actual defects on a physical appliance may then be added as a label or metadata to an associated digital file of the appliance and / or an image of the appliance. Possible defects of the digital file of the appliance may be determined by processing the digital design of the model using numerical simulation, as further described below. For example, a digital file of the appliance may be processed using numerical simulation to determine possible defects. Digital files of appliances with associated defects (as provided by real-world data) and digital files of appliances with associated possible defects (as provided by the output of numerical simulations) can be used together to generate a robust machine learning model that can predict the likely defects of a new appliance from the digital files of those appliances.

[0173] A machine learning model may consist of a single level of linear or nonlinear operations (e.g., a support vector machine (SVM) or a single level of a neural network), or may be a deep neural network consisting of multiple levels of nonlinear operations. Examples of deep networks and neural networks include convolutional neural networks and / or recurrent neural networks with one or more hidden layers. Some neural networks may consist of interconnected nodes, where each node receives input from the previous node, performs one or more operations, and sends the resulting output to one or more other connected nodes for further processing.

[0174] As mentioned, information related to whether a plastic appliance has experienced a defect can be obtained from the patient's historical feedback. For example, a patient can provide a report indicating a defect in a plastic appliance, and the location of the defect can be determined (e.g., from the report, from scanning the appliance, etc.). Moreover, the patient can specify which appliance (e.g., top or bottom) failed at a particular stage of the treatment plan. In some cases, the patient can return the defective appliance to a site, and the defective appliance can be scanned at the site to obtain an image of a digital model of the plastic appliance including the location of the defect. Therefore, images of defective appliances can be collected for an image corpus (a set of image corpora that can include a large set of images) and used as part of the training data. The information provided by the patient about the defective appliance or the information determined by the scanned image can be associated to determine the ID of the appliance, which can then be used to obtain a digital file of the particular appliance. The location of the defect can be placed in a digital file of the plastic appliance with a label indicating that there is a defect at that location.

[0175] The digital file can be applied as an input to a predictive model using numerical simulation. Numerical simulations can be performed on the digital file of the plastic appliance to simulate one or more forces on the plastic appliance. In some embodiments, the force simulation removes the appliance from the teeth or mold. The numerical simulation can determine when the amount of force required to remove the appliance from the mold or dental arch reaches a stress or strain level that exceeds a threshold at any point on the plastic appliance, which can indicate that a specific point will break. In some embodiments, strain or stress thresholds can be used during the numerical simulation to determine when a point on the digital design of the appliance may fail. In this way, the numerical simulation can be used as a predictive model that predicts possible defects on the digital file of the appliance by identifying one or more high-risk areas for defects. This simulation can be run dynamically on the digital file of the plastic appliance to identify high-risk areas for defects to allow targeted inspections at those locations. In addition, these simulations can be run multiple times on multiple digital files of the plastic appliance, and labels indicating whether the digital file includes one or more possible failure points can be included with the digital file. The digital file including labels indicating whether the digital file includes one or more possible defects can be used as input for training a machine learning model.

[0176] Numerical simulation may include finite element method, finite difference method, finite volume method, meshless method, smooth particle Galerkin method, combination of these methods, etc. Finite element method (also known as finite element analysis) may refer to a numerical method for solving structural problems related to the appliance by using a series of partial differential equations to generate approximate values ​​of unknown numbers at a discrete number of points on the domain. Finite difference method may refer to a numerical method for solving differential equations by approximating differential equations with difference equations and calculating approximate values ​​at discrete points. Finite volume method may refer to a method for representing and evaluating partial differential equations in the form of algebraic equations. Finite volume method can also calculate values ​​(e.g., strain, force) at discrete locations on the mesh geometry of the digital design of the appliance. "Finite volume" may refer to a small volume around each point on the grid. Meshless method may refer to a method based on the interaction of a node or point with all adjacent nodes or points. In other words, meshless method does not require connections between nodes of the simulation domain. Smooth particle Galerkin method may be a form of meshless method.

[0177] Figure 7 A flow chart of a method 700 for determining an inspection plan using a rule engine according to one embodiment is shown. One or more operations of the method 700 are performed by processing logic of a computing device. The processing logic may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed by a processing device), firmware, or a combination thereof. For example, one or more operations of the method 700 may be performed by an execution Fig.21 The method 700 is performed by the processing device of the image-based quality control module 2150. It should be noted that the method 700 can be performed for each unique appliance manufactured for each patient's treatment plan.

[0178] At block 702, processing logic may generate one or more rules for a rule engine. The rules may be generated based on at least one of the following: a) historical data (e.g., images of defective appliances, reports, etc.) including reported defects of a set of plastic shells and locations of the reported defects on the set of plastic shells, b) a digital file of a set of plastic shells with a label indicating whether each of the set of plastic shells is subject to a defect, or c) a digital file of a set of plastic shells with a label indicating whether each of the set of plastic shells includes a likelihood of a defect in the plastic shell. The rules may be determined based on observations, outputs of numerical simulations, and the like. For example, a customer may provide a report describing an appliance that broke during removal or included another defect, a manufacturing technician may observe damage to an appliance during removal of the appliance from a mold, and the like. Hundreds or thousands of observations of appliances that include defects may be used to determine a pattern or combination of features included in a defective appliance that may cause a defect. A rule may be determined that specifies that a defect may exist when a pattern or combination of these features is present in a subsequent design. Additionally, numerical simulations can be performed and possible defects identified as output. The output from hundreds or thousands of numerical simulations can be aggregated and patterns or combinations of features associated with possible defects can be identified. Rules can be determined that specify that a defect is likely to exist when a pattern or combination of these features is present in a subsequent design.

[0179] At block 704, processing logic may determine an inspection scheme based on at least one of first information associated with the first image of the plastic housing or second information associated with the digital file. Block 706 may include operations of performing blocks 706, 708, and 710. At block 706, processing logic may apply the digital file to a rule engine using one or more rules. At block 708, processing logic may determine whether one or more features, one or more defects, and / or one or more possible defects are included at one or more locations in the plastic housing. For example, processing logic may determine whether one or more defects are detected or one or more possible defects (e.g., high-risk areas for defects) are detected based on first information associated with the first image of the plastic housing and / or based on second information associated with the digital file. The rule may specify that there are defects or possible defects associated with the features at certain locations. In addition, if defects are detected in the plastic housing, the rule may specify that certain images are captured. If defects or possible defects are not included at one or more locations, method 700 may end. If it is determined that there are one or more defects or possible defects at one or more locations, at block 710, processing logic may specify that one or more additional images of the plastic housing are generated according to one or more rules.

[0180] The rules may include rules associated with feature groups (e.g., multiple features within a threshold proximity of each other) and / or with individual parameters. The processing logic may determine features of the plastic appliance based on first information associated with the first image or second information associated with a digital file of the plastic appliance. The features may include at least one of the following: an angle of a cutting line at a location of the plastic appliance associated with an interproximal region of a patient's dental arch, a curvature of the plastic appliance, a thickness of the plastic appliance, an undercut height associated with an attachment to a tooth of a tooth of a patient's dental arch, a precise cutting line, a distance between cavities of the plastic appliance associated with an attachment to a tooth of a patient's dental arch, and / or the number of cavities of the plastic appliance. Any one or more of these features in combination may indicate a high risk area where defects may exist in the plastic appliance. Therefore, the processing logic may determine to generate one or more additional images in the inspection plan at a high risk area of ​​possible defects in the plastic appliance.

[0181] Fig. 8A A flow chart of a method 800 for performing image-based quality control on a housing is shown according to one embodiment. One or more operations of the method 800 are performed by processing logic of a computing device. The processing logic may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed by a processing device), firmware, or a combination thereof. For example, one or more operations of the method 800 may be performed by an execution Fig.21 The method 800 is performed by the processing device of the image-based quality control module 2150. It should be noted that the method 800 can be performed for each unique appliance manufactured for each patient's treatment plan.

[0182] At box 802, processing logic may obtain one or more images of a first shell (e.g., an appliance). As described above, the first shell may have been manufactured for the patient's dental arch. The first shell may be received by an automatic supply mechanism at an image-based quality control (IBQC) site, or placed in the IBQC site by a user. The IBQC site may include one or more cameras and a fixed or rotating table on which the received shell is placed. The IBQC site may also include a lighting system configured to provide consistent exposure and image capture with consistent environmental parameters. In one embodiment, processing logic may configure the position of the camera so that one camera obtains a top view image of the shell, while another camera obtains a side view image and / or a diagonal view image of the shell. The rotating table may enable the shell being inspected in the IBQC site to be rotated so that images from different sides of the shell can be obtained.

[0183] The image obtained may include a first image including a component number identifier of the first shell. In some embodiments, an image of the component number identifier with a light (e.g., white) background may facilitate reading a laser marking that identifies the component number identifier (e.g., a barcode, a serial number, etc.). An image of the appliance for quality control analysis may have a dark (e.g., black) background, and the appliance is uniformly illuminated. Illumination of the appliance with a dark background may facilitate distinguishing the edges and shape of the appliance for quality control analysis.

[0184] At box 804, a technician or processing logic can use the image to identify an identifier (laser mark) on the first shell. For example, in some embodiments, the processing logic can use optical character recognition to read a serial number or other text to identify a component number identifier of the imaged appliance. Optionally, the technician can identify and enter the component number identifier by visually inspecting the appliance. As described above, the identifier can represent the component number and can be laser marked on the appliance. The processing logic can use the first image with a light background to identify the identifier. The identifier can be associated with a digital model of the appliance generated by the processing logic. In particular, prior to receiving the image of the shell, the processing logic can receive a file including a digital model of a mold used to create the specific appliance being inspected.

[0185] At block 806, processing logic may determine a first digital model of a first shell from a set of digital models of shells based on the identifier. Each digital model in the set of digital models is for a specific shell customized for a specific patient at a specific stage of the patient's treatment plan. The digital model of the shell may be generated based on the digital model of the mold at each corresponding stage of the patient's treatment plan, such as reference. Fig.16 The method 1600 is discussed in detail.

[0186] At block 808, processing logic may compare the image of the first shell to the projection of the first digital model. In one embodiment, processing logic may compare the top view image of the first shell to the top view of the first digital model to determine whether the shape of the first shell is deformed. If one or more differences between the first shape of the first projection of the digital model of the shell and the second shape of the first shell exceed a first threshold, then processing logic may determine that the shape of the first shell is deformed, as described below with reference to Fig.9A and Fig.11 As further described in methods 900 and 1100, and as Fig.10 , Fig.12 and Figures 13A-13B as shown in the example.

[0187] However, if one or more differences do not exceed the first threshold, the processing logic may perform additional comparisons. For example, the processing logic may generate a modified projection of the digital model by deforming the first shape of the first projection of the digital model of the first shell toward the second shape of the first shell to approximately match the second shape of the first shell. The processing logic may determine whether one or more remaining differences between the third shape of the modified projection and the second shape of the first shell exceed the second threshold. If so, the processing logic may determine that the cut line (or other feature) of the first shell is deformed, as described below with reference to Fig.14A The method 1400 is further described in and as Figures 15A-15C as shown in the example.

[0188] At box 810, processing logic can perform quality control on the first shell based on the comparison. The results of the IBQC analysis can be compiled and a classification can be assigned to the appliance being inspected. If any comparison indicates the presence of a manufacturing defect, the processing logic can classify the appliance as defective. If each comparison does not indicate the presence of a defect, the processing logic can classify the appliance as not defective. Optionally, when the analysis is inconclusive, the system can indicate that the appliance needs further inspection by a technician. The results can be presented to the user in a user interface.

[0189] Figure 8B A flow chart of another method 820 for performing image-based quality control on a housing is shown according to one embodiment. One or more operations of the method 820 are performed by processing logic of a computing device. The processing logic may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed by a processing device), firmware, or a combination thereof. For example, one or more operations of the method 820 may be performed by an execution Fig.21 The method 820 is performed by the processing device of the image-based quality control module 2150. It should be noted that the method 820 can be performed for each unique appliance manufactured for each patient's treatment plan.

[0190] At box 822, processing logic can obtain one or more images of a first shell (e.g., an appliance). As described above, the first shell may have been manufactured for the patient's dental arch. The first shell can be received by an automatic supply mechanism at an image-based quality control (IBQC) site, or placed in an IBQC site by a user. The IBQC site can include one or more cameras and a fixed or rotating table on which the received shell is placed. The IBQC site can also include a lighting system configured to provide consistent exposure and image capture with consistent environmental parameters. In one embodiment, processing logic can configure the position of the camera so that one camera obtains a top view image of the shell, while another camera obtains a side view image and / or a diagonal view image of the shell. The rotating table can enable the shell being inspected in the IBQC site to be rotated so that images from different sides of the shell can be obtained.

[0191] The image obtained may include a first image including a component number identifier of the first shell. In some embodiments, an image of the component number identifier with a light (e.g., white) background may facilitate reading a laser marking that identifies the component number identifier (e.g., a barcode, a serial number, etc.). An image of the appliance for quality control analysis may have a dark (e.g., black) background, and the appliance is uniformly illuminated. Illumination of the appliance with a dark background may facilitate distinguishing the edges and shape of the appliance for quality control analysis.

[0192] At box 824, a technician or processing logic can use the image to identify an identifier (laser mark) on the first housing. For example, in some embodiments, processing logic can use optical character recognition to read a serial number or other text to identify a component number identifier of the imaged appliance. Alternatively, a technician can identify and enter the component number identifier by visually inspecting the appliance. As described above, the identifier can represent the component number and can be laser marked on the appliance. Processing logic can use the first image with a light background to identify the identifier. The identifier can be associated with a digital file.

[0193] At block 826, processing logic may determine a first digital file associated with the first housing from a set of digital files based on the identifier. Each digital file in the set of digital files includes a digital model of at least one of a housing (e.g., an appliance) or a digital model of a mold for manufacturing the appliance. Each digital file is for a specific housing customized for a specific patient at a specific stage of the patient's treatment plan.

[0194] In one embodiment, the digital file associated with the identifier includes a digital model of a first shell (e.g., an appliance) that is dynamically generated by processing logic or received from another source. The digital model of the first shell can be dynamically generated by manipulating a digital model of a mold for manufacturing the first shell. The digital model of the first shell can be generated by expanding the digital model of the mold into an expanded digital model (e.g., by scaling or expanding the surface of the digital model) to simulate the process of thermoforming a film on the digital model of the mold. In addition, the generation of the digital model of the first shell can include calculating a projection of a cutting line onto the expanded digital model, virtually cutting the expanded digital model along the cutting line to create a cut expanded digital model, and selecting the outer surface of the cut expanded digital model. In one embodiment, the digital model of the first shell includes an outer surface of the first shell, but does not necessarily have a thickness and / or does not include an inner surface of the first shell, although in other embodiments it may include a thickness or an inner surface.

[0195] In one embodiment, the digital file includes a mold for manufacturing the first shell. In one embodiment, the digital file may include multiple files associated with the first shell, wherein the multiple files include a first digital file and a second digital file, the first digital file includes a digital model of the mold, and the second digital file includes a digital model of the first shell. Alternatively, a single digital file may include both the digital model of the mold and the digital model of the first shell.

[0196] At box 828, processing logic determines an approximate first characteristic of the first shell from the first digital file. In one embodiment, the approximate first characteristic is based on a projection of the digital model of the first shell onto a plane defined by the image of the first shell. In one embodiment, the approximate first characteristic is based on a manipulation of a digital model of a mold used to create the first shell. For example, in some embodiments, the approximate first characteristic may be based on a projection of the digital model of the mold onto a plane defined by the image of the first shell. In this case, the projection of the mold may be scaled or otherwise expanded to approximate the projection of the appliance thermoformed on the mold. In another embodiment, the approximate first characteristic is based on a manipulation of the digital model, wherein the manipulation causes the outer surface of the digital model to have an approximate shape of the first shell, and is also based on a projection of the outer surface of the digital model onto a surface defined by the image of the first shell. In some embodiments, the approximate first characteristic may include an approximate outer surface of the first shell. The approximate outer surface of the first shell may be referred to as a digital model of the first shell. In some embodiments, the approximate first characteristic may include a first shape of the projection of the approximate outer surface of the first shell onto a plane defined by the image of the first shell.

[0197] At block 830, processing logic determines a second characteristic of the first shell from the one or more images. The image of the first shell may define a plane. The second characteristic may include a second shape of the first shell or a projection thereof. The second characteristic may be determined directly from the one or more images (e.g., a top view, a side view, etc.). In one embodiment, an outline of the second shape is drawn from the image.

[0198] At block 832, processing logic may compare the approximated first characteristic to the second characteristic. If one or more differences between the approximated first characteristic and the second characteristic exceed a first threshold, processing logic may determine a first housing deformation, as described below with reference to Fig. 9B Method 920 is further described in and as Fig.10 For example, if the first shape cannot approximately match the second shape, then it can be determined that the first shell is deformed.

[0199] However, if one or more differences do not exceed the first threshold, the processing logic may perform additional comparisons. For example, the processing logic may generate a modified projection of the approximate outer surface of the first shell by deforming the curvature of the projected first shape toward a second curvature of the second shape of the first shell so that the curvature of the projected deformed first shell approximately matches the second curvature of the second shape of the first shell. The processing logic may determine whether one or more additional differences between the deformed first shape and the second shape exceed a second threshold. If so, the processing logic may determine that the cut line (or other characteristic) of the first shell is inaccurate, as described below with reference to Fig. 14B and 14C As described in method 1420 and method 1440.

[0200] At box 834, processing logic can perform quality control on the first shell based on the comparison. The results of the IBQC analysis can be compiled, and a category can be assigned to the shell being inspected. If any comparison indicates the presence of a manufacturing defect, the processing logic can classify the shell as defective. If each comparison does not indicate the presence of a defect, the processing logic can classify the shell as not defective. Optionally, when the analysis is inconclusive, the system can indicate that the shell needs further inspection by a technician. The results can be presented to the user in a user interface.

[0201] Fig.9A A flowchart of a method 900 for determining whether the shape of a housing is deformed according to one embodiment is shown. One or more operations of the method 900 are performed by processing logic of a computing device. The processing logic may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed by a processing device), firmware, or a combination thereof. For example, one or more operations of the method 900 may be performed by an execution Fig.21The method 900 may be executed by the processing device of the image-based quality control module 2150. The method 900 may be executed to determine whether the shape of the shell is deformed. It should be noted that in some embodiments, before executing the method 900, the processing logic may have executed blocks 802, 804, and 806 of the method 800 (e.g., obtaining a top view image of the first shell and determining a first digital model for the first shell based on the identifier).

[0202] At block 902, processing logic may determine a plane associated with a top view image of a first shell. The top view image of the first shell may include a two-dimensional object or a three-dimensional object including pixels representing an image of the first shell located in the image plane. At block 904, processing logic may project a first digital model of a first mold (or a manipulated digital model of a mold for manufacturing the first shell) into the determined plane to generate a first projection. For example, Fig.10 As shown, a first projection 1000 of the digital model is projected onto an image 1002 of the appliance. In some embodiments, the first projection 1000 is projected into the same plane as the image 1002 of the first shell, such that the first projection 1000 overlays the image 1002. The method 900 will be discussed with reference to a first digital model of the first appliance. However, it should be understood that the described operations work equally well using a manipulated digital model of a mold of the first shell.

[0203] At block 906, processing logic may identify one or more differences between the first shape of first projection 1000 and the second shape of the first shell based on the comparison performed at block 808 of method 800. In some embodiments, processing logic may identify the one or more differences by determining (block 908) one or more regions where the first shape of first projection 1000 does not match the second shape of the first shell. Processing logic may also determine (block 910) differences in regions (e.g., at least one of a thickness of the one or more regions or an area of ​​the one or more regions).

[0204] At box 912, processing logic may determine whether one or more differences (e.g., thickness, area, etc.) between the first shape of the first projection 1000 and the second shape of the first shell exceed a first threshold. The first threshold can be any suitable configurable amount (e.g., a thickness greater than three millimeters (mm), 5 mm, 10 mm, an area having an area greater than one hundred square millimeters, etc.). At box 914, processing logic may determine whether the first shell is deformed based on whether the one or more differences exceed the first threshold. When the differences exceed the first threshold, processing logic may classify the appliance as deformed. When the one or more differences do not exceed the first threshold, processing logic may determine that the shape of the first shell is not deformed, and may continue to perform additional quality control (e.g., cutting line deformation detection).

[0205] Fig. 9B A flowchart of another method 920 for determining whether the shape of a housing is deformed according to one embodiment is shown. One or more operations of method 920 are performed by processing logic of a computing device. The processing logic may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed by a processing device), firmware, or a combination thereof. For example, one or more operations of method 820 may be performed by an execution Fig.21 The processing device of the image-based quality control module 2150 may be executed. Method 920 may be executed to determine whether the shape of the shell is deformed. It should be noted that in some embodiments, before executing method 920, the processing logic may have executed blocks 822, 824, 826, and 828 of method 820 (e.g., obtaining a top view image of the first shell, identifying an identifier of the first shell, determining a first digital model for the first shell based on the identifier, and determining an approximate first characteristic of the first shell from the first digital file).

[0206] At block 922, processing logic may determine a plane associated with the top view image of the first shell. The top view image of the first shell may include pixels representing an image of the first shell that is located in the image plane. At block 924, processing logic may calculate a projection of an approximate outer surface of the first shell into the first plane. For example, Fig.10 As shown, a first projection 1000 of an approximate outer surface of the first shell is projected onto an image 1002 of the appliance. In some embodiments, the first projection 1000 is projected into the same plane as the image 1002 of the first shell so that the first projection 1000 covers the image 1002. In some embodiments, the digital model of the mold of the first shell is manipulated by inflating or expanding the size of the digital model, wherein the amount of expansion or expansion is based on the thickness of the first shell. The inflated or expanded digital model of the mold can then be cut along the cutting line to calculate the approximate outer surface of the first shell. Then, at box 924, the approximate outer surface of the first shell can be projected onto the plane.

[0207] At block 926, processing logic may identify one or more differences between the first shape of first projection 1000 and the second shape of the first shell based on the comparison performed at block 832 of method 820. In some embodiments, processing logic may identify the one or more differences by determining (block 928) one or more regions where the first shape of first projection 1000 does not match the second shape of the first shell. Processing logic may also determine (block 930) differences in regions (e.g., at least one of a thickness of the one or more regions or an area of ​​the one or more regions).

[0208] In box 932, processing logic may determine whether one or more differences (e.g., thickness, area, etc.) between the second shape of the first shell and the first shape of the first projection 1000 exceed a first threshold. The first threshold can be any suitable configurable amount (e.g., a thickness greater than three millimeters (mm), 5 mm, 10 mm, an area having an area greater than one hundred square millimeters, etc.). At box 934, processing logic may determine whether the first shell is deformed based on whether the one or more differences exceed the first threshold. When the differences exceed the first threshold, processing logic may classify the appliance as deformed. When the one or more differences do not exceed the first threshold, processing logic may determine that the shape of the first shell is not deformed, and may continue to perform additional quality control (e.g., cutting line deformation detection).

[0209] Fig.11 A flow chart of a method 1100 for determining a shape difference between a digital model of a shell (e.g., an approximate outer surface of the shell without thickness) and an image of the shell is shown according to one embodiment. One or more operations of the method 1100 are performed by processing logic of a computing device. The processing logic may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed by a processing device), firmware, or a combination thereof. For example, one or more operations of the method 1100 may be performed by a processor. Fig.21 The image-based quality control module 2150 is executed by the processing device.

[0210] In some embodiments, method 1100 may be performed to identify (block 906 of method 900 , block 926 of method 920 ) one or more differences between the shape of first projection 1000 and the shape of the first shell. Fig.12 An example user interface 1200 is shown depicting the first projection 1000 overlaid on a top view image 1202 of a first housing. Image-based quality control may include coloring an image of an appliance obtained with a dark / black background so that the area occupied by the appliance is highlighted in the plane on the image. The user interface 1200 may be implemented in computer instructions stored on one or more storage devices and executable by a computing device (e.g., Fig.21 The user interface 1200 may be executed by one or more processing devices of the computing device 2100 in the embodiment of the present invention. The user interface 1200 may be displayed on a display of the computing device.

[0211] At block 1102, processing logic may generate an outline of a first shell from the overhead view image 1202. As shown, the first projection 1000 has a first color (e.g., red), and the outline of the first shell has a second color (e.g., blue). The processing logic may set (block 1104) an area 1204 where the outline and projection 1000 overlap to a third color (e.g., white). The first color, the second color, and the third color may be different from each other. In addition, the processing logic may determine one or more differences by identifying any area having the first color or the second color. Based on the first and second colors bordering the third color of area 1204, the user interface 1200 may be used to easily identify portions of the projection that do not overlap. For example, the first color (e.g., red) and the second color (e.g., blue) may overlap. Fig.12 A visible region in the image may be identified and certain measurements may be taken, such as the thickness, area, and / or perimeter of the region. If the measurement exceeds a first threshold, processing logic may determine that the appliance is deformed.

[0212] The user interface 1200 displays a pop-up message providing various options and results. These options can include connecting a camera, loading an image from a camera, loading an image from a file, and performing an IBQC process. The results can include a classification (e.g., deformed in this case) and the actual results for the inward parameters and the outward parameters. The defect area for the inward parameters is 96.9 and the defect thickness for the inward parameters is 2.13. The defect area for the outward parameters is 60.9 and the defect thickness for the outward parameters is 1.85. Because one or more of these measurements exceeds the desired threshold, the processing logic can determine that the appliance being inspected is deformed. The technician can read this result and can create another appliance, repair the deformation, etc.

[0213] Figures 13A-13B Additional example comparisons of the contour of a digital model of an appliance (eg, an approximate outer surface of the appliance without thickness) and the contour of an image of the appliance to detect deformation are shown in accordance with one embodiment. Fig.13A A top view image 1202 of an appliance is depicted having a first line 1300 representing a contour edge of a first projection 1000 of a digital model of the appliance and a second line 1302 representing a contour edge of the image 1202 of the appliance. In some cases, the first line 1300 may be set to a first color (e.g., red) and the second line 1302 may be set to a second color (e.g., blue) that is different from the first color. A defective area of ​​the appliance may be determined to be between the first line 1300 and the second line 1302. As described above, one or more measurements may be obtained from the defective area. For example, the thickness of the defective area, the area of ​​the defective area, etc. If a measurement exceeds a threshold, it may be determined that the appliance is deformed.

[0214] Fig. 13B A close-up display of a portion of image 1202 is shown. As shown, a first line 1300 representing the outline edge of the projected digital model of the appliance does not match a second line 1302 representing the outline edge of the image 1202 of the appliance. As a result, the appliance appears wider than expected based on the first projection of the digital model of the appliance. In this case, processing logic can measure the thickness, area, or perimeter of the region between line 1300 and line 1302 and determine that the appliance is deformed.

[0215] Fig.14A Deforming the outline of the digital model to more closely match the outline of the image of the appliance to detect other manufacturing defects (e.g., cut line variations, debris, webbing, trimmed accessories, and missing accessories, etc.) according to one embodiment is shown. One or more operations of method 1400 are performed by processing logic of a computing device. The processing logic may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed by a processing device), firmware, or a combination thereof. For example, one or more operations of method 1400 may be performed by a processor that executes Fig.21 The method 1400 may include an operation performed to detect other manufacturing defects of the shell when the processing logic determines that the difference between the shape of the first projection and the shape of the image of the first shell is within a first threshold. Although the method 1400 may be described below as specifically detecting a cut line deviation, it should be understood that the method 1400 is also applicable to detecting other manufacturing defects in the aligner.

[0216] For example, at block 1402, processing logic may determine that one or more differences (e.g., thickness, area, perimeter, etc.) do not exceed a first threshold. Thus, processing logic may perform additional comparisons to identify other deformations. In some embodiments, at block 1404, processing logic may generate a modified projection of the first digital model of the appliance by deforming the curvature of the first shape of the first projection 1000 so that the curvature of the first shape approximately matches the curvature of the second shape of the first shell. After the curvature deformation, the modified projection may have a new third shape. To generate the modified projection, processing logic may perform operations at blocks 1406-1414.

[0217] For the sake of clarity, Figures 15A-15C Discussed together with method 1400, because Figures 15A-15C An example of generating a modified projection to detect cut line changes by deforming the digital model contour to more closely match the contour of the image of the appliance is shown according to one embodiment. Fig.15AA top view of the contour edges of the contour of the image of the appliance and the contour edges of the contour of the first projection 1000 of the digital model of the appliance is shown. Fig. 15B The point on the midline is shown being pulled or shifted on the intersection line so that the entire outline of the digital model of the appliance is shifted outward to more closely match the outline of the image of the appliance. Fig. 15C A modified projection is shown which has been deformed so that its curvature approximately matches the curvature of the outline of the second shape of the housing.

[0218] At block 1406 of method 1400, processing logic may identify a median line 1500 of the first projection 1000 of the digital model of the appliance, such as Fig.15A In one example, the median line may be approximated based on the thickness between the contoured edges of the digital model. Multiple pairs of points may be added to the contour edges of the contours of both the image and the digital model of the appliance. At block 1410, processing logic may project multiple intersection lines 1502 that intersect the median line 1500 perpendicularly, which are also in Fig.15A . Pairs of points on the first projection 1000 of the digital model and pairs of points on the image of the appliance may be matched, and an intersection line 1502 perpendicular to the middle line 1500 may intersect the matched pairs and the middle line 1500. The middle line 1500 may be projected on the first projection 1000. The intersection line 1502 may also intersect at least two points on the outline of the first shape of the first projection 1000 and at least two points on the outline of the second shape of the shell. At block 1412, processing logic may identify points on the intersection line 1502 at the intersection between each corresponding line and the middle line 1500.

[0219] At block 1414, processing logic may move the point along intersection line 1502 to approximately match the outline of the first shape of first projection 1000 to the outline of the second shape of the housing, such as Fig. 15B As shown. Processing logic may offset points on the middle line 1500 of the first projection 1000 of the digital model to move the middle line 1500 of the digital model to a position where it is located in the middle of the image of the appliance. Once processing logic determines that the middle line 1500 of the first projection 1000 of the digital model is close enough to the middle of the image of the appliance, processing logic may stop offsetting the points. Moving the points may generate a modified projection 1504 having a third shape. The third shape (e.g., the outline of the modified projection 1504) may more closely match the outline of the image 1202 of the appliance, as shown. Fig. 15C shown.

[0220] When the contour of the first projection 1000 of the digital model more closely matches the image 1202 of the appliance, the processing logic may compare the image of the cut line of the appliance with the cut line of the modified projection 1504 of the digital model of the appliance to identify (block 1416) the remaining differences between the second shape of the first shell and the third shape of the modified projection 1504. For example, the processing logic may identify one or more additional areas where the second shape of the first shell does not match the third shape of the modified projection 1504. The one or more additional areas may correspond to the cut line of the first shell. At block 1418, the processing logic may determine whether the remaining differences exceed a second threshold. For example, a variation of up to 1 millimeter between the contour of the modified digital model and the contour of the image may be tolerated. Any variation greater than or equal to 1 millimeter may be determined to be a cut line defect. It should be noted that any suitable measurement threshold may be used. If the measured area exceeds the second threshold, the processing logic may determine that there is a cut line deformation. That is, the processing logic may determine that the cut line of the first shell may interfere with the fit of the first shell on the patient's dental arch. In some embodiments, when determining cut line deformation, a side view image of the appliance may be used to compare with a side view of the digital model.

[0221] Fig. 14B A flow chart of another method 1420 for deforming the contour of an approximate outer surface of an appliance (e.g., a digital model of the appliance without thickness) to more closely match the contour of an image of the appliance to detect cut line changes is shown according to one embodiment. One or more operations of the method 1420 are performed by processing logic of a computing device. The processing logic may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed by a processing device), firmware, or a combination thereof. For example, one or more operations of the method 1420 may be performed by an execution Fig.21 The method 1420 may include an operation of determining a cut line deformation of the shell when the processing logic determines that the difference between the shape of the first projection and the shape of the image of the first shell is within a first threshold.

[0222] For example, at block 1422, processing logic may determine that one or more differences between the approximated characteristic of the first shell and the measured characteristic of the first shell (e.g., thickness, area, perimeter, etc.) do not exceed a first threshold. Thus, processing logic may perform additional comparisons to identify other deformations. In some embodiments, at block 1424, processing logic may generate a modified projection of the approximate outer surface of the first shell by deforming the first shape of the first projection 1000 so that the first curvature of the deformed first shape approximately matches the second curvature of the second shape of the first shell. After deformation, the modified projection may have a new third shape. To generate the modified projection, processing logic may perform the operations at blocks 1426-1424.

[0223] For the sake of clarity, Figures 15A-15C Discussed together with method 1420, because Figures 15A-15C An example of generating a modified projection to detect cut line changes by deforming the approximate outer surface contour to more closely match the contour of the image of the appliance is shown according to one embodiment. Fig.15A A top view of the contour edge of the contour of the image of the appliance and the contour edge of the contour of the first projection 300 of the approximate outer surface of the appliance is shown. Fig. 15B Points on the midline are shown being pulled or shifted on the intersection line so that the entire contour of the approximate outer surface of the appliance is shifted outward to more closely match the contour of the image of the appliance. Fig. 15C A modified projection is shown which has been deformed so that its shape approximately matches the contour of the shape of the housing.

[0224] At block 1426 of method 1420, processing logic may identify a median line 1500 of the first projection 1000 of the approximate outer surface of the appliance, such as Fig.15A In one example, the median line may be approximated based on the width between the contoured edges of the approximated outer surface. Multiple pairs of points may be added to the contoured edges of the contours of both the image of the appliance and the approximated outer surface. At block 1428, processing logic may calculate projections of multiple intersection lines 1502 that intersect perpendicularly with the median line 1500, which are also in Fig.15A . Pairs of points on the first projection 1000 of the approximate outer surface and pairs of points on the image of the appliance may be matched, and an intersection line 1502 perpendicular to the middle line 1500 may intersect the matched pairs and the middle line 1500. The middle line 1500 may be projected on the first projection 1000. The intersection line 1502 may also intersect at least two points on the outline of the first shape of the first projection 1000 and at least two points on the outline of the shape of the shell. At block 1430, processing logic may identify points on the intersection line 1502 at the intersection between each corresponding line and the middle line 1500.

[0225] At block 1432, processing logic may move the points along intersection line 1502 so that the contour of the first shape of first projection 1000 approximately matches the contour of the second shape of the housing, such as Fig. 15B As shown. Processing logic can offset points on the middle line 1500 of the first projection 1000 of the approximate outer surface of the first shell to move the middle line 1500 of the approximate outer surface to a position where it is located in the middle of the image of the aligner. Once processing logic determines that the middle line 1500 of the first projection 1000 of the approximate outer surface is close enough to the middle of the image of the aligner, processing logic can stop offsetting these points. Moving these points can generate a modified projection 1504 with a deformed first shape, the deformed first shape including a curvature that approximately matches the curvature of the second shape.

[0226] When the contour of the first projection 1000 of the approximate outer surface more closely matches the contour of the image 1202 of the appliance, the processing logic may compare the image of the cut line of the appliance with the image of the cut line of the modified projection 1504 of the approximate outer surface of the appliance to identify (block 1434) additional differences between the first curvature of the deformed second shape and the second curvature of the first shape of the first shell. For example, the processing logic may identify one or more additional areas where the second curvature and the first curvature do not match. This comparison may be performed based on the remaining differences between the third shape of the modified projection and the second shape of the first shell measured from the top view image. Alternatively or additionally, a new projection onto the second plane defined by the side view image may be calculated based on the modified projection in the first plane defined by the top view image. One or more additional areas may correspond to the cut line of the first shell. At block 1436, the processing logic may determine whether the additional difference exceeds a second threshold. For example, a variation of up to 1 mm between the contour of the modified approximate outer surface and the contour of the image may be tolerated. Any variation greater than or equal to 1 mm may be determined as a cut line defect. It should be noted that any suitable measurement threshold may be used. If the measured area exceeds the second threshold, the processing logic may determine that there is a cutting line deformation. That is, the processing logic may determine that the cutting line of the first shell may interfere with the fit of the first shell on the patient's dental arch.

[0227] Fig. 14CA flow chart of another method 1440 for deforming the contour of an approximate outer surface of an appliance (e.g., a digital model of the appliance without thickness) to more closely match the contour of an image of the appliance to detect cut line changes is shown according to one embodiment. One or more operations of the method 1440 are performed by processing logic of a computing device. The processing logic may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed by a processing device), firmware, or a combination thereof. For example, one or more operations of the method 1440 may be performed by an execution Fig.21 The method 1440 may include an operation of determining a cut line deformation of the shell when the processing logic determines that the difference between the shape of the first projection and the shape of the image of the first shell is within a first threshold.

[0228] At block 1442, processing logic may generate a modified projection of an approximate outer surface of the first shell by deforming the first projection's first shape so that a first curvature of the deformed first shape approximately matches a second curvature of a second shape of the first shell. The deformation of the shape of the first projection may be performed as described above.

[0229] At block 1444, processing logic may determine a second plane associated with the side-view image. At block 1446, processing logic may deform the approximate outer surface of the first shell according to the deformation of the second shape of the first projection. At block 1448, processing logic may calculate a second projection of the deformed approximate outer surface of the first shell onto the second plane. At block 1450, processing logic may determine an additional difference between a third shape of the first shell represented in the side-view image and an approximate fourth shape of the first shell represented in the second projection.

[0230] At box 1452, the processing logic may determine whether one or more additional differences exceed a second threshold. For example, a change of up to 1 millimeter between the contour of the second projection of the deformed approximated outer surface and the contour of the second image may be tolerated. Any change greater than or equal to 1 millimeter may be determined to be a cut line defect. It should be noted that any suitable measurement threshold may be used. If the measured area exceeds the second threshold, the processing logic may determine that there is a cut line deformation. That is, the processing logic may determine that the cut line of the first shell may interfere with the fit of the first shell on the patient's dental arch.

[0231] Fig.16A flow chart of method 1600 for generating a digital model of a shell (e.g., an approximate outer surface of an appliance) that can be included in a digital file is shown according to one embodiment. Alternatively or additionally, method 1600 can be performed using a digital model of a mold of the shell to approximate characteristics of the shell that can be compared to measured characteristics of the shell that can be determined from an image of the shell. Thus, method 1600 can be performed without generating a digital model of the shell. One or more operations of method 1600 are performed by processing logic of a computing device. The processing logic may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed by a processing device), firmware, or a combination thereof. For example, one or more operations of method 1600 may be performed by an executable Fig.21 The digital model of the specific shell for a specific stage in the patient treatment plan can be generated based on the digital model of the mold of the patient's dental arch at that specific stage of the patient treatment plan. The mold is manufactured using the digital model of the mold (e.g., by 3D printing), and the shell is manufactured using the mold using a thermoforming process. The digital model of the shell may not be used to manufacture the shell.

[0232] At box 1602, processing logic can simulate the process of thermoforming a film on the digital model of the mold by expanding the digital model of the mold into an expanded digital model. The expansion performed can take into account the thickness of the shell. The expansion or expansion can scale the surface of the digital model of the mold by a predetermined factor. The processing logic can determine the cutting line of the expanded digital model of the shell. In one example, the processing logic can determine the cutting line of the surface of the expanded digital model of the shell by finding the intersection line between the gums and the teeth and modifying the intersection line by raising the intersection line so that it does not contact the gums. At box 1604, the processing logic can project the cutting line onto the expanded digital model. In some embodiments, the cutting line can be displayed to the user as a line with a certain color (e.g., yellow) superimposed on the expanded digital model of the surface of the aligner.

[0233] At box 1606, the processing logic can virtually cut the enlarged digital model along the cutting line to create a cut enlarged digital model. That is, the determined cutting line can be used during the simulation process to remove the redundant enlarged surface to generate the virtual surface of the appliance by cutting along the determined virtual cutting line. At box 1608, the processing logic can select the outer surface of the cut enlarged digital model as the digital model of the shell. The digital model of the shell can be obtained by the simulated trimming, and the digital model can represent the outer surface of the appliance. The digital model of the appliance can be three-dimensional, and the digital model of the appliance can be used to obtain various two-dimensional views (e.g., top view, side view) or three-dimensional views. In addition, the digital model of the appliance can be associated with the component number identifier of the corresponding appliance, so that the digital model of the appliance can be obtained when the processing logic recognizes the component number identifier using the image of the manufactured appliance. In an alternative embodiment, the technician can read the component number identifier and enter it into the IBQC system. Alternatively, the IBQC system can read other computer-readable labels associated with the appliance for component number identification. The system can receive the component number identifier information and then retrieve a previously generated digital model of the appliance or a digital model of a mold associated with the appliance from a database to generate a digital model of the appliance. Thus, before or during the quality control process, a separate digital model of the appliance can be generated for each appliance at each stage in the patient's treatment plan. The processing logic can use the identified component number identifier to perform IBQC for each uniquely manufactured appliance.

[0234] Fig.17 A flow chart of a general method 1700 for determining a stationary position of a digital model of a shell on a flat surface is shown according to one embodiment. The digital model may be included in a digital file, and the digital model may include an approximation of a first characteristic (e.g., an outer surface of the shell). The digital model of the shell may be generated based on manipulation of the digital model of the mold. One or more operations of method 1700 are performed by processing logic of a computing device. The processing logic may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed by a processing device), firmware, or a combination thereof. For example, one or more operations of method 1700 may be performed by an execution Fig.21The method 1700 may be performed by a processing device of the image-based quality control module 2150 of the embodiment of the present invention. The method 1700 may be performed to project a first projection of the digital model of the shell into the same plane associated with the top view of the shell. The method 1700 may also be used when the digital model of the shell is projected into other planes associated with images of the shell taken from other angles. The method 1700 may be performed, for example, in blocks 904 and / or 924 of methods 900 and / or 920, respectively, to correctly calculate the projection of the digital model into the plane associated with the top view image of the appliance.

[0235] At block 1702 , processing logic may determine a stationary position of a first digital model of a first housing on a planar surface. Fig.18 The method 1800 describes determining a stationary position of a digital model of a shell on a flat surface using a two-dimensional digital model, Figures 19A-19C Depicted is an example of determining a stationary position using a two-dimensional digital model. Fig. 20 Method 2000 describes determining a static position of a digital model of a housing on a flat surface using a three-dimensional digital model. Once the static position is determined, processing logic may calculate a projection of the first digital model having the static position onto the plane of the top view image (block 1704).

[0236] Fig.18 A flow chart of a method 1800 for determining a stationary position of a digital model of a shell (e.g., an approximate outer surface of the shell) on a flat surface using a two-dimensional digital model is shown according to one embodiment. One or more operations of method 1800 are performed by processing logic of a computing device. The processing logic may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed by a processing device), firmware, or a combination thereof. For example, one or more operations of method 1440 may be performed by an executable Fig.21 The image-based quality control module 2150 is executed by the processing device. For clarity, the method 1800 and Figures 19A-19C .

[0237] Fig.19A A two-dimensional outline of an arbitrary object 1900 is shown. Although an arbitrary object 1900 is depicted, it should be understood that method 1800 can be applied to the outline of a digital model of a shell. At box 1802, processing logic can determine the center of mass 1902 of the object 1900. At box 1804, processing logic can determine a convex hull 1904 (e.g., a polygon) of the object 1900. The convex hull can include a plurality of vertices linking the outermost points of the object 1900. In particular, three vertices F1, F2, and F3 are identified on the convex hull 1904.

[0238] For each vertex of convex hull 1904, processing logic may calculate (block 1806) a line 1906 that contains the vertex. At block 1808, processing logic may calculate the projection of centroid 1902 onto line 1906, such as Fig.19B 1908 in FIG. 1810. At block 1810, processing logic may determine whether projection point 1908 is outside of a vertex. For vertex F1, projection point 1908 is outside of vertex F1, so object 1900 may not be stationary on side F1 because object 1900 will roll to the left. The same is true for vertex F2. If projection point 1908 is outside of a vertex, processing logic may return to repeat blocks 1806, 1808, and 1810 until a stationary position is found.

[0239] If the projected point 1908 is located within a vertex, processing logic may determine that the particular vertex is a stationary position of the first digital model of the first shell. Fig.19C A line 1910 is shown drawn containing vertex F3. The centroid 1902 is projected onto the line 1910, and as shown, the projected point 1912 is located within vertex F3 defining the line 1910. Thus, the object 1900 can be stationary on this side without rolling. Using this approach, processing logic can determine the stationary position and identify the appropriate projection of the digital model of the appliance for quality control analysis depending on the image view (e.g., top view, side view, etc.).

[0240] Fig. 20 A flow chart of a method 2000 for determining a stationary position of a digital model of a shell on a flat surface using a three-dimensional digital model is shown according to one embodiment. One or more operations of the method 2000 are performed by processing logic of a computing device. The processing logic may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed by a processing device), firmware, or a combination thereof. For example, one or more operations of the method 2000 may be performed by an executable program. Fig.21 The image-based quality control module 2150 is executed by the processing device.

[0241] At block 2002, processing logic may determine the centroid of a first digital model of a first shell. At block 2004, processing logic may determine a convex hull of the digital model (e.g., a polyhedron for a three-dimensional digital model). The convex hull may include a number of faces linking the outermost points of the first digital model. For each face of the convex hull, processing logic may calculate (block 2006) a plane containing the face. At block 2008, processing logic may calculate a projection of the centroid onto the plane. At block 2010, processing logic may determine whether the projected point on the plane is outside the face. If the projected point is outside the face, processing logic may return to repeat blocks 2006, 2008, and 2010 until a stationary position is found. If the projected point is not outside the face, processing logic may determine (block 2012) that the face is the stationary position of the first digital model.

[0242] Fig.21 A graphical representation of a machine in the example form of a computing device 2100 is shown, wherein a set of instructions is used to cause the machine to perform any one or more methods discussed herein. In some embodiments, the machine may be part of an IBQC site, or may be communicatively coupled to an IBQC site. In alternative embodiments, the machine may be connected (e.g., networked) to other machines in a local area network (LAN), an intranet, an extranet, or the Internet. For example, the machine may be networked to an IBQC site and / or a rapid prototyping device, such as a 3D printer or an SLA device. The machine may be run in a client-server network environment with the capabilities of a server or client machine, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet computer, a set-top box (STB), a personal digital assistant (PDA), a cellular phone, a network device, a server, a network router, a switch or a bridge, or any machine capable of executing a set of instructions (sequentially or otherwise) specifying the actions to be taken by the machine. Further, while a single machine is illustrated, the term "machine" shall also be taken to include any collection of machines (e.g., computers) that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0243] The example computing device 2100 includes a processing device 2102, a main memory 2104 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM)), etc.), a static memory 2106 (e.g., flash memory, static random access memory (SRAM), etc.), and an auxiliary memory (e.g., a data storage device 2128), which communicate with each other via a bus 2108.

[0244] The processing device 2102 represents one or more general-purpose processors such as a microprocessor, a central processing unit, etc. More specifically, the processing device 2102 can be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor that implements other instruction sets, or a processor that implements a combination of instruction sets. The processing device 2102 can also be one or more special-purpose processing devices, such as an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, etc. The processing device 2102 is configured to execute processing logic (instructions 2126) for performing the operations and steps discussed herein.

[0245] The computing device 2100 may also include a network interface device 2122 for communicating with a network 2164. The computing device 2100 may also include a video display unit 2110 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 2112 (e.g., a keyboard), a cursor control device 2114 (e.g., a mouse), and a signal generating device 2120 (e.g., a speaker).

[0246] The data storage device 2128 may include a machine-readable storage medium (or more specifically, a non-transitory computer-readable storage medium) 2124 on which is stored one or more sets of instructions 2126 that embody any one or more of the methods or functions described herein. A non-transitory storage medium refers to a storage medium other than a carrier wave. During the execution of the instructions by the computer device 2100, the instructions 2126 may also reside completely or at least partially in the main memory 2104 and / or in the processing device 2102, and the main memory 2104 and the processing device 2102 also constitute computer-readable storage media.

[0247] The computer-readable storage medium 2124 may also be used to store one or more virtual 3D models (also referred to as electronic models) and / or IBQC modules 2150, which may perform one or more operations of the methods described herein. The computer-readable storage medium 2124 may also store a software library containing a method for calling the IBQC module 2150. Although the computer-readable storage medium 2124 is shown as a single medium in the example embodiment, the term "computer-readable storage medium" should be considered to include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) storing one or more sets of instructions. The term "computer-readable storage medium" should also be considered to include any medium capable of storing or encoding a set of instructions for execution by a machine and causing the machine to perform any one or more methods of the present invention. Therefore, the term "computer-readable storage medium" should be considered to include, but is not limited to, solid-state memory, and optical and magnetic media.

[0248] It should be understood that the above description is intended to be illustrative rather than restrictive. Upon reading and understanding the above description, many other embodiments will be apparent. Although embodiments of the present invention have been described with reference to specific example embodiments, it should be appreciated that the present invention is not limited to the described embodiments, but can be practiced by modification and alteration within the spirit and scope of the appended claims. Therefore, the description and drawings should be regarded as illustrative rather than restrictive. Therefore, the scope of the present invention should be determined with reference to the appended claims and the full scope of equivalents to the rights of these claims.

Claims

1. A method for inspecting a dental appliance for manufacturing defects, the method comprising: obtaining one or more images of a customized orthodontic appliance that is customized for a specific dental arch of a specific patient and a specific stage of orthodontic treatment; Identifiers that identify custom orthodontic appliances; determining a digital file associated with the customized orthodontic appliance from a plurality of digital files based on the identified identifier, the digital file associated with the customized orthodontic appliance comprising a digital model of an intermediate component used during manufacture of the customized orthodontic appliance; determining, based on the digital file, desired characteristics of the customized orthodontic appliance, wherein the desired characteristics of the customized orthodontic appliance include desired cut lines for the customized orthodontic appliance; determining an actual characteristic of the customized orthodontic appliance from the one or more images of the customized orthodontic appliance, wherein the actual characteristic of the customized orthodontic appliance includes an actual cut line of the customized orthodontic appliance captured by the one or more images of the customized orthodontic appliance; determining whether a manufacturing defect exists in the customized orthodontic appliance by comparing an expected characteristic of the customized orthodontic appliance with an actual characteristic of the customized orthodontic appliance, wherein comparing the expected characteristic of the customized orthodontic appliance with the actual characteristic comprises comparing an expected cut line with the actual cut line and determining whether a difference exceeds a threshold value; and Output associated with determining whether a manufacturing defect exists is output.

2. The method according to claim 1, wherein: The identifier of the customized orthodontic appliance is printed on the customized orthodontic appliance, and wherein the identifier of the customized orthodontic appliance is identified by analyzing the one or more images of the customized orthodontic appliance.

3. The method according to claim 1, wherein: Identifying the identifier of the customized orthodontic appliance includes receiving user input of the identifier.

4. The method according to claim 1, wherein: Desired properties of the customized orthodontic appliance are determined by digitally manipulating a portion of the surface of the digital model of the intermediate component to approximate the surface of the intermediate component.

5. The method according to claim 1, wherein: The intermediate assembly comprises a positive mold associated with the customized orthodontic appliance, and wherein the desired characteristics of the customized orthodontic appliance are determined by calculating a profile of a digital model of the positive mold in a plane.

6. The method according to claim 5, wherein: The expected characteristics of the customized orthodontic appliance are calculated by offsetting the calculated perimeter of the contour of the digital model of the positive mold in this plane.

7. A method for inspecting a dental appliance for manufacturing defects, the method comprising: obtaining one or more images of the dental appliance; an identifier to identify the dental appliance; determining a digital file associated with the dental appliance from a plurality of digital files based on the identified identifier, the digital file associated with the dental appliance comprising a digital model of an intermediate component used during manufacture of the dental appliance; determining desired characteristics of the dental appliance based on the digital file, wherein the desired characteristics of the dental appliance include an desired contour that approximates an outer surface of the dental appliance in a plane; determining an actual characteristic of the dental appliance from the one or more images of the dental appliance, wherein the actual characteristic of the dental appliance comprises an actual contour of an outer surface of the dental appliance in the plane as captured by the one or more images of the dental appliance; determining whether a manufacturing defect exists in the dental appliance by comparing an expected characteristic of the dental appliance with an actual characteristic of the dental appliance, wherein comparing the expected characteristic of the dental appliance with the actual characteristic comprises comparing an expected profile with the actual profile and determining whether a difference exceeds a threshold; and outputs associated with determining whether a manufacturing defect exists, wherein the desired characteristic of the dental appliance is determined by digitally manipulating a portion of the surface of the digital model of the intermediate component to approximate the surface of the intermediate component, and Therein, the surface of the dental appliance is approximated by offsetting the portion of the surface of the digital model of the intermediate component by a distance, wherein the intermediate component is a positive mold.

8. The method according to claim 7, wherein: The expected characteristics of the dental appliance are determined by virtually projecting a cutting line associated with the dental appliance onto an approximate surface of the dental appliance.

9. The method according to claim 7, wherein: The expected profile of the dental appliance is determined by calculating an approximate surface profile of the dental appliance in a plane.

10. A computer readable medium comprising instructions, which when executed by a processing device, cause the processing device to perform the method of any one of claims 1-9.

11. A system for executing the method according to any one of claims 1 to 9, the system comprising: one or more cameras for generating the one or more images; as well as A processing device is used to process the one or more images.

12. A system for inspecting a custom dental device for manufacturing defects, wherein: The customized dental device is associated with a dental application and is customized for a specific patient, the system comprising: Memory; and A processor operatively coupled to the memory, the processor configured to: obtaining one or more images of the customized dental device; an identifier identifying the customized dental device; determining one or more digital files associated with the customized dental device from a plurality of digital files based on the identifier, the one or more digital files comprising at least one of a first digital model of the customized dental device or a second digital model of a mold used during manufacture of the customized dental device; determining desired characteristics of the customized dental device based on at least one of the first digital model or the second digital model; determining actual characteristics of the customized dental device from the one or more images of the customized dental device; determining whether at least one of a gingival cut line change, a dental arch change, or a curvature of the customized dental device is detected between the customized dental device and at least one of the first digital model or the second digital model by comparing an expected characteristic of the customized dental device with an actual characteristic of the customized dental device; determining whether a manufacturing defect exists in the customized dental device based on at least one of whether a gum cut line change exceeds a gum cut line change threshold, whether a dental arch change exceeds a dental arch change threshold, or whether a bend exceeds a bend threshold; and Output associated with determining whether a manufacturing defect exists is output.

13. The system according to claim 12, wherein: Desired properties of the customized dental device are determined by digitally manipulating a second digital model of a mold used during fabrication of the customized dental device to approximate a surface of the customized dental device by offsetting a portion of the surface of the second digital model of the mold by a distance.

14. The system according to claim 12, wherein: The desired characteristics of the custom dental device are determined by: determining an approximate surface of the customized dental device from at least one of the first digital model or the second digital model; as well as A cutting line associated with the customized dental device is virtually projected onto the approximated surface of the customized dental device.

15. The system of claim 12, wherein: The expected characteristics of the customized dental device include an expected contour of the customized dental device in a plane, and wherein the actual characteristics of the customized dental device include an actual contour of the customized dental device in the plane as captured by the one or more images of the customized dental device.

16. The system of claim 15, wherein: The desired profile of the customized dental device is determined by: determining an approximate surface of the customized dental device from at least one of the first digital model or the second digital model; as well as An approximate surface contour of the customized dental device in the plane is calculated.

17. The system of claim 15, wherein: The desired characteristics of the custom dental device are determined by: calculating the contour of the second digital model of the mold in the plane; and The offset calculates the perimeter of the contour of the second digital model of the mold in the plane.

18. The system of claim 15, wherein: Comparing the expected characteristic to the actual characteristic of the customized dental device includes comparing the expected contour to the actual contour.

19. The system of claim 12, wherein: The expected characteristics of the customized dental device include an expected cut line of the customized dental device, and wherein the actual characteristics of the customized dental device include an actual cut line of the customized dental device determined from the one or more images.

20. The system of claim 12, wherein: The output includes a determination that a defect exists, and wherein the output includes a suggested digital modification to a second digital model of the mold used during fabrication of the customized dental device to limit future defects, wherein the suggested digital modification to the second digital model of the mold includes at least one of added virtual fill material, a correction to a cut line, or a modification to one or more accessories of the mold.

21. The system of claim 12, wherein: The customized dental device includes a customized orthodontic appliance.

22. The system of claim 12, wherein: The identifier includes at least one of a barcode, text, or a number.

23. A non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to perform operations for inspecting a customized dental device for manufacturing defects, wherein: Customized dental devices are associated with dental applications and are customized for a specific patient, including: obtaining one or more images of the customized dental device; an identifier identifying the customized dental device; determining, based on the identifier, a digital file associated with the customized dental appliance from the plurality of digital files, the digital file associated with the customized dental appliance comprising a digital model of the customized dental appliance; determining desired characteristics of the customized dental device from the digital model of the customized dental device; determining actual characteristics of the customized dental device from the one or more images; determining whether at least one of a gingival cut line change, a dental arch change, or a curvature of the customized dental appliance is detected between the customized dental appliance and the digital model of the customized dental appliance by comparing expected characteristics of the customized dental appliance with actual characteristics of the customized dental appliance; determining whether a manufacturing defect exists in the customized dental device based on at least one of whether a gum cut line change exceeds a gum cut line change threshold, whether a dental arch change exceeds a dental arch change threshold, or whether a bend exceeds a bend threshold; and Output associated with determining whether a manufacturing defect exists is output.

24. The non-transitory computer readable medium of claim 23, the operations further comprising: The digital model of the customized dental appliance is generated by digitally manipulating a second digital model of a staged dental arch of a particular patient associated with a particular stage of orthodontic treatment.

25. The non-transitory computer readable medium of claim 24, wherein: The digitally manipulating is performed to approximate a surface of the customized dental device by offsetting a portion of a surface of the second digital model of the staged dental arch by a distance.

26. The non-transitory computer readable medium of claim 23, wherein: The expected characteristics of the customized dental device include an expected contour of the customized dental device in a plane, and wherein the actual characteristics of the customized dental device include an actual contour of the customized dental device in the plane determined from the one or more images.

27. The non-transitory computer readable medium of claim 26, wherein: The desired profile of the customized dental device is determined by: determining an approximate surface of the customized dental device from at least one of the digital model or a second digital model; as well as An approximate surface contour of the customized dental device in the plane is calculated.

28. The non-transitory computer readable medium of claim 26, wherein: The desired characteristics of the custom dental device are determined by: calculating a contour in the plane of a second digital model of a mold for manufacturing the customized dental appliance; and The offset calculates the perimeter of the contour of the second digital model of the mold in the plane.

29. The non-transitory computer readable medium of claim 26, wherein: Comparing the expected characteristic to the actual characteristic of the customized dental device includes comparing the expected contour to the actual contour.

30. The non-transitory computer readable medium of claim 23, wherein: Determining the desired characteristics includes: determining an approximate surface of the customized dental device from at least one of the digital model or a second digital model of a mold used to manufacture the customized dental device; and A cutting line associated with the customized dental device is virtually projected onto an approximate surface of the customized dental device.

31. The non-transitory computer readable medium of claim 23, wherein: The expected characteristics of the customized dental device include an expected cut line of the customized dental device, and wherein the actual characteristics of the customized dental device include an actual cut line of the customized dental device determined from the one or more images.

32. The non-transitory computer readable medium of claim 23, wherein: The customized dental device includes an orthodontic appliance.

33. The non-transitory computer readable medium of claim 23, wherein: The output includes a determination that a defect exists, and wherein the output includes a suggested digital modification to a second digital model of the mold used during fabrication of the customized dental device to limit future defects, wherein the suggested digital modification to the second digital model of the mold includes at least one of added virtual fill material, a correction to a cut line, or a modification to one or more accessories of the mold.

34. The non-transitory computer readable medium of claim 23, wherein: The identifier includes at least one of a barcode, text, or a number.

35. A non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to perform operations for inspecting a customized dental device for manufacturing defects, wherein: Customized dental devices are associated with dental applications and are customized for a specific patient, including: obtaining one or more images of the customized dental device; an identifier identifying the customized dental device; determining a digital model of a mold used in a manufacturing process of the customized dental appliance based on an identifier of the customized dental appliance; determining desired characteristics of the customized dental device based on digitally manipulating a digital model of a mold used in a manufacturing process of the customized dental device; determining actual characteristics of the customized dental device from the one or more images; determining whether at least one of a gingival cut line change, a dental arch change, or a bend in the customized dental device is detected between the customized dental device and the digital model of the mold by comparing expected characteristics of the customized dental device with actual characteristics of the customized dental device; determining whether a manufacturing defect exists in the customized dental device based on at least one of whether a gum cut line change exceeds a gum cut line change threshold, whether a dental arch change exceeds a dental arch change threshold, or whether a bend exceeds a bend threshold; and Output associated with determining whether a manufacturing defect exists is output.

36. The non-transitory computer readable medium of claim 35, wherein: The expected characteristics of the customized dental device include an expected contour of the customized dental device in a plane, and wherein the actual characteristics of the customized dental device include an actual contour of the customized dental device in the plane determined from the one or more images.

37. The non-transitory computer readable medium of claim 35, wherein: The expected characteristics of the customized dental device include an expected cut line of the customized dental device, and wherein the actual characteristics of the customized dental device include an actual cut line of the customized dental device determined from the one or more images.

38. The non-transitory computer readable medium of claim 35, wherein: Determining whether a manufacturing defect exists in the customized dental device includes: determining a difference between the actual characteristic and the expected characteristic based on a comparison of the expected characteristic of the customized dental device with the actual characteristic of the customized dental device; and A determination is made as to whether the difference exceeds a threshold.

39. The non-transitory computer readable medium of claim 35, wherein: The customized dental device includes an orthodontic appliance.

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