A 3D printed part defect online evaluation device

Abstract

An embodiment of the present invention discloses an online defect evaluation device, method and system for 3D printed molded parts. The method includes: S00, receiving the geometric shape and appearance data of the 3D printed molded part to be evaluated collected by a 3D scanner; S10, analyzing the key points of quality control of the molded part and establishing the assessment model of the molded part; S20, reverse modeling the collected geometric shape and appearance data of the molded part; S30, filtering and smoothing the image after the reverse modeling; S40, comparing the assessment model with the image after the reverse modeling that has been filtered and smoothed to determine whether the molded part is a qualified product.

Description

Technical Field

[0001] The present invention relates to the field of product surface defect detection, and more specifically, to an online defect evaluation device, method and system for 3D printed parts. Background Art

[0002] At present, the detection of surface defects of printed products is mainly based on manual inspection. The manual inspection method can no longer meet the needs of production and modern process manufacturing. In addition, manual inspection depends on the experience of the inspectors, and the inspection efficiency and accuracy cannot be guaranteed. Therefore, manual inspection will cause problems such as low inspection efficiency and large inspection errors. Summary of the invention

[0003] In order to solve at least one of the above problems, a first aspect of the present invention provides a method for online defect evaluation of a 3D printed part, comprising:

[0004] S00, receiving the geometric shape and appearance data of the 3D printed part to be evaluated collected by the 3D scanner;

[0005] S10, analyzing the key points of quality control of the formed parts and establishing the assessment model of the formed parts;

[0006] S20, performing reverse modeling on the collected geometric shape and appearance data of the formed part;

[0007] S30, performing filtering and smoothing processing on the image after the reverse modeling;

[0008] S40, comparing the assessment model with the image after reverse modeling that has been filtered and smoothed to determine whether the formed part is a qualified product.

[0009] In a specific embodiment, the S40 further includes:

[0010] S400, dividing the assessment model into a plurality of sub-images, and dividing the image after the filtering and smoothing processing and the reverse modeling into a plurality of sub-images corresponding to the plurality of sub-images of the assessment model;

[0011] S402, respectively comparing the sub-image of the reverse modeling with the sub-image of the assessment model, and if all the comparison results are within the threshold range, the product is qualified;

[0012] S404. If the sub-image of the reverse modeling and the sub-image of the assessment model have a portion exceeding the threshold range, the portion exceeding the threshold range is classified into defect types.

[0013] In a specific embodiment, classifying the defect type of the portion exceeding the threshold range includes:

[0014] S4040, selecting three feature points of the sub-image of the assessment model that are not coplanar, selecting three feature points of the sub-image of the reverse modeling that correspond to the three feature points of the assessment model, and roughly matching the three feature points of the sub-image of the reverse modeling with the three feature points of the assessment model;

[0015] S4042, setting the matching accuracy, and accurately matching the three feature points of the sub-image of the reverse modeling with the three feature points of the assessment model;

[0016] S4044. The precisely matched model is displayed in different colors according to the size of the error. The depth, position and size of the color correspond to different types of defects. The error range is set manually.

[0017] A second aspect of the present invention provides a computer device, comprising a processor and a memory storing a program, wherein the processor implements the method described in the first aspect of the present invention when executing the program.

[0018] A third aspect of the present invention provides a storage medium storing a program, which, when executed, implements the method described in the first aspect of the present invention.

[0019] A fourth aspect of the present invention provides an online defect evaluation device for 3D printed parts, comprising:

[0020] A receiving module, used for receiving the geometric shape and appearance data of the 3D printed part to be evaluated collected by the 3D scanner;

[0021] A design module for analyzing the key points of quality control of the formed parts and establishing an assessment model for the formed parts;

[0022] A modeling module, used for reverse modeling the collected geometric shape and appearance data of the formed part;

[0023] A processing module, used for filtering and smoothing the image after the inverse modeling;

[0024] The judgment module is used to compare the assessment model with the image after reverse modeling that has been filtered and smoothed to determine whether the formed part is a qualified product.

[0025] A fifth aspect of the present invention provides an online defect evaluation system for 3D printed parts, comprising:

[0026] A turntable, a 3D scanner and a computer device according to the second aspect of the invention, wherein

[0027] The turntable is used to place the formed part;

[0028] The 3D scanner is used to collect the geometric shape and appearance data of the formed part.

[0029] In a specific embodiment, the system further includes: a power cord for supplying power to the 3D scanner and the computer device.

[0030] In a specific embodiment, the system further includes: a data line for connecting the 3D scanner and the computer device.

[0031] In a specific embodiment, the system further includes: a scanner bracket for fixing the 3D scanner.

[0032] The beneficial effects of the present invention are as follows:

[0033] The present invention can overcome the technical defects of low manual inspection efficiency and large inspection error caused by the current manual inspection of surface defects of 3D printed products. This system is mainly used for online inspection of pits, scratches, defects and other defects on the surface of small 3D printed industrial products. The system performance can meet the surface defect size detection of more than 0.5mm, and the detection accuracy is 0.5mm~1mm. The defect data and evaluation results can be set according to the requirements and can be output online. The system improves the detection accuracy and efficiency of online detection of surface defects of 3D printed products. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The specific implementation modes of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0035] Figure 1 A schematic diagram of an online defect evaluation system for 3D printed parts according to an embodiment of the present invention is shown.

[0036] Figure 2 A flow chart of a method for online defect assessment of 3D printed parts according to an embodiment of the present invention is shown.

[0037] Figure 3 A schematic diagram showing the structure of a computer device for implementing the evaluation method of the present invention according to an embodiment of the present invention is shown.

[0038] Figure 4 A schematic diagram of an online defect evaluation device for 3D printed parts according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0039] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0040] like Figure 1As shown, an embodiment of the present invention provides an online detection system for surface defects of additively manufactured printed products, the system comprising: a turntable 1, a 3D scanner 3 and a computer device 5, wherein the turntable 1 is used to place the 3D printed part to be evaluated; the 3D scanner 3 is used to collect the geometric shape and appearance data of the 3D printed part to be evaluated; the computer device 5 is used to perform data processing to obtain the detection result of the 3D printed part to be evaluated.

[0041] Among them, the 3D scanner can be used for fixed turntable scanning and handheld scanning. When scanning, the required scanning method can be selected according to the size and needs of the 3D printed parts to be evaluated. The 3D printed parts to be evaluated can be scanned and spliced ​​multiple times and finally saved as STL format files.

[0042] Embodiment 1

[0043] like Figure 2 As shown, an embodiment of the present invention provides a method for online defect evaluation of 3D printed parts. In one example, the method is implemented in Figure 1 The computer device 5 comprises:

[0044] S00, receiving the geometric shape and appearance data of the 3D printed part to be evaluated collected by the 3D scanner.

[0045] S10, analyzing the key points of quality control of the formed parts and establishing the assessment model of the formed parts.

[0046] In a specific embodiment, the molded part assessment model is established using existing three-dimensional software such as solidworks, creo, ug, etc.

[0047] S20, reverse modeling the collected geometric shape and appearance data of the formed part.

[0048] In a specific embodiment, when collecting the geometric shape and appearance data of the 3D printed part to be evaluated, the light source intensity and the position of the turntable and the 3D scanner are adjusted so that the object can be clearly viewed and the cross pattern is clear. After the scanning is completed, the scanning results are checked. If there are unscanned parts in the image, the placement of the 3D printed part to be evaluated can be changed and scanned again. The collected geometric shape and appearance data of the 3D printed part to be evaluated are imported into the three-dimensional software for reverse modeling, and the modeled size is consistent with the standard model.

[0049] S30, performing filtering and smoothing processing on the image after the inverse modeling.

[0050] In a specific embodiment, filtering and smoothing are performed to correct errors to obtain more accurate data. The present invention selects a Gaussian filter for filtering. Any filter that can achieve the clarity of the image after filtering required by the present invention can be used as the filter used in the present invention, and the present invention does not limit this.

[0051] S40, comparing the assessment model with the image after reverse modeling that has been filtered and smoothed to determine whether the formed part is a qualified product.

[0052] In a specific embodiment, the S40 further includes:

[0053] S400, dividing the assessment model into a number of sub-images, and dividing the filtered and smoothed inverse modeled image into a number of sub-images corresponding to the sub-images of the assessment model, for example, setting the size of the sub-image to 15mm*30mm.

[0054] S402, respectively comparing the sub-image of the reverse modeling with the sub-image of the assessment model, using the principle of visual detection for image comparison, and if all comparison results are within the threshold range, the product is qualified;

[0055] S404. If the sub-image of the reverse modeling and the sub-image of the assessment model have a portion exceeding the threshold range, the portion exceeding the threshold range is classified into defect types.

[0056] In a specific embodiment, classifying the defect type of the portion exceeding the threshold range includes:

[0057] S4040, select three non-coplanar feature points of the sub-image of the assessment model, select three feature points of the sub-image of the reverse modeling corresponding to the three feature points of the assessment model, and roughly match the three feature points of the sub-image of the reverse modeling with the three feature points of the assessment model.

[0058] In a specific embodiment, the data of the sub-image of the reverse modeling and the sub-image of the assessment model are first imported into the analysis software, and then three non-coplanar feature points of the sub-image of the assessment model are selected, and three feature points corresponding to the three feature points of the sub-image of the reverse modeling and the assessment model are selected, and the three feature points of the sub-image of the reverse modeling are roughly matched with the three feature points of the assessment model.

[0059] S4042, setting the matching accuracy, accurately matching the three feature points of the reverse modeled sub-image with the three feature points of the assessment model, wherein the default matching accuracy of the analysis software is 0.0001;

[0060] S4044. The precisely matched model is displayed in different colors according to the size of the error. The depth, position and size of the color correspond to different types of defects. The error range is set manually.

[0061] In a specific embodiment, for example, the error range, i.e., the color on the chromatogram, represents the degree of deviation of the surface size between the reverse modeling image and the assessment model image, green for similarity, yellow-red for protrusion, light blue and dark blue for depression, and the type of defect can be judged by color, position and size. The non-green areas of the chromatogram obtained by software analysis are all defects, which are attributed to surface quality defects. They can also be roughly divided into scratches (thinner, blue), pits (coarser, blue), pores (small area, blue), support residues (yellow-red, small area, usually located on the overhanging surface), cutting errors (errors when shoveling from the tray, located on the bottom surface), and splashes (smaller, yellow-red, usually located on the outer surface) according to the size, type, and distribution of the area.

[0062] In a specific example, the handheld scanning and fixed turntable scanning parameters and indicators are listed as shown in Table 1.

[0063] Table 1 Parameters and indicators of handheld scanning and fixed turntable scanning

[0064]

[0065]

[0066] It can be seen from Table 1 that the scanning accuracy of handheld scanning is 0.1mm, and the scanning accuracy of fixed scanning is 0.05mm. Both methods can meet the detection of surface defect sizes above 0.5mm, and the detection effect is good.

[0067] In view of the existing problems, the present invention develops an online evaluation method for defects of 3D printed parts, which can overcome the technical defects of low efficiency and large detection error caused by manual detection of surface defects of 3D printed products at the current stage. This method is mainly used for online detection of pits, scratches, defects and other defects on the surface of small 3D printed industrial products. Defect data can be set according to requirements, and the evaluation results can be output online. This method improves the detection accuracy and efficiency of online detection of surface defects of 3D printed products.

[0068] Embodiment 2

[0069] like Figure 3 As shown, an embodiment of the present invention provides a schematic diagram of the structure of a computer device. Figure 3 The computer device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0070] like Figure 3 As shown, the computer device 12 is in the form of a general-purpose computer device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0071] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0072] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0073] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 3 not shown, usually called a "hard drive"). Although Figure 3 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0074] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0075] The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), one or more devices that enable a user to interact with the computer device 12, and / or any device that enables the computer device 12 to communicate with one or more other computer devices (e.g., network cards, modems, etc.). Such communication may be performed through an input / output (I / O) interface 22. Furthermore, the computer device 12 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 20. Figure 3 As shown, the network adapter 20 communicates with other modules of the computer device 12 via the bus 18. It should be understood that although Figure 3 Not shown, other hardware and / or software modules may be used in conjunction with computer device 12, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0076] The processor unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the method provided in the first embodiment of the present invention.

[0077] In view of the existing problems, the present invention develops a computer device, which can overcome the technical defects of low manual inspection efficiency and large inspection errors caused by the current manual inspection of surface defects of three-dimensional printed products. The method used by this computer device is mainly used for online detection of defects such as pits, scratches, and defects on the surface of small 3D printed industrial products. This method improves the detection accuracy and efficiency of online detection of surface defects of three-dimensional printed products.

[0078] Embodiment 3

[0079] An embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method provided in the above-mentioned embodiment 1 is implemented.

[0080] In practical applications, the computer-readable storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0081] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0082] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0083] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0084] In view of the current existing problems, the present invention develops a storage medium, which can overcome the technical defects of low manual inspection efficiency and large inspection errors caused by the current manual inspection of surface defects of three-dimensional printed products. The method used by this storage medium is mainly used for online detection of defects such as pits, scratches, and defects on the surface of small 3D printed industrial products. This method improves the detection accuracy and efficiency of online detection of surface defects of three-dimensional printed products.

[0085] Embodiment 4

[0086] like Figure 4 As shown, one embodiment of the present invention provides an online defect evaluation device for 3D printed parts, comprising:

[0087] A receiving module, used for receiving the geometric shape and appearance data of the 3D printed part to be evaluated collected by the 3D scanner;

[0088] A design module for analyzing the key points of quality control of the formed parts and establishing an assessment model for the formed parts;

[0089] A modeling module, used for reverse modeling the collected geometric shape and appearance data of the formed part;

[0090] A processing module, used for filtering and smoothing the image after the inverse modeling;

[0091] The judgment module is used to compare the assessment model with the image after reverse modeling that has been filtered and smoothed to determine whether the formed part is a qualified product.

[0092] Embodiment 5

[0093] A 3D printed part defect online evaluation system, comprising:

[0094] The turntable 1, the 3D scanner 3 and the computer device 5 according to the second aspect of the present invention, wherein

[0095] The turntable 1 is used to place the formed part;

[0096] The 3D scanner 3 is used to collect the geometric shape and appearance data of the formed part.

[0097] In a specific embodiment, the system further includes: a power cord 7 for supplying power to the 3D scanner 3 and the computer device 5 .

[0098] In a specific embodiment, the system further includes: a data line 9 for connecting the 3D scanner 3 and the computer device 5 .

[0099] In a specific embodiment, the system further includes: a scanner bracket 4 for fixing the 3D scanner 3 .

[0100] In view of the current existing problems, the present invention develops an online evaluation system for defects of 3D printed parts, which can overcome the technical defects of low efficiency and large detection errors caused by the current manual detection of surface defects of 3D printed products. This system is mainly used for online detection of pits, scratches, defects and other defects on the surface of small 3D printed industrial products. The system performance can meet the detection of surface defect sizes above 0.5mm, and the detection accuracy is 0.5mm~1mm. The system improves the detection accuracy and efficiency of online detection of surface defects of 3D printed products.

[0101] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. A method for online defect evaluation of 3D printed parts, characterized in that: include: S00, receiving the geometric shape and appearance data of the 3D printed part to be evaluated collected by the 3D scanner; S10, analyzing the key points of quality control of the formed parts and establishing the assessment model of the formed parts; S20, performing reverse modeling on the collected geometric shape and appearance data of the formed part; S30, performing filtering and smoothing processing on the image after the reverse modeling; S40, comparing the assessment model with the image after reverse modeling that has been subjected to filtering and smoothing processing, to determine whether the formed part is a qualified product; The S40 further includes: S400, dividing the assessment model into a plurality of sub-images, and dividing the image after the filtering and smoothing processing and the reverse modeling into a plurality of sub-images corresponding to the plurality of sub-images of the assessment model; S402, respectively comparing the sub-image of the reverse modeling with the sub-image of the assessment model, and if all the comparison results are within the threshold range, the product is qualified; S404, if the sub-image of the reverse modeling and the sub-image of the assessment model have a portion exceeding the threshold range, classify the portion exceeding the threshold range into defect types; The defect classification of the part exceeding the threshold range includes: S4040, selecting three feature points of the sub-image of the assessment model that are not coplanar, selecting three feature points of the sub-image of the reverse modeling that correspond to the three feature points of the assessment model, and roughly matching the three feature points of the sub-image of the reverse modeling with the three feature points of the assessment model; S4042, setting the matching accuracy, and accurately matching the three feature points of the sub-image of the reverse modeling with the three feature points of the assessment model; S4044. The precisely matched model is displayed in different colors according to the size of the error. The depth, position and size of the color correspond to different types of defects. The error range is set manually.

2. A computer device comprising a processor and a memory storing a program, characterized in that: The processor implements the method of claim 1 when executing the program.

3. A storage medium storing a program, characterized in that: When the program is executed, the method according to claim 1 is implemented.

4. A 3D printed part defect online evaluation device, characterized in that: include: A receiving module, used for receiving the geometric shape and appearance data of the 3D printed part to be evaluated collected by the 3D scanner; A design module for analyzing the key points of quality control of the formed parts and establishing an assessment model for the formed parts; A modeling module, used for reverse modeling the collected geometric shape and appearance data of the formed part; A processing module, used for filtering and smoothing the image after the inverse modeling; A judgment module, used to compare the assessment model with the image after reverse modeling that has been filtered and smoothed, to judge whether the formed part is a qualified product; The assessment model is divided into a plurality of sub-images, and the image after the filtering and smoothing processing and the reverse modeling is divided into a plurality of sub-images corresponding to the plurality of sub-images of the assessment model; The sub-images of the reverse modeling are compared with the sub-images of the assessment model respectively. If all the comparison results are within the threshold range, the product is qualified. If the sub-image of the reverse modeling and the sub-image of the assessment model have a portion exceeding the threshold range, the portion exceeding the threshold range is classified into defect types; The defect classification of the part exceeding the threshold range includes: Select three feature points of the sub-image of the assessment model that are not coplanar, select three feature points of the sub-image of the reverse modeling that correspond to the three feature points of the assessment model, and roughly match the three feature points of the sub-image of the reverse modeling with the three feature points of the assessment model; Set the matching accuracy to accurately match the three feature points of the reverse modeled sub-image with the three feature points of the assessment model; The precisely matched model is displayed in different colors according to the size of the error. The depth, position and size of the color correspond to different types of defects. The error range is set manually.

5. A 3D printed part defect online evaluation system, characterized in that: include: A turntable, a 3D scanner and a computer device according to claim 2, wherein The turntable is used to place the formed part; The 3D scanner is used to collect the geometric shape and appearance data of the formed part.

6. The system according to claim 5, characterized in that The system also includes: a power cord for supplying power to the 3D scanner and the computer device.

7. The system according to claim 5, characterized in that The system also includes: a data line for connecting the 3D scanner and the computer device.

8. The system according to claim 5, characterized in that The system also includes: a scanner bracket, used for fixing the 3D scanner.

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