3D printing prediction method and system based on equipment part analysis

Abstract

The invention discloses a 3D printing prediction method and system based on equipment component analysis. The method comprises the steps that to-be-printed model data and corresponding printing equipment are obtained; determining a printing operation sequence corresponding to the model data according to the model data and a preset printing task rule; determining a plurality of associated working parts in the printing equipment according to the printing operation sequence and a preset equipment working part rule; and calculating a printing success rate corresponding to the model data according to the historical working record of each associated working part. It can be seen that accurate printing success rate evaluation based on the task sequence and the component history can be achieved, the accuracy and reliability of 3D printing task planning are improved, and the printing failure risk is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a 3D printing prediction method and system based on equipment component analysis. BACKGROUND

[0002] With the wide application of 3D printing technology in the field of high-precision manufacturing, enterprises and users pay more and more attention to improving the printing success rate and reliability by optimizing task planning. The existing technology usually obtains model data to be printed and equipment information, evaluates the printing feasibility based on the basic state monitoring of the equipment, so as to reduce the risk of printing failure. The existing solution lacks dynamic identification of associated working components of the equipment and in-depth analysis of historical working records, and it is difficult to accurately evaluate the printing success rate of the model data. The commonly used static task planning method cannot adapt to complex printing scenarios, resulting in insufficient accuracy of printing success rate prediction, easy to cause printing failure or resource waste, and limiting the accuracy and efficiency of 3D printing task planning. It can be seen that the existing technology has defects and needs to be solved. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a 3D printing prediction method and system based on equipment component analysis, which can realize accurate printing success rate evaluation based on task sequence and component history, improve the accuracy and reliability of 3D printing task planning, and reduce the risk of printing failure.

[0004] In order to solve the above technical problems, the first aspect of the present application discloses a 3D printing prediction method based on equipment component analysis, the method comprising: obtaining model data to be printed and corresponding printing equipment; determining a printing operation sequence corresponding to the model data according to the model data and a preset printing task rule; determining a plurality of associated working components in the printing equipment according to the printing operation sequence and a preset equipment working component rule; calculating the printing success rate corresponding to the model data according to the historical working record of each associated working component.

[0005] As an optional implementation, in the first aspect of the present application, the determination of the printing operation sequence corresponding to the model data according to the model data and the preset printing task rule comprises: determining the corresponding printing task parameters according to the model form in the model data; determining the printing operation sequence corresponding to the model data according to the printing task parameters and a preset printing operation simulation algorithm.

[0006] As an optional implementation, in the first aspect of the present application, the determining of the corresponding printing task parameters according to the model shape in the model data comprises: inputting the model data into a trained three-dimensional model segmentation algorithm model to obtain a plurality of three-dimensional parts corresponding to the model data; inputting each of the three-dimensional parts into a trained shape recognition model to obtain a three-dimensional shape corresponding to each of the three-dimensional parts; the three-dimensional shape is a standard shape closest to the overall shape of the three-dimensional part among a plurality of standard shapes; determining a printing task parameter corresponding to the three-dimensional shape of each of the three-dimensional parts according to a preset correspondence between the three-dimensional shape and the printing parameter; the printing task parameter comprises a consumable amount, an optimal printing speed and an optimal printing direction.

[0007] As an optional implementation, in the first aspect of the present application, the determining of the printing operation sequence corresponding to the model data according to the printing task parameter and a preset printing operation simulation algorithm comprises: inputting the printing task parameter corresponding to the three-dimensional shape of each of the three-dimensional parts into a trained printing operation prediction model to obtain a printing operation corresponding to each of the three-dimensional parts; the printing operation prediction model is trained by a training data set comprising a plurality of training printing task parameters and corresponding printing operation annotations; sorting all of the corresponding printing operations from high to low based on the position of each of the three-dimensional parts in the model data to obtain the printing operation sequence corresponding to the model data.

[0008] As an optional implementation, in the first aspect of the present application, the printing operation sequence comprises a plurality of printing operations sorted according to execution time; the printing operation comprises at least one component operation parameter of an execution work component; the component operation parameter comprises a motion direction, a motion speed and an operation type.

[0009] As an optional implementation, in the first aspect of the present application, the determining of the plurality of associated work components in the printing device according to the printing operation sequence and a preset device work component rule comprises: determining a set of execution work components corresponding to each of the printing operations in the printing operation sequence; calculating an information similarity between any two adjacent printing operations in the printing operation sequence; judging whether the information similarity is greater than a preset first similarity threshold, and if not, no processing is needed; If yes, the execution work component sets corresponding to the two print operations are deduplicated to obtain new execution work component sets corresponding to the two print operations. All work components in the execution work component sets corresponding to the print operations are determined as a plurality of associated work components in the print device.

[0010] As an optional implementation, in the first aspect of the present application, the deduplication optimization of the execution work component sets corresponding to the two print operations to obtain new execution work component sets corresponding to the two print operations includes: At least one component combination in the execution work component sets corresponding to the two print operations is determined; the component combination includes two components respectively from the execution work component sets corresponding to the two print operations, and the parameter similarity between the component operation parameters corresponding to the two components is greater than a preset second similarity threshold; Each component combination is input into a trained substitution possibility prediction model to obtain a substitution possibility corresponding to each component combination; the substitution possibility prediction model is trained by a training data set including a plurality of training component combinations and corresponding labels of whether they can be substituted for each other; The product value of the substitution possibility corresponding to each component combination and the parameter similarity is calculated; Two components in each component combination with a product value higher than a preset threshold are randomly deleted to obtain new execution work component sets corresponding to the two print operations.

[0011] As an optional implementation, in the first aspect of the present application, the calculation of the print success rate corresponding to the model data according to the historical work records of each associated work component includes: For each associated work component, a plurality of similar work records are obtained by screening records in the historical work records of the associated work component, in which the similarity between the operation parameters and the corresponding component operation parameters is greater than a preset third similarity threshold. The operation success rates corresponding to all the similar work records are calculated. The average value of the operation success rates corresponding to all the associated work components is calculated to obtain the print success rate corresponding to the model data.

[0012] The second aspect of the embodiment of the present application discloses a 3D printing prediction system based on device component analysis, which comprises: An acquisition module is configured to acquire model data to be printed and a corresponding print device. a first determining module, configured to determine a printing operation sequence corresponding to the model data according to the model data and a preset printing task rule; a second determining module, configured to determine a plurality of associated working components in the printing device according to the printing operation sequence and a preset device working component rule; a calculating module, configured to calculate a printing success rate corresponding to the model data according to a historical working record of each associated working component.

[0013] As an optional implementation, in the second aspect of the present application, the specific manner in which the first determining module determines the printing operation sequence corresponding to the model data according to the model data and a preset printing task rule comprises: determining a printing task parameter corresponding to the model form in the model data; determining the printing operation sequence corresponding to the model data according to the printing task parameter and a preset printing operation simulation algorithm.

[0014] As an optional implementation, in the second aspect of the present application, the specific manner in which the first determining module determines the printing task parameter corresponding to the model form in the model data comprises: inputting the model data into a trained three-dimensional model segmentation algorithm model to obtain a plurality of three-dimensional parts corresponding to the model data; inputting each three-dimensional part into a trained form recognition model to obtain a three-dimensional form corresponding to each three-dimensional part; the three-dimensional form is a standard form that is closest to the overall form of the three-dimensional part among a plurality of standard forms; determining a printing task parameter corresponding to the three-dimensional form corresponding to each three-dimensional part according to a preset correspondence between a three-dimensional form and a printing parameter; the printing task parameter comprises a consumable amount, an optimal printing speed and an optimal printing direction.

[0015] As an optional implementation, in the second aspect of the present application, the specific manner in which the first determining module determines the printing operation sequence corresponding to the model data according to the printing task parameter and a preset printing operation simulation algorithm comprises: inputting the printing task parameter corresponding to the three-dimensional form corresponding to each three-dimensional part into a trained printing operation prediction model to obtain a printing operation corresponding to each three-dimensional part; the printing operation prediction model is trained by a training data set comprising a plurality of training printing task parameters and corresponding printing operation annotations; sorting all corresponding printing operations from high to low based on the position of each three-dimensional part in the model data to obtain the printing operation sequence corresponding to the model data.

[0016] As an optional implementation, in the second aspect of the present application, the sequence of printing operations comprises a plurality of printing operations sorted according to execution time; the printing operations comprise at least one component operation parameter of an execution work component; the component operation parameter comprises a motion direction, a motion speed and an operation type.

[0017] As an optional implementation, in the second aspect of the present application, the second determining module determines the specific manner of the plurality of associated work components in the printing device according to the sequence of printing operations and a preset device work component rule, comprising: determining a set of execution work components corresponding to each of the printing operations in the sequence of printing operations; calculating an information similarity between any two adjacent printing operations in the sequence of printing operations; judging whether the information similarity is greater than a preset first similarity threshold value, if not, no processing is needed; if yes, performing deduplication optimization on the set of execution work components corresponding to the two printing operations to obtain a new set of execution work components corresponding to the two printing operations; determining all the work components in the set of execution work components corresponding to all the printing operations as the plurality of associated work components in the printing device.

[0018] As an optional implementation, in the second aspect of the present application, the specific manner of the second determining module performing deduplication optimization on the set of execution work components corresponding to the two printing operations to obtain a new set of execution work components corresponding to the two printing operations, comprising: determining at least one component combination in the set of execution work components corresponding to the two printing operations; the component combination comprises two components respectively from the set of execution work components corresponding to the two printing operations, and a parameter similarity between the component operation parameters corresponding to the two components is greater than a preset second similarity threshold value; inputting each of the component combinations into a trained substitution possibility prediction model to obtain a substitution possibility corresponding to each of the component combinations; the substitution possibility prediction model is trained by a training data set comprising a plurality of training component combinations and corresponding labels of whether the training component combinations can be substituted for each other; calculating a product value of the substitution possibility and the parameter similarity corresponding to each of the component combinations; randomly deleting one component from the two components in each of the component combinations with all the product values higher than a preset threshold value to obtain a new set of execution work components corresponding to the two printing operations.

[0019] As an optional implementation, in the second aspect of the present application, the specific manner of calculating the printing success rate corresponding to the model data by the calculation module according to the historical working records of each associated working component comprises: For each associated working component, filtering out records in the historical working records of the associated working component, in which the similarity between the operation parameters and the corresponding component operation parameters is greater than a preset third similarity threshold, to obtain a plurality of similar working records; Calculating the operation success rates corresponding to all the similar working records; Calculating the average value of the operation success rates corresponding to all the associated working components to obtain the printing success rate corresponding to the model data.

[0020] The third aspect of the present application discloses another 3D printing prediction system based on equipment component analysis, which comprises: a memory storing executable program codes; a processor coupled with the memory; The processor invokes the executable program codes stored in the memory to execute part or all of the steps of the 3D printing prediction method based on equipment component analysis disclosed in the first aspect of the present application.

[0021] The fourth aspect of the present application discloses a computer storage medium storing computer instructions, which when invoked, are used to execute part or all of the steps of the 3D printing prediction method based on equipment component analysis disclosed in the first aspect of the present application.

[0022] Compared with the prior art, the embodiments of the present application have the following beneficial effects: The present application can realize accurate printing success rate evaluation based on task sequence and component history, improve the accuracy and reliability of 3D printing task planning, and reduce the risk of printing failure by obtaining the model data to be printed and the corresponding printing equipment, determining the printing operation sequence in combination with the preset printing task rules, identifying a plurality of associated working components based on the equipment working component rules, and calculating the printing success rate of the model data according to the historical working records of each associated working component. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0024] Figure 1 is a flowchart of a 3D printing prediction method based on equipment component analysis disclosed by an embodiment of the present application.

[0025] Figure 2 is a structural schematic diagram of a 3D printing prediction system based on equipment component analysis disclosed by an embodiment of the present application.

[0026] Figure 3 is a structural schematic diagram of another 3D printing prediction system based on equipment component analysis disclosed by an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor fall within the scope of protection of the present application.

[0028] The terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, and are not used to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or equipment.

[0029] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily mutually exclusive of other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] The present application discloses a 3D printing prediction method and system based on equipment component analysis. By obtaining the to-be-printed model data and the corresponding printing equipment, the printing operation sequence is determined in combination with the preset printing task rules, a plurality of associated working components are identified based on the equipment working component rules, and the printing success rate of the model data is calculated according to the historical working records of each associated working component, so as to realize accurate printing success rate evaluation based on task sequence and component history, improve the accuracy and reliability of 3D printing task planning, and reduce the risk of printing failure. The following will be described in detail.

[0031] Embodiment one Please refer to Figure 1 , Figure 1 is a flowchart of a 3D printing prediction method based on equipment component analysis disclosed by an embodiment of the present application. Among them, Figure 1 The 3D printing prediction method based on equipment component analysis described can be applied in a data processing system / data processing equipment / data processing server (wherein the server includes a local processing server or a cloud processing server). As Figure 1 shown, the 3D printing prediction method based on equipment component analysis can include the following operations: 101, obtaining model data to be printed and corresponding printing equipment.

[0032] Optionally, the model data can be a three-dimensional model file, point cloud data or a CAD design file, which is not limited by the present application.

[0033] Optionally, the acquisition process can be based on local storage, cloud transmission or real-time generation, which is not limited by the present application.

[0034] 102, determining the printing operation sequence corresponding to the model data according to the model data and the preset printing task rule. Optionally, the printing task rule can be a rule set based on model complexity, material type or printing accuracy, which is not limited by the present application.

[0035] Optionally, the printing operation sequence can be a time-ordered operation list, a priority queue or a hierarchical operation set, which is not limited by the present application.

[0036] 103, determining a plurality of associated working components in the printing equipment according to the printing operation sequence and the preset equipment working component rule. Optionally, the equipment working component rule can be a rule set based on function division, operation dependence or component cooperation, which is not limited by the present application.

[0037] Optionally, the associated working component can be a print head, a platform leveling mechanism, a feeding system or a cooling system, which is not limited by the present application.

[0038] 104, calculating the printing success rate corresponding to the model data according to the historical working record of each associated working component.

[0039] Optionally, the historical working record can be an operation log, a fault record or performance statistical data, which is not limited by the present application.

[0040] Optionally, the printing success rate can be a success probability, a success score or a success level, which is not limited by the present application.

[0041] It can be seen that the above embodiments of the application can obtain model data to be printed and a corresponding printing device, determine a printing operation sequence in combination with a preset printing task rule, identify a plurality of associated working components based on a device working component rule, and calculate a printing success rate of the model data according to historical working records of the associated working components, so as to realize accurate printing success rate evaluation based on a task sequence and component history, improve the accuracy and reliability of 3D printing task planning, and reduce the risk of printing failure.

[0042] As an optional embodiment, in the above step, the printing operation sequence corresponding to the model data is determined according to the model data and a preset printing task rule, including: determining a printing task parameter corresponding to the model data according to the model morphology in the model data; determining the printing operation sequence corresponding to the model data according to the printing task parameter and a preset printing operation simulation algorithm.

[0043] Optionally, the model morphology can be a geometric shape, a topological structure or a surface feature, which is not limited by the application.

[0044] Optionally, the printing task parameter can include a consumable amount, an optimal printing speed, an optimal printing direction or a layer thickness setting, which is not limited by the application.

[0045] Optionally, the printing operation simulation algorithm can be a path planning algorithm, a motion simulation algorithm or an optimization scheduling algorithm, which is not limited by the application.

[0046] It can be seen that through the above optional embodiments, the printing task parameter is determined by analyzing the model morphology in the model data, and the printing operation sequence corresponding to the model data is generated in combination with the preset printing operation simulation algorithm, so as to realize accurate printing operation sequence formulation based on morphology analysis and simulation algorithm, assist in realizing accurate printing success rate evaluation based on a task sequence and component history, improve the accuracy and reliability of 3D printing task planning, and reduce the risk of printing failure.

[0047] As an optional embodiment, in the above step, the printing task parameter corresponding to the model morphology in the model data is determined, including: inputting the model data into a trained three-dimensional model segmentation algorithm model to obtain a plurality of three-dimensional parts corresponding to the model data; inputting each three-dimensional part into a trained morphology recognition model to obtain a three-dimensional morphology corresponding to each three-dimensional part; optionally, the three-dimensional morphology is a standard morphology that is closest to the overall morphology of the three-dimensional part among a plurality of standard morphologies; According to a preset correspondence relationship between the three-dimensional shape and the printing parameter, a printing task parameter corresponding to each three-dimensional part is determined, and the printing task parameter includes a consumable amount, an optimal printing speed, and an optimal printing direction.

[0048] Optionally, the three-dimensional model segmentation algorithm model can be an image segmentation-based model, a mesh segmentation-based model, or a deep learning segmentation model, and the application is not limited in this regard.

[0049] Optionally, the three-dimensional part can be an independent geometric body, a functional area, or a printing level, and the application is not limited in this regard.

[0050] Optionally, the shape recognition model can be a convolutional neural network, a point cloud classification model, or a feature matching model, and the application is not limited in this regard.

[0051] Optionally, the standard shape can be a cube, a cylinder, a complex curved surface, or a support structure, and the application is not limited in this regard.

[0052] As can be seen, through the above optional embodiments, the model data is input into the trained three-dimensional model segmentation algorithm model to segment into multiple three-dimensional parts, each three-dimensional part is input into the trained shape recognition model to determine the closest standard three-dimensional shape, and the printing task parameter of each three-dimensional part is determined based on the preset correspondence relationship between the three-dimensional shape and the printing parameter, thereby realizing accurate printing parameter determination based on model segmentation and shape recognition, assisting in realizing accurate printing success rate evaluation based on a task sequence and a component history, improving the accuracy and reliability of 3D printing task planning, and reducing the risk of printing failure.

[0053] As an optional embodiment, in the above step, according to the printing task parameter and a preset printing operation simulation algorithm, a printing operation sequence corresponding to the model data is determined, including: The printing task parameter corresponding to each three-dimensional part is input into a trained printing operation prediction model to obtain a printing operation corresponding to each three-dimensional part; optionally, the printing operation prediction model is trained by a training data set including a plurality of training printing task parameters and corresponding printing operation labels; All corresponding printing operations are sorted in descending order based on the position of each three-dimensional part in the model data to obtain a printing operation sequence corresponding to the model data.

[0054] Optionally, the printing operation prediction model can be a regression model, a classification model, or a reinforcement learning model, and the application is not limited in this regard.

[0055] Optionally, the training data set can include historical printing data, simulation data, or experimental data, and the application is not limited in this regard.

[0056] It can be seen that through the above optional embodiments, by inputting the three-dimensional form corresponding printing task parameters of each three-dimensional part into the trained printing operation prediction model to obtain the corresponding printing operation, and sorting all printing operations according to the positions of the three-dimensional parts in the model data from high to low to form a printing operation sequence, the accurate printing operation sequence generation based on parameter prediction and position sorting is realized, which assists in realizing the accurate printing success rate evaluation based on the task sequence and the part history, improving the accuracy and reliability of 3D printing task planning, and reducing the risk of printing failure.

[0057] As an optional embodiment, in the above step, the printing operation sequence includes a plurality of printing operations sorted according to execution time; the printing operation includes at least one part operation parameter for executing a work part; and the part operation parameter includes a motion direction, a motion speed, and an operation type.

[0058] Optionally, the positions from high to low can be based on Z-axis height, printing level, or geometric priority sorting, which is not limited by the application.

[0059] Optionally, the part operation parameter can include additional parameters such as operation strength or operation frequency, which is not limited by the application.

[0060] It can be seen that through the above optional embodiments, the data content of the printing operation sequence is limited to comprehensively represent the operation characteristics of the printing device during printing, which assists in realizing the accurate printing success rate evaluation based on the task sequence and the part history, improving the accuracy and reliability of 3D printing task planning, and reducing the risk of printing failure.

[0061] As an optional embodiment, in the above step, according to the printing operation sequence and a preset device work part rule, a plurality of associated work parts in the printing device are determined, including: determining a set of execution work parts corresponding to each printing operation in the printing operation sequence; calculating the information similarity between any two adjacent printing operations in the printing operation sequence; judging whether the information similarity is greater than a preset first similarity threshold, and if not, no processing is needed; if yes, the set of execution work parts corresponding to the two printing operations is optimized to obtain a new set of execution work parts corresponding to the two printing operations; determining the work parts in the set of execution work parts corresponding to all printing operations as the plurality of associated work parts in the printing device.

[0062] Optionally, the information similarity can be calculated based on operation parameter vectors, operation types, or execution times, which is not limited by the application.

[0063] Optionally, the information similarity can be cosine similarity, Euclidean distance or Jaccard coefficient, and the application does not make any limitation.

[0064] Optionally, the state without processing can record a log or trigger a low-priority monitoring, and the application does not make any limitation.

[0065] Optionally, the deduplication optimization can be implemented based on set operation, substitution analysis or optimization algorithm, and the application does not make any limitation.

[0066] As can be seen, through the above optional embodiments, by determining the execution work component set of each printing operation in the printing operation sequence, calculating the information similarity between adjacent printing operations, if the similarity is higher than the first threshold value, performing deduplication optimization on the corresponding execution work component set to generate a new set, and finally determining the work components in the execution work component set of all printing operations as the associated work components of the printing device, the application realizes accurate work component identification based on similarity and deduplication optimization, assists in realizing accurate printing success rate evaluation based on task sequence and component history, improves the accuracy and reliability of 3D printing task planning, and reduces the risk of printing failure.

[0067] As an optional embodiment, in the above step, the deduplication optimization on the execution work component sets corresponding to the two printing operations to obtain new execution work component sets corresponding to the two printing operations comprises: determining at least one component combination in the execution work component sets corresponding to the two printing operations; optionally, the component combination includes two components respectively from the execution work component sets corresponding to the two printing operations, and the parameter similarity between the component operation parameters corresponding to the two components is greater than a preset second similarity threshold value; inputting each component combination into a trained substitution possibility prediction model to obtain a substitution possibility corresponding to each component combination; optionally, the substitution possibility prediction model is trained by a training data set including a plurality of training component combinations and corresponding labels of whether they can be substituted for each other; calculating the product value of the substitution possibility and the parameter similarity corresponding to each component combination; randomly deleting one component from the two components in each component combination with a product value higher than a preset threshold value to obtain new execution work component sets corresponding to the two printing operations.

[0068] Optionally, the parameter similarity can be calculated based on vector distance, feature matching or statistical analysis, and the application does not make any limitation.

[0069] Optionally, the second similarity threshold value can be a fixed threshold value, a dynamic threshold value or a threshold value adjusted based on the component type, and the application does not make any limitation.

[0070] Optionally, the alternative possibility prediction model can be a classification model, a regression model or a probability model, and the present application is not limited thereto.

[0071] Optionally, the preset threshold value can be a fixed threshold value, a dynamic threshold value or a threshold value adjusted based on an optimization target, and the present application is not limited thereto.

[0072] Optionally, the random deletion can be based on uniform random selection, weighted random selection or priority selection, and the present application is not limited thereto.

[0073] As can be seen, through the above optional embodiments, by identifying at least one component combination in the two adjacent printing operation execution work component set, inputting each component combination into the trained alternative possibility prediction model to obtain the alternative possibility, calculating the product of the alternative possibility and the parameter similarity, and randomly deleting one component in the product value exceeding the threshold combination to generate a new execution work component set, the precision component deduplication optimization based on the parameter similarity and the alternative possibility is realized, the precision printing success rate evaluation based on the task sequence and the component history is assisted to realize, the accuracy and reliability of the 3D printing task planning are improved, and the printing failure risk is reduced.

[0074] As an optional embodiment, in the above steps, the printing success rate corresponding to the model data is calculated according to the historical work record of each associated work component, including: For each associated work component, the records in the historical work record of the associated work component are filtered out, in which the similarity between the operation parameter and the corresponding component operation parameter is greater than a preset third similarity threshold value, to obtain a plurality of similar work records; The operation success rates corresponding to all similar work records are calculated; The average value of the operation success rates corresponding to all associated work components is calculated to obtain the printing success rate corresponding to the model data.

[0075] Optionally, the operation success rate can be a success rate, a success probability or a weighted success score, and the present application is not limited thereto.

[0076] Optionally, the calculation process can be based on statistical analysis, a probability model or historical trend analysis, and the present application is not limited thereto.

[0077] Optionally, the calculation of the operation success rate can be combined with task complexity or environmental conditions for correction, and the present application is not limited thereto.

[0078] It can be seen that, through the above optional embodiments, by screening similar work records in the historical work records of each associated work component, the operation success rate of these records is calculated, and the average value of the operation success rates of all associated work components is taken as the printing success rate of the model data, so as to realize accurate printing success rate evaluation based on parameter similarity and historical records, improve the accuracy and reliability of 3D printing task planning, and reduce the risk of printing failure.

[0079] Embodiment two Please refer to Figure 2 , Figure 2 is a structural schematic diagram of a 3D printing prediction system based on equipment component analysis disclosed by the embodiments of the present application. Among them, Figure 2 The 3D printing prediction system based on equipment component analysis described can be applied in a data processing system / data processing equipment / data processing server (wherein the server includes a local processing server or a cloud processing server). As Figure 2 indicated, the 3D printing prediction system based on equipment component analysis can include: The acquisition module 201 is configured to acquire model data to be printed and a corresponding printing equipment.

[0080] The first determination module 202 is configured to determine a printing operation sequence corresponding to the model data according to the model data and a preset printing task rule. The second determination module 203 is configured to determine a plurality of associated work components in the printing equipment according to the printing operation sequence and a preset equipment work component rule. The calculation module 204 is configured to calculate a printing success rate corresponding to the model data according to historical work records of each associated work component.

[0081] It can be seen that, through the above embodiments, by acquiring the model data to be printed and the corresponding printing equipment, determining the printing operation sequence in combination with the preset printing task rule, identifying the plurality of associated work components based on the equipment work component rule, and calculating the printing success rate of the model data according to the historical work records of each associated work component, accurate printing success rate evaluation based on the task sequence and the component history can be realized, the accuracy and reliability of 3D printing task planning can be improved, and the risk of printing failure can be reduced.

[0082] As an optional embodiment, the specific manner in which the first determination module determines the printing operation sequence corresponding to the model data according to the model data and the preset printing task rule includes: determining the printing task parameters corresponding to the model data according to the model form in the model data; determining the printing operation sequence corresponding to the model data according to the printing task parameters and a preset printing operation simulation algorithm.

[0083] As can be seen, through the above optional embodiments, the printing task parameters are determined by analyzing the model morphology in the model data, and the printing operation sequence corresponding to the model data is generated by combining the preset printing operation simulation algorithm. This enables the formulation of accurate printing operation sequences based on morphological analysis and simulation algorithms, assists in the accurate evaluation of printing success rate based on task sequence and part history, improves the accuracy and reliability of 3D printing task planning, and reduces the risk of printing failure.

[0084] As an optional embodiment, the first determining module determines the specific method of the corresponding printing task parameters based on the model morphology in the model data, including: The model data is input into the trained 3D model segmentation algorithm to obtain multiple 3D parts corresponding to the model data; Each three-dimensional part is input into the trained shape recognition model to obtain the three-dimensional shape corresponding to each three-dimensional part; optionally, the three-dimensional shape is the standard shape that is closest to the overall shape of the three-dimensional part among a variety of preset standard shapes. Based on the pre-defined correspondence between the three-dimensional shape and the printing parameters, the printing task parameters corresponding to the three-dimensional shape of each three-dimensional part are determined; the printing task parameters include the amount of consumables, the optimal printing speed, and the optimal printing direction.

[0085] As can be seen, through the above optional embodiments, by inputting model data into a trained 3D model segmentation algorithm to segment into multiple 3D parts, and inputting each 3D part into a trained morphology recognition model to determine the closest standard 3D shape, the printing task parameters for each 3D part are determined based on the preset correspondence between the 3D shape and printing parameters. This achieves accurate determination of printing parameters based on model segmentation and morphology recognition, assists in achieving accurate printing success rate assessment based on task sequence and part history, improves the accuracy and reliability of 3D printing task planning, and reduces the risk of printing failure.

[0086] As an optional embodiment, the first determining module determines the specific method of the printing operation sequence corresponding to the model data based on the printing task parameters and the preset printing operation simulation algorithm, including: The printing task parameters corresponding to the three-dimensional shape of each three-dimensional part are input into the trained printing operation prediction model to obtain the printing operation corresponding to each three-dimensional part; optionally, the printing operation prediction model is trained using a training dataset that includes multiple training printing task parameters and corresponding printing operation annotations. Based on the position of each 3D part in the model data from high to low, all corresponding printing operations are sorted to obtain the printing operation sequence corresponding to the model data.

[0087] As can be seen, through the above optional embodiments, by inputting the printing task parameters corresponding to the three-dimensional shape of each three-dimensional part into the trained printing operation prediction model to obtain the corresponding printing operation, and sorting all printing operations from high to low position of the three-dimensional part in the model data to form a printing operation sequence, the generation of accurate printing operation sequence based on parameter prediction and position sorting can be achieved. This helps to achieve accurate printing success rate assessment based on task sequence and part history, improve the accuracy and reliability of 3D printing task planning, and reduce the risk of printing failure.

[0088] As an optional embodiment, the printing operation sequence includes multiple printing operations ordered in chronological order; each printing operation includes component operation parameters for at least one executing component; the component operation parameters include movement direction, movement speed, and operation type.

[0089] As can be seen, the data content of the printing operation sequence is defined through the above optional embodiments to comprehensively characterize the operating characteristics of the printing device during printing, assist in achieving accurate printing success rate assessment based on task sequence and part history, improve the accuracy and reliability of 3D printing task planning, and reduce the risk of printing failure.

[0090] As an optional embodiment, the second determining module determines the specific method of multiple associated working parts in the printing device according to the printing operation sequence and preset device working part rules, including: Determine the set of execution work components corresponding to each print operation in the print operation sequence; For any two adjacent printing operations in the printing operation sequence, calculate the information similarity between the two printing operations; Determine whether the information similarity is greater than the preset first similarity threshold; if not, no processing is required. If so, the sets of execution work components corresponding to the two printing operations are optimized to remove duplicates, so as to obtain new sets of execution work components corresponding to the two printing operations; The work parts in the set of execution work parts corresponding to all printing operations are identified as multiple associated work parts in the printing device.

[0091] As can be seen, through the above optional embodiments, by determining the set of execution working parts for each printing operation in the printing operation sequence, calculating the information similarity between adjacent printing operations, and if the similarity exceeds a first threshold, the corresponding set of execution working parts is deduplicated and optimized to generate a new set. Finally, the working parts in the set of execution working parts of all printing operations are determined as the associated working parts of the printing device, thereby achieving accurate working part identification based on similarity and deduplication optimization, assisting in the accurate printing success rate assessment based on task sequence and part history, improving the accuracy and reliability of 3D printing task planning, and reducing the risk of printing failure.

[0092] As an optional embodiment, the second determining module optimizes the set of execution work components corresponding to the two printing operations to obtain a new set of execution work components corresponding to the two printing operations in the following specific ways: Determine at least one component combination from the set of execution work components corresponding to the two printing operations; optionally, the component combination includes two components respectively from the set of execution work components corresponding to the two printing operations, and the parameter similarity between the component operation parameters corresponding to the two components is greater than a preset second similarity threshold; Each component combination is input into a trained substitution probability prediction model to obtain the substitution probability corresponding to each component combination; optionally, the substitution probability prediction model is trained using a training dataset that includes multiple training component combinations and corresponding annotations indicating whether they can be substituted for each other. Calculate the product of the substitution probability and parameter similarity for each component combination; Randomly remove one component from any two components in a combination of components whose product values ​​are higher than a preset threshold to obtain a new set of execution components corresponding to the two printing operations.

[0093] As can be seen, through the above optional embodiments, by identifying at least one component combination in the set of work parts for two adjacent printing operations, inputting each component combination into a trained substitution probability prediction model to obtain substitution probability, calculating the product of substitution probability and parameter similarity, and randomly deleting one component from the combination whose product value exceeds the threshold to generate a new set of work parts, accurate component deduplication optimization based on parameter similarity and substitution probability is achieved. This assists in achieving accurate printing success rate assessment based on task sequence and component history, improving the accuracy and reliability of 3D printing task planning, and reducing the risk of printing failure.

[0094] As an optional embodiment, the calculation module calculates the printing success rate corresponding to the model data based on the historical work records of each associated work component in the following specific ways: For each associated work component, records in the historical work records of that associated work component whose similarity between the operation parameters and the corresponding component operation parameters is greater than a preset third similarity threshold are selected to obtain multiple similar work records. Calculate the success rate of operations for all similar work records; Calculate the average success rate of operations corresponding to all associated work components to obtain the printing success rate corresponding to the model data.

[0095] As can be seen, through the above optional embodiments, by filtering similar work records in the historical work records of each associated work part whose similarity between the operation parameters and the part operation parameters exceeds the third threshold, calculating the operation success rate of these records, and taking the average of the operation success rates of all associated work parts as the printing success rate of the model data, an accurate printing success rate assessment based on parameter similarity and historical records can be achieved, thereby improving the accuracy and reliability of 3D printing task planning and reducing the risk of printing failure.

[0096] Example 3 Please see Figure 3 , Figure 3 This is another 3D printing prediction system based on equipment component analysis disclosed in the embodiments of the present invention. Figure 3 The described 3D printing prediction system based on device component analysis is applied in a data processing system / data processing equipment / data processing server (wherein the server includes a local processing server or a cloud processing server). For example... Figure 3 As shown, the 3D printing prediction system based on equipment component analysis may include: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the 3D printing prediction method based on device component analysis described in Embodiment 1.

[0097] Example 4 This invention discloses a computer read storage medium that stores a computer program for electronic data interchange, wherein the computer program causes a computer to execute the steps of the 3D printing prediction method based on device component analysis described in Embodiment 1.

[0098] Example 5 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the 3D printing prediction method based on device component analysis described in Embodiment 1.

[0099] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0100] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0101] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0102] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0103] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0104] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0105] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0106] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0107] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0108] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0109] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0110] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0111] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0112] Finally, it should be noted that the 3D printing prediction method and system based on equipment component analysis disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A 3D printing prediction method based on equipment component analysis, characterized in that, The method includes: Obtain the model data to be printed and the corresponding printing device; Based on the model data and the preset printing task rules, determine the printing operation sequence corresponding to the model data; Based on the printing operation sequence and the preset device working component rules, multiple associated working components in the printing device are determined; The printing success rate corresponding to the model data is calculated based on the historical work records of each associated work component.

2. The 3D printing prediction method based on equipment component analysis according to claim 1, characterized in that, The step of determining the printing operation sequence corresponding to the model data based on the model data and preset printing task rules includes: Based on the model shape in the model data, the corresponding printing task parameters are determined; Based on the print task parameters and the preset print operation simulation algorithm, the print operation sequence corresponding to the model data is determined.

3. The 3D printing prediction method based on equipment component analysis according to claim 2, characterized in that, The step of determining the corresponding printing task parameters based on the model shape in the model data includes: The model data is input into a trained 3D model segmentation algorithm to obtain multiple 3D parts corresponding to the model data; Each of the three-dimensional parts is input into the trained morphology recognition model to obtain the three-dimensional morphology corresponding to each of the three-dimensional parts; the three-dimensional morphology is the standard morphology that is closest to the overall morphology of the three-dimensional part among a variety of preset standard morphologies. Based on the preset correspondence between the three-dimensional shape and the printing parameters, the printing task parameters corresponding to the three-dimensional shape of each of the three-dimensional parts are determined; the printing task parameters include the amount of consumables, the optimal printing speed, and the optimal printing direction.

4. The 3D printing prediction method based on equipment component analysis according to claim 3, characterized in that, The step of determining the printing operation sequence corresponding to the model data based on the printing task parameters and a preset printing operation simulation algorithm includes: The printing task parameters corresponding to the three-dimensional shape of each of the three-dimensional parts are input into the trained printing operation prediction model to obtain the printing operation corresponding to each of the three-dimensional parts; the printing operation prediction model is trained using a training dataset that includes multiple training printing task parameters and corresponding printing operation annotations. Based on the position of each of the three-dimensional parts in the model data from high to low, all the corresponding printing operations are sorted to obtain the printing operation sequence corresponding to the model data.

5. The 3D printing prediction method based on equipment component analysis according to claim 1, characterized in that, The printing operation sequence includes multiple printing operations ordered by execution time; each printing operation includes component operation parameters for at least one executing component; the component operation parameters include movement direction, movement speed, and operation type.

6. The 3D printing prediction method based on equipment component analysis according to claim 5, characterized in that, The step of determining multiple associated working components in the printing device based on the printing operation sequence and preset device working component rules includes: Determine the set of execution work components corresponding to each printing operation in the printing operation sequence; For any two adjacent printing operations in the printing operation sequence, calculate the information similarity between the two printing operations; Determine whether the information similarity is greater than a preset first similarity threshold; if not, no processing is required. If so, the sets of execution work components corresponding to the two printing operations are optimized to remove duplicates, so as to obtain new sets of execution work components corresponding to the two printing operations; The working components in the set of executing working components corresponding to all the printing operations are determined as multiple associated working components in the printing device.

7. The 3D printing prediction method based on equipment component analysis according to claim 6, characterized in that, The step of deduplicating and optimizing the set of execution work components corresponding to the two printing operations to obtain a new set of execution work components corresponding to the two printing operations includes: Determine at least one component combination from the set of execution work components corresponding to the two printing operations; the component combination includes two components respectively from the set of execution work components corresponding to the two printing operations, and the parameter similarity between the component operation parameters corresponding to the two components is greater than a preset second similarity threshold; Each component combination is input into a trained substitution probability prediction model to obtain the substitution probability corresponding to each component combination; the substitution probability prediction model is trained using a training dataset that includes multiple training component combinations and corresponding annotations indicating whether they can be substituted for each other. Calculate the product of the substitution probability corresponding to each component combination and the parameter similarity. Randomly delete one component from any two components in the component combinations whose product values ​​are higher than a preset threshold to obtain a new set of execution components corresponding to the two printing operations.

8. The 3D printing prediction method based on equipment component analysis according to claim 5, characterized in that, The step of calculating the printing success rate corresponding to the model data based on the historical work records of each associated work component includes: For each associated working component, records in the historical working records of the associated working component whose similarity between the operating parameters and the corresponding operating parameters of the component is greater than a preset third similarity threshold are selected to obtain multiple similar working records; Calculate the success rate of the operation for all the similar work records; Calculate the average success rate of the operation corresponding to all the associated working components to obtain the printing success rate corresponding to the model data.

9. A 3D printing prediction system based on equipment component analysis, characterized in that, The system includes: The acquisition module is used to acquire the model data to be printed and the corresponding printing device; The first determining module is used to determine the printing operation sequence corresponding to the model data based on the model data and the preset printing task rules; The second determining module is used to determine multiple associated working parts in the printing device based on the printing operation sequence and preset device working part rules; The calculation module is used to calculate the printing success rate corresponding to the model data based on the historical work records of each associated work component.

10. A 3D printing prediction system based on equipment component analysis, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the 3D printing prediction method based on device component analysis as described in any one of claims 1-8.

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