3D printing success rate evaluation method and system based on big data analysis

Abstract

The present application relates to a 3D printing success rate evaluation method and system based on big data analysis, relating to the field of 3D printing, comprising: obtaining model parameters for complexity analysis to obtain model complexity; obtaining slice accuracy, as well as printing material characteristic parameters and environmental characteristic parameters; performing basic printing success rate analysis according to the model complexity, printing material characteristic parameters and environmental characteristic parameters to obtain the basic printing success rate; obtaining a printing difficulty coefficient according to the slice accuracy classification to correct the basic printing success rate to obtain the printing success rate. Through the present application, the technical problem of high failure rate of the printing of the product due to the inability to accurately evaluate the printing success rate of the product in the prior art can be solved; by comprehensively evaluating the printing success rate of the product in combination with various factors, the accuracy and reliability of the printing success rate evaluation can be significantly improved, and the overall success rate and production efficiency of the 3D printing can be effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of 3D printing, in particular to a 3D printing success rate evaluation method and system based on big data analysis. BACKGROUND

[0002] 3D printing is a manufacturing technology that uses computer-aided design (CAD) files to create three-dimensional objects by layering materials. Unlike traditional subtractive manufacturing methods, 3D printing builds complex geometries through layer-by-layer accumulation, greatly improving design flexibility and production efficiency.

[0003] With the rapid development of 3D printing technology, its application in manufacturing, medical, aerospace and consumer products is becoming more and more widespread, however, although 3D printing has high design flexibility and manufacturing efficiency, how to accurately evaluate the printing success rate is still a technical problem to be solved.

[0004] Currently, the existing 3D printing success rate evaluation method has the limitation of not being able to effectively combine the actual printing scene, resulting in insufficient accuracy of the evaluation result, which not only affects the optimization process of the printed object, but also causes a high printing failure rate, increasing the waste of time and resources. SUMMARY

[0005] The present application provides a 3D printing success rate evaluation method and system based on big data analysis to solve the technical problem that the existing 3D printing success rate evaluation method cannot accurately evaluate the printing success rate of the object due to the inability to combine the actual printing scene, which makes it difficult to achieve optimization of the object printing and results in a high object printing failure rate.

[0006] The technical solution of the present application to solve the above technical problems is as follows:

[0007] In a first aspect, the present application provides a 3D printing success rate evaluation method based on big data analysis, comprising: obtaining the model parameters of the object model currently being 3D printed, performing model complexity analysis to obtain the model complexity; obtaining the slicing accuracy of the object model, and collecting the printing material characteristic parameters and environmental characteristic parameters of the current printing; performing basic printing success rate analysis according to the model complexity, printing material characteristic parameters and environmental characteristic parameters to obtain the basic printing success rate; obtaining the printing difficulty coefficient according to the slicing accuracy, and correcting the basic printing success rate to obtain the printing success rate as the printing success rate evaluation result.

[0008] In a second aspect, the present application provides a 3D printing success rate evaluation system based on big data analysis, comprising: a model complexity analysis module for obtaining model parameters of an article model currently subjected to 3D printing, performing model complexity analysis, and obtaining model complexity; an information collection module for obtaining slicing accuracy of the article model subjected to slicing, and collecting printing material characteristic parameters and environmental characteristic parameters currently subjected to printing; a basic printing success rate analysis module for performing basic printing success rate analysis according to the model complexity, the printing material characteristic parameters, and the environmental characteristic parameters, and obtaining a basic printing success rate; and a basic printing success rate correction module for obtaining a printing difficulty coefficient according to the slicing accuracy, correcting the basic printing success rate, and obtaining a printing success rate as a printing success rate evaluation result.

[0009] In a third aspect, the present application further provides an electronic device, comprising:

[0010] at least one processor; a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of any one of the methods in the first aspect.

[0011] In a fourth aspect, a computer readable storage medium stores a computer program, and the computer program implements the steps of any one of the methods in the first aspect when executed.

[0012] The present application has the following beneficial effects: by obtaining model parameters of an article model currently subjected to 3D printing, performing model complexity analysis according to the model parameters, and obtaining model complexity, then obtaining slicing accuracy of the article model subjected to slicing, and collecting printing material characteristic parameters and environmental characteristic parameters currently subjected to printing, then performing basic printing success rate analysis according to the model complexity, the printing material characteristic parameters, and the environmental characteristic parameters, and obtaining a basic printing success rate, and finally obtaining a printing difficulty coefficient according to the slicing accuracy, correcting the basic printing success rate, and obtaining a printing success rate as a printing success rate evaluation result. By combining multiple factors to comprehensively evaluate the printing success rate of the article, the precision and reliability of the printing success rate evaluation can be significantly improved, thereby providing a basis for printing parameter optimization and adjustment, and achieving the technical effect of effectively improving the overall success rate and production efficiency of 3D printing. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 A flowchart of a 3D printing success rate evaluation method based on big data analysis provided by the present application;

[0014] Figure 2 A structural schematic diagram of a 3D printing success rate evaluation system based on big data analysis provided by the present application is shown in the figure;

[0015] Figure 3 A structural schematic diagram of an electronic device provided by the present application is shown in the figure;

[0016] Figure 4 A structural schematic diagram of a computer readable storage medium provided by the present application is shown in the figure.

[0017] In the drawings, the components represented by the respective reference numerals are described as follows:

[0018] Model complexity analysis module 01, information collection module 02, basic printing success rate analysis module 03, basic printing success rate correction module 04, electronic device 500, memory 510, processor 520, first computer program 511, computer readable storage medium 600, second computer program 611. DETAILED DESCRIPTION

[0019] 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 those skilled in the art without creative work fall within the scope of protection of the present application.

[0020] In the description of the present application, the terms "first", "second" are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0021] In the description of the present application, the term "for example" is used to indicate "as an example, illustration or explanation". Any embodiment described as "for example" in the present application is not necessarily interpreted as more preferred or more advantageous than other embodiments. The following description is given in order to enable any person skilled in the art to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that those skilled in the art can realize the present application without using these specific details. In other examples, well-known structures and processes will not be described in detail to avoid unnecessary details making the description of the present application obscure. Therefore, the present application is not intended to be limited to the shown embodiments, but is consistent with the broadest scope of principles and features disclosed.

[0022] Embodiment one, asFigure 1 As shown, the embodiment of the present application provides a 3D printing success rate evaluation method based on big data analysis, which specifically comprises:

[0023] S100: Obtain the model parameters of the current 3D printing object model, perform model complexity analysis, and obtain the model complexity.

[0024] In one embodiment, the step S100 of the present application further comprises:

[0025] S110: Train the model complexity recognizer.

[0026] In one embodiment, the step S110 of the present application further comprises:

[0027] S111: According to the 3D printing record data in the historical time, collect a sample model parameter set, process the labeling according to the minimum size parameter in each sample model parameter, and obtain a sample model complexity set; S112: Use the sample model parameter set and the sample model complexity set to train the model complexity recognizer to convergence.

[0028] Specifically, first, collect the historical printing data of the 3D printing equipment within a predetermined time period, which can be set according to the demand, for example, within the last three months, wherein the historical printing data includes model parameters (such as size, shape, material type, etc.), printing results (success rate, time, cost, etc.) and other data. Then collect sample model parameters in the historical printing data to obtain a sample model parameter set; further obtain the minimum size parameter in each sample model parameter, wherein the minimum size parameter refers to the smallest feature size that affects the printing quality and feasibility in the 3D printing model, such as the minimum wall thickness; then according to the minimum size parameter, the model complexity of the sample model parameter is labeled, wherein the minimum size parameter and the model complexity are negatively correlated, that is, the smaller the minimum size parameter, the greater the model complexity, a plurality of sample model parameters corresponding to a plurality of sample model complexity are obtained, and a sample model complexity set is constructed.

[0029] Then, a model complexity identifier based on the BP neural network is constructed. The model complexity identifier is a neural network model that can be iteratively optimized in machine learning, including an input layer, multiple hidden layers, and an output layer. The input data of the input layer is the model parameter, and the output data of the output layer is the model complexity. Then, the sample model parameter is used as the input data, and the sample model complexity is used as the supervised data. The sample model parameter set and the sample model complexity set are used as the training data to supervise the training of the model complexity identifier. First, the sample model parameter is input into the neural network, and the output of each layer is calculated until the output layer generates a predicted complexity value. Then, the loss function (such as mean square error or cross-entropy loss) is used to calculate the difference between the predicted value and the true label. Then, according to the result of the loss function, the gradient of each parameter (weight and bias) to the loss is calculated, and the gradient is propagated from the output layer to the input layer through the chain rule to update the weight and bias of each layer. Further, the optimization algorithm (such as gradient descent) is used to update the weight and bias according to the calculated gradient, and the learning rate determines the step size of the update. Finally, the process of forward propagation, loss calculation, back propagation, and weight update is repeated until the set number of iterations or the loss converges, and the trained model complexity identifier is obtained.

[0030] By constructing the model complexity identifier based on the BP neural network, the complexity of the sample model can be effectively analyzed, and the efficiency and accuracy of the model complexity prediction can be improved.

[0031] S120: obtaining the model parameter of the current 3D printing object model; S130: inputting the model parameter into the model complexity identifier to obtain the model complexity.

[0032] Specifically, the model parameter of the current object is extracted from the 3D printing software or model file, including size parameter, material parameter, etc. Then, the model parameter is input into the model complexity identifier for analysis, and the model complexity is output. Through the above steps, the model parameter of the current 3D printing object can be effectively obtained, and the model complexity identifier can be used to predict the complexity, providing support for the printing process.

[0033] S200: obtaining the slicing precision of the object model, and collecting the printing material characteristic parameter and the environmental characteristic parameter of the current printing.

[0034] In one embodiment, the step S200 of the present application further comprises:

[0035] S210: obtaining the minimum slicing size of the object model, calculating the ratio of the average slicing size and the minimum slicing size as the slicing precision information; S220: collecting the printing material characteristic parameter and the current environmental parameter.

[0036] Specifically, the minimum slicing size of the object model is obtained, i.e., the minimum slicing size of the current object model is extracted from the slicing software used, and the minimum slicing size refers to the minimum layer thickness that can be achieved in the slicing process, which affects the printing precision and detail performance. Then, slicing data is collected from previous printing records to calculate the average slicing size in the historical time, which can be obtained by database query or extraction from log files, such as calculating the average slicing size in the last three days. Then, the ratio of the average slicing size and the minimum slicing size is calculated, and the ratio is taken as the slicing precision information, wherein if the ratio is close to 1, it means that the slicing precision is high; if the ratio is greater than 1, it means that the average slicing size is relatively large, which may affect the detail performance of printing.

[0037] On the other hand, the printing material characteristic parameters and the current environmental parameters of the current printing are collected, wherein the printing material characteristic parameters include material type, material density, melting point, glass transition temperature, and flowability, etc. The environmental parameters include environmental temperature, environmental humidity, etc. The environmental parameters will affect the printing success rate, such as, too high or too low environmental temperature will affect the melting characteristics and cooling speed of the material, low temperature may cause incomplete solidification of the material, and high temperature may cause thermal deformation.

[0038] S300: According to the model complexity, the printing material characteristic parameters and the environmental characteristic parameters, a basic printing success rate analysis is performed to obtain a basic printing success rate.

[0039] In one embodiment, the step S300 of the present application further comprises:

[0040] S310: According to the historical data of 3D printing, a sample model complexity set, a sample printing material characteristic parameter set and a sample environmental characteristic parameter set are collected, and a sample basic printing success rate set is calculated according to the proportion of successful printing; S320: The sample model complexity set, the sample printing material characteristic parameter set, the sample environmental characteristic parameter set and the sample basic printing success rate set are used to train a basic printing predictor; S330: The model complexity, the printing material characteristic parameters and the environmental characteristic parameters are input into the basic printing predictor to output a basic printing success rate.

[0041] Specifically, the historical data of 3D printing is obtained, and information extraction is performed in the historical printing data to collect a sample model complexity set, a sample printing material characteristic parameter set and a sample environmental characteristic parameter set; further, the proportion of successful printing under different sample model complexity, different sample printing material characteristic parameters and different sample environmental characteristic parameters, i.e., the ratio of the number of successful printing to the total number of printing, is counted to calculate the sample basic printing success rate, and a sample basic printing success rate set is obtained.

[0042] Then, a basic printing predictor is constructed based on the BP neural network, the basic printing predictor including an input layer, a plurality of hidden layers and an output layer, wherein the input data of the input layer is the model complexity, the printing material characteristic parameter and the environment characteristic parameter, and the output data is the basic printing success rate; then, taking the sample model complexity, the sample printing material characteristic parameter and the sample environment characteristic parameter as input, and taking the sample basic printing success rate as supervision, the sample model complexity set, the sample printing material characteristic parameter set, the sample environment characteristic parameter set and the sample basic printing success rate set are used as training data to perform supervised training and verification training on the basic printing predictor. First, the data set is divided into a training set and a verification set (for example, 80% for training and 20% for verification); then, the basic printing predictor is supervised trained through the training set, for example, the input feature data is input into the neural network, and the output value (the predicted basic printing success rate) is obtained through the calculation of each layer; then, the error between the predicted value and the true value is calculated using a loss function (such as mean square error), and the gradient of the loss function to each weight and bias is calculated, the gradient is propagated from the output layer to the input layer through the chain rule, and the weight and bias are updated; further, the network parameters are updated using an optimization algorithm (such as stochastic gradient descent, Adam, etc.), and the step size of weight update is adjusted according to the learning rate; the process of forward propagation, loss calculation, back propagation and weight update is repeated until the set number of iterations or the loss converges. Then, the basic printing predictor is verified through the verification set, that is, the verification set is used for evaluation, the prediction accuracy and the loss of the model on the verification set are calculated, and the generalization ability of the model is judged; according to the verification result, the model structure and the hyperparameters (such as learning rate, number of hidden layer nodes, etc.) are adjusted to optimize the model performance; finally, the basic printing predictor meeting the expected conditions is obtained.

[0043] Finally, the model complexity, the printing material characteristic parameter and the environment characteristic parameter are input into the basic printing predictor, and the basic printing success rate is output. Through intelligent prediction by constructing the basic printing predictor, the efficiency, accuracy and reliability of the basic printing success rate can be improved, and thus the accuracy and reliability of the final printing success rate evaluation can be improved.

[0044] S400: According to the slicing accuracy, a printing difficulty coefficient is obtained by classification, the basic printing success rate is corrected, and a printing success rate is obtained as a printing success rate evaluation result.

[0045] In one embodiment, the step S400 of the present application further includes:

[0046] S410: According to the slicing accuracy, a printing difficulty coefficient is obtained by classification.

[0047] In one embodiment, step S410 of the present application further comprises:

[0048] S411: According to the historical data of 3D printing, a sample slice accuracy set is collected, and an average proportion of the decrease in printing success rate under different sample slice accuracies is collected to obtain a sample printing difficulty coefficient set; S412: a mapping relationship between the sample slice accuracy set and the sample printing difficulty coefficient set is constructed to obtain a printing difficulty classifier; and S413: the slice accuracy is input into the printing difficulty classifier to obtain the printing difficulty coefficient through mapping classification.

[0049] Specifically, according to the historical data of 3D printing, a sample slice accuracy set is collected, and a sample decrease proportion of the decrease in printing success rate under different sample slice accuracies is collected to obtain a plurality of sample decrease proportions, and the plurality of sample decrease proportions are subjected to mean value calculation to obtain a sample average decrease proportion, and the sample average decrease proportion is set as a sample printing difficulty coefficient to obtain a sample printing difficulty coefficient set. Through the above steps, the sample slice accuracy set and the printing success rate decrease proportion can be effectively collected, and the sample printing difficulty coefficient can be calculated, thereby providing a reference basis for optimizing the 3D printing process.

[0050] Based on the principle of decision tree, the sample slice accuracy is taken as a child node of a tree, and the sample printing difficulty coefficient is taken as a leaf node of the child node, a printing difficulty classifier is constructed by taking the sample slice accuracy set and the sample printing difficulty coefficient set as construction data according to the mapping relationship between the sample slice accuracy and the sample printing difficulty coefficient. Finally, the slice accuracy is input into the printing difficulty classifier for matching to obtain the printing difficulty coefficient through mapping classification.

[0051] S420: The printing success rate is obtained by correcting and calculating the base printing success rate according to the printing difficulty coefficient, as a printing success rate evaluation result.

[0052] Specifically, a correction coefficient is set according to the printing difficulty coefficient, wherein the correction coefficient is used to adjust the printing success rate and reflects the influence of the printing difficulty coefficient on the success rate. The greater the printing difficulty coefficient, the greater the correction coefficient should be, indicating that the greater the amplitude of the decrease in success rate. Then, the base printing success rate is corrected and calculated according to the correction coefficient, for example, 1 is subtracted from the correction coefficient as a correction weight, and the product of the correction weight and the base printing success rate is taken as the printing success rate, and the printing success rate is obtained as the printing success rate evaluation result. By correcting the base printing success rate according to the printing difficulty coefficient, the accuracy and reliability of the printing success rate evaluation can be further improved.

[0053] The 3D printing success rate evaluation method based on big data analysis provided by the embodiment of the present application has at least the following technical effects:

[0054] By acquiring model parameters of an object model currently undergoing 3D printing, performing model complexity analysis according to the model parameters to obtain model complexity, then acquiring slicing precision of slicing the object model, and collecting printing material characteristic parameters and environmental characteristic parameters of the printing currently undergoing, then performing basic printing success rate analysis according to the model complexity, the printing material characteristic parameters and the environmental characteristic parameters to obtain a basic printing success rate, and finally correcting the basic printing success rate according to the slicing precision to obtain a printing success rate as a printing success rate evaluation result. By combining various factors to comprehensively evaluate the printing success rate of the object, the accuracy and reliability of the printing success rate evaluation can be significantly improved, thereby providing a basis for printing parameter optimization and adjustment, and achieving the technical effect of effectively improving the overall success rate and production efficiency of 3D printing.

[0055] Embodiment two, as shown in Figure 2 based on the same inventive concept of the 3D printing success rate evaluation method based on big data analysis provided in embodiment one, the present embodiment also provides a 3D printing success rate evaluation system based on big data analysis, comprising:

[0056] A model complexity analysis module 01 is configured to acquire model parameters of an object model currently undergoing 3D printing, perform model complexity analysis, and obtain model complexity. An information collection module 02 is configured to acquire slicing precision of slicing the object model, and collect printing material characteristic parameters and environmental characteristic parameters of the printing currently undergoing. A basic printing success rate analysis module 03 is configured to perform basic printing success rate analysis according to the model complexity, the printing material characteristic parameters and the environmental characteristic parameters, and obtain a basic printing success rate. A basic printing success rate correction module 04 is configured to correct the basic printing success rate according to the slicing precision to obtain a printing success rate as a printing success rate evaluation result.

[0057] In one embodiment, the 3D printing success rate evaluation system based on big data analysis further comprises:

[0058] A model complexity recognizer is trained. Model parameters of an object model currently undergoing 3D printing are acquired. The model parameters are input into the model complexity recognizer to output model complexity.

[0059] In one embodiment, the 3D printing success rate evaluation system based on big data analysis further comprises:

[0060] According to the 3D printing record data in the historical time, a sample model parameter set is collected, a sample model complexity set is obtained by processing and labeling according to the minimum size parameter in each sample model parameter, and a model complexity identifier is trained to convergence by using the sample model parameter set and the sample model complexity set.

[0061] In one embodiment, the 3D printing success rate evaluation system based on big data analysis further comprises:

[0062] The minimum slicing size of the slicing of the object model is obtained, the ratio of the average slicing size and the minimum slicing size is calculated as the slicing precision information, the printing material characteristic parameters and the current environmental parameters of the current printing are collected.

[0063] In one embodiment, the 3D printing success rate evaluation system based on big data analysis further comprises:

[0064] According to the historical data of 3D printing, a sample model complexity set, a sample printing material characteristic parameter set and a sample environmental characteristic parameter set are collected, and a sample basic printing success rate set is obtained by processing and labeling according to the printing success rate, and a basic printing predictor is trained by using the sample model complexity set, the sample printing material characteristic parameter set, the sample environmental characteristic parameter set and the sample basic printing success rate set; the model complexity, the printing material characteristic parameter and the environmental characteristic parameter are input into the basic printing predictor, and a basic printing success rate is output.

[0065] In one embodiment, the 3D printing success rate evaluation system based on big data analysis further comprises:

[0066] According to the slicing precision, a printing difficulty coefficient is classified and obtained, and the basic printing success rate is corrected and calculated by using the printing difficulty coefficient to obtain a printing success rate as a printing success rate evaluation result.

[0067] In one embodiment, the 3D printing success rate evaluation system based on big data analysis further comprises:

[0068] According to the historical data of 3D printing, a sample slicing precision set is collected, and a sample printing difficulty coefficient set is obtained by collecting the average proportion of the printing success rate under different sample slicing precisions; a mapping relationship between the sample slicing precision set and the sample printing difficulty coefficient set is constructed to obtain a printing difficulty classifier; the slicing precision is input into the printing difficulty classifier to map and classify to obtain the printing difficulty coefficient.

[0069] Embodiment three, please refer to Figure 3 , Figure 3 An embodiment of an electronic device provided by the embodiment of the application is shown in the figure. Figure 3As shown in the figure, the embodiment of the present application provides an electronic device 500, which comprises a memory 510, a processor 520, and a first computer program 511 stored in the memory 510 and capable of running on the processor 520. When the processor 520 executes the first computer program 511, the following steps are implemented: obtaining model parameters of an object model currently undergoing 3D printing, performing model complexity analysis to obtain model complexity; obtaining slicing precision of slicing the object model, and collecting printing material characteristic parameters and environmental characteristic parameters of the printing currently undergoing; performing basic printing success rate analysis according to the model complexity, the printing material characteristic parameters, and the environmental characteristic parameters to obtain a basic printing success rate; according to the slicing precision, obtaining a printing difficulty coefficient by classification, and correcting the basic printing success rate to obtain a printing success rate as a printing success rate evaluation result.

[0070] Embodiment four, please refer to Figure 4 , Figure 4 An embodiment of a computer readable storage medium provided by the embodiment of the present application is shown in the figure. As Figure 4 shown in the figure, the embodiment provides a computer readable storage medium 600, which stores a second computer program 611. When the processor executes the second computer program 611, the following steps are implemented: obtaining model parameters of an object model currently undergoing 3D printing, performing model complexity analysis to obtain model complexity; obtaining slicing precision of slicing the object model, and collecting printing material characteristic parameters and environmental characteristic parameters of the printing currently undergoing; performing basic printing success rate analysis according to the model complexity, the printing material characteristic parameters, and the environmental characteristic parameters to obtain a basic printing success rate; according to the slicing precision, obtaining a printing difficulty coefficient by classification, and correcting the basic printing success rate to obtain a printing success rate as a printing success rate evaluation result.

[0071] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0072] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application 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.

[0073] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or blocks of the flowcharts. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or blocks of the flowcharts.

[0074] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or blocks of the flowcharts. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or blocks of the flowcharts.

[0075] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or blocks of the flowcharts. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or blocks of the flowcharts.

[0076] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the embodiments described and shown, without departing from the spirit and scope of the application.

[0077] It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover the modifications and variations of this application and its equivalent.

Claims

1. A method for 3D printing success rate assessment based on big data analysis, characterized in that, The method comprises: Obtaining model parameters of a model of an article currently undergoing 3D printing, performing model complexity analysis, and obtaining model complexity; Obtaining slicing precision of slicing of the model of the article, and collecting printing material characteristic parameters and environmental characteristic parameters of the printing currently being performed, the printing material characteristic parameters comprising material type, material density, melting point, glass transition temperature, and fluidity, and the environmental characteristic parameters comprising environmental temperature and environmental humidity; Performing basic printing success rate analysis according to the model complexity, the printing material characteristic parameters, and the environmental characteristic parameters, and obtaining a basic printing success rate; According to the slicing precision, a printing difficulty coefficient is obtained by classification, the basic printing success rate is corrected, and a printing success rate is obtained as a printing success rate evaluation result; Obtaining model parameters of a model of an article currently undergoing 3D printing, performing model complexity analysis, and obtaining model complexity, comprising: Training a model complexity recognizer; Obtaining model parameters of a model of an article currently undergoing 3D printing; Inputting the model parameters into the model complexity recognizer, and outputting the obtained model complexity; Obtaining slicing precision of slicing of the model of the article, and collecting printing material characteristic parameters and environmental characteristic parameters of the printing currently being performed, comprising: Obtaining the minimum slicing size of slicing of the model of the article, calculating the ratio of the average slicing size to the minimum slicing size as slicing precision information; Collecting printing material characteristic parameters and environmental characteristic parameters of the printing currently being performed; Performing basic printing success rate analysis according to the model complexity, the printing material characteristic parameters, and the environmental characteristic parameters, and obtaining a basic printing success rate, comprising: According to historical data of 3D printing, a sample model complexity set, a sample printing material characteristic parameter set, and a sample environmental characteristic parameter set are collected, and a sample basic printing success rate set is calculated according to the proportion of printing success; The sample model complexity set, the sample printing material characteristic parameter set, the sample environmental characteristic parameter set, and the sample basic printing success rate set are used to train a basic printing predictor; The model complexity, the printing material characteristic parameters, and the environmental characteristic parameters are input into the basic printing predictor, and the basic printing success rate is output.

2. The big data analytics based 3D printing success rate evaluation method according to claim 1, wherein, Training a model complexity recognizer, comprising: According to 3D printing record data in a historical period, a sample model parameter set is collected, and a sample model complexity set is processed and labeled according to the minimum size parameter in each sample model parameter; The sample model parameter set and the sample model complexity set are used to train the model complexity recognizer to convergence.

3. The big data analytics based 3D printing success rate evaluation method according to claim 1, wherein, According to the slicing precision, a printing difficulty coefficient is obtained by classification, the basic printing success rate is corrected, and a printing success rate is obtained as a printing success rate evaluation result, comprising: According to the slicing precision, a printing difficulty coefficient is obtained by classification; The printing difficulty coefficient is used to correct and calculate the basic printing success rate, and a printing success rate is obtained as a printing success rate evaluation result.

4. The big data analytics based 3D printing success rate evaluation method according to claim 3, wherein, According to the slicing precision, a printing difficulty coefficient is obtained by classification, comprising: According to the historical data of 3D printing, a sample slice accuracy set is collected, and an average proportion of printing success rate reduction under different sample slice accuracies is collected to obtain a sample printing difficulty coefficient set; A mapping relationship between the sample slice accuracy set and the sample printing difficulty coefficient set is constructed to obtain a printing difficulty classifier; The slice accuracy is input into the printing difficulty classifier to obtain the printing difficulty coefficient through mapping classification.

5. A 3D printing success rate evaluation system based on big data analysis, characterized by, Steps for implementing the 3D printing success rate evaluation method based on big data analysis according to any one of claims 1 to 4, comprising: A model complexity analysis module is configured to obtain model parameters of an article model currently subjected to 3D printing, perform model complexity analysis, and obtain model complexity. An information collection module is configured to obtain slice accuracy of the article model subjected to slicing, and collect printing material characteristic parameters and environmental characteristic parameters of the current printing; A basic printing success rate analysis module is configured to perform basic printing success rate analysis according to the model complexity, printing material characteristic parameters, and environmental characteristic parameters, and obtain a basic printing success rate. A basic printing success rate correction module is configured to classify and obtain a printing difficulty coefficient according to the slice accuracy, correct the basic printing success rate, and obtain a printing success rate as a printing success rate evaluation result.

6. An electronic device, comprising: Comprise: A memory is configured to store a computer software program; A processor is configured to read and execute the computer software program, thereby realizing the steps of the 3D printing success rate evaluation method based on big data analysis according to any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium, comprising: The storage medium stores a computer software program, and the computer software program is executed by the processor to realize the steps of the 3D printing success rate evaluation method based on big data analysis according to any one of claims 1 to 4.

Images