Resin matching method and system based on slice data analysis

Abstract

The invention discloses a resin matching method and system based on slice data analysis. The method comprises the following steps: acquiring slice data of a model to be subjected to 3D printing; determining a plurality of printing process parameters corresponding to the model according to the slice data; according to a preset resin database, a resin scheme corresponding to each printing process parameter is matched; and according to the resin schemes corresponding to all the printing process parameters, based on a resin performance prediction model, determining a resin matching scheme corresponding to the model. Therefore, precise resin matching based on slice analysis and performance prediction can be achieved, the adaptability and printing quality of the 3D printing material are improved, and the stability of the printing process and the performance of a finished product are optimized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a resin matching method and system based on slice data analysis. BACKGROUND

[0002] With the rapid popularization of 3D printing technology in the field of high-precision manufacturing, enterprises and users pay more and more attention to improving printing quality and process stability by optimizing resin matching, and how to realize accurate resin matching to optimize product performance becomes a key technical problem. The existing technology usually obtains slice data of a model to be printed, and selects a resin scheme by using simple process parameter analysis or fixed resin matching rules. The existing solution is difficult to generate a resin matching scheme that adapts to complex models due to the lack of comprehensive analysis of multiple printing process parameters and dynamic prediction of resin performance, and cannot meet diversified printing needs, resulting in insufficient material adaptability, easy to cause printing defects or poor performance due to improper resin selection, and limiting the stability of the 3D printing process and the quality of the finished product. 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 resin matching method and system based on slice data analysis, which can realize accurate resin matching based on slice analysis and performance prediction, improve the adaptability of 3D printing materials and printing quality, and optimize the stability of the printing process and the performance of the finished product.

[0004] To solve the above technical problems, the first aspect of the present application discloses a resin matching method based on slice data analysis, which comprises: obtaining slice data of a model to be printed by 3D printing; determining a plurality of printing process parameters corresponding to the model according to the slice data; matching a resin scheme corresponding to each printing process parameter according to a preset resin database; determining a resin matching scheme corresponding to the model based on a resin performance prediction model according to resin schemes corresponding to all printing process parameters.

[0005] As an optional implementation, in the first aspect of the present application, the process parameter types of the printing process parameters are part structure type, part physical parameter, model maximum thickness, model minimum thickness, model support overall stability, and model support weak place quantity.

[0006] As an optional implementation, in the first aspect of the present application, the determination of a plurality of printing process parameters corresponding to the model according to the slice data comprises: for each process parameter type, obtaining historical analysis records corresponding to the process parameter type in a historical database; Based on the historical analysis records, determine the core slice location corresponding to this process parameter type; The slice portion data corresponding to the core slice position is determined from the slice data; The sliced ​​data is input into the calculation model corresponding to the process parameter type to obtain the printing process parameters corresponding to the process parameter type.

[0007] As an optional implementation, in the first aspect of the present invention, the historical analysis record includes slice data records and historical calculation results corresponding to multiple historical time points for calculating the process parameter type.

[0008] As an optional implementation, in the first aspect of the present invention, determining the core slice location corresponding to the process parameter type based on the historical analysis records includes: For each slice data record, multiple sample data are obtained by randomly sampling the slice data record. Each of the aforementioned sampling data is input into the calculation model corresponding to the process parameter type to obtain the corresponding sampling calculation result; Calculate the similarity between the sampling calculation results and the historical calculation results; All sampled data with similarity lower than a preset similarity threshold are selected to obtain candidate sampled data; Based on the candidate sampling data, determine the core slice location corresponding to the process parameter type.

[0009] As an optional implementation, in the first aspect of the present invention, determining the core slice location corresponding to the process parameter type based on the candidate sampling data includes: The position of each candidate sampled data relative to the missing slice data of the slice data record is determined as the candidate slice position; For each candidate slice location, calculate the weighted sum of the time proximity parameters corresponding to all slice data records that exist at that candidate slice location, and obtain the priority parameter corresponding to that candidate slice location; The candidate slice positions for which the priority parameter is greater than the preset parameter threshold are selected to obtain the core slice position corresponding to the process parameter type.

[0010] As an optional implementation, in the first aspect of the present invention, the step of matching the resin scheme corresponding to each of the printing process parameters according to a preset resin database includes: For each of the aforementioned printing process parameters, the parameter similarity between the printing record of each candidate resin scheme in the preset resin database and the printing process parameter is calculated. All candidate resin schemes with a parameter similarity greater than a preset threshold are selected to obtain multiple similar resin schemes; Determine the finished product quality corresponding to each of the aforementioned similar resin solutions; The resin scheme with the highest finished product quality is determined as the resin scheme corresponding to the printing process parameters; the resin scheme includes multiple resin materials.

[0011] As an optional implementation, in the first aspect of the present invention, determining the resin selection scheme corresponding to the resin scheme based on the resin performance prediction model according to the resin scheme corresponding to all the printing process parameters includes: Based on the random sampling combination algorithm, the resin materials in the resin schemes corresponding to all the printing process parameters are sampled and combined to obtain multiple resin material combination schemes. Each of the resin material combination schemes is input into the trained resin printing performance prediction model to obtain the corresponding predicted performance; the resin printing performance prediction model is trained using a training dataset that includes multiple training resin combination schemes and corresponding post-printing quality performance annotations. The resin material combination scheme with the highest predicted performance is determined as the resin selection scheme corresponding to the model.

[0012] A second aspect of this invention discloses a resin selection system based on slice data analysis, the system comprising: The acquisition module is used to acquire slice data of the model to be 3D printed; The first determining module is used to determine multiple printing process parameters corresponding to the model based on the slice data; The matching module is used to match the resin scheme corresponding to each of the printing process parameters according to the preset resin database. The second determining module is used to determine the resin selection scheme corresponding to the model based on the resin scheme corresponding to all the printing process parameters and the resin performance prediction model.

[0013] As an optional implementation, in the second aspect of the present invention, the process parameter type of the printing process parameters is the component structure type, component physical parameters, maximum model thickness, minimum model thickness, overall stability of model support, and number of weak points in model support.

[0014] As an optional implementation, in a second aspect of the invention, the specific method by which the first determining module determines the multiple printing process parameters corresponding to the model based on the slice data includes: For each process parameter type, retrieve the corresponding historical analysis records from the historical database; Based on the historical analysis records, determine the core slice location corresponding to this process parameter type; The slice portion data corresponding to the core slice position is determined from the slice data; The sliced ​​data is input into the calculation model corresponding to the process parameter type to obtain the printing process parameters corresponding to the process parameter type.

[0015] As an optional implementation, in a second aspect of the invention, the historical analysis record includes slice data records and historical calculation results corresponding to multiple historical time points for calculating the process parameter type.

[0016] As an optional implementation, in a second aspect of the invention, the specific method by which the first determining module determines the core slice location corresponding to the process parameter type based on the historical analysis records includes: For each slice data record, multiple sample data are obtained by randomly sampling the slice data record. Each of the aforementioned sampling data is input into the calculation model corresponding to the process parameter type to obtain the corresponding sampling calculation result; Calculate the similarity between the sampling calculation results and the historical calculation results; All sampled data with similarity lower than a preset similarity threshold are selected to obtain candidate sampled data; Based on the candidate sampling data, determine the core slice location corresponding to the process parameter type.

[0017] As an optional implementation, in a second aspect of the invention, the specific method by which the first determining module determines the core slice location corresponding to the process parameter type based on the candidate sampling data includes: The position of each candidate sampled data relative to the missing slice data of the slice data record is determined as the candidate slice position; For each candidate slice location, calculate the weighted sum of the time proximity parameters corresponding to all slice data records that exist at that candidate slice location, and obtain the priority parameter corresponding to that candidate slice location; The candidate slice positions for which the priority parameter is greater than the preset parameter threshold are selected to obtain the core slice position corresponding to the process parameter type.

[0018] As an optional implementation, in a second aspect of the invention, the matching module matches the resin scheme corresponding to each printing process parameter according to a preset resin database in the following manner: For each of the aforementioned printing process parameters, the parameter similarity between the printing record of each candidate resin scheme in the preset resin database and the printing process parameter is calculated. All candidate resin schemes with a parameter similarity greater than a preset threshold are selected to obtain multiple similar resin schemes; Determine the finished product quality corresponding to each of the aforementioned similar resin solutions; The resin scheme with the highest finished product quality is determined as the resin scheme corresponding to the printing process parameters; the resin scheme includes multiple resin materials.

[0019] As an optional implementation, in a second aspect of the invention, the second determining module determines the specific method of the resin selection scheme corresponding to the model based on the resin scheme corresponding to all the printing process parameters and the resin performance prediction model, including: Based on the random sampling combination algorithm, the resin materials in the resin schemes corresponding to all the printing process parameters are sampled and combined to obtain multiple resin material combination schemes. Each of the resin material combination schemes is input into the trained resin printing performance prediction model to obtain the corresponding predicted performance; the resin printing performance prediction model is trained using a training dataset that includes multiple training resin combination schemes and corresponding post-printing quality performance annotations. The resin material combination scheme with the highest predicted performance is determined as the resin selection scheme corresponding to the model.

[0020] A third aspect of the present invention discloses another resin selection system based on slice data analysis, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute some or all of the steps in the resin selection method based on slice data analysis disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the resin selection method based on slice data analysis disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention acquires slice data of the model to be 3D printed and determines multiple printing process parameters. It matches resin schemes according to a preset resin database and determines resin selection schemes based on resin performance prediction models. This enables precise resin selection based on slice analysis and performance prediction, improves the compatibility of 3D printing materials and printing quality, and optimizes the stability of the printing process and the performance of the finished product. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic flowchart of a resin selection method based on slice data analysis disclosed in an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of a resin selection system based on slice data analysis disclosed in an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of another resin selection system based on slice data analysis disclosed in an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

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

[0030] This invention discloses a resin selection method and system based on slice data analysis. By acquiring slice data of the model to be 3D printed and determining multiple printing process parameters, resin schemes are matched according to a preset resin database, and a resin selection scheme is determined based on a resin performance prediction model. This enables precise resin selection based on slice analysis and performance prediction, improving the compatibility and printing quality of 3D printing materials, and optimizing the stability of the printing process and the performance of the finished product. Detailed descriptions follow.

[0031] Example 1 Please see Figure 1 , Figure 1 This is a schematic flowchart of a resin selection method based on slice data analysis disclosed in an embodiment of the present invention. Figure 1 The resin selection method based on slice data analysis described herein can be applied to data processing systems / data processing equipment / data processing servers (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 1 As shown, the resin selection method based on slice data analysis may include the following operations: 101. Obtain the slice data of the model to be 3D printed.

[0032] Optionally, the slice data may include slice layer data, contour path data, fill structure data, and layer height information, which are not limited in this invention.

[0033] Optionally, this acquisition process can be implemented based on slicing software output, database query, file upload, or real-time generation; this invention does not impose any limitations.

[0034] 102. Based on the slice data, determine the multiple printing process parameters corresponding to the model. Optionally, the printing process parameters may include printing speed, layer thickness, infill density, support density, or temperature parameters, which are not limited in this invention.

[0035] Optionally, the determination of the printing process parameters can be based on parameter optimization algorithms, historical data matching, or machine learning models, and this invention does not limit the scope of the invention.

[0036] Optionally, the determination of the printing process parameters can be optimized by combining the geometric characteristics of the model, material properties, or equipment performance, and this invention does not limit this.

[0037] 103. Based on the preset resin database, match the resin solution corresponding to each printing process parameter. Optionally, the resin database can be a local database, a cloud database, or a distributed database; this invention does not impose any limitations.

[0038] Optionally, the resin solution may include resin type, curing parameters, mixing ratio, or material supplier information, which are not limited in this invention.

[0039] Optionally, the matching process can be implemented based on similarity calculation, rule matching, or query algorithms, and this invention does not limit it.

[0040] 104. Based on the resin schemes corresponding to all printing process parameters, determine the resin selection scheme corresponding to the model based on the resin performance prediction model.

[0041] Optionally, the resin performance prediction model can be a neural network model, a regression model, or a simulation model; this invention does not impose any limitations.

[0042] Optionally, the resin selection scheme can be a single resin scheme, a mixed resin scheme, or a set of alternative schemes, and the present invention does not limit it.

[0043] Optionally, this determination process can be combined with predictive performance evaluation, cost optimization, or user preferences, and the present invention does not limit it.

[0044] As can be seen, the above-described embodiments of the invention acquire slice data of the model to be 3D printed and determine multiple printing process parameters, match resin schemes according to a preset resin database, and determine resin selection schemes based on resin performance prediction models. This enables precise resin selection based on slice analysis and performance prediction, improves the compatibility and printing quality of 3D printing materials, and optimizes the stability of the printing process and the performance of the finished product.

[0045] As an optional embodiment, the process parameter type of the printing process parameters in the above steps is the component structure type, component physical parameters, maximum model thickness, minimum model thickness, overall stability of model support, and number of weak points in model support.

[0046] As can be seen, the above optional embodiments define the types of process parameters for printing processes, so as to comprehensively characterize the process requirements of model printing, assist in achieving accurate resin selection based on slicing analysis and performance prediction, improve the compatibility and printing quality of 3D printing materials, and optimize the stability of the printing process and the performance of the finished product.

[0047] As an optional embodiment, the step above, determining multiple printing process parameters corresponding to the model based on the slice data, includes: For each process parameter type, retrieve the corresponding historical analysis records from the historical database; Based on historical analysis records, the core slice location corresponding to this process parameter type was determined; Identify the slice portion data corresponding to the core slice location from the slice data; Input the sliced ​​data into the calculation model corresponding to the process parameter type to obtain the printing process parameters corresponding to that process parameter type.

[0048] Optionally, the historical analysis record may include historical slice data, historical calculation results, or historical performance indicators, which are not limited in this invention.

[0049] Optionally, the process of obtaining the historical analysis records can be based on database queries, time range filtering, or type matching, and this invention does not limit this.

[0050] Optionally, the core slice location can be a slice layer number, position coordinates, or relative position; this invention does not impose any limitations on this.

[0051] Optionally, the determination of the core slice location can be based on analysis and extraction, clustering calculation or feature screening, and this invention does not limit it.

[0052] Optionally, the determination of the core slice location can be optimized by combining historical data distribution or parameter correlation, and this invention does not limit it.

[0053] Optionally, the process of determining the data of the slice can be based on location matching, data extraction or slice segmentation, and the present invention does not limit it.

[0054] Optionally, the determination of the slice data can be optimized by combining slice resolution or positional accuracy, and this invention does not limit it.

[0055] Optionally, the calculation model can be an automated engineering calculation model, a regression model, or a neural network model; this invention does not limit the specific model.

[0056] Optionally, the calculation process of the printing process parameters can be optimized by combining historical calculation results or parameter constraints, and this invention does not limit this.

[0057] As can be seen, through the above optional embodiments, by obtaining historical analysis records corresponding to the process parameter types in the historical database and determining the core slice position, the slice data is input into the calculation model to obtain the printing process parameters. This achieves accurate process parameter calculation based on historical records and core position analysis, improves the accuracy and relevance of 3D printing parameter evaluation, and enhances the reliability of resin selection schemes and printing efficiency.

[0058] As an optional embodiment, the historical analysis record in the above steps includes slice data records and historical calculation results corresponding to multiple historical time points for calculating the process parameter type.

[0059] As can be seen, the above optional embodiments limit the content of historical analysis records to comprehensively characterize the use of slice data records and calculation results of different process parameters in the historical analysis process, assisting in the accurate resin selection based on slice analysis and performance prediction, improving the compatibility and printing quality of 3D printing materials, and optimizing the stability of the printing process and the performance of finished products.

[0060] As an optional embodiment, the step above, determining the core slice location corresponding to the process parameter type based on historical analysis records, includes: For each slice of data record, multiple sample data are obtained by randomly sampling that slice of data record; Each sampled data point is input into the calculation model corresponding to the process parameter type to obtain the corresponding sampling calculation result. Calculate the similarity between the sampling calculation results and the historical calculation results; All sampled data with similarity below a preset similarity threshold are filtered out to obtain candidate sampled data; Based on the candidate sampling data, determine the core slice location corresponding to this process parameter type.

[0061] Optionally, the random sampling can be uniform sampling, weighted sampling, or stratified sampling; the present invention does not limit the type of sampling.

[0062] Optionally, the sampling data can be slice subset data, feature sampling data, or parameter sampling data; this invention does not impose any limitations.

[0063] Optionally, the sampling process can be optimized by combining sampling ratio, data distribution, or random seed; this invention does not limit this.

[0064] Optionally, the similarity can be cosine similarity, Euclidean distance, or Jaccard coefficient; this invention does not limit the specific similarity.

[0065] Optionally, the similarity calculation process can be based on numerical comparison, statistical analysis, or vector matching, and this invention does not limit it.

[0066] Optionally, the similarity calculation can be optimized by combining the result type or the calculation precision, which is not limited in this invention.

[0067] Optionally, the similarity threshold can be a fixed threshold, a dynamic threshold, or a threshold adjusted based on parameter type; this invention does not impose any limitations.

[0068] Optionally, the screening process for the candidate sampling data can be implemented based on threshold filtering, sorting algorithms, or classification models, and this invention does not limit it.

[0069] As can be seen, through the above optional embodiments, by randomly sampling the slice data records and calculating the similarity between the sampling calculation results and the historical calculation results, the core slice position is determined by screening low similarity sampling data, thereby achieving accurate core position identification based on random sampling and similarity analysis, improving the accuracy of process parameter calculation and data utilization, and enhancing the optimization effect of the 3D printing process.

[0070] As an optional embodiment, the step of determining the core slice location corresponding to the process parameter type based on the candidate sampling data in the above steps includes: The position of the missing slice data relative to the slice data record for each candidate sampled data is determined as the candidate slice position; For each candidate slice location, calculate the weighted sum of the time proximity parameters of all slice data records that exist at that candidate slice location, and obtain the priority parameter corresponding to that candidate slice location. The candidate slice positions with priority parameters greater than the preset parameter threshold are selected to obtain the core slice position corresponding to the process parameter type.

[0071] Optionally, the time proximity parameter is the reciprocal of the time difference between the calculation time point corresponding to each slice of data record and the current time point, used to characterize the proximity of the slice of data record to the current time point.

[0072] Optionally, the weighted sum can be calculated using fixed weights, dynamic weights, or adaptive weights; this invention does not impose any limitations on this.

[0073] Optionally, the calculation process of this priority parameter can be based on time analysis, statistical summation or data fusion, and this invention does not limit it.

[0074] Optionally, the threshold parameter can be a fixed threshold, a dynamic threshold, or a threshold adjusted based on priority distribution; this invention does not impose any limitations.

[0075] As can be seen, through the above optional embodiments, by determining the missing locations of candidate sampling data as candidate slice locations and calculating the weighted sum of time proximity parameters to filter high-priority parameter locations as core slice locations, accurate location filtering based on missing data analysis and time weighting is achieved, improving the accuracy and temporal relevance of core slice location determination, and enhancing the reliability and printing adaptability of process parameter extraction.

[0076] As an optional embodiment, the step above, matching the resin scheme corresponding to each printing process parameter according to a preset resin database, includes: For each printing process parameter, calculate the parameter similarity between the printing record of each candidate resin scheme in the preset resin database and the printing process parameter. All candidate resin schemes with parameter similarity greater than a preset threshold are selected to obtain multiple similar resin schemes; Determine the finished product quality corresponding to each similar resin scheme; The resin solution with the highest finished product quality is selected as the resin solution corresponding to the printing process parameters; the resin solution includes multiple resin materials.

[0077] Optionally, the similarity parameter can be cosine similarity, Euclidean distance, or Jaccard coefficient; this invention does not impose any limitation.

[0078] Optionally, the quality of the finished product can be a quality score, performance index, or defect rate; this invention does not limit this.

[0079] Optionally, the process of determining the quality of the finished product can be based on historical record analysis, simulation evaluation, or model prediction, and this invention does not limit this.

[0080] Optionally, the quality of the finished product can be optimized by combining resin properties or printing parameters, and this invention does not limit this.

[0081] As can be seen, through the above optional embodiments, by calculating the parameter similarity between the printing records of candidate resin schemes in the resin database and the printing process parameters, and screening out highly similar schemes, the best resin scheme is determined based on the quality of the finished product, thereby achieving accurate resin matching based on similarity and quality optimization, improving the adaptability of resin schemes and the quality of finished products, and increasing the efficiency of 3D printing material selection.

[0082] As an optional embodiment, the step above, determining the resin selection scheme corresponding to the model based on the resin performance prediction model according to the resin scheme corresponding to all printing process parameters, includes: Based on the random sampling combination algorithm, the resin materials in the resin schemes corresponding to all printing process parameters are sampled and combined to obtain multiple resin material combination schemes. Each resin material combination scheme is input into the trained resin printing performance prediction model to obtain the corresponding predicted performance; optionally, the resin printing performance prediction model is trained using a training dataset that includes multiple training resin combination schemes and corresponding post-printing quality performance annotations. The resin material combination scheme with the highest predicted performance is determined as the resin selection scheme corresponding to the model.

[0083] Optionally, the random sampling combination algorithm can be a Monte Carlo algorithm, a genetic algorithm, or a random search algorithm; this invention does not limit the algorithm.

[0084] Optionally, the resin material combination scheme can be a single combination, a multi-material combination, or a layered combination; the present invention does not limit this.

[0085] Optionally, the sampling combination process can be optimized by combining combination size, random seed or constraints, and this invention does not limit it.

[0086] Optionally, the resin printing performance prediction model can be a neural network model, a regression model, or a classification model; this invention does not impose any limitations.

[0087] Optionally, the predicted performance can be a performance score, quality indicator, or risk assessment, and the present invention does not limit it.

[0088] Optionally, the training dataset may include historical combined data, simulated data, or labeled data, and this invention does not impose any limitations.

[0089] As can be seen, through the above optional embodiments, by generating resin material combination schemes based on random sampling combination algorithms and inputting them into the resin printing performance prediction model to predict performance, the highest performance scheme is selected as the resin selection scheme, thereby achieving accurate material optimization based on sampling combination and performance prediction, improving the comprehensive performance and adaptability of resin selection, and enhancing the durability and economy of 3D printed products.

[0090] Example 2 Please see Figure 2 , Figure 2 This is a schematic diagram of a resin selection system based on slice data analysis disclosed in an embodiment of the present invention. Figure 2 The described resin selection system based on slice data analysis can be applied to data processing systems / data processing equipment / data processing servers (including local processing servers or cloud processing servers). For example... Figure 2 As shown, the resin selection system based on slice data analysis may include: The acquisition module 201 is used to acquire slice data of the model to be 3D printed.

[0091] The first determining module 202 is used to determine multiple printing process parameters corresponding to the model based on the slice data. The matching module 203 is used to match the resin scheme corresponding to each printing process parameter according to the preset resin database. The second determining module 204 is used to determine the resin selection scheme corresponding to the model based on the resin scheme corresponding to all printing process parameters and the resin performance prediction model.

[0092] As can be seen, the above-described embodiments of the invention acquire slice data of the model to be 3D printed and determine multiple printing process parameters, match resin schemes according to a preset resin database, and determine resin selection schemes based on resin performance prediction models. This enables precise resin selection based on slice analysis and performance prediction, improves the compatibility and printing quality of 3D printing materials, and optimizes the stability of the printing process and the performance of the finished product.

[0093] As an optional embodiment, the process parameter types of the printing process parameters are component structure type, component physical parameters, maximum model thickness, minimum model thickness, overall stability of model support, and number of weak points in model support.

[0094] As can be seen, the above optional embodiments define the types of process parameters for printing processes, so as to comprehensively characterize the process requirements of model printing, assist in achieving accurate resin selection based on slicing analysis and performance prediction, improve the compatibility and printing quality of 3D printing materials, and optimize the stability of the printing process and the performance of the finished product.

[0095] As an optional embodiment, the first determining module determines the specific method of multiple printing process parameters corresponding to the model based on the slice data, including: For each process parameter type, retrieve the corresponding historical analysis records from the historical database; Based on historical analysis records, the core slice location corresponding to this process parameter type was determined; Identify the slice portion data corresponding to the core slice location from the slice data; Input the sliced ​​data into the calculation model corresponding to the process parameter type to obtain the printing process parameters corresponding to that process parameter type.

[0096] As can be seen, through the above optional embodiments, by obtaining historical analysis records corresponding to the process parameter types in the historical database and determining the core slice position, the slice data is input into the calculation model to obtain the printing process parameters. This achieves accurate process parameter calculation based on historical records and core position analysis, improves the accuracy and relevance of 3D printing parameter evaluation, and enhances the reliability of resin selection schemes and printing efficiency.

[0097] As an optional embodiment, the historical analysis record includes slice data records and historical calculation results corresponding to multiple historical time points for calculating this type of process parameter.

[0098] As can be seen, the above optional embodiments limit the content of historical analysis records to comprehensively characterize the use of slice data records and calculation results of different process parameters in the historical analysis process, assisting in the accurate resin selection based on slice analysis and performance prediction, improving the compatibility and printing quality of 3D printing materials, and optimizing the stability of the printing process and the performance of finished products.

[0099] As an optional embodiment, the first determining module determines the specific method for the core slice location corresponding to the process parameter type based on historical analysis records, including: For each slice of data record, multiple sample data are obtained by randomly sampling that slice of data record; Each sampled data point is input into the calculation model corresponding to the process parameter type to obtain the corresponding sampling calculation result. Calculate the similarity between the sampling calculation results and the historical calculation results; All sampled data with similarity below a preset similarity threshold are filtered out to obtain candidate sampled data; Based on the candidate sampling data, determine the core slice location corresponding to this process parameter type.

[0100] As can be seen, through the above optional embodiments, by randomly sampling the slice data records and calculating the similarity between the sampling calculation results and the historical calculation results, the core slice position is determined by screening low similarity sampling data, thereby achieving accurate core position identification based on random sampling and similarity analysis, improving the accuracy of process parameter calculation and data utilization, and enhancing the optimization effect of the 3D printing process.

[0101] As an optional embodiment, the first determining module determines the specific method for determining the core slice location corresponding to the process parameter type based on the candidate sampling data, including: The position of the missing slice data relative to the slice data record for each candidate sampled data is determined as the candidate slice position; For each candidate slice location, calculate the weighted sum of the time proximity parameters of all slice data records that exist at that candidate slice location, and obtain the priority parameter corresponding to that candidate slice location. The candidate slice positions with priority parameters greater than the preset parameter threshold are selected to obtain the core slice position corresponding to the process parameter type.

[0102] As can be seen, through the above optional embodiments, by determining the missing locations of candidate sampling data as candidate slice locations and calculating the weighted sum of time proximity parameters to filter high-priority parameter locations as core slice locations, accurate location filtering based on missing data analysis and time weighting is achieved, improving the accuracy and temporal relevance of core slice location determination, and enhancing the reliability and printing adaptability of process parameter extraction.

[0103] As an optional embodiment, the matching module matches the resin solution corresponding to each printing process parameter according to a preset resin database in the following ways: For each printing process parameter, calculate the parameter similarity between the printing record of each candidate resin scheme in the preset resin database and the printing process parameter. All candidate resin schemes with parameter similarity greater than a preset threshold are selected to obtain multiple similar resin schemes; Determine the finished product quality corresponding to each similar resin scheme; The resin solution with the highest finished product quality is selected as the resin solution corresponding to the printing process parameters; the resin solution includes multiple resin materials.

[0104] As can be seen, through the above optional embodiments, by calculating the parameter similarity between the printing records of candidate resin schemes in the resin database and the printing process parameters, and screening out highly similar schemes, the best resin scheme is determined based on the quality of the finished product, thereby achieving accurate resin matching based on similarity and quality optimization, improving the adaptability of resin schemes and the quality of finished products, and increasing the efficiency of 3D printing material selection.

[0105] As an optional embodiment, the second determining module determines the specific method of the resin selection scheme corresponding to the model based on the resin scheme corresponding to all printing process parameters and the resin performance prediction model, including: Based on the random sampling combination algorithm, the resin materials in the resin schemes corresponding to all printing process parameters are sampled and combined to obtain multiple resin material combination schemes. Each resin material combination scheme is input into the trained resin printing performance prediction model to obtain the corresponding predicted performance; optionally, the resin printing performance prediction model is trained using a training dataset that includes multiple training resin combination schemes and corresponding post-printing quality performance annotations. The resin material combination scheme with the highest predicted performance is determined as the resin selection scheme corresponding to the model.

[0106] As can be seen, through the above optional embodiments, by generating resin material combination schemes based on random sampling combination algorithms and inputting them into the resin printing performance prediction model to predict performance, the highest performance scheme is selected as the resin selection scheme, thereby achieving accurate material optimization based on sampling combination and performance prediction, improving the comprehensive performance and adaptability of resin selection, and enhancing the durability and economy of 3D printed products.

[0107] Example 3 Please see Figure 3 , Figure 3 This is another resin selection system based on slice data analysis disclosed in the embodiments of the present invention. Figure 3 The described resin selection system based on slice data analysis is applied in data processing systems / data processing equipment / data processing servers (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 3 As shown, the resin selection system based on slice data 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 resin selection method based on slice data analysis described in Embodiment 1.

[0108] 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 resin selection method based on slice data analysis described in Embodiment 1.

[0109] 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 resin selection method based on slice data analysis described in Embodiment 1.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. 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.

[0123] Finally, it should be noted that the resin selection method and system based on slice data 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 resin selection method based on slice data analysis, characterized in that, The method includes: Obtain slice data of the model to be 3D printed; Based on the slice data, determine multiple printing process parameters corresponding to the model; Based on a preset resin database, match the resin solution corresponding to each of the aforementioned printing process parameters. Based on the resin options corresponding to all the printing process parameters, and using the resin performance prediction model, the resin selection scheme corresponding to the model is determined.

2. The resin selection method based on slice data analysis according to claim 1, characterized in that, The printing process parameters are of the following types: component structure type, component physical parameters, maximum model thickness, minimum model thickness, overall stability of model support, and number of weak points in model support.

3. The resin selection method based on slice data analysis according to claim 1, characterized in that, The step of determining multiple printing process parameters corresponding to the model based on the slice data includes: For each process parameter type, retrieve the corresponding historical analysis records from the historical database; Based on the historical analysis records, determine the core slice location corresponding to this process parameter type; The slice portion data corresponding to the core slice position is determined from the slice data; The sliced ​​data is input into the calculation model corresponding to the process parameter type to obtain the printing process parameters corresponding to the process parameter type.

4. The resin selection method based on slice data analysis according to claim 3, characterized in that, The historical analysis records include slice data records and historical calculation results corresponding to multiple historical time points used to calculate this type of process parameter.

5. The resin selection method based on slice data analysis according to claim 4, characterized in that, The step of determining the core slice location corresponding to the process parameter type based on the historical analysis records includes: For each slice data record, multiple sample data are obtained by randomly sampling the slice data record. Each of the aforementioned sampling data is input into the calculation model corresponding to the process parameter type to obtain the corresponding sampling calculation result; Calculate the similarity between the sampling calculation results and the historical calculation results; All sampled data with similarity lower than a preset similarity threshold are selected to obtain candidate sampled data; Based on the candidate sampling data, determine the core slice location corresponding to the process parameter type.

6. The resin selection method based on slice data analysis according to claim 5, characterized in that, The step of determining the core slice location corresponding to the process parameter type based on the candidate sampling data includes: The position of each candidate sampled data relative to the missing slice data of the slice data record is determined as the candidate slice position; For each candidate slice location, calculate the weighted sum of the time proximity parameters corresponding to all slice data records that exist at that candidate slice location, and obtain the priority parameter corresponding to that candidate slice location; The candidate slice positions for which the priority parameter is greater than the preset parameter threshold are selected to obtain the core slice position corresponding to the process parameter type.

7. The resin selection method based on slice data analysis according to claim 1, characterized in that, The step of matching the resin scheme corresponding to each of the printing process parameters according to the preset resin database includes: For each of the aforementioned printing process parameters, the parameter similarity between the printing record of each candidate resin scheme in the preset resin database and the printing process parameter is calculated. All candidate resin schemes with a parameter similarity greater than a preset threshold are selected to obtain multiple similar resin schemes; Determine the finished product quality corresponding to each of the aforementioned similar resin solutions; The resin scheme with the highest finished product quality is determined as the resin scheme corresponding to the printing process parameters; the resin scheme includes multiple resin materials.

8. The resin selection method based on slice data analysis according to claim 1, characterized in that, The step of determining the resin selection scheme corresponding to the model based on the resin scheme corresponding to all the printing process parameters and the resin performance prediction model includes: Based on the random sampling combination algorithm, the resin materials in the resin schemes corresponding to all the printing process parameters are sampled and combined to obtain multiple resin material combination schemes. Each of the resin material combination schemes is input into the trained resin printing performance prediction model to obtain the corresponding predicted performance; the resin printing performance prediction model is trained using a training dataset that includes multiple training resin combination schemes and corresponding post-printing quality performance annotations. The resin material combination scheme with the highest predicted performance is determined as the resin selection scheme corresponding to the model.

9. A resin selection system based on slice data analysis, characterized in that, The system includes: The acquisition module is used to acquire slice data of the model to be 3D printed; The first determining module is used to determine multiple printing process parameters corresponding to the model based on the slice data; The matching module is used to match the resin scheme corresponding to each of the printing process parameters according to the preset resin database. The second determining module is used to determine the resin selection scheme corresponding to the model based on the resin scheme corresponding to all the printing process parameters and the resin performance prediction model.

10. A resin selection system based on slice data 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 resin selection method based on slice data analysis as described in any one of claims 1-8.

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