Plastic product precise secondary forming method based on 3D printing technology

Abstract

The invention provides a plastic product precise secondary forming method based on a 3D printing technology, and relates to the technical field of plastic forming. The method comprises the steps that base material specification information is collected; index matching is conducted, multiple secondary forming areas and multiple pieces of area forming specification information are obtained, and multiple pieces of thickness information and component information are collected; carrying out forming compensation difficulty analysis; weight for secondary forming optimization is distributed, and optimization of thermal forming parameters is carried out; secondary thermal forming is carried out, multiple pieces of actual forming specification information are collected, and multiple pieces of forming error specification information are obtained through calculation in combination with the multiple pieces of area forming specification information; and forming compensation printing is carried out, and a secondarily-formed plastic product is obtained. According to the method and the device, the technical problem that the secondary forming quality is further affected due to the fact that the accuracy of extracting the deviation to be repaired is low in the prior art can be solved, the technical target of improving the obtaining accuracy of the repaired deviation is achieved, and the technical effect of improving the secondary forming quality is achieved.

Description

Technical Field

[0001] The present application relates to the field of plastic molding technology, and in particular to a method for accurate secondary molding of plastic products based on 3D printing technology. Background Art

[0002] Secondary thermoforming of plastic substrates is an important step in the production process of plastic products. The preformed plastic substrate is further heated and shaped to achieve the desired shape and size.

[0003] At present, in the existing secondary molding process of plastic products, the shrinkage and deformation degree of different thickness areas during the thermoforming process will be different. Different components may lead to different shrinkage rates, thermal expansion coefficients and other characteristics, thus affecting the accuracy of secondary molding. At the same time, the lack of molding compensation difficulty analysis may lead to the need to find the best repair solution through repeated trials and adjustments, which not only reduces the repair efficiency, but also may reduce the success rate of the repair. Therefore, a method is needed to solve the above problems.

[0004] In summary, the prior art has a technical problem in that most of the molding compensation difficulty analysis is not performed based on the thickness information and composition information of the secondary molding area, resulting in low accuracy in extracting the deviation to be repaired, which further affects the quality of the secondary molding. Summary of the invention

[0005] The purpose of this application is to provide a precise secondary molding method for plastic products based on 3D printing technology, so as to solve the technical problem in the prior art that most of them do not perform molding compensation difficulty analysis based on thickness information and composition information of the secondary molding area, resulting in low accuracy in extracting deviations to be repaired, which further affects the quality of secondary molding.

[0006] In view of the above problems, the present application provides a method for precise secondary molding of plastic products based on 3D printing technology.

[0007] The present application provides a method for accurate secondary molding of plastic products based on 3D printing technology. The method is based on the method for accurate secondary molding of plastic products based on 3D printing technology, wherein the method comprises: collecting substrate specification information of a plastic substrate to be subjected to secondary molding, wherein the substrate specification information comprises size specification information of the plastic substrate; performing index matching on the substrate specification information according to the molding specification information of the secondary molding, obtaining multiple secondary molding areas and multiple regional molding specification information, and collecting thickness information and composition information of the multiple secondary molding areas on the plastic substrate; performing molding compensation difficulty analysis on the molding specification information of the multiple regions, obtaining multiple molding compensation difficulty information; performing index matching ... substrate specification information of the multiple regions, obtaining multiple molding compensation difficulty information; performing index matching on the substrate specification information of the multiple regions, obtaining multiple molding compensation difficulty information; performing index matching The method comprises the following steps: compensating difficulty information, allocating weights for optimizing the secondary molding of the multiple secondary molding areas, optimizing the thermoforming parameters of the multiple secondary molding areas in combination with multiple thickness information, multiple component information and multiple regional molding specification information, and obtaining optimal thermoforming parameters, wherein the thermoforming parameters include molding temperature, molding speed and setting temperature; adopting the optimal thermoforming parameters, performing secondary thermoforming on the multiple secondary molding areas, and collecting and obtaining multiple actual molding specification information after completion, and calculating and obtaining multiple molding error specification information in combination with the multiple regional molding specification information; performing molding compensation printing based on 3D printing according to the multiple molding error specification information to obtain a secondary molded plastic product.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: By collecting substrate specification information of a plastic substrate to be subjected to secondary molding, wherein the substrate specification information includes size specification information of the plastic substrate; performing index matching on the substrate specification information according to the molding specification information of the secondary molding, obtaining a plurality of secondary molding regions and a plurality of regional molding specification information, and collecting thickness information and composition information of the plurality of secondary molding regions on the plastic substrate; performing molding compensation difficulty analysis on the molding specification information of the plurality of regions, obtaining a plurality of molding compensation difficulty information; allocating weights for secondary molding optimization of the plurality of secondary molding regions according to the plurality of molding compensation difficulty information, combining the plurality of thickness information, the plurality of composition information and the plurality of regional molding specification information, optimize the thermoforming parameters of the multiple secondary molding areas to obtain optimal thermoforming parameters, wherein the thermoforming parameters include molding temperature, molding speed and setting temperature; adopt the optimal thermoforming parameters to perform secondary thermoforming on the multiple secondary molding areas, and after completion, collect and obtain multiple actual molding specification information, combine the multiple regional molding specification information, and calculate and obtain multiple molding error specification information; according to the multiple molding error specification information, based on 3D printing, perform molding compensation printing to obtain secondary molded plastic products, thereby achieving the technical goal of improving the accuracy of obtaining repair deviations and achieving the technical effect of improving the quality of secondary molding.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0011] Figure 1 This is a schematic diagram of the process of the precise secondary molding method of plastic products based on 3D printing technology in this application; Figure 2 This is a schematic diagram of the process of obtaining multiple secondary molding areas and multiple regional molding specification information in the precise secondary molding method of plastic products based on 3D printing technology in this application. DETAILED DESCRIPTION

[0012] This application provides a method for accurate secondary molding of plastic products based on 3D printing technology, which solves the technical problem in the prior art that most of the molding compensation difficulty analysis based on the thickness information and composition information of the secondary molding area is not performed, resulting in low accuracy in extracting the deviation to be repaired, which further affects the quality of secondary molding. The technical goal of improving the accuracy of obtaining the repair deviation is achieved, and the technical effect of improving the quality of secondary molding is achieved.

[0013] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0014] Embodiment 1 Please refer to the attached Figure 1The present application provides a method for accurate secondary molding of plastic products based on 3D printing technology, wherein the method specifically comprises the following steps: Step 1: collecting substrate specification information of the plastic substrate to be subjected to secondary molding, wherein the substrate specification information includes size specification information of the plastic substrate; Specifically, the plastic substrate to be secondary molded refers to a plastic product formed by secondary thermoforming of the plastic substrate, such as thermoforming and pressing a plastic sheet with a mold to obtain a plastic of a preset shape, such as a basin or other products. Due to the molding size deviation, the error is printed and repaired based on 3D printing to complete the secondary molding. Furthermore, the substrate specification information of the plastic substrate is the size specification of the plastic substrate, for example, it can be a three-dimensional model of the plastic substrate.

[0015] Step 2: According to the molding specification information of secondary molding, index matching is performed on the substrate specification information to obtain multiple secondary molding areas and multiple area molding specification information, and thickness information and composition information of the multiple secondary molding areas on the plastic substrate are collected; Specifically, coordinates are unified according to substrate specification information and molding specification information, and then indexed and matched to obtain regions with different specifications, which are multiple regions that need to be secondary molded. The specifications, thickness, and composition information of multiple secondary molding regions are obtained for subsequent secondary molding. Among them, since the plastic on some plastic products may be unevenly molded, which affects the molding quality, the composition information includes the density of the plastic component.

[0016] Step 3: Performing a molding compensation difficulty analysis on the molding specification information of the plurality of regions to obtain a plurality of molding compensation difficulty information; Specifically, according to the specification information of multiple regions, the difficulty of compensation after molding is analyzed, and then multiple molding compensation difficulty information is obtained. Among them, the more complex the molding area is, the greater the molding compensation difficulty is, and vice versa.

[0017] Step 4: according to the plurality of molding compensation difficulty information, weights for optimizing the secondary molding of the plurality of secondary molding regions are allocated, and the thermoforming parameters of the plurality of secondary molding regions are optimized by combining the plurality of thickness information, the plurality of component information and the plurality of regional molding specification information to obtain optimal thermoforming parameters, wherein the thermoforming parameters include molding temperature, molding speed and setting temperature; Specifically, weights are allocated based on multiple pieces of molding compensation difficulty information to reflect the difficulty of different regions reaching the target specifications during the thermoforming process. The lower the difficulty, the greater the allocation weight, indicating that the deviation between the actual specifications of multiple regions and the preset regional specifications is easier to repair, and vice versa, the smaller the weight, and then the parameters of the secondary thermoforming of the plastic substrate are optimized to reduce the deviation between the actual specifications of multiple regions after molding and the preset regional specifications. The optimal parameters of the thermoforming parameters are obtained, including molding temperature, molding speed and setting temperature.

[0018] Step 5: performing secondary thermoforming on the plurality of secondary molding areas by using the optimal thermoforming parameters, and after completion, collecting and obtaining a plurality of actual molding specification information, and calculating and obtaining a plurality of molding error specification information by combining the molding specification information of the plurality of areas; Specifically, multiple secondary molding areas are subjected to secondary thermoforming based on optimal thermoforming parameters, and dimensional errors between actual specifications of multiple areas after thermoforming and preset area specifications are collected to compensate the plastic product according to the errors.

[0019] Step 6: Perform molding compensation printing based on 3D printing according to the multiple molding error specification information to obtain a secondary molded plastic product.

[0020] Specifically, according to multiple molding error specification information, compensation printing is performed based on 3D printing to meet the preset specification requirements. The 3D printing is performed using materials that are the same or similar to the plastic substrate, and after printing, curing is performed to obtain a secondary molded plastic product.

[0021] The precise secondary molding method for plastic products based on 3D printing technology can achieve the technical goal of improving the accuracy of obtaining repair deviations and achieve the technical effect of improving the quality of secondary molding.

[0022] Furthermore, the present application also includes the following steps: Unifying coordinates and establishing a position index relationship according to the molding specification information and the substrate specification information; According to the position index relationship and the substrate specification information, index matching is performed in the molding specification information to obtain regions with different specifications as multiple secondary molding regions, and molding specification information of multiple regions of the secondary molding regions is obtained; The plurality of thickness information and the plurality of composition information of the plurality of secondary molding areas on the plastic substrate are detected and collected.

[0023] Specifically, if Figure 2As shown, determine the coordinate system of the molding specification information and the substrate specification information. If the coordinate systems of the two are not unified, coordinate conversion is required to ensure that the two are in the same coordinate system. For example, by operations such as translation, rotation, or scaling, the coordinate origin, coordinate axis direction, and unit length of the two are consistent. Establish a position index relationship. Among them, this is achieved by associating each position point in the substrate specification information with the corresponding position point in the molding specification information. For example, the corresponding relationship can be stored by creating an index table or using a data structure such as a database for subsequent quick query and matching.

[0024] Then, according to the position index relationship and the substrate specification information, the index matching in the molding specification information is used to obtain the areas with different specifications. For example, this can be achieved by comparing the size, shape and other parameters of the corresponding position points in the substrate specification information and the molding specification information. Find the area with large differences, that is, the area that needs to be secondary molded. For each secondary molding area, obtain the regional molding specification information. Among them, the size, shape, position and other information of the area are included as the basis for secondary molding.

[0025] Next, the thickness information and composition information are detected and collected. Among them, non-destructive testing techniques such as ultrasonic testing and X-ray testing can be used to measure the thickness of each secondary molding area. At the same time, chemical analysis or spectral analysis can be used to detect composition information.

[0026] By identifying the areas that need to be overmolded, detailed specification information and physical and chemical properties can be obtained to provide strong support for the subsequent overmolding process.

[0027] Furthermore, the present application also includes the following steps: According to the historical data of secondary molding of plastic products, a set of molding specification information of the sample area is obtained; Mark the difficulty of compensation adjustment after molding according to molding specification information of different sample areas, and obtain a sample molding compensation difficulty information set; The sample area molding specification information set and the sample molding compensation difficulty information set are used to construct an area molding compensation difficulty analyzer, and molding compensation difficulty analysis is performed on the multiple area molding specification information to obtain the multiple molding compensation difficulty information.

[0028] Specifically, the molding specification information of different sample areas is extracted from the historical data of secondary molding of plastic products. For example, it may include the size, shape, material type, molding process parameters, etc. of the area. Multiple molding specification information is sorted into a set, and the molding specification information set of the sample area is obtained as input data for subsequent analysis.

[0029] Then, for each sample area, the difficulty is marked according to the difficulty of compensation adjustment after molding in the historical data records. Among them, the compensation difficulty can be quantitatively evaluated based on indicators such as the number of adjustments, adjustment time, and adjustment cost in actual production. For example, the higher the number of adjustments, adjustment time, and adjustment cost, the higher the compensation difficulty, and vice versa. The compensation difficulty information is sorted into a set to obtain a sample molding compensation difficulty information set, which corresponds to the sample area molding specification information set.

[0030] Next, from the sample regional forming specification information set, select features that have a significant impact on the difficulty of forming compensation. For example, the features may include regional complexity, material plasticity, and the stability of the forming process. Based on the sample regional forming specification information set and the sample forming compensation difficulty information set as input data, a regional forming compensation difficulty analyzer is constructed. Among them, according to the characteristics of the data, a suitable machine learning model, such as a decision tree, a random forest, a neural network, etc., can be selected as the regional forming compensation difficulty analyzer. The training data and the verification data are obtained by dividing the sample regional forming specification information set and the sample forming compensation difficulty information set, and the regional forming compensation difficulty analyzer is trained by the training data. Among them, the division ratio is obtained by a technician in this field according to the actual situation. When the output of the regional forming compensation difficulty analyzer tends to be stable, the regional forming compensation difficulty analyzer is verified by the verification data, and the output accuracy of the regional forming compensation difficulty analyzer is extracted. If it is judged that the output accuracy threshold of the regional forming compensation difficulty analyzer is met, the training of the regional forming compensation difficulty analyzer is completed. Among them, the output accuracy threshold of the regional forming compensation difficulty analyzer is obtained by a technician in this field according to the actual situation. Then, the regional forming specification information is analyzed by the regional forming compensation difficulty analyzer to obtain the forming compensation difficulty information.

[0031] By building a regional molding compensation difficulty analyzer, it helps to improve production efficiency and optimize product quality for secondary molding of plastic products.

[0032] Furthermore, the present application also includes the following steps: Allocating a plurality of area weights for performing secondary molding optimization on the plurality of secondary molding areas according to the plurality of molding compensation difficulty information; Acquire a thermoforming parameter space for secondary molding of a plastic product, wherein the thermoforming parameter space is constructed according to a molding temperature range, a molding speed range, and a qualitative temperature range; In the thermoforming parameter space, the multiple region weights are used to optimize the thermoforming parameters according to the multiple thickness information, the multiple component information and the multiple region forming specification information to obtain the optimal thermoforming parameters.

[0033] Specifically, the smaller the area with the smaller molding compensation difficulty, the smaller the weight assigned, and vice versa, the larger the weight assigned, and then multiple weights of the secondary molding optimization are assigned to multiple secondary molding areas so as to give different attentions in the optimization process.

[0034] Then, the thermoforming parameter space includes multiple dimensions such as the molding temperature range, molding speed range, and qualitative temperature range. Among them, it is determined according to the thermal properties of the material, the performance range of the equipment, and the actual production needs, and then the upper and lower limits of each parameter range are determined according to the material characteristics, equipment capabilities, and process requirements. Construct a multi-dimensional parameter space for subsequent parameter optimization search.

[0035] Next, the target of thermoforming parameter optimization is determined based on multiple thickness information, multiple component information, and multiple regional molding specification information. For example, the optimization target can be to improve molding accuracy, etc. The thermoforming parameters are searched and optimized by calculating parameter fitness, etc. During the optimization process, the influence of different regions on the optimization target is adjusted by using the assigned regional weights. The optimization algorithm is used to search in the thermoforming parameter space to find the optimal thermoforming parameter combination that meets the optimization target. The optimal parameters are output for production optimization.

[0036] Weights are assigned to them based on the molding compensation difficulty information of multiple areas, and parameter optimization is performed within the thermoforming parameter space to obtain the optimal thermoforming parameters for specific plastic products, which helps to improve the accuracy and efficiency of secondary molding.

[0037] Furthermore, the present application also includes the following steps: In the thermoforming parameter space, a plurality of initial thermoforming parameters and a plurality of target thermoforming parameters are randomly generated, and a mapping relationship between the plurality of initial thermoforming parameters and the plurality of target thermoforming parameters is randomly constructed; Taking the multiple target thermoforming parameters as adjustment directions, and adjusting the multiple initial thermoforming parameters according to the mapping relationship, to obtain multiple adjusted thermoforming parameters; Randomly cross-update the parameter data in the plurality of adjustment thermoforming parameters and the plurality of target thermoforming parameters to obtain a plurality of test thermoforming parameters, wherein at least one of the molding temperature, the molding speed and the setting temperature is cross-updated; Perform secondary thermoforming simulation according to the multiple test thermoforming parameters, multiple thickness information and multiple composition information, and calculate multiple test fitnesses in combination with the multiple regional weights to determine whether they are greater than the target fitnesses of the corresponding target thermoforming parameters, and if so, replace the target thermoforming parameters, and if not, do not replace them, to obtain multiple target thermoforming parameters that are replaced and updated; Continue to randomly generate initial thermoforming parameters, combine the multiple target thermoforming parameters that are replaced and updated for iterative optimization, until the number of convergence times is reached, output the thermoforming parameters with the maximum fitness, and obtain the optimal thermoforming parameters.

[0038] Specifically, multiple initial thermoforming parameters are randomly generated in the thermoforming parameter space, where each initial thermoforming parameter includes parameters such as molding temperature, molding speed, and setting temperature. At the same time, multiple target thermoforming parameters are randomly generated as target references in the optimization process. The mapping relationship between the initial thermoforming parameters and the target thermoforming parameters is randomly constructed so that the initial parameters can be adjusted according to the target parameters later.

[0039] Then, the target thermoforming parameters are used as the adjustment direction, and the initial thermoforming parameters are adjusted according to the mapping relationship. During the adjustment process, more attention can be paid to the regions with larger weights according to the regional weights, so that the adjustment is more in line with the actual production needs. Multiple adjusted thermoforming parameters are obtained as candidate parameters for the next round of optimization.

[0040] Then, the parameter data in the adjusted thermoforming parameters and the target thermoforming parameters are randomly cross-updated. The cross-updating may be at least one of the forming temperature, the forming speed and the setting temperature to increase the diversity of the parameters. Through the cross-updating, a plurality of experimental thermoforming parameters are obtained.

[0041] Next, multiple experimental thermoforming parameters are used to perform secondary thermoforming simulations, and multiple thickness information and multiple component information are combined to simulate the forming process. The fitness of each experimental thermoforming parameter is calculated based on the simulation results and the regional weight. It is determined whether the fitness of the experimental thermoforming parameter is greater than the target fitness of the corresponding target thermoforming parameter. If greater, the target thermoforming parameter is replaced with the experimental thermoforming parameter. If not, the target thermoforming parameter remains unchanged. After replacement and update, a new set of target thermoforming parameters is obtained.

[0042] Next, continue to randomly generate new initial thermoforming parameters, and combine them with the target thermoforming parameters to perform the next round of iterative optimization. Repeat the steps of adjustment, cross-update, simulation, and evaluation until the preset number of convergences is reached or other stop conditions are met. During the iteration process, the fitness changes of each iteration are recorded for subsequent analysis. When the convergence conditions are reached, the thermoforming parameters with the largest fitness are output, which are the optimal thermoforming parameters required.

[0043] Through iterative optimization, the optimal thermoforming parameters can be searched in the thermoforming parameter space, thereby improving the accuracy and efficiency of secondary molding.

[0044] Furthermore, the present application also includes the following steps: According to the secondary thermoforming data record of the plastic product, a sample thermoforming parameter set, a plurality of sample thickness information sets and a plurality of sample composition information sets of a plurality of secondary molding regions are obtained, and an actual specification information set of a plurality of sample regions after secondary thermoforming is obtained; Constructing a thermoforming simulator using the sample thermoforming parameter set, multiple sample thickness information sets, multiple sample composition information sets, and multiple sample area actual specification information sets; According to the thermoforming simulator, performing secondary thermoforming simulation on the plurality of experimental thermoforming parameters, the plurality of thickness information and the plurality of composition information to obtain a plurality of sets of actual specification information of the regions; A thermoforming function is constructed, and a plurality of test fitnesses are calculated based on the plurality of area actual specification information sets and the plurality of area forming specification information.

[0045] Specifically, the parameters in the molding records within the historical time are extracted from the secondary thermoforming data records as the sample thermoforming parameter set, including molding temperature, molding speed, setting temperature, etc. For each secondary molding area, the corresponding sample thickness information set and sample composition information set are obtained to reflect the material properties and initial state of different areas. The actual specification information set of each area after secondary thermoforming, including size, shape, etc., is collected to evaluate the actual effect of thermoforming parameters.

[0046] Then, a machine learning or physical simulation method is used, combined with a sample thermoforming parameter set, a sample thickness information set, a sample composition information set, and a sample area actual specification information set as input data to construct a thermoforming simulator. Among them, the input data is divided into sample training data and sample verification data. The division ratio is obtained by a technician in this field according to the actual situation. The thermoforming simulator is trained with sample training data, and when the output of the thermoforming simulator tends to be stable, the output of the thermoforming simulator is verified by sample verification data. If the output accuracy of the thermoforming simulator meets the output accuracy threshold of the thermoforming simulator, the training of the thermoforming simulator is completed. Among them, the output accuracy threshold of the thermoforming simulator is obtained by a technician in this field according to the actual situation. The thermoforming simulator can simulate the secondary thermoforming process of plastic products under different thermoforming parameters.

[0047] Next, the constructed thermoforming simulator is used to perform secondary thermoforming simulations for multiple experimental thermoforming parameters, multiple thickness information, and multiple component information. Through the secondary thermoforming simulation, the actual specification information set of each area under each experimental thermoforming parameter is obtained to reflect the influence of thermoforming parameters on the molding effect of plastic products.

[0048] Next, a thermoforming function is constructed to quantify the difference between the actual specification information of the simulated area and the molding specification information of the target area. This function is used to calculate the fitness of multiple experiments and evaluate the advantages and disadvantages of each experimental thermoforming parameter.

[0049] By recording the secondary thermoforming data of plastic products, a thermoforming simulator is constructed, and the experimental thermoforming parameters are simulated and the fitness evaluation is performed, which helps to predict and optimize the thermoforming parameters before actual production and improve the molding quality and efficiency of plastic products.

[0050] Furthermore, the present application also includes the following steps: Construct the hot forming function as follows: ; Where THF is the fitness, M is the number of multiple secondary molding regions, is the area weight of the i-th secondary molding area, is the deviation between the actual regional specification information and the regional molding specification information of the i-th secondary molding region; According to the actual specification information sets of the multiple regions and the forming specification information of the multiple regions, multiple deviation degree sets are calculated and obtained, and in combination with the hot forming function, multiple test fitness are calculated and obtained.

[0051] Specifically, THF is the fitness, which represents the comprehensive effect of the thermoforming parameters. M is the number of multiple secondary molding regions, is the area weight of the i-th secondary molding area, is the deviation between the actual regional specification information and the regional molding specification information of the i-th secondary molding area. The lower the value, the better the THF fitness, otherwise, the worse. The higher the fitness, the better the molding parameter value, otherwise, the worse.

[0052] Then, for each secondary molding area, the deviation between its actual specification information and the molding specification information is calculated. The calculation of the deviation can be based on a variety of methods, such as Euclidean distance, absolute error, etc., depending on the type and measurement of the specification information. For example, if the specification information includes size and shape, the deviation can be calculated using a comprehensive error index. The thermoforming function is used to calculate the fitness by combining the deviation set of each experimental thermoforming parameter and the corresponding regional weight. The fitness calculation takes into account the weights of different regions and the deviation of each region, so that the advantages and disadvantages of the thermoforming parameters can be comprehensively evaluated.

[0053] The thermoforming function is used to quantitatively evaluate the fitness of different experimental thermoforming parameters, thus providing a basis for subsequent parameter optimization. The fitness calculation comprehensively considers the weights of multiple regions and the deviation of specification information, making the evaluation results more accurate and comprehensive.

[0054] Furthermore, the present application also includes the following steps: Using the optimal thermoforming parameters, performing secondary thermoforming on the multiple secondary molding areas, and after completion, collecting and obtaining multiple actual molding specification information of the multiple secondary molding areas; Combine the plurality of regional molding specification information to calculate and obtain a plurality of molding error specification information of the plurality of actual molding specification information.

[0055] Specifically, the optimal thermoforming parameters, including molding temperature, molding speed, and setting temperature, are input into the thermoforming equipment. Ensure that the equipment is correctly set up and ready for secondary thermoforming. Prepare the corresponding plastic products according to the material thickness and composition information of the required multiple secondary molding areas. Place the prepared plastic products into the thermoforming equipment and perform the thermoforming operation according to the optimal thermoforming parameters. After the secondary thermoforming is completed, use appropriate measuring tools or equipment to collect multiple actual molding specification information for multiple secondary molding areas. Record the collected actual molding specification information in detail to ensure the accuracy and completeness of the data.

[0056] Then, the collected multiple actual forming specification information is compared with the preset multiple regional forming specification information. By comparing the differences between the two, the quality of the thermoforming effect can be evaluated. For example, by calculating the difference, deviation rate or other relevant indicators between the actual specification and the target specification, the forming error specification information can be obtained.

[0057] Performing secondary thermoforming operations with optimal thermoforming parameters and collecting actual molding specification information to calculate molding error specification information helps to evaluate thermoforming effects, optimize production processes, and improve product quality.

[0058] In summary, the method for precise secondary molding of plastic products based on 3D printing technology provided in this application has the following technical effects: By collecting substrate specification information of a plastic substrate to be subjected to secondary molding, wherein the substrate specification information includes size specification information of the plastic substrate; performing index matching on the substrate specification information according to the molding specification information of the secondary molding, obtaining a plurality of secondary molding regions and a plurality of regional molding specification information, and collecting thickness information and composition information of the plurality of secondary molding regions on the plastic substrate; performing molding compensation difficulty analysis on the molding specification information of the plurality of regions, obtaining a plurality of molding compensation difficulty information; allocating weights for secondary molding optimization of the plurality of secondary molding regions according to the plurality of molding compensation difficulty information, combining the plurality of thickness information, the plurality of composition information and the plurality of regional molding specification information, optimize the thermoforming parameters of the multiple secondary molding areas to obtain optimal thermoforming parameters, wherein the thermoforming parameters include molding temperature, molding speed and setting temperature; adopt the optimal thermoforming parameters to perform secondary thermoforming on the multiple secondary molding areas, and after completion, collect and obtain multiple actual molding specification information, combine the multiple regional molding specification information, and calculate and obtain multiple molding error specification information; according to the multiple molding error specification information, based on 3D printing, perform molding compensation printing to obtain secondary molded plastic products, thereby achieving the technical goal of improving the accuracy of obtaining repair deviations and achieving the technical effect of improving the quality of secondary molding.

[0059] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0060] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A method for accurate secondary molding of plastic products based on 3D printing technology, characterized in that: The method comprises: Collecting substrate specification information of a plastic substrate to be subjected to secondary molding, wherein the substrate specification information includes size specification information of the plastic substrate; According to the molding specification information of the secondary molding, the substrate specification information is indexed and matched to obtain a plurality of secondary molding regions and a plurality of regional molding specification information, and the thickness information and composition information of the plurality of secondary molding regions on the plastic substrate are collected; Performing a molding compensation difficulty analysis on the molding specification information of the plurality of regions to obtain a plurality of molding compensation difficulty information; According to the plurality of molding compensation difficulty information, weights for optimizing the secondary molding of the plurality of secondary molding regions are allocated, and the thermoforming parameters of the plurality of secondary molding regions are optimized by combining the plurality of thickness information, the plurality of component information and the plurality of regional molding specification information to obtain optimal thermoforming parameters, wherein the thermoforming parameters include molding temperature, molding speed and setting temperature; Using the optimal thermoforming parameters, performing secondary thermoforming on the multiple secondary molding areas, and after completion, collecting and obtaining multiple actual molding specification information, and combining the molding specification information of the multiple areas to calculate and obtain multiple molding error specification information; According to the plurality of molding error specification information, molding compensation printing is performed based on 3D printing to obtain a secondary molded plastic product.

2. The method according to claim 1, characterized in that According to the molding specification information of the secondary molding, the substrate specification information is indexed and matched to obtain multiple secondary molding areas and multiple area molding specification information, including: Unifying coordinates and establishing a position index relationship according to the molding specification information and the substrate specification information; According to the position index relationship and the substrate specification information, index matching is performed in the molding specification information to obtain regions with different specifications as multiple secondary molding regions, and molding specification information of multiple regions of the secondary molding regions is obtained; The plurality of thickness information and the plurality of composition information of the plurality of secondary molding areas on the plastic substrate are detected and collected.

3. The method according to claim 1, characterized in that Performing a molding compensation difficulty analysis on the molding specification information of the plurality of regions to obtain a plurality of molding compensation difficulty information, including: According to the historical data of secondary molding of plastic products, a set of molding specification information of the sample area is obtained; Mark the difficulty of compensation adjustment after molding according to molding specification information of different sample areas, and obtain a sample molding compensation difficulty information set; The sample area molding specification information set and the sample molding compensation difficulty information set are used to construct an area molding compensation difficulty analyzer, and molding compensation difficulty analysis is performed on the multiple area molding specification information to obtain the multiple molding compensation difficulty information.

4. The method according to claim 1, characterized in that: According to the plurality of molding compensation difficulty information, weights for optimizing the secondary molding of the plurality of secondary molding regions are allocated, and the thermoforming parameters of the plurality of secondary molding regions are optimized by combining the plurality of thickness information, the plurality of component information, and the plurality of regional molding specification information, including: Allocating a plurality of area weights for performing secondary molding optimization on the plurality of secondary molding areas according to the plurality of molding compensation difficulty information; Acquire a thermoforming parameter space for secondary molding of a plastic product, wherein the thermoforming parameter space is constructed according to a molding temperature range, a molding speed range, and a qualitative temperature range; In the thermoforming parameter space, the multiple region weights are used to optimize the thermoforming parameters according to the multiple thickness information, the multiple component information and the multiple region forming specification information to obtain the optimal thermoforming parameters.

5. The method according to claim 4, characterized in that In the thermoforming parameter space, the multiple region weights are used to optimize the thermoforming parameters according to the multiple thickness information, the multiple component information and the multiple region forming specification information, including: In the thermoforming parameter space, a plurality of initial thermoforming parameters and a plurality of target thermoforming parameters are randomly generated, and a mapping relationship between the plurality of initial thermoforming parameters and the plurality of target thermoforming parameters is randomly constructed; Taking the multiple target thermoforming parameters as adjustment directions, adjusting the multiple initial thermoforming parameters according to the mapping relationship to obtain multiple adjusted thermoforming parameters; Randomly cross-update the parameter data in the plurality of adjustment thermoforming parameters and the plurality of target thermoforming parameters to obtain a plurality of test thermoforming parameters, wherein at least one of the molding temperature, the molding speed and the setting temperature is cross-updated; Perform secondary thermoforming simulation according to the multiple test thermoforming parameters, multiple thickness information and multiple composition information, and calculate multiple test fitnesses in combination with the multiple regional weights to determine whether they are greater than the target fitnesses of the corresponding target thermoforming parameters, and if so, replace the target thermoforming parameters, and if not, do not replace them, to obtain multiple target thermoforming parameters that are replaced and updated; Continue to randomly generate initial thermoforming parameters, combine the multiple target thermoforming parameters that are replaced and updated for iterative optimization, until the number of convergence times is reached, output the thermoforming parameters with the maximum fitness, and obtain the optimal thermoforming parameters.

6. The method according to claim 5, characterized in that Performing secondary hot forming simulation according to the multiple test hot forming parameters, multiple thickness information and multiple composition information, and obtaining multiple test fitnesses in combination with the multiple region weight calculations, including: According to the secondary thermoforming data record of the plastic product, a sample thermoforming parameter set, a plurality of sample thickness information sets and a plurality of sample composition information sets of a plurality of secondary molding regions are obtained, and an actual specification information set of a plurality of sample regions after secondary thermoforming is obtained; Constructing a thermoforming simulator using the sample thermoforming parameter set, multiple sample thickness information sets, multiple sample composition information sets, and multiple sample area actual specification information sets; According to the thermoforming simulator, performing secondary thermoforming simulation on the plurality of experimental thermoforming parameters, the plurality of thickness information and the plurality of composition information to obtain a plurality of sets of actual specification information of the regions; A thermoforming function is constructed, and a plurality of test fitnesses are calculated based on the plurality of area actual specification information sets and the plurality of area forming specification information.

7. The method according to claim 6, characterized in that Constructing a hot forming function, and calculating and obtaining multiple test fitnesses according to the multiple area actual specification information sets and the multiple area forming specification information, including: Construct the hot forming function as follows: ; Where THF is the fitness, M is the number of multiple secondary molding regions, is the area weight of the i-th secondary molding area, is the deviation between the actual regional specification information and the regional molding specification information of the i-th secondary molding region; According to the actual specification information sets of the multiple regions and the forming specification information of the multiple regions, multiple deviation degree sets are calculated and obtained, and in combination with the hot forming function, multiple test fitness are calculated and obtained.

8. The method according to claim 1, characterized in that The optimal thermoforming parameters are used to perform secondary thermoforming on the multiple secondary molding areas, and after completion, multiple actual molding specification information is collected and obtained, and multiple molding error specification information is calculated and obtained by combining the molding specification information of the multiple areas, including: Performing secondary thermoforming on the plurality of secondary molding areas by using the optimal thermoforming parameters, and acquiring a plurality of actual molding specification information of the plurality of secondary molding areas after completion; Combine the plurality of regional molding specification information to calculate and obtain a plurality of molding error specification information of the plurality of actual molding specification information.

Images