Protective mask 3D printing process parameter optimization method

Abstract

The invention relates to the field of data processing, in particular to a protective mask 3D printing process parameter optimization method which comprises the steps that the feasible value range of each process parameter of protective mask 3D printing is obtained, and the space range formed by the feasible value ranges of all the process parameters serves as a solution space; iteratively optimizing by utilizing a genetic algorithm on the basis of the solution space until a stop condition is met to obtain an optimal solution, and taking each process parameter in the optimal solution as an optimized process parameter; in the one-time optimization process, the protective mask quality indexes corresponding to all the current feasible solutions serve as fitness, the selection possibility of any current feasible solution is calculated, and part of the current feasible solutions are selected as reserved individuals according to the selection possibility. Local optimum is avoided, and proper process parameters can be positioned more accurately.

Description

Technical Field

[0001] The present invention relates to the field of data processing. More specifically, the present invention relates to a method for optimizing the 3D printing process parameters of a protective mask. Background Art

[0002] With the advantages of rapid response and flexible production, 3D printing technology has played an important role in the manufacturing of personal protective equipment (PPE) such as protective masks. However, the quality and performance of 3D printed protective masks are affected by various process parameters. For example, an unreasonable printing temperature setting may lead to a decrease in strength or surface roughness. Therefore, it is necessary to optimize the process parameters to ensure their protective effect and wearing comfort.

[0003] Due to the differences in the types, sizes, and printing materials of protective masks, there are differences in the processing process parameters of different protective masks. In order to set appropriate process parameters according to the individual processing requirements of protective masks, it is necessary to evaluate the quality and performance of the protective masks obtained with each process parameter to obtain better process parameters. As an optimization algorithm, the genetic algorithm can effectively improve the optimization efficiency. However, this algorithm is prone to falling into local optima during the optimization process, thus unable to obtain better process parameters. How to jump out of the local optimum and obtain better process parameters has become the research focus of the present invention.

[0004] The patent document with the authorization announcement number CN118428244B discloses a method for adaptively optimizing the reflow soldering process parameters. The key point of the method in this patent document is to achieve the adaptive adjustment of the reflow soldering process parameters by adjusting the reflow optimization step size. The method in this patent document does not involve how to solve the problem of jumping out of the local optimum. Therefore, the method in this patent document cannot solve the technical problems of this solution. Summary of the Invention

[0005] To solve the problem of how to jump out of the local optimum and obtain better process parameters, the present invention proposes a method for optimizing the 3D printing process parameters of a protective mask. The method includes the following steps: Obtain the feasible value range of each process parameter for 3D printing of a protective mask, and use the spatial range formed by the feasible value ranges of all process parameters as the solution space; Based on the solution space, use the genetic algorithm to iteratively optimize until the stopping condition is met to obtain the optimal solution, and use each process parameter in the optimal solution as the optimized process parameter; During an optimization process, the quality indicators of the protective masks corresponding to each current feasible solution are used as fitness values. The solution space is divided into several subspaces, and the exploration degree is calculated based on the distribution of the feasible solutions traversed in the previous iteration process in the subspaces. The variation degree is calculated based on the variation difference of the traversed feasible solutions in different regions within the subspaces. The selection probability of the current feasible solution is calculated based on the exploration degree, the variation degree, the fitness value of the current feasible solution, and the estimated fitness value at the peak position of the peak where the current feasible solution is located. The selection probability is positively correlated with the variation degree, the fitness value of the current feasible solution, and the estimated fitness value at the peak position of the peak where the current feasible solution is located, and is negatively correlated with the exploration degree. Select a part of the current feasible solutions as the retained individuals according to the selection probability.

[0006] The present invention controls the retention of individuals by adjusting the selection probability of each current feasible solution, thereby effectively jumping out of the local optimum and more accurately locating the global optimum. Further, when calculating the selection probability, the exploration degree of the subspace is considered, so as to explore each subspace in the solution space to a certain extent, thereby effectively preventing the situation of falling into the local optimum caused by insufficient exploration of some subspaces. Further, when calculating the selection probability, a peak prediction part is introduced, so as to effectively solve the situation where the current feasible solution cannot reflect the existence of the optimal solution in each subspace, thereby setting a greater selection probability for the current feasible solution in the subspace with a greater possibility of the existence of the optimal solution, avoiding falling into the local optimum, and more accurately obtaining the optimal solution.

[0007] Preferably, the selection probability of the current feasible solution satisfies the relational expression: ; where T represents the exploration degree of the subspace where the current feasible solution is located, C represents the variation degree in the subspace where the current feasible solution is located, represents the estimated fitness value at the peak position of the peak where the current feasible solution is located, represents the prediction accuracy of the estimated fitness value, represents the fitness value of the current feasible solution, represents the selection probability of the current feasible solution.

[0008] Preferably, the division of the solution space into several subspaces includes: The solution space is evenly divided into a preset first number of subspaces.

[0009] The present invention obtains subspaces by means of even division, effectively avoiding the problem of inaccurate analysis caused by volume differences of subspaces, and providing a basis for subsequent accurate analysis.

[0010] Preferably, the method for obtaining the exploration degree includes: Obtain the positions of the feasible solutions that have been traversed in the previous iteration process and record them as the traversed positions. Divide each subspace evenly into a preset second number of small spaces and record them as analysis spaces. Count the number of traversed positions included in each analysis space, and take the reciprocal of the variance of the number of traversed positions in all analysis spaces of each subspace as the exploration uniformity degree of each subspace; Multiply the number of traversed positions included in each subspace by the exploration uniformity degree to obtain the exploration degree of each subspace.

[0011] When analyzing the exploration degree in the present invention, not only the exploration uniformity degree is introduced, but also the number of traversed positions is introduced, so as to more comprehensively and accurately analyze the exploration situation of each sub-region, providing a basis for subsequent analysis.

[0012] Preferably, the method for obtaining the variation difference degree includes: Use the traversed feasible solutions and their corresponding fitness values in the solution space to fit a polynomial, denoted as the overall relationship; divide the fitting model of the overall variation relationship at the subspace into several independent peaks, obtain the kurtosis and skewness of each independent peak, multiply the kurtosis difference between every two independent peaks by the skewness difference to obtain the law difference degree between the two independent peaks, and take the mean of the law difference degrees of all two independent peaks as the law difference degree of the subspace; Take the mean of the interval distances between each independent peak and its surrounding independent peaks as the interval distance of each independent peak, and take the variance of the interval distances of all independent peaks in the subspace as the distribution difference degree of the subspace; Multiply the law difference degree of the subspace by the distribution difference degree to obtain the variation difference degree of the subspace.

[0013] When analyzing the variation difference degree in the present invention, not only the law difference degree is introduced to reflect the fluctuation difference, but also the distribution interval difference is introduced to reflect the interval distribution information of different fluctuation laws, more comprehensively and accurately evaluating the variation difference situation in the subspace.

[0014] Preferably, the step of dividing the fitting model of the overall variation relationship at the subspace into several independent peaks includes: Obtain the closed region surrounded by the positions corresponding to the minimum points of the overall variation relationship as the independent region, and take the part of the relationship model corresponding to the overall variation relationship within the independent region as the independent peak.

[0015] Preferably, the prediction accuracy of the fitness prediction value includes: Obtain the estimated fitness values of each feasible solution within the independent peak where the current feasible solution is located. Take the absolute value of the difference between the fitness of each traversed feasible solution and the estimated fitness value as the fitness value prediction deviation of each traversed feasible solution. Take the reciprocal of the fitness prediction deviations of all traversed feasible solutions within the peak where the current feasible solution is located as the prediction accuracy of the estimated fitness value.

[0016] Preferably, the step of selecting some current feasible solutions as reserved individuals according to the selection possibility includes: Divide the selection possibility of each current feasible solution by the sum of the selection possibilities of all current feasible solutions to obtain the selection probability of each current feasible solution; Take the selection probability of each current feasible solution as the extraction probability, and randomly extract a preset third number of current feasible solutions from all current feasible solutions as reserved individuals.

[0017] Preferably, take the iteration number reaching the preset cut-off number as the stop condition.

[0018] Preferably, the method for obtaining the quality index of the protective mask includes: On the simulation software, use the printing parameters corresponding to each process parameter in the current feasible solution to control the printing of the protective mask, and obtain the processed protective mask; Conduct various performance simulation tests on the protective mask, obtain the performance scores of each performance test, and take the average value of the performance scores of all performance tests as the quality index of the protective mask.

[0019] The present invention has the following beneficial effects: The present invention controls the retention of individuals by adjusting the selection possibility of each current feasible solution, thereby effectively jumping out of the local optimum and more accurately locating the global optimum; Furthermore, when calculating the selection possibility, consider the exploration degree of the subspace, so as to explore each subspace in the solution space to a certain extent, thereby effectively preventing the situation of falling into the local optimum caused by insufficient exploration of some subspaces; Furthermore, when calculating the selection possibility, a peak prediction part is introduced, so as to effectively solve the situation that the current feasible solution cannot reflect the existence of the optimal solution in each subspace, thereby setting a greater selection possibility for the current feasible solution in the subspace with a greater possibility of the existence of the optimal solution, avoiding falling into the local optimum and more accurately obtaining the optimal solution. Description of the Drawings

[0020] Figure 1 It is a flowchart of the steps of a method for optimizing the process parameters of 3D printing of a protective mask according to an embodiment of the present invention. Detailed Embodiment

[0021] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0022] The following will specifically describe the embodiments of the present invention in conjunction with the accompanying drawings.

[0023] Please refer to Figure 1 , which shows a step flowchart of a method for optimizing the 3D printing process parameters of a protective mask provided by an embodiment of the present invention. The method includes the following steps: S1: Obtain the feasible value range of each process parameter for 3D printing of the protective mask, and use the spatial range formed by the feasible value ranges of all process parameters as the solution space.

[0024] Specifically, obtain the feasible value range of each process parameter for 3D printing of the protective mask, and use the spatial range formed by the feasible value ranges of all process parameters as the solution space.

[0025] The types of process parameters include but are not limited to the following aspects: printing temperature, printing speed, filling density, and support structure. The feasible value range of each process parameter is the value range of each process parameter obtained based on experience.

[0026] S2: Based on the solution space, use the genetic algorithm to iteratively optimize until the stopping condition is met, obtain the optimal solution, and use the process parameters in the optimal solution as the optimized process parameters.

[0027] Preferably, as an example, based on the solution space, use the genetic algorithm to iteratively optimize until the stopping condition is met, obtain the optimal solution, and use the process parameters in the optimal solution as the optimized process parameters, including: Use the solution space as the optimization search space, and use the genetic algorithm to perform iterative optimization within the optimization search space until the stopping condition is met, obtain the optimal solution, and use the process parameters in the optimal solution as the optimized process parameters.

[0028] It should be added that in this embodiment, the iteration count reaching the preset cut-off count is used as the stopping condition. Other embodiments can set other cut-off conditions, and this embodiment does not make specific limitations.

[0029] Among them, using the genetic algorithm to perform iterative optimization within the optimization search space includes step S20-step S21.

[0030] It should be noted that in the traditional genetic algorithm, individuals with large fitness values are selected and retained for genetic reproduction. In this selection method, it is very easy for some individuals in the local optimum to be retained, resulting in the retained individuals being too concentrated in a small area, so that other areas are not searched, leading to the inability to jump out of the local optimum. For example, in some areas, the individuals being traversed are at the peak, so the fitness value of this individual is large, while in some areas, the individuals being traversed are at the valley, so the fitness value of this individual is small. Therefore, by selecting individuals to retain based on the fitness values of the individuals being traversed, it is very easy for there to be no individuals retained in some areas, resulting in the phenomenon of missing the optimal solution due to weak search intensity.

[0031] It should be further noted that in order to prevent falling into the local optimum, while ensuring that better individuals are retained, it is also necessary to judge the distribution of the traversed solutions, so as to search each area of the solution space. At the same time, when judging whether an individual is retained, not only the fitness value of the currently traversed individual in each area needs to be considered, but also the maximum value situation at the peak where the individual is located needs to be appropriately estimated, so as to prevent the phenomenon that the individual is removed due to the currently traversed individual being in the valley, resulting in missing the optimal solution.

[0032] S20: In a single optimization process, use the quality index of the protective mask corresponding to each current feasible solution as the fitness, and calculate the selection probability of any current feasible solution.

[0033] S200: In a single optimization process, use the quality index of the protective mask corresponding to each current feasible solution as the fitness.

[0034] Optionally, as an example, using the quality index of the protective mask corresponding to each current feasible solution as the fitness includes: In the historical process data, obtain the historical process data with the smallest difference from the current feasible solution. Use the protective mask printed with the historical process data as a reference object, conduct various performance tests on the reference object, obtain the performance scores obtained from each performance test, and use the average value of the performance scores obtained from all performance tests as the quality index of the protective mask corresponding to the current feasible solution.

[0035] It should be noted that there is still a difference between the current feasible solution and the historical process data with the smallest difference. Therefore, the protective mask printed with the historical process data with differences cannot fully reflect the object corresponding to the current feasible solution, and the obtained quality index of the protective mask is also inaccurate.

[0036] Preferably, as an example, in a single optimization process, using the quality index of the protective mask corresponding to each current feasible solution as the fitness includes: On the simulation software, using the process parameters in the current feasible solution to set the corresponding printing parameters, controlling the printing of the protective mask, and obtaining the printed protective mask; Conduct various performance simulation tests on the protective mask, obtain the performance scores of each performance test, and take the mean of the performance scores of all performance tests as the quality index of the protective mask.

[0037] It should be added that the types of performance simulation tests for the protective mask include but are not limited to the following aspects: Filtration efficiency, sealing performance, impact resistance, weight index, air permeability index.

[0038] S201: In one optimization process, calculate the selection possibility of any current feasible solution.

[0039] It should be noted that in order to prevent falling into local optima, a certain degree of search needs to be ensured in each subspace. At the same time, in order to prevent the differences in peaks and valleys among the current feasible solutions in different subspaces from resulting in the situation where the fitness value of the current feasible solution cannot reflect the existence of the optimal value in the subspace, it is necessary to estimate the fitness value at the peak position to represent the existence of the optimal value in the subspace, and then more accurately set the selection possibility of the current feasible solution.

[0040] Optionally, as an example, in one optimization process, calculating the selection possibility of any current feasible solution includes:

[0041] Among them, the solution space is divided into several subspaces, and the exploration degree of each subspace is obtained. T represents the exploration degree of the subspace where the current feasible solution is located, C represents the variation degree in the subspace where the current feasible solution is located, represents the estimated fitness value at the peak position of the peak where the current feasible solution is located, represents the fitness value of the feasible solution, represents the selection possibility of the current feasible solution.

[0042] It can be understood that the variation degree C reflects the difference in the variation laws of different regions in the subspace where the current feasible solution is located. The larger this value is, the greater the difference in the variation laws. Therefore, the fitness information at the current feasible solution cannot represent the fitness information of other regions in the subspace. Therefore, it is necessary to explore this subspace in detail to prevent falling into local optima. Therefore, it is necessary to increase the selection possibility of the current feasible solution, so that the current feasible solution has a greater probability of being retained, providing a data basis for subsequent traversal. It reflects the traversed situation in this subspace. The larger this value is, the greater the traversed degree of this subspace, so the possibility of falling into a local optimum in this subspace is smaller. Therefore, it is necessary to appropriately reduce the selection possibility of the current feasible solution, so as to reduce the retention probability of the current feasible solution, and then save more energy to traverse other regions.

[0043] Since the current feasible solution may be at the valley position, the fitness value of the current feasible solution at the valley position cannot accurately reflect the situation of the existence of the optimal solution in this subspace. Therefore, it is necessary to estimate the information of the peak where the current feasible solution is located, so as to judge the situation of the existence of the optimal solution in this subspace according to the peak information, and then set an appropriate selection possibility. Due to the existence of deviation in the estimation, relying solely on the estimated value cannot accurately reflect the situation of the existence of the optimal solution in this subspace. Therefore, it is also necessary to combine the true value to comprehensively reflect the situation of the existence of the optimal solution in this subspace. It reflects the situation of the existence of the optimal solution in this subspace. The larger this value is, the greater the possibility of the existence of the optimal solution in this subspace. Therefore, it is more necessary to carefully search this subspace. Therefore, it is necessary to increase the selection possibility of the current feasible solution, so as to provide a data basis for subsequent searching of this subspace.

[0044] It should be noted that in the above process, the fitness estimated value at the peak position and the current feasible solution are combined by taking the mean. This combination method does not consider the influence of the estimation accuracy, resulting in inaccurate calculation results.

[0045] Preferably, as an example, in an optimization process, calculating the selection possibility of any current feasible solution includes:

[0046] where C represents the degree of variation difference in the subspace where the current feasible solution is located, represents the fitness estimated value at the peak position of the peak where the current feasible solution is located, represents the prediction accuracy of the fitness estimated value, represents the fitness value of the feasible solution, represents the selection possibility of the current feasible solution.

[0047] It can be understood that in the process of calculating the selection possibility, the fitness value of the current feasible solution and the predicted value of the fitness value at the peak position are combined according to the prediction accuracy. When the prediction accuracy is high, more reference is made to the fitness prediction value at the peak position of the peak where the feasible solution is located, so as to prevent skipping the optimal solution just because the current feasible solution takes a smaller value for a while and is not retained. At the same time, when the prediction accuracy is low, more reference is made to the fitness value of the current feasible solution, so as to prevent the wrong prediction information from interfering with the selection of the feasible solution.

[0048] The above embodiments relate to subspaces, exploration degree, variation difference degree, fitness predicted value at the peak position, and prediction accuracy. Next, the determination methods of subspaces, exploration degree, variation difference degree, fitness predicted value at the peak position, and prediction accuracy will be described.

[0049] First, the method for obtaining subspaces will be introduced.

[0050] Preferably, as an example, the solution space is divided into several subspaces, including: The solution space is evenly divided into a preset first number of subspaces. In this embodiment, the preset first number is taken as 25 for description. Other embodiments can take other values, and this embodiment does not make specific limitations.

[0051] Then, the method for obtaining the exploration degree will be introduced.

[0052] Optionally, as an example, obtaining the exploration degree of each subspace includes: Obtain the positions of each feasible solution that has been traversed in the previous iteration process and record them as the traversed positions. Take the number of traversed positions in this subspace as the exploration degree.

[0053] It should be noted that uneven search will also lead to being trapped in local optima. Therefore, when judging the exploration degree, the uniformity of the search needs to be evaluated.

[0054] Preferably, as an example, obtaining the exploration degree of each subspace includes: Obtain the positions of each feasible solution that has been traversed in the previous iteration process and record them as the traversed positions. Evenly divide each subspace into a preset second number of small spaces and record them as analysis spaces. Take the number of traversed positions included in each analysis space. Take the reciprocal of the variance of the number of traversed positions in all analysis spaces of each subspace as the exploration uniformity degree of each subspace; Multiply the number of traversed positions included in each subspace by the exploration uniformity degree as the exploration degree of each subspace.

[0055] In this embodiment, the preset second number is taken as 9 for description. Other embodiments can take other values, and this embodiment does not make specific limitations.

[0056] After that, the method for obtaining the variation difference degree will be introduced.

[0057] Optionally, as an example, the method for obtaining the variation difference degree in the subspace where the current feasible solution is located includes: Using the least squares method to perform polynomial fitting on all feasible solutions and their corresponding fitness values in the solution space, and denoting the fitted polynomial as the overall change relationship; dividing the fitting model of the overall change relationship at the subspace into several independent peaks, obtaining the kurtosis and skewness of each independent peak, multiplying the kurtosis difference between two independent peaks by the skewness difference to obtain the change difference degree between the two independent peaks, and taking the mean of the change law differences between all pairs of independent peaks in the subspace as the change difference degree of the subspace.

[0058] It should be noted that when analyzing the change law differences in different regions of the subspace, the distribution of the change laws is not considered, resulting in inaccurate measurement results.

[0059] Preferably, as an example, the method for obtaining the change difference degree in the subspace where the current feasible solution is located includes: Using the least squares method to perform polynomial fitting on all feasible solutions and their corresponding fitness values in the solution space, and denoting the fitted polynomial as the overall change relationship, dividing the fitting model of the overall change relationship at the subspace into several independent peaks, obtaining the kurtosis and skewness of each independent peak, multiplying the kurtosis difference between every two independent peaks by the skewness difference to obtain the law difference degree between the two independent peaks, and taking the mean of the law difference degrees between all pairs of independent peaks as the law difference degree of the subspace; Taking the mean of the interval distances between each independent peak and its surrounding independent peaks as the interval distance of each independent peak, and taking the variance of the interval distances of all independent peaks in the subspace as the distribution difference degree of the subspace; Multiplying the law difference degree of the subspace by the distribution difference degree to obtain the change difference degree of the subspace.

[0060] It should be added that dividing the fitting model of the overall change relationship at the subspace into several independent peaks includes: Obtaining the closed region surrounded by the positions corresponding to the minimum points of the overall change relationship as the independent region, and taking the part of the relationship model corresponding to the overall change relationship within the independent region as the independent peak.

[0061] Subsequently, the method for obtaining the predicted fitness value at the peak position is introduced.

[0062] Preferably, as an example, the method for obtaining the predicted fitness value at the peak position of the peak where the current feasible solution is located includes: Obtaining the independent peak where the current feasible solution is located, and using the overall change relationship to fit the fitness value at the peak position of the independent peak where the current feasible solution is located, which is denoted as the predicted fitness value at the peak position of the peak where the current feasible solution is located.

[0063] Finally, the method for obtaining the prediction accuracy is introduced.

[0064] Preferably, as an example, the method for obtaining the prediction accuracy of the fitness prediction value includes: Dividing the selection possibility of each current feasible solution by the sum of the selection possibilities of all current feasible solutions to obtain the selection probability of each current feasible solution; Taking the selection probability of each current feasible solution as the extraction probability, and randomly extracting a preset third number of current feasible solutions from all current feasible solutions as the retained individuals.

[0065] S21: In one optimization process, select some current feasible solutions as the retained individuals according to the selection possibility.

[0066] Preferably, as an example, in one optimization process, selecting some current feasible solutions as the retained individuals according to the selection possibility includes: Dividing the selection possibility of each current feasible solution by the sum of the selection possibilities of all current feasible solutions to obtain the selection probability of each current feasible solution; Taking the selection probability of each current feasible solution as the extraction probability, and randomly extracting a preset third number of current feasible solutions from all current feasible solutions as the retained individuals.

[0067] In this embodiment, it is described by taking the preset third number as 10 as an example. Other embodiments can take other values, and this embodiment does not make specific limitations.

[0068] So far, this embodiment is completed.

[0069] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing process parameters of 3D printing of protective masks, characterized in that: include: Obtain the feasible value range of each process parameter of 3D printing of protective masks, and use the space range formed by the feasible value ranges of all process parameters as the solution space; Based on the solution space, the genetic algorithm is used to iteratively search for the best solution until the stopping condition is met and the optimal solution is obtained. The process parameters in the optimal solution are used as the optimized process parameters. In an optimization process, the protective mask quality index corresponding to each current feasible solution is used as the fitness, the solution space is divided into several subspaces, and the exploration degree is calculated according to the distribution of feasible solutions traversed in the previous iteration process in the subspace; the degree of change difference is calculated according to the change difference of the traversed feasible solutions in different regions in the subspace, and the possibility of selection of the current feasible solution is calculated according to the exploration degree, the degree of change difference, the fitness value of the current feasible solution and the estimated fitness value at the peak position of the peak where the current feasible solution is located, wherein the possibility of selection is positively correlated with the degree of change difference, the fitness value of the current feasible solution and the estimated fitness value at the peak position of the peak where the current feasible solution is located, and negatively correlated with the exploration degree; Select some current feasible solutions as retained individuals according to the selection probability.

2. A method for optimizing process parameters of 3D printing of protective masks according to claim 1, characterized in that: The possibility of selecting the current feasible solution satisfies the relationship: ； Among them, T represents the degree of exploration of the subspace where the current feasible solution is located, and C represents the degree of change difference in the subspace where the current feasible solution is located. Indicates the estimated fitness value at the peak position of the current feasible solution. represents the prediction accuracy of the fitness estimate, represents the fitness value of the current feasible solution, Indicates the possibility of selecting the current feasible solution.

3. A method for optimizing process parameters of 3D printing of protective masks according to claim 1, characterized in that: The solution space is divided into several subspaces, including: The solution space is evenly divided into a preset first number of subspaces.

4. A method for optimizing process parameters of 3D printing of protective masks according to claim 1, characterized in that: The method for obtaining the exploration degree includes: Obtain the positions of each feasible solution that has been traversed in the previous iteration process as the traversed positions, divide each subspace evenly into a preset second number of small spaces as analysis spaces, and take the number of traversed positions in each analysis space and the inverse of the variance of the number of traversed positions in all analysis spaces of each subspace as the exploration uniformity of each subspace; The exploration degree of each subspace is obtained by multiplying the number of traversed positions in each subspace by the exploration uniformity.

5. A method for optimizing process parameters of 3D printing of protective masks according to claim 1, characterized in that: The method for obtaining the degree of change difference includes: The polynomial is fitted using the feasible solutions that have been traversed in the solution space and the corresponding fitness values, which are recorded as the overall relationship; the fitting model of the overall change relationship in the subspace is divided into several independent peaks, the kurtosis and skewness of each independent peak are obtained, and the degree of regular difference between the two independent peaks is obtained by multiplying the kurtosis difference between each two independent peaks by the skewness difference, and the average of the degree of regular difference between all two independent peaks is taken as the degree of regular difference of the subspace; The mean of the distances between each independent peak and the surrounding independent peaks is taken as the distance between each independent peak, and the variance of the distances between all independent peaks in the subspace is taken as the degree of distribution difference of the subspace; The degree of variation difference of the subspace is obtained by multiplying the degree of regularity difference of the subspace by the degree of distribution difference.

6. A method for optimizing process parameters of 3D printing of protective masks according to claim 5, characterized in that: The fitting model of the overall variation relationship in the subspace is divided into several independent peaks, including: The closed area surrounded by the positions corresponding to the minimum value points of the overall change relationship is obtained as an independent area, and the part of the relationship model corresponding to the overall change relationship in the independent area is taken as an independent peak.

7. A method for optimizing process parameters of 3D printing of protective masks according to claim 2, characterized in that: The predictive accuracy of the fitness estimate includes: Get the estimated fitness value of each feasible solution in the independent peak where the current feasible solution is located, take the absolute value of the difference between the fitness of each traversed feasible solution and the estimated fitness value as the predicted deviation of the fitness value of each traversed feasible solution, and take the inverse of the predicted deviation of the fitness of all traversed feasible solutions in the peak where the current feasible solution is located as the predicted accuracy of the estimated fitness value.

8. A method for optimizing process parameters of 3D printing of protective masks according to claim 1, characterized in that: The step of selecting part of the current feasible solutions as the retained individuals according to the selected possibilities includes: The probability of selection of each current feasible solution is obtained by dividing the possibility of selection of each current feasible solution by the cumulative sum of the possibilities of selection of all current feasible solutions; The probability of selection of each current feasible solution is used as the extraction probability, and a preset third number of current feasible solutions are randomly extracted from all current feasible solutions as retained individuals.

9. A method for optimizing process parameters of 3D printing of protective masks according to claim 1, characterized in that: The stopping condition is when the number of iterations reaches the preset cutoff number.

10. A method for optimizing process parameters of 3D printing of protective masks according to claim 1, characterized in that: The method for obtaining the quality index of the protective mask comprises: In the simulation software, the corresponding printing parameters are set using the process parameters in the current feasible solution, the printing of the protective mask is controlled, and the processed protective mask is obtained; Conduct multiple performance simulation tests on the protective mask, obtain the performance score of each performance test, and take the average of the performance scores of all performance tests as the quality indicator of the protective mask.

Images