New energy battery thin-wall rectangular shell stamping process parameter optimization method

Abstract

The invention provides a method for optimizing stamping process parameters of a thin-wall rectangular shell of a new energy battery, and relates to the technical field of metal plate stamping forming. The method systematically explores a process parameter space through an orthogonal test design, and accurately quantifies a parameter coupling effect in combination with a dual-target regression model to optimize the stamping process parameters of the thin-wall rectangular shell of the new energy battery. An NSGA-I algorithm is utilized to generate a Pareto optimal solution set giving consideration to the maximum / minimum molding thinning rate, and the limitation of traditional single-target optimization is broken through; furthermore, material fluctuation and equipment deviation are compensated in real time through PI D dynamic control, closed-loop control of modeling-optimization-feedback is formed, the consistency of the forming quality of the thin-wall shell is remarkably improved, the defect rate is reduced, meanwhile, the process debugging period is shortened, the adaptability of a production line to complex working conditions is enhanced, and the production efficiency is improved. And reliable technical support is provided for efficient and precise stamping of the new energy battery shell.

Description

Technical Field

[0001] The present invention relates to the technical field of metal sheet stamping forming, and particularly to a method for optimizing stamping process parameters of a thin-walled rectangular shell of a new energy battery. Background Technique

[0002] For the external protective shell of a new energy vehicle-mounted battery pack, due to the overall space layout and lightweight problems inside the vehicle machine, there are high requirements for the wall thickness. Generally, it is made into a thin-walled rectangular box part of a deep cavity type, and is formed by multiple processes such as stamping, drawing, thinning, and trimming using 3003-H14 aluminum alloy. It has the characteristics of thin material, many processes, and high precision. Its forming law is not easy to control, and quality defects such as wrinkling, cracking, and uneven wall thickness are extremely likely to occur. According to the numerical simulation analysis of the stamping process of the battery shell, the forming quality of the shell is greatly affected by various stamping process parameters, such as stamping speed, blank holder force, friction coefficient, die clearance, etc.

[0003] The existing stamping process flow analyzes the influencing factors of the forming quality, verifies through numerical simulation, and optimizes the stamping process parameters with reference to the analysis and verification results. Since the influencing factors of the stamping process parameters are diversified, and there is a correlation between various stamping process parameters, there is no clear linear mapping relationship between the process parameters and the forming quality. The existing simulation verification method cannot establish an optimization mechanism for stamping process parameters under the combined action of multiple parameters, and often can only perform specific optimization for a single process parameter, ignoring the parameter interaction effect, resulting in a local optimum of the parameter combination. In actual production, batch quality instability is easily caused by dynamic factors such as material property fluctuations and die wear.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and therefore it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for optimizing stamping process parameters of a thin-walled rectangular shell of a new energy battery to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A method for optimizing stamping process parameters of a thin-walled rectangular shell of a new energy battery, the specific steps include:

[0008] S1: Conduct an orthogonal experiment design based on multiple groups of process parameters, and perform a number of stamping forming experiments under different process parameters respectively;

[0009] S2: Detect the wall thickness values of the stamping shell before and after each stamping forming test respectively, and detect the overall forming quality of the stamping shell after each stamping forming test;

[0010] S3: Analyze the wall thickness values and the overall forming quality after each stamping forming test, and generate the maximum forming thinning rate and the minimum forming thinning rate of the stamping shell corresponding to this stamping forming test according to the analysis results;

[0011] S4: Use multiple regression analysis to establish a relationship model between the maximum forming thinning rate, the minimum forming thinning rate of the stamping shell and the process parameters, and construct an objective function with the maximum forming thinning rate and the minimum forming thinning rate to solve the relationship model to obtain the optimal process parameters.

[0012] Preferably, the process parameters include stamping speed, friction coefficient, R angle, die clearance, and blank holding force;

[0013] Before and after each stamping forming test, measure the wall thickness values on different surfaces of the stamping shell, and calculate the corresponding forming thinning rate according to the wall thickness values on different surfaces. The calculation method is:

[0014]

[0015] In the formula represents the forming thinning rate at the coordinate (x, y) on the i-th surface of the stamping shell during the j-th stamping, respectively represent the thickness of the stamping shell before the stamping forming test and the thickness after the stamping forming test. The superscript i represents the index of the inner surface of the stamping shell, i ∈ [1, 5], the subscript j represents the number of stamping forming tests, and (x, y) represents the coordinate of the inner surface of the stamping shell.

[0016] Preferably, obtain the maximum forming thinning rate and the minimum forming thinning rate corresponding to this stamping forming test according to the forming thinning rates on different surfaces of the stamping shell, and mark them as and

[0017] Then construct the relationship models between the maximum forming thinning rate, the minimum forming thinning rate and the process parameters respectively. The relationship models are respectively expressed as:

[0018]

[0019] In the formula u k 、v k respectively represent the linear regression coefficients of the two groups of models, u k,k′ 、v k,k′ respectively represent the interaction regression coefficients of the two groups of models, X k 、X k′They respectively represent the k-th process parameter and the k'-th process parameter. Both k and k' represent the indices of the process parameters, and δ1 and δ2 respectively represent the model residuals of the two groups of models.

[0020] Preferably, the maximum forming thinning rate, the minimum forming thinning rate, and the stamping process parameters collected under each stamping forming test are divided into a training set and a validation set according to a ratio of 8:2, and the relationship models between the maximum forming thinning rate, the minimum forming thinning rate, and the process parameters are trained and optimized respectively.

[0021] Preferably, a target function is constructed, and the NSGA-II algorithm is used to solve the Pareto front for multi-objective optimization to obtain the optimal process parameters. The target function is expressed as:

[0022]

[0023] In the formula, F represents the target function, min() and max() respectively represent taking the minimum value and the maximum value of the content in the brackets, respectively represent the expected maximum forming thinning rate and the expected minimum forming thinning rate.

[0024] Preferably, after obtaining the optimal process parameters, multi-pass stamping forming tests are carried out with the optimal process parameters, the real-time process parameters of the stamping shell during the stamping test are collected, and the PID control algorithm is used to adjust the real-time process parameters. The calculation method of the PID control algorithm is:

[0025]

[0026] In the formula represents the adjustment amount of the process parameter, represents the real-time process parameter, represents the optimal process parameter, KP k 、KI k 、KD k respectively represent the proportional coefficient, the integral coefficient, and the differential coefficient of the PID control algorithm. e(j) and e(j') respectively represent the errors during the j-th and j'-th stamping forming tests. Both j and j' represent the number of stamping forming tests. The calculation method of the error is:

[0027]

[0028] In the formula, both a1 and a2 represent model weights, both of which are greater than 0, and a1 + a2 = 1.

[0029] Preferably, the root mean square of the relative errors of all real-time process parameters is calculated. The calculation method is:

[0030]

[0031] When σ j ≤ σ y is satisfied, it is considered that the adjustment is completed and the adjustment is stopped;

[0032] σ y represents a preset error threshold, and σ y ∈ [0.02, 0.05].

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0034] The present invention systematically explores the process parameter space through orthogonal experimental design, precisely quantifies the parameter coupling effect by combining a double-objective regression model, and uses the NSGA-II algorithm to generate a Pareto optimal solution set that takes into account the maximum / minimum forming thinning rate, breaking through the limitations of traditional single-objective optimization; further, through PID dynamic control, it compensates for material fluctuations and equipment deviations in real time, forming a closed-loop control of "modeling-optimization-feedback", significantly improving the consistency of the forming quality of thin-walled shells, reducing the defect rate, shortening the process debugging cycle at the same time, enhancing the adaptability of the production line to complex working conditions, and providing reliable technical support for the high-efficiency and precision stamping of new energy battery shells. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to specific embodiments.

[0037] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those of ordinary skill in the field to which the present invention belongs. The "first", "second" and similar terms used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left" and "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0038] Embodiment:

[0039] Please refer to Figure 1 , the present invention provides a technical solution:

[0040] An optimization method for stamping process parameters of a thin-walled rectangular shell of a new energy battery, the specific steps include:

[0041] S1: Conduct an orthogonal experiment design based on multiple groups of process parameters, and conduct a number of stamping forming experiments under different process parameters respectively. The process parameters include stamping speed, friction coefficient, R angle, die clearance, and blank holding force. Specifically, the number of experiments in the orthogonal experiment design should satisfy greater than or equal to 2 5 +1, that is, at least 33 times, and the number of experiments can be increased according to actual needs to improve the model accuracy.

[0042] In this step, the constructed orthogonal experiment design can systematically cover the combination space of process parameters, reduce the number of redundant experiments through mathematical orthogonality, quickly locate the influence weights of key parameters, provide evenly distributed data samples for subsequent modeling, avoid model distortion caused by parameter deviation, and can also significantly shorten the process development cycle through a structured experimental plan, laying a data foundation for multi-objective optimization.

[0043] S2: Detect the wall thickness value of the stamping shell before and after each stamping forming experiment respectively, and detect the overall forming quality of the stamping shell after each stamping forming experiment.

[0044] Before and after each stamping forming experiment, measure the wall thickness values on different surfaces of the stamping shell, and calculate the corresponding forming thinning rate according to the wall thickness values on different surfaces. The calculation method is:

[0045]

[0046] In the formula represents the forming thinning rate at the coordinate (x, y) on the i-th surface of the stamping shell during the j-th stamping, respectively represent the thickness of the stamping shell before the stamping forming experiment and after the stamping forming experiment. The superscript i represents the index of the inner surface of the stamping shell, i ∈ [1, 5], the subscript j represents the number of stamping forming experiments, and (x, y) represents the coordinate of the inner surface of the stamping shell.

[0047] Specifically, the wall thickness data can be measured by a high-precision measuring device, such as a laser thickness gauge, before and after stamping.

[0048] It can be understood that cracking is a common defect in the stamping process. Under the condition that other influencing factors are the same, the magnitude of the wall thickness reduction can largely characterize whether the part will crack. Therefore, the wall thickness forming thinning rate is selected as the evaluation index of the forming quality. During the stamping process, the die is in contact with the inner surface of the part. Therefore, when measuring the wall thickness data on different surfaces, the inner surface is used as the reference.

[0049] In this step, through wall thickness measurement at the coordinate point level, local deformation characteristics can be accurately captured, avoiding the masking of defects in key areas by the overall mean. Further, by combining the thinning rate and defect detection, the forming quality can be comprehensively quantified, providing multi-dimensional inputs for model construction. Additionally, the wall thickness change between passes can be dynamically tracked to reveal the temporal influence law of parameters on material flow.

[0050] S3: Analyze the wall thickness values and overall forming quality after each stamping forming test, and generate the maximum forming thinning rate and minimum forming thinning rate of the stamping shell corresponding to the current stamping forming test according to the analysis results.

[0051] Obtain the maximum forming thinning rate and minimum forming thinning rate corresponding to the current stamping forming test based on the forming thinning rates on different surfaces of the stamping shell, and label them as and

[0052] Then, establish the relationship models between the maximum forming thinning rate, minimum forming thinning rate and process parameters respectively. The relationship models are expressed as:

[0053]

[0054] In the formula, u k , v k represent the linear regression coefficients of the two groups of models respectively, u k,k′ , v k,k′ represent the interaction regression coefficients of the two groups of models respectively, X k , X k′ represent the k-th process parameter and the k'-th process parameter respectively. Both k and k' represent the indices of process parameters, and δ1 and δ2 represent the model residuals of the two groups of models respectively.

[0055] The linear regression model coefficients and interaction regression coefficients in the two groups of models can be obtained by fitting from the orthogonal test data through multiple regression analysis, and the least squares method is used to optimize the error between the model and the measured data. The model residuals represent random errors and can be obtained through residual analysis based on the orthogonal test data.

[0056] In this step, by separately modeling the maximum forming thinning rate and minimum forming thinning rate, the driving factors of the forming limit can be accurately distinguished. The influence intensity of single parameters and parameter interactions on the thinning rate should be quantified through regression coefficients. At the same time, by introducing interaction terms into the two models respectively, the synergistic or antagonistic effects between different process parameters can also be revealed, further improving the accuracy of the model.

[0057] S4: Establish a relationship model between the maximum forming thinning rate, minimum forming thinning rate of the stamping shell and process parameters using multiple regression analysis, and construct an objective function with the maximum forming thinning rate and minimum forming thinning rate to solve the relationship model to obtain the optimal process parameters.

[0058] Divide the maximum forming thinning rate, minimum forming thinning rate and stamping process parameters collected under each stamping forming test into a training set and a validation set according to a ratio of 8:2, and train and optimize the relationship model between the maximum forming thinning rate, minimum forming thinning rate and process parameters respectively.

[0059] Construct an objective function, and use the NSGA-II algorithm to solve the Pareto front for multi-objective optimization to obtain the optimal process parameters. The objective function is expressed as:

[0060]

[0061] In the formula, F represents the objective function, min() and max() respectively represent taking the minimum value and maximum value inside the parentheses, respectively represent the expected maximum forming thinning rate and minimum forming thinning rate. Specifically, the expected maximum forming thinning rate and minimum forming thinning rate can be obtained according to the plastic deformation limit of the shell material (such as aluminum alloy, stainless steel) (such as obtaining the elongation at break through a tensile test), or can be obtained by referring to the requirements of the new energy battery shell industry standard (such as QC / T743-2018) for the wall thickness uniformity of the shell.

[0062] In this step, the objective function can simultaneously satisfy the minimization of the maximum thinning rate and the maximization of the minimum thinning rate, that is, keep the thinning rate of the stamping shell at an intermediate value as much as possible, avoid overall forming quality problems such as breakage and cracks caused by too small thinning rate, and cost increase problems caused by too large thinning rate. In other words, it is to avoid the performance imbalance caused by single-objective optimization. Then, through the NSGA-II algorithm, through non-dominated sorting and crowding degree calculation, multiple feasible parameter combinations are provided, which can adapt to different production requirements, and the Pareto solution set covers the parameter sensitive area, which can enhance the adaptability of the scheme to production fluctuations.

[0063] After obtaining the optimal process parameters, conduct multi-pass stamping forming tests with the optimal process parameters, collect the real-time process parameters of the stamping shell during the stamping test, and use the PID control algorithm to adjust the real-time process parameters. The calculation method of the PID control algorithm is:

[0064]

[0065] In the formula represents the adjustment amount of the process parameter, represents the real-time process parameter, denotes the optimal process parameters, that is, the Pareto optimal solution set output by the NSGA-II algorithm, KP k , KI k , KD k respectively represent the proportional coefficient, integral coefficient, and differential coefficient of the PID control algorithm. e(j) and e(j′) respectively represent the errors during the j-th and j′-th stamping forming tests. j and j′ both represent the number of stamping forming tests. The calculation method of the error is as follows:

[0066]

[0067] In the formula, a1 and a2 both represent model weights, both of which are greater than 0, and a1 + a2 = 1.

[0068] where represent the initial values of the proportional coefficient, integral coefficient, and differential coefficient of the PID control algorithm, which can be determined according to expert experience or calculated by using the Ziegler-Nichols tuning method to determine the critical gain and oscillation period through step response experiments.

[0069] In this step, using the PID control algorithm to independently adjust each process parameter can avoid control inaccuracy caused by parameter coupling, and can also achieve dynamic error compensation, respond in real time to interference factors such as material property fluctuations and equipment aging, and maintain the stability of the forming quality. Further, it can also accumulate historical errors through the integral term and gradually approach the theoretical optimal state to achieve the purpose of adaptive optimization.

[0070] Calculate the root mean square of the relative errors of all real-time process parameters. The calculation method is as follows:

[0071]

[0072] When σ j ≤ σ y is satisfied, it is considered that the adjustment is completed and the adjustment is stopped;

[0073] σ y represents the preset error threshold, and σ y ∈ [0.02, 0.05].

[0074] In this step, through statistical threshold judgment, the balance between control accuracy and production efficiency is achieved, forming a complete link of "optimization - execution - feedback", verifying the engineering applicability of the theoretical model, and also avoiding resource waste caused by over-adjustment, ensuring that the parameters converge in the effective interval.

[0075] In summary, the present invention systematically explores the process parameter space through orthogonal experimental design, combines a dual-objective regression model to accurately quantify the parameter coupling effect, and uses the NSGA-II algorithm to generate a Pareto optimal solution set that takes into account the maximum / minimum forming thinning rate, breaking through the limitations of traditional single-objective optimization; further, through PID dynamic control, it compensates for material fluctuations and equipment deviations in real time, forming a closed-loop control of "modeling - optimization - feedback", significantly improving the consistency of the forming quality of thin-walled shells, reducing the defect rate, shortening the process debugging cycle at the same time, enhancing the adaptability of the production line to complex working conditions, and providing reliable technical support for the high-efficiency and precision stamping of new energy battery shells.

[0076] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0077] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0078] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0079] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all of them should be covered by the protection scope of this application.

Claims

1. A method for optimizing stamping process parameters of a thin-walled rectangular shell of a new energy battery, characterized in that, The specific steps include: S1: Conduct an orthogonal experiment design based on multiple groups of process parameters, and conduct several stamping forming experiments under different process parameters respectively; S2: Detect the wall thickness values of the stamping shell before and after each stamping forming experiment respectively, and detect the overall forming quality of the stamping shell after each stamping forming experiment; S3: Analyze the wall thickness values and the overall forming quality after each stamping forming experiment, and generate the maximum forming thinning rate and the minimum forming thinning rate of the stamping shell corresponding to this stamping forming experiment according to the analysis results; S4: Use multiple regression analysis to establish a relationship model between the maximum forming thinning rate, the minimum forming thinning rate of the stamping shell and the process parameters, and construct an objective function with the maximum forming thinning rate and the minimum forming thinning rate to solve the relationship model to obtain the optimal process parameters.

2. The optimization method of stamping process parameters for a thin-walled rectangular housing of a new energy battery according to claim 1, wherein: The process parameters include stamping speed, friction coefficient, R angle, die clearance, and blank holding force; Before and after each stamping forming experiment, measure the wall thickness values of different surfaces of the stamping shell, and calculate the corresponding forming thinning rate according to the wall thickness values of different surfaces. The calculation method is: In the formula represents the forming thinning rate at the coordinate (x, y) on the i-th surface of the stamping shell during the j-th stamping respectively represent the thickness of the stamping shell before the stamping forming test and the thickness after the stamping forming test. The superscript i represents the index of the inner surface of the stamping shell, i ∈ [1, 5], the subscript j represents the number of stamping forming tests, and (x, y) represents the coordinates of the inner surface of the stamping shell 3. A method for optimizing the stamping process parameters of a thin-walled rectangular housing of a new energy battery according to claim 2, characterized in that: The maximum and minimum forming thinning rates corresponding to the current stamping forming test are obtained based on the forming thinning rates on different surfaces of the stamping shell, and are respectively calibrated as and Then establish relationship models between the maximum forming thinning rate, the minimum forming thinning rate and the process parameters respectively. The relationship models are respectively expressed as: where u k and v k represent the linear regression coefficients of two groups of models respectively, u k,k′ and v k,k′ represent the interaction regression coefficients of two groups of models respectively, X k and X k′ represent the k-th process parameter and the k'-th process parameter respectively, k and k' both represent the indices of process parameters, and δ1 and δ2 represent the model residuals of two groups of models respectively.

4. The optimization method of stamping process parameters for a thin-walled rectangular housing of a new energy battery according to claim 3, wherein: Divide the maximum forming thinning rate, the minimum forming thinning rate and the stamping process parameters collected under each stamping forming experiment into a training set and a validation set according to a ratio of 8:2, and train and optimize the relationship models between the maximum forming thinning rate, the minimum forming thinning rate and the process parameters respectively.

5. The optimization method of stamping process parameters for a thin-walled rectangular shell of a new energy battery according to claim 3, characterized in that: Construct an objective function, and use the NSGA-II algorithm to solve the Pareto front for multi-objective optimization to obtain the optimal process parameters. The objective function is expressed as: In the formula, F represents the objective function, and min() and max() respectively represent taking the minimum value and the maximum value of the content within the parentheses. They respectively represent the expected maximum forming thinning rate and the minimum forming thinning rate.

6. A method for optimizing the stamping process parameters of a thin-walled rectangular housing of a new energy battery according to claim 5, characterized in that: After obtaining the optimal process parameters, conduct multi-pass stamping forming experiments with the optimal process parameters, collect the real-time process parameters of the stamping shell during the stamping experiment, and use the PID control algorithm to adjust the real-time process parameters. The calculation method of the PID control algorithm is: wherein represents the adjustment amount of the process parameter, represents the real-time process parameter, represents the optimal process parameter, KP k 、KI k 、KD k respectively represent the proportional coefficient, integral coefficient, and differential coefficient of the PID control algorithm. e(j) and e(j′) respectively represent the errors during the j-th and j′-th stamping forming tests. j and j′ both represent the number of stamping forming tests. The calculation method of the error is as follows: In the formula, both a1 and a2 represent model weights, both are greater than 0, and a1 + a2 = 1.

7. A method for optimizing the stamping process parameters of a thin-walled rectangular housing of a new energy battery according to claim 6, characterized in that: Calculate the root mean square of the relative errors of all real-time process parameters. The calculation method is: When σ j ≤ σ y is satisfied, it is considered that the adjustment is completed and the adjustment is stopped; σ y represents a preset error threshold, and σ y ∈ [0.02, 0.05].

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