New energy power battery shell deep drawing forming process parameter optimization method

Abstract

The invention relates to the technical field of metal plate stamping forming, and provides a new energy power battery shell deep drawing forming process parameter optimization method which comprises the steps that orthogonal test design is carried out based on a plurality of sets of parameters, and multiple times of shell deep drawing forming tests are carried out under different process parameters; the method comprises the following steps: analyzing a shell drawing forming test by adopting a response surface method, and establishing a maximum forming reduction rate mathematical model and a minimum forming reduction rate mathematical model based on test data; taking the established mathematical model as a target function, solving an optimal solution of the process parameters by adopting a particle swarm optimization algorithm, and adjusting the process parameters in real time by adopting a PID control algorithm; and combining the optimal solution of the process parameters with left and right double-shaft load monitoring to realize deviation verification analysis of multi-objective optimization numerical values of the process parameters. According to the method, dynamic adjustment of shell deep drawing forming process parameters is achieved, the stability and consistency of the machining process are ensured, and the quality of the formed shell is improved.

Description

Technical Field

[0001] The embodiments of the present invention generally relate to the field of metal sheet stamping technology, and in particular to a method for optimizing process parameters of deep drawing forming of a new energy power battery housing. Background Art

[0002] The external protective casing of new energy vehicle battery packs has high wall thickness requirements due to the overall spatial layout and lightweight design of the vehicle's internal computer. Typically, it is manufactured as a thin-walled rectangular box with a deep cavity. It is formed from 3003-H14 aluminum alloy through multiple processes including stamping, drawing, thinning, and trimming. This thin material, multiple steps, and high precision make it difficult to control the forming process, making it prone to quality defects such as wrinkling, cracking, and uneven wall thickness. Numerical simulation analysis of the battery casing stamping process shows that the casing's forming quality is significantly affected by various stamping process parameters, such as stamping speed, blank holder force, friction factor, and die clearance.

[0003] The existing stamping process flow analyzes the factors affecting the forming quality, verifies it through numerical simulation, and optimizes the stamping process parameters with reference to the analysis and verification results. Since the factors affecting the stamping process parameters are diverse and there is a correlation between various stamping process parameters, and there is no clear linear mapping relationship between the process parameters and the forming quality, the existing simulation verification method cannot establish a stamping process parameter optimization mechanism under the joint action of multiple parameters. It can often only perform specific optimization on a single process parameter, ignoring the parameter interaction effect, resulting in a local optimal parameter combination. In actual production, it is easy to cause batch quality instability due to dynamic factors such as material property fluctuations and mold wear. Summary of the Invention

[0004] To solve the above problems, the present invention systematically optimizes the shell deep drawing process parameters through the response surface methodology and particle swarm optimization algorithm, improves the stamping stability, and improves the quality of the formed shell. Combined with the real-time PID control algorithm, it further realizes the dynamic adjustment of the shell deep drawing process parameters to ensure the stability and consistency of the processing process, and combines the left and right biaxial load monitoring to perform verification analysis of the optimized values, which greatly reduces the number and cost of experiments, improves data quality and analysis depth, and effectively controls the shell deep drawing stability and forming thinning rate, significantly improving the processing quality and production efficiency.

[0005] According to an embodiment of the present invention, a method for optimizing process parameters of a new energy power battery housing deep drawing is provided.

[0006] In a first aspect of the present invention, a method for optimizing the deep drawing process parameters of a new energy power battery housing is provided. The method comprises:

[0007] Step S01: using several sets of drawing parameters as central composite design factors, designing an orthogonal experiment with the shell forming thinning rate corresponding to each set of drawing parameters as a response indicator, and performing several shell drawing tests under different process parameters;

[0008] Step S02: Analyzing the shell deep drawing test using the response surface methodology, and establishing a maximum forming thinning rate mathematical model and a minimum forming thinning rate mathematical model based on the test data;

[0009] Step S03: using the established mathematical model as the objective function, a particle swarm optimization algorithm is used to find the optimal solution for the process parameters, and a PID control algorithm is used to adjust the process parameters in real time;

[0010] Step S04: The optimal solution of process parameters is combined with left and right biaxial load monitoring to achieve data fitting of the left and right load force difference and calculate the stable output air pressure control value. The air pressure output controls the measurement and analysis of the left and right verticality errors, and realizes the deviation verification analysis of the multi-objective optimization values ​​of the process parameters.

[0011] Furthermore, the process parameters described in step S01 include: punching speed, blank holding force and die gap.

[0012] Furthermore, the specific steps of step S02 are:

[0013] Step S021: using a three-coordinate measuring machine, a digital display vernier caliper, and an electron microscope to measure the thickness of the shell wall after processing, and obtain the shell forming thinning rate corresponding to multiple sets of drawing parameters;

[0014] Step S022: Using the punching speed, blank holding force and die clearance as parameter variables, and the maximum forming thinning rate and minimum forming thinning rate of the shell as response variables, the response surface method is used to calculate the expected values ​​of the maximum forming thinning rate and the minimum forming thinning rate of the shell corresponding to multiple sets of drawing parameters, that is, to establish a mathematical model of the maximum forming thinning rate of the shell and a mathematical model of the minimum forming thinning rate of the shell.

[0015] Furthermore, the specific steps of step S03 are:

[0016] Step S031: Use weighted summation to convert multiple objectives into a single objective. The single objective function formula is as follows:

[0017] minF(v,f,a p )=min(w1Ra+w2FR)

[0018] Where, w1 represents the weight coefficient of the mathematical model of the maximum forming thinning rate of the shell; w2 represents the weight coefficient of the mathematical model of the minimum forming thinning rate of the shell; Ra represents the mathematical model of the maximum forming thinning rate of the shell; FR represents the mathematical model of the minimum forming thinning rate of the shell; v, f, ap represent punching speed, blank holding force and friction factor respectively;

[0019] Step S032: using a particle swarm optimization algorithm to perform a global search within a given drawing parameter range to obtain an optimal solution for the process parameters that minimizes the single objective function.

[0020] 5. The method for optimizing the deep drawing process parameters of a new energy power battery housing according to claim 4, wherein the specific steps of step S032 are:

[0021] Step S0321: Unify the dimensions of the mathematical model of the maximum forming thinning rate and the mathematical model of the minimum forming thinning rate of the shell according to the following formula:

[0022]

[0023] Where Ra (max) Indicates the maximum value of the mathematical model of the maximum forming thinning rate of the shell; Ra (min) Indicates the minimum value of the mathematical model of shell forming thinning rate; FR (max) Indicates the maximum value of the mathematical model of the minimum thinning rate of the shell; FR (min) It represents the minimum value of the mathematical model of the minimum forming thinning rate of the shell;

[0024] Step S0322: Substitute the unified mathematical model of the maximum forming thinning rate and the mathematical model of the minimum forming thinning rate into the single objective function formula to obtain the single objective function formula after dimension transformation:

[0025]

[0026] Step S0323: Calculate and generate a set of random solutions of the optimization model, and continuously iterate to search for the optimal solution of the population. In each iteration, the particle will track the optimal solution pBest found by itself and the optimal solution qbest found by the entire population, and update its position and velocity by comparing the fitness value of the particle at this time with its historical optimal solution;

[0027] The update formula of the historical optimal solution is:

[0028]

[0029] Where i represents the particle; j represents the current iteration number; f(.) represents the single objective function after dimension transformation;

[0030] The position of particles in the population and speed The update formula is:

[0031]

[0032] Where, represents the optimal solution found by the i-th particle itself; Represents the optimal solution of the current entire population optimization; r 1j 、r 2j represents a random number in the interval [0,1]; C1 represents the acceleration coefficient for updating the optimal solution of the particle itself; c2 represents the acceleration coefficient for updating the optimal solution of the population; t represents the t-dimensional search space; ω represents the inertia weight coefficient.

[0033] Furthermore, after obtaining the optimal solution of the process parameters that minimize the single objective function in step S032, a shell deep drawing trial is performed with the optimal process parameters, and real-time process parameters during the trial processing are collected. The PID control algorithm is used to adjust the real-time process parameters. The calculation method of the PID control algorithm is:

[0034]

[0035] Where P(t) represents the punching speed during the tth trial processing, K p (P), K i (P), K d (P) represents the proportional coefficient, integral coefficient, and differential coefficient of punching speed, blank holder force, or die clearance, respectively. e(t-1) represents the total error during the t-1th trial processing. The calculation method of e(t) is:

[0036]

[0037] In the formula are the maximum forming thinning rate and target maximum thinning rate of the shell during the t-th trial processing, ω s 、ω min are the minimum forming thinning rate and target minimum thinning rate of the shell during the tth trial processing, respectively.

[0038] In a second aspect of the present invention, a device for optimizing the deep drawing process parameters of a new energy power battery housing is provided. The device comprises:

[0039] Orthogonal test module: used to design orthogonal experiments with several sets of drawing parameters as central composite design factors and the shell forming thinning rate corresponding to each set of drawing parameters as the response index, and to conduct several shell drawing tests under different process parameters;

[0040] Model building module: used to analyze the shell deep drawing test using the response surface methodology and establish the maximum forming thinning rate mathematical model and the minimum forming thinning rate mathematical model based on the test data;

[0041] Model solving module: used to use the established mathematical model as the objective function and adopt the particle swarm optimization algorithm to find the optimal solution for process parameters;

[0042] Numerical Verification Module: This module is used to combine the optimal solution of process parameters with left and right biaxial load monitoring, to achieve data fitting of the left and right load force difference and calculate the stable output air pressure control value. The air pressure output controls the measurement and analysis of the left and right verticality errors, thus realizing the deviation verification analysis of the multi-objective optimization values ​​of process parameters.

[0043] In a third aspect of the present invention, an electronic device is provided, comprising: a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method according to the first aspect of the present invention is implemented.

[0044] In a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method according to the first aspect of the present invention is implemented.

[0045] The present invention uses the response surface methodology and particle swarm optimization algorithm to systematically optimize the shell deep drawing process parameters, improve stamping stability, and improve the quality of the formed shell. Combined with the real-time PID control algorithm, it further realizes the dynamic adjustment of the shell deep drawing process parameters to ensure the stability and consistency of the processing process, and combines the left and right biaxial load monitoring to perform verification analysis of the optimized values, which greatly reduces the number of tests and costs, improves data quality and analysis depth, and effectively controls the shell deep drawing stability and forming thinning rate, significantly improving processing quality and production efficiency.

[0046] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The above and other features, advantages and aspects of the embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings, in which:

[0048] Figure 1 A flow chart of a method for optimizing deep drawing process parameters of a new energy power battery housing according to an embodiment of the present invention is shown;

[0049] Figure 2 shows a biaxial load detection diagram according to an embodiment of the present invention;

[0050] Figure 3 A blank sheet material diagram according to an embodiment of the present invention is shown;

[0051] Figure 4shows a first sequence forming diagram according to an embodiment of the present invention;

[0052] Figure 5 shows a second sequence diagram according to an embodiment of the present invention;

[0053] Figure 6 shows a third sequence forming diagram according to an embodiment of the present invention;

[0054] Figure 7 shows a fourth sequence forming diagram according to an embodiment of the present invention;

[0055] Figure 8 shows a fifth sequence forming diagram according to an embodiment of the present invention;

[0056] Figure 9 shows a sixth sequence forming diagram according to an embodiment of the present invention;

[0057] Figure 10 A schematic diagram of a full-pass deep drawing effect according to an embodiment of the present invention is shown;

[0058] Figure 11 A block diagram of a device for optimizing the deep drawing process parameters of a new energy power battery housing according to an embodiment of the present invention is shown;

[0059] Figure 12 A schematic diagram of equipment for optimizing the deep drawing process parameters of a new energy power battery housing according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0061] According to an embodiment of the present invention, a method for optimizing the deep drawing process parameters of a new energy power battery shell is proposed. Through the response surface methodology and the particle swarm optimization algorithm, the shell deep drawing process parameters are systematically optimized, the stamping stability is improved, and the quality of the formed shell is improved. Combined with the real-time PID control algorithm, the dynamic adjustment of the shell deep drawing process parameters is further realized to ensure the stability and consistency of the processing process. The optimized values ​​are verified and analyzed in combination with the left and right biaxial load monitoring, which greatly reduces the number of tests and costs, improves the data quality and analysis depth, and effectively controls the shell deep drawing stability and forming thinning rate, significantly improving the processing quality and production efficiency.

[0062] The principles and spirit of the present invention are explained in detail below with reference to several representative embodiments of the present invention.

[0063] Figure 1 The figure is a flow chart of a method for optimizing the deep drawing process parameters of a new energy power battery housing according to an embodiment of the present invention. The method comprises:

[0064] Step S01: using several sets of drawing parameters as central composite design factors, designing an orthogonal experiment with the shell forming thinning rate corresponding to each set of drawing parameters as a response indicator, and performing several shell drawing tests under different process parameters;

[0065] Step S02: Analyzing the shell deep drawing test using the response surface methodology, and establishing a maximum forming thinning rate mathematical model and a minimum forming thinning rate mathematical model based on the test data;

[0066] Step S03: using the established mathematical model as the objective function, a particle swarm optimization algorithm is used to find the optimal solution for the process parameters, and a PID control algorithm is used to adjust the process parameters in real time;

[0067] Step S04: The optimal solution of process parameters is combined with left and right biaxial load monitoring to achieve data fitting of the left and right load force difference and calculate the stable output air pressure control value. The air pressure output controls the measurement and analysis of the left and right verticality errors, and realizes the deviation verification analysis of the multi-objective optimization values ​​of the process parameters.

[0068] It should be noted that although the operations of the method of the present invention are described in a specific order in the above embodiments and drawings, this does not require or imply that these operations must be performed in this specific order, or that all illustrated operations must be performed to achieve the desired results. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0069] In order to more clearly explain the method for optimizing the deep drawing process parameters of the new energy power battery housing, a specific embodiment is described below. However, it is worth noting that this embodiment is only for better illustrating the present invention and does not constitute an improper limitation to the present invention.

[0070] The following is a specific example to further illustrate the method of optimizing the deep drawing process parameters of new energy power battery housing:

[0071] Step S01: using several sets of drawing parameters as central composite design factors and the shell forming thinning rate corresponding to each set of drawing parameters as the response index to design an orthogonal experiment, and performing several shell drawing experiments under different process parameters.

[0072] In this embodiment, based on the Dynaform finite element simulation software and the various properties of the aluminum alloy in Table 1, a corresponding constitutive model was constructed and applied to a multi-pass deep drawing simulation test of a shell. Combined with the actual production test results, a comparative analysis of various values ​​such as the shell forming morphology, height, wall thickness, and forming thinning rate was performed.

[0073] Table 1

[0074]

[0075] Specifically, the process parameters include punching speed, blank holding force and die clearance.

[0076] Step S02: Analyze the shell deep drawing test using the response surface methodology, and establish a maximum forming thinning rate mathematical model and a minimum forming thinning rate mathematical model based on the test data.

[0077] Step S021: Use a three-coordinate measuring machine, a digital display vernier caliper and an electron microscope to measure the thickness of the shell wall after processing, and obtain the shell forming thinning rate,

[0078] In this embodiment, the thinning rates of each forming pass are 3.15%, 4.21%, 9.63%, 9.56%, 14.7% and 30.91% respectively. This proves that in the six-pass progressive forming process of the shell, the thinning rate increases step by step, and is most obvious in the last two sequences of squaring and finishing drawing.

[0079] Step S022: Using the punching speed, blank holding force and die clearance as parameter variables, and the maximum forming thinning rate and minimum forming thinning rate of the shell as response variables, the response surface method is used to calculate the expected values ​​of the maximum forming thinning rate and the minimum forming thinning rate of the shell corresponding to multiple sets of drawing parameters, that is, to establish a mathematical model of the maximum forming thinning rate of the shell and a mathematical model of the minimum forming thinning rate of the shell.

[0080] Step S03: Using the established mathematical model as the objective function, a particle swarm optimization algorithm is used to find the optimal solution for the process parameters, and a PID control algorithm is used to adjust the process parameters in real time.

[0081] Step S031: Use weighted summation to convert multiple objectives into a single objective. The single objective function formula is as follows:

[0082] minF(v,f,a p )=min(w1Ra+w2FR)

[0083] Where, w1 represents the weight coefficient of the mathematical model of the maximum forming thinning rate of the shell; w2 represents the weight coefficient of the mathematical model of the minimum forming thinning rate of the shell; Ra represents the mathematical model of the maximum forming thinning rate of the shell; FR represents the mathematical model of the minimum forming thinning rate of the shell; v, f, ap They represent punching speed, blank holding force and friction coefficient respectively.

[0084] In this embodiment, w1 is set to 0.416, w2 is set to 0.512, and FR represents the mathematical model of the minimum forming thinning rate of the shell.

[0085] Step S032: A particle population optimization algorithm is used to perform a global search within a given drawing parameter range to obtain the optimal solution for the process parameters that minimizes the single objective function. The shell is subjected to a deep drawing trial using the optimal process parameters. Real-time process parameters during the trial process are collected and adjusted using a PID control algorithm. The calculation method of the PID control algorithm is as follows:

[0086]

[0087] Where P(t) represents the punching speed during the tth trial processing, K p (P), K i (P), K d (P) represents the proportional coefficient, integral coefficient, and differential coefficient of punching speed, blank holder force, or die clearance, respectively. e(t-1) represents the total error during the t-1th trial processing. The calculation method of e(t) is:

[0088]

[0089] In the formula are the maximum forming thinning rate and target maximum thinning rate of the shell during the t-th trial processing, ω s 、ω min are the minimum forming thinning rate and target minimum thinning rate of the shell during the tth trial processing, respectively.

[0090] Furthermore, the specific steps of step S032 are:

[0091] Step S0321: Unify the dimensions of the mathematical model of the maximum forming thinning rate and the mathematical model of the minimum forming thinning rate of the shell according to the following formula:

[0092]

[0093] Where Ra (max) Indicates the maximum value of the mathematical model of the maximum forming thinning rate of the shell; Ra (min) Indicates the minimum value of the mathematical model of shell forming thinning rate; FR (max) Indicates the maximum value of the mathematical model of the minimum thinning rate of the shell; FR (min) It represents the minimum value of the mathematical model of the minimum forming thinning rate of the shell.

[0094] Step S0322: Substitute the unified mathematical model of the maximum forming thinning rate and the mathematical model of the minimum forming thinning rate into the single objective function formula to obtain the single objective function formula after dimension transformation:

[0095]

[0096] Step S0323: Calculate and generate a set of random solutions of the optimization model, and continuously iterate to search for the optimal solution of the population. In each iteration, the particle will track the optimal solution pBest found by itself and the optimal solution qbest found by the entire population, and update its position and velocity by comparing the fitness value of the particle at this time with its historical optimal solution;

[0097] The update formula of the historical optimal solution is:

[0098]

[0099] Where i represents the particle; j represents the current iteration number; f(.) represents the single objective function after dimension transformation;

[0100] The position of particles in the population and speed The update formula is:

[0101]

[0102] Where, represents the optimal solution found by the i-th particle itself; Represents the optimal solution of the current entire population optimization; r 1j 、r 2j represents a random number in the interval [0,1]; c1 represents the acceleration coefficient for updating the optimal solution of the particle itself; c2 represents the acceleration coefficient for updating the optimal solution of the population; t represents the t-dimensional search space; ω represents the inertia weight coefficient.

[0103] Step S04: The optimal solution of process parameters is combined with left and right biaxial load monitoring to achieve data fitting of the left and right load force difference and calculate the stable output air pressure control value. The air pressure output controls the measurement and analysis of the left and right verticality errors, and realizes the deviation verification analysis of the multi-objective optimization values ​​of the process parameters.

[0104] like Figure 2 As shown in the figure, it is a biaxial load detection diagram. As the stamping process proceeds step by step, the stamping process gradually tends to be stable, and the left and right differences of the biaxial load tend to converge.

[0105] like Figure 3As shown in FIG. 1 , the blank sheet of the embodiment is transported to the loading area of ​​the stamping machine by the suction cup device at the end of the industrial robot arm. The blank is transferred to the first drawing die under the push of the conveyor belt. The positioning is accurate. The double-axis stamping machine is loaded and started. The first punch falls. The specific stroke is 495. The blank is formed into the shell shape of the first pass during the punching process of the male and female dies. Figure 4 As shown, the first pass only has a forming effect and does not involve thinning; the preformed shell is transferred to the second pass drawing die under the push of the conveyor line, accurately positioned, and the punching machine is started. The preformed shell of the first pass is formed into an elliptical shell during the second pass punching process of the male and female dies, as shown in FIG. Figure 5 As shown in the figure, the gap between the dies is narrowed in the second pass, and a partial thinning effect is involved; the shell is pushed to the third drawing die via the conveyor line, and is punched by the third convex and concave dies to form a square shell, as shown in the figure. Figure 6 As shown in the figure, the gap between the dies is further narrowed in the third pass, and the thinning gradually increases; the shell is transferred to the fourth pass punch and die, and is stamped into a nearly rectangular shell, as shown in the figure. Figure 7 As shown in the figure, the thinning is further increased; the shell is transferred to the fifth punch and die, and is formed into a square shell during the drawing process, as shown in the figure. Figure 8 As shown in the figure, in this process, thinning replaces the forming function and occupies the main function; the shell is transferred to the sixth pass punch and die, and is formed into a precision drawn shell during the drawing process, as shown in the figure. Figure 9 As shown in the figure, the shell thinning rate is the largest during the forming process of this pass, which is the final forming sequence.

[0106] In this embodiment, taking 30194 as an example, it is a thin-walled rectangular shell with a length of 194.3 mm, a width of 30.2 mm, a height of 8.4 mm, a long side wall thickness of 0.48 mm, a short side wall thickness of 0.63 mm, and a height-to-width ratio (H / B) greater than 0.7, belonging to the field of high box-shaped parts, wherein the first forming process of the shell is as follows Figure 4 As shown, after forming, the wall thickness values ​​of the four points A, B, C, and D of the large and small surfaces are 1.047mm, 1.020mm, 0.985mm, and 1.037mm respectively. The preformed shell of the first pass is transferred to the second pass die via the conveyor line. Under the operation of the biaxial punching machine, the second deep drawing is carried out. After the second forming, the wall thickness values ​​of the four points A, B, C, and D of the large and small surfaces are 1.007mm, 1.014mm, 0.994mm, and 1.115mm respectively. The shell formed in the second pass is elliptical as shown in FIG. Figure 5 As shown, the second to fifth forming steps are as follows Figure 6-9 As shown, after the sixth pass of fine deep drawing, the wall thickness values ​​of the shell at points A, B, C, and D are 0.485mm, 0.499mm, 0.480mm, and 0.578mm respectively, which meet the actual processing size error requirements and have excellent overall quality.

[0107] Figure 10 A schematic diagram of the full-pass deep drawing effect of this embodiment is shown. Based on the self-developed stamping stability control system of the App designer module on the MATLAB platform, the multi-pass forming stamping process parameters are optimized, and the anisotropy of material flow is effectively suppressed. The multi-pass stamping forming numerical simulation test is carried out by Dynaform, revealing the specific factors affecting the wall thickness of the 30194 shell forming. After production verification, the surface quality of the formed shell is highly consistent with the simulation prediction, with no scratches and high uniformity as a whole. The wall thickness variation coefficient is reduced by 3.7%, and the forming quality stability is improved by 20.5%, providing theoretical support and practical guidance for the stamping forming process of thin-walled rectangular shells.

[0108] Based on the same inventive concept, the present invention also proposes a device for optimizing the deep drawing process parameters of a new energy power battery housing. The implementation of this device can refer to the implementation of the above method, and the repeated parts will not be repeated.

[0109] like Figure 11 As shown, the device 100 includes:

[0110] Orthogonal test module 101: used to design an orthogonal test using several sets of drawing parameters as central composite design factors and the shell forming thinning rate corresponding to each set of drawing parameters as a response indicator, and to conduct several shell drawing tests under different process parameters;

[0111] Model building module 102: used to analyze the shell deep drawing test using response surface methodology, and to establish a maximum forming thinning rate mathematical model and a minimum forming thinning rate mathematical model based on the test data;

[0112] Model solving module 103: used to use the established mathematical model as the objective function and adopt the particle swarm optimization algorithm to find the optimal solution of the process parameters;

[0113] Numerical verification module 104: used to combine the optimal solution of process parameters with left and right dual-axis load monitoring, realize data fitting of the left and right load force difference and calculate the stable output air pressure control value, and control the measurement and analysis of the left and right verticality errors through the air pressure output to realize the deviation verification analysis of the multi-objective optimization value of the process parameters.

[0114] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0115] like Figure 12As shown, the device includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The CPU, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0116] Many components in a device are connected to the I / O interface, including: input units, such as a keyboard and mouse; output units, such as various types of displays and speakers; storage units, such as magnetic disks and optical disks; and communication units, such as network cards, modems, and wireless communication transceivers. The communication unit allows the device to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks.

[0117] The processing unit performs the various methods and processes described above, such as method steps S01 to S04. For example, in some embodiments, method steps S01 to S04 can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more of the method steps S01 to S04 described above can be executed. Alternatively, in other embodiments, the CPU can be configured to execute method steps S01 to S04 in any other appropriate manner (for example, by means of firmware).

[0118] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), and the like.

[0119] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0120] In the context of the present invention, machine-readable medium can be a tangible medium that can contain or store a program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0121] In addition, although adopting specific order to describe each operation, this should be understood as requiring such operation to be carried out in the specific order shown or in sequential order, or requiring all illustrated operations to be carried out to obtain desired result.Under certain environment, multitasking and parallel processing may be advantageous.Similarly, although comprising some specific implementation details in the above discussion, these should not be construed as limiting the scope of the present invention.Some features described in the context of independent embodiment can also be realized in single realization in combination.On the contrary, the various features described in the context of independent realization also can be realized in multiple realizations individually or in the mode of any suitable subcombination.

[0122] Although the subject matter has been described in language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims.

Claims

1. A method for optimizing the deep drawing process parameters of a new energy power battery housing, characterized in that: The method includes: Step S01: using several sets of drawing parameters as central composite design factors, designing an orthogonal experiment with the shell forming thinning rate corresponding to each set of drawing parameters as a response indicator, and performing several shell drawing tests under different process parameters; Step S02: Analyzing the shell deep drawing test using the response surface methodology, and establishing a maximum forming thinning rate mathematical model and a minimum forming thinning rate mathematical model based on the test data; Step S03: using the established mathematical model as the objective function, a particle swarm optimization algorithm is used to find the optimal solution for the process parameters, and a PID control algorithm is used to adjust the process parameters in real time; Step S04: The optimal solution of process parameters is combined with left and right biaxial load monitoring to achieve data fitting of the left and right load force difference and calculate the stable output air pressure control value. The air pressure output controls the measurement and analysis of the left and right verticality errors, and realizes the deviation verification analysis of the multi-objective optimization values ​​of the process parameters.

2. The method for optimizing the deep drawing process parameters of a new energy power battery housing according to claim 1, characterized in that: The process parameters described in step S01 include: punching speed, blank holding force and die gap.

3. The method for optimizing the deep drawing process parameters of a new energy power battery housing according to claim 1, characterized in that: The specific steps of step S02 are: Step S021: using a three-coordinate measuring machine, a digital display vernier caliper, and an electron microscope to measure the thickness of the shell wall after processing, and obtain the shell forming thinning rate corresponding to multiple sets of drawing parameters; Step S022: Using the punching speed, blank holding force and die clearance as parameter variables, and the maximum forming thinning rate and minimum forming thinning rate of the shell as response variables, the response surface method is used to calculate the expected values ​​of the maximum forming thinning rate and the minimum forming thinning rate of the shell corresponding to multiple sets of drawing parameters, that is, to establish a mathematical model of the maximum forming thinning rate of the shell and a mathematical model of the minimum forming thinning rate of the shell.

4. The method for optimizing the deep drawing process parameters of a new energy power battery housing according to claim 1, characterized in that: The specific steps of step S03 are: Step S031: Use weighted summation to convert multiple objectives into a single objective. The single objective function formula is as follows: minF(v,f,a p )=min(w1Ra+w2FR) Where, w1 represents the weight coefficient of the mathematical model of the maximum forming thinning rate of the shell; w2 represents the weight coefficient of the mathematical model of the minimum forming thinning rate of the shell; Ra represents the mathematical model of the maximum forming thinning rate of the shell; FR represents the mathematical model of the minimum forming thinning rate of the shell; v, f, a p represent punching speed, blank holding force and friction factor respectively; Step S032: using a particle swarm optimization algorithm to perform a global search within a given drawing parameter range to obtain an optimal solution for the process parameters that minimizes the single objective function.

5. The method for optimizing the deep drawing process parameters of a new energy power battery housing according to claim 4, characterized in that: The specific steps of step S032 are: Step S0321: Unify the dimensions of the mathematical model of the maximum forming thinning rate and the mathematical model of the minimum forming thinning rate of the shell according to the following formula: Where Ra (max) Indicates the maximum value of the mathematical model of the maximum forming thinning rate of the shell; Ra (min) Indicates the minimum value of the mathematical model of shell forming thinning rate; FR (max) Indicates the maximum value of the mathematical model of the minimum thinning rate of the shell; FR (min) It represents the minimum value of the mathematical model of the minimum forming thinning rate of the shell; Step S0322: Substitute the unified mathematical model of the maximum forming thinning rate and the mathematical model of the minimum forming thinning rate into the single objective function formula to obtain the single objective function formula after dimension transformation: Step S0323: Calculate and generate a set of random solutions of the optimization model, and continuously iterate to search for the optimal solution of the population. In each iteration, the particle will track the optimal solution pBest found by itself and the optimal solution qBest found by the entire population, and update its own position and velocity by comparing the fitness value of the particle at this time with its historical optimal solution; The update formula of the historical optimal solution is: Where i represents the particle; j represents the current iteration number; f(.) represents the single objective function after dimension transformation; The position of particles in the population and speed The update formula is: Where, represents the optimal solution found by the i-th particle itself; Represents the optimal solution of the current entire population optimization; r 1j 、r 2j represents a random number in the interval [0,1]; c1 represents the acceleration coefficient for updating the optimal solution of the particle itself; c2 represents the acceleration coefficient for updating the optimal solution of the population; t represents the t-dimensional search space; ω represents the inertia weight coefficient.

6. The method for optimizing the deep drawing process parameters of a new energy power battery housing according to claim 4, characterized in that: After obtaining the optimal solution of the process parameters that minimize the single objective function in step S032, a shell deep drawing trial is performed with the optimal process parameters. The real-time process parameters during the trial processing are collected and adjusted using a PID control algorithm. The calculation method of the PID control algorithm is: Where P(t) represents the punching speed during the tth trial processing, K p (P), K i (P), K d (P) represents the proportional coefficient, integral coefficient, and differential coefficient of punching speed, blank holder force, or die clearance, respectively. e(t-1) represents the total error during the t-1th trial processing. The calculation method of e(t) is: In the formula are the maximum forming thinning rate and target maximum thinning rate of the shell during the t-th trial processing, ω s 、ω min are the minimum forming thinning rate and target minimum thinning rate of the shell during the tth trial processing, respectively.

7. A device for optimizing the deep drawing process parameters of a new energy power battery housing, characterized in that: The device implements the method according to any one of claims 1 to 6, comprising: Orthogonal test module: used to design orthogonal experiments with several sets of drawing parameters as central composite design factors and the shell forming thinning rate corresponding to each set of drawing parameters as the response index, and to conduct several shell drawing tests under different process parameters; Model building module: used to analyze the shell deep drawing test using the response surface methodology and establish the maximum forming thinning rate mathematical model and the minimum forming thinning rate mathematical model based on the test data; Model solving module: used to use the established mathematical model as the objective function and adopt the particle swarm optimization algorithm to find the optimal solution for process parameters; Numerical Verification Module: This module is used to combine the optimal solution of process parameters with left and right biaxial load monitoring, to achieve data fitting of the left and right load force difference and calculate the stable output air pressure control value. The air pressure output controls the measurement and analysis of the left and right verticality errors, thus realizing the deviation verification analysis of the multi-objective optimization values ​​of process parameters.

8. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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