Methods for optimizing process parameters of deep drawing forming of new energy power battery casing

Abstract

This invention relates to the field of metal sheet stamping technology, and provides a method for optimizing the process parameters of deep drawing forming of new energy power battery casings. The method includes: conducting orthogonal experimental design based on several sets of parameters, and performing several deep drawing forming experiments under different process parameters; analyzing the deep drawing forming experiments using response surface methodology, and establishing mathematical models for maximum and minimum forming thinning rates based on the experimental data; using the established mathematical models as objective functions, employing particle swarm optimization to find the optimal solution for the process parameters, and using a PID control algorithm to adjust the process parameters in real time; combining the optimal solution for the process parameters with left and right dual-axis load monitoring to verify and analyze the deviation of the multi-objective optimization values ​​of the process parameters. This invention achieves dynamic adjustment of the deep drawing forming process parameters for casings, ensuring the stability and consistency of the processing, and improving the quality of the formed casing.

Description

Technical Field

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

[0002] Due to the overall internal space layout and weight reduction requirements of new energy vehicle battery packs, the external protective shell requires high wall thickness. It is generally manufactured as a deep-cavity, thin-walled rectangular box, formed from 3003-H14 aluminum alloy through multiple processes including stamping, deep drawing, thinning, and edge trimming. This process is characterized by thin material, numerous processes, and high precision, making its forming process difficult to control and prone to quality defects such as wrinkling, cracking, and uneven wall thickness. Numerical simulation analysis of the battery shell stamping process shows that the shell forming quality is significantly affected by various stamping process parameters, such as stamping speed, blank holder force, friction coefficient, and die clearance.

[0003] Existing stamping processes analyze the factors affecting forming quality, verify them through numerical simulation, and optimize stamping process parameters based on the analysis and verification results. However, since the factors affecting stamping process parameters are diverse and there are correlations between various stamping process parameters, the relationship between process parameters and forming quality is not a clear linear mapping. Existing simulation verification methods cannot establish an optimization mechanism for stamping process parameters under the combined effect of multiple parameters. They often can only perform specific optimization for a single process parameter, ignoring the interaction effect of parameters, resulting in local optima for parameter combinations. In actual production, batch quality instability is easily caused by dynamic factors such as material property fluctuations and mold wear. Summary of the Invention

[0004] To address the above issues, this invention utilizes response surface methodology and particle swarm optimization to systematically optimize the deep drawing process parameters for shells, thereby improving stamping stability and the quality of the formed shells. Combined with a real-time PID control algorithm, it further enables dynamic adjustment of the deep drawing process parameters, ensuring the stability and consistency of the processing. Furthermore, by integrating dual-axis load monitoring for left and right axes, the optimized numerical values ​​are validated and analyzed, significantly reducing the number of experiments and costs, improving data quality and analysis depth, and effectively controlling the stability and thinning rate of the deep drawing process, thus significantly improving processing quality and production efficiency.

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

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

[0007] Step S01: Using several sets of deep drawing parameters as the central composite design factor, and using the shell forming thinning rate corresponding to each set of deep drawing parameters as the response index, design an orthogonal experiment, and conduct several shell deep drawing forming experiments under different process parameters.

[0008] Step S02: The response surface methodology is used to analyze the shell deep drawing test, and mathematical models for the maximum forming thinning rate and the minimum forming thinning rate are established based on the test data;

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

[0010] Step S04: Combining the optimal solution of process parameters with left and right dual-axis load monitoring, the data of the difference between left and right load forces is fitted and the stable output air pressure control value is calculated. Through the measurement and analysis of the left and right verticality error controlled by air pressure output, the deviation verification analysis of the multi-objective optimization value of process parameters is realized.

[0011] Furthermore, the process parameters mentioned in step S01 include: stamping speed, blank holder force, and die clearance.

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

[0013] Step S021: Use a coordinate measuring machine, digital vernier caliper and electron microscope to measure the wall thickness of the processed shell and obtain the shell forming thinning rate corresponding to multiple sets of deep drawing parameters;

[0014] Step S022: Using stamping speed, blank holder force, and die clearance as parameter variables, and the maximum and minimum forming thinning rates of the shell as response variables, the expected values ​​of the maximum and minimum forming thinning rates of the shell corresponding to multiple sets of deep drawing parameters are calculated using the response surface method, that is, the mathematical models of the maximum and minimum forming thinning rates of the shell are constructed.

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

[0016] Step S031: Use weighted summation to convert the multi-objective function into a single objective. The formula for the single objective function is as follows:

[0017]

[0018] In the formula, The weighting coefficients represent the mathematical model of the maximum forming thinning rate of the shell; The weighting coefficients represent the mathematical model for the minimum forming thinning rate of the shell. A mathematical model representing the maximum forming thinning rate of the shell; A mathematical model representing the minimum forming thinning rate of the shell; These represent stamping speed, blank holder force, and coefficient of friction, respectively.

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

[0020] Furthermore, the specific steps of step S032 are as follows:

[0021] Step S0321: The dimensions of the mathematical models for the maximum and minimum forming thinning rates of the shell are standardized according to the following formula:

[0022]

[0023]

[0024] In the formula, This represents the maximum value of the mathematical model for the maximum forming thinning rate of the shell; This represents the minimum value of the mathematical model for the shell forming thinning rate; This represents the maximum value of the mathematical model for the minimum forming thinning rate of the shell; This represents the minimum value of the mathematical model for the minimum forming thinning rate of the shell;

[0025] Step S0322: Substitute the unified mathematical models of the maximum and minimum forming thinning rates of the shell into the single-objective function formula to obtain the dimensionally transformed single-objective function formula as follows:

[0026]

[0027]

[0028] Step S0323: Calculate a set of random solutions to the optimization model, and iterate hierarchically to search for the optimal solution of the population. In each iteration, the particle will track the optimal solution it has found. And the optimal solution found by searching the entire population. The particle updates its position and velocity by comparing its current fitness value with its historical best solution.

[0029] The update formula for the historical optimal solution is:

[0030]

[0031] In the formula, Represents particles; Indicates the current iteration number; This represents a single-objective function after dimensional transformation.

[0032] Position of particles in the population and speed The update formula is:

[0033]

[0034] In the formula, Indicates the first The optimal solution that each particle finds on its own; This represents the optimal solution found in the entire population at present; Represents a random number in the interval [0,1]. This represents the acceleration factor for updating the particle's own optimal solution; This represents the speedup factor for updating the population's optimal solution; express 3D search space; This represents the inertia weighting coefficient.

[0035] Furthermore, after obtaining the optimal solution of process parameters that minimizes the single objective function in step S032, a trial deep drawing of the shell is performed using the optimal process parameters. Real-time process parameters are collected during the trial processing, and a PID control algorithm is used to adjust the real-time process parameters. The calculation method of the PID control algorithm is as follows:

[0036]

[0037] In the formula Indicates the first Stamping speed during the first trial processing , , These represent the proportional coefficient, integral coefficient, and differential coefficient with respect to stamping speed, blank holder force, or die clearance, respectively. Indicates the first Total error during the first trial processing The calculation method is as follows:

[0038]

[0039] In the formula , The first The maximum forming thinning rate and the target maximum thinning rate of the shell during the first trial processing. , The first Minimum forming thinning rate and target minimum thinning rate of the shell during the first trial processing.

[0040] In a second aspect of the invention, an apparatus for optimizing the process parameters of deep drawing forming of new energy power battery casings is provided. The apparatus includes:

[0041] Orthogonal test module: Used to design orthogonal tests with several sets of deep drawing parameters as the central composite design factor and the shell forming thinning rate corresponding to each set of deep drawing parameters as the response index, and to conduct several shell deep drawing forming tests under different process parameters;

[0042] Model building module: used to analyze shell deep drawing tests using the response surface methodology, and to establish mathematical models for the maximum forming thinning rate and the minimum forming thinning rate based on the test data;

[0043] Model Solving Module: Used to solve for the optimal process parameters using the established mathematical model as the objective function and the particle swarm optimization algorithm.

[0044] Numerical verification module: It is used to combine the optimal solution of process parameters with left and right dual-axis load monitoring to realize the data fitting of the difference between left and right load forces and calculate the stable output air pressure control value. Through the measurement and analysis of the left and right verticality error controlled by air pressure output, the deviation verification analysis of the multi-objective optimization values ​​of process parameters is realized.

[0045] In a third aspect of the invention, an electronic device is provided. The electronic device includes a memory and a processor, the memory storing a computer program, the processor executing the program to implement the method according to a first aspect of the invention.

[0046] In a fourth aspect of the invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method according to a first aspect of the invention.

[0047] This invention utilizes response surface methodology and particle swarm optimization to systematically optimize the deep drawing process parameters of shells, thereby improving stamping stability and the quality of the formed shells. Combined with a real-time PID control algorithm, it further enables dynamic adjustment of the deep drawing process parameters, ensuring the stability and consistency of the processing. Furthermore, by incorporating dual-axis load monitoring for left and right axis verification and analysis, the invention significantly reduces the number of experiments and costs, improves data quality and analysis depth, and effectively controls the stability and thinning rate of the deep drawing process, thus significantly improving processing quality and production efficiency.

[0048] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of the present invention, nor is it intended to restrict the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0049] The above and other features, advantages, and aspects of the various embodiments of the present invention will become more apparent from the accompanying drawings and the following detailed description. Wherein:

[0050] Figure 1 A flowchart illustrating a method for optimizing process parameters in the deep drawing forming of new energy power battery casings according to an embodiment of the present invention is shown.

[0051] Figure 2 A biaxial load detection diagram according to an embodiment of the present invention is shown;

[0052] Figure 3 A drawing of a blank sheet material according to an embodiment of the present invention is shown;

[0053] Figure 4 A first-order forming diagram according to an embodiment of the present invention is shown;

[0054] Figure 5 A second-order forming diagram according to an embodiment of the present invention is shown;

[0055] Figure 6 A third-order forming diagram according to an embodiment of the present invention is shown;

[0056] Figure 7 A fourth-order forming diagram according to an embodiment of the present invention is shown;

[0057] Figure 8 A fifth-order forming diagram according to an embodiment of the present invention is shown;

[0058] Figure 9 A sixth-order forming diagram according to an embodiment of the present invention is shown;

[0059] Figure 10 A schematic diagram of the full-pass drawing effect according to an embodiment of the present invention is shown;

[0060] Figure 11 A block diagram of an apparatus for optimizing the process parameters of deep drawing forming of new energy power battery casings according to an embodiment of the present invention is shown.

[0061] Figure 12 A schematic diagram of an apparatus for optimizing the deep drawing process parameters of a new energy power battery casing according to an embodiment of the present invention is shown. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] According to an embodiment of the present invention, a method for optimizing the process parameters of deep drawing forming of new energy power battery casings is proposed. By using response surface methodology and particle swarm optimization algorithm, the deep drawing forming process parameters of the casing are systematically optimized to improve stamping stability and the quality of the formed casing. Combined with a real-time PID control algorithm, the dynamic adjustment of the deep drawing forming process parameters of the casing is further realized to ensure the stability and consistency of the processing. Furthermore, the optimized values ​​are verified and analyzed by combining left and right dual-axis load monitoring, which significantly reduces the number of experiments and costs, improves data quality and analysis depth, and effectively controls the stability and thinning rate of deep drawing forming of the casing, thereby significantly improving processing quality and production efficiency.

[0064] The principles and spirit of the present invention will be explained in detail below with reference to several representative embodiments.

[0065] Figure 1 This is a schematic flowchart illustrating a method for optimizing the process parameters of deep drawing forming of a new energy power battery casing according to an embodiment of the present invention. The method includes:

[0066] Step S01: Using several sets of deep drawing parameters as the central composite design factor, and using the shell forming thinning rate corresponding to each set of deep drawing parameters as the response index, design an orthogonal experiment, and conduct several shell deep drawing forming experiments under different process parameters.

[0067] Step S02: The response surface methodology is used to analyze the shell deep drawing test, and mathematical models for the maximum forming thinning rate and the minimum forming thinning rate are established based on the test data;

[0068] Step S03: Using the established mathematical model as the objective function, the particle swarm optimization algorithm is used to find the optimal solution of the process parameters, and the PID control algorithm is used to adjust the process parameters in real time.

[0069] Step S04: Combining the optimal solution of process parameters with left and right dual-axis load monitoring, the data of the difference between left and right load forces is fitted and the stable output air pressure control value is calculated. Through the measurement and analysis of the left and right verticality error controlled by air pressure output, the deviation verification analysis of the multi-objective optimization value of process parameters is realized.

[0070] It should be noted that although the operation of the method of the present invention has been described in a specific order in the above embodiments and figures, this does not require or imply that the operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. 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.

[0071] To provide a clearer explanation of the method for optimizing the process parameters of the deep drawing forming process for new energy power battery casings, 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 of the present invention.

[0072] The following specific example will further illustrate the method for optimizing the process parameters of deep drawing forming of new energy power battery casings:

[0073] Step S01: Using several sets of deep drawing parameters as the central composite design factor, and using the shell forming thinning rate corresponding to each set of deep drawing parameters as the response index, design an orthogonal experiment, and conduct several shell deep drawing forming experiments under different process parameters.

[0074] In this embodiment, based on the Dynaform finite element simulation software and the various properties of aluminum alloy in Table 1, a corresponding constitutive model is constructed and incorporated into the multi-pass deep drawing simulation test of the shell. The results of actual production tests are then combined to conduct comparative analysis of various values ​​such as shell forming morphology, height, wall thickness, and forming thinning rate.

[0075] Table 1

[0076]

[0077] Specifically, the process parameters include stamping speed, blank holder force, and die clearance.

[0078] Step S02: The response surface methodology is used to analyze the shell deep drawing test, and mathematical models for the maximum forming thinning rate and the minimum forming thinning rate are established based on the test data.

[0079] Step S021: Measure the wall thickness of the processed shell using a coordinate measuring machine, digital vernier caliper, and electron microscope to obtain the shell forming thinning rate corresponding to multiple sets of deep drawing parameters.

[0080] 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 confirms that in the six-pass progressive forming process of the shell, the thinning rate increases step by step, with the most significant increase in the final two steps of squaring and fine drawing.

[0081] Step S022: Using stamping speed, blank holder force, and die clearance as parameter variables, and the maximum and minimum forming thinning rates of the shell as response variables, the expected values ​​of the maximum and minimum forming thinning rates of the shell corresponding to multiple sets of deep drawing parameters are calculated using the response surface method, that is, the mathematical models of the maximum and minimum forming thinning rates of the shell are constructed.

[0082] Step S03: Using the established mathematical model as the objective function, the particle swarm optimization algorithm is used to find the optimal solution of the process parameters, and the PID control algorithm is used to adjust the process parameters in real time.

[0083] Step S031: Use weighted summation to convert the multi-objective function into a single objective. The formula for the single objective function is as follows:

[0084]

[0085] In the formula, The weighting coefficients represent the mathematical model of the maximum forming thinning rate of the shell; The weighting coefficients represent the mathematical model for the minimum forming thinning rate of the shell. A mathematical model representing the maximum forming thinning rate of the shell; A mathematical model representing the minimum forming thinning rate of the shell; These represent stamping speed, blank holder force, and friction coefficient, respectively.

[0086] In this embodiment, Take 0.416, Take 0.512, A mathematical model representing the minimum forming thinning rate of the shell.

[0087] Step S032: A particle swarm optimization algorithm is used to perform a global search within the given deep drawing parameter range to obtain the optimal solution of the process parameters that minimizes the single objective function. A trial deep drawing of the shell is then performed using the optimal process parameters. Real-time process parameters are collected during the trial process, and a PID control algorithm is used to adjust the real-time process parameters. The calculation method of the PID control algorithm is as follows:

[0088]

[0089] In the formula Indicates the first Stamping speed during the first trial processing , , These represent the proportional coefficient, integral coefficient, and differential coefficient with respect to stamping speed, blank holder force, or die clearance, respectively. Indicates the first Total error during the first trial processing The calculation method is as follows:

[0090]

[0091] In the formula , The first The maximum forming thinning rate and the target maximum thinning rate of the shell during the first trial processing. , The first Minimum forming thinning rate and target minimum thinning rate of the shell during the first trial processing.

[0092] Furthermore, the specific steps of step S032 are as follows:

[0093] Step S0321: The dimensions of the mathematical models for the maximum and minimum forming thinning rates of the shell are standardized according to the following formula:

[0094]

[0095]

[0096] In the formula, This represents the maximum value of the mathematical model for the maximum forming thinning rate of the shell; This represents the minimum value of the mathematical model for the shell forming thinning rate; This represents the maximum value of the mathematical model for the minimum forming thinning rate of the shell; This represents the minimum value of the mathematical model for the minimum forming thinning rate of the shell.

[0097] Step S0322: Substitute the unified mathematical models of the maximum and minimum forming thinning rates of the shell into the single-objective function formula to obtain the dimensionally transformed single-objective function formula as follows:

[0098]

[0099]

[0100] Step S0323: Calculate a set of random solutions to the optimization model, and iterate hierarchically to search for the optimal solution of the population. In each iteration, the particle will track the optimal solution it has found. And the optimal solution found by searching the entire population. The particle updates its position and velocity by comparing its current fitness value with its historical best solution.

[0101] The update formula for the historical optimal solution is:

[0102]

[0103] In the formula, Represents particles; Indicates the current iteration number; This represents a single-objective function after dimensional transformation.

[0104] Position of particles in the population and speed The update formula is:

[0105]

[0106] In the formula, Indicates the first The optimal solution that each particle finds on its own; This represents the optimal solution found in the entire population at present; Represents a random number in the interval [0,1]. This represents the acceleration factor for updating the particle's own optimal solution; This represents the speedup factor for updating the population's optimal solution; express 3D search space; This represents the inertia weighting coefficient.

[0107] Step S04: Combining the optimal solution of process parameters with left and right dual-axis load monitoring, the data of the difference between left and right load forces is fitted and the stable output air pressure control value is calculated. Through the measurement and analysis of the left and right verticality error controlled by air pressure output, the deviation verification analysis of the multi-objective optimization value of process parameters is realized.

[0108] like Figure 2 As shown, the biaxial load detection diagram shows that as the stamping process progresses step by step, the stamping process gradually stabilizes, and the difference between the left and right sides of the biaxial load tends to converge.

[0109] like Figure 3 As shown, this is a diagram of the blank sheet material for the entire process in this embodiment. The sheet material is conveyed to the loading area of ​​the stamping machine by a suction cup device at the end of the industrial robotic arm. Under the pushing action of the conveyor belt, the sheet material is transferred to the first-pass drawing die. With accurate positioning, the dual-axis stamping machine starts under load, and the first-pass punch descends, with a specific stroke of 495°. During the punching process, the sheet material is formed into the shell shape of the first pass, as shown in the diagram. Figure 4 As shown, the first pass only performs forming and does not involve thinning; the preformed shell is transferred to the second-pass drawing die by the conveyor line, where it is accurately positioned. The press starts, and the preformed shell from the first pass is formed into an elliptical shell during the second-pass punching process, specifically as follows... Figure 5 As shown, the die gap narrows in the second pass, incorporating a thinning effect; the shell is pushed by the conveyor line to the third deep drawing die, where it is stamped by the third punch and die to form a roughly square shell, as shown. Figure 6 As shown, in the third pass, the die gap narrows further, and the thinning gradually increases; the shell is transferred to the fourth pass's punch and die, where it is stamped into a near-rectangular shell, as shown. Figure 7 As shown, the thinning further increases the size; the shell is transferred to the fifth die and formed into a square shell during the deep drawing process, as shown. Figure 8As shown, in this process, thinning replaces forming and plays a major role; the shell is transferred to the sixth die and formed into a precision-drawn shell during the deep drawing process, as shown in the figure. Figure 9 As shown, this forming pass has the largest shell thinning rate and is the final forming sequence.

[0110] 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. It belongs to the field of tall box-shaped parts, wherein the first-pass forming shell is as follows: Figure 4 As shown, after forming, the wall thicknesses at points A, B, C, and D on the large and small faces are 1.047mm, 1.020mm, 0.985mm, and 1.037mm, respectively. The first-pass preformed shell is transferred to the second-pass mold via a conveyor line, where it undergoes a second deep drawing under the operation of a twin-shaft press. After the second forming, the wall thicknesses at points A, B, C, and D on the large and small faces are 1.007mm, 1.014mm, 0.994mm, and 1.115mm, respectively. The shell formed in the second pass is a near-elliptical shape. Figure 5 As shown, the second to fifth passes are formed as follows: Figure 6-9 As shown, the wall thicknesses at points A, B, C, and D in the sixth pass of the precision deep drawing process of the shell are 0.485mm, 0.499mm, 0.480mm, and 0.578mm, respectively, which meet the actual processing dimensional error requirements and demonstrate excellent overall quality.

[0111] Figure 10 This diagram illustrates the full-pass deep drawing effect of this embodiment. A stamping stability control system, developed based on the App designer module of the MATLAB platform, was constructed. The multi-pass forming stamping process parameters were optimized, effectively suppressing material flow anisotropy. Numerical simulation experiments of multi-pass stamping forming were conducted using Dynaform to reveal the specific factors affecting the wall thickness of the 30194 shell. Production verification showed that the surface quality of the formed shell highly matched the simulation predictions, exhibiting scratch-free and highly uniform forming. The wall thickness variation coefficient decreased by 3.7%, and the forming quality stability improved by 20.5%, providing theoretical support and practical guidance for the stamping forming process of thin-walled rectangular shells.

[0112] Based on the same inventive concept, this invention also proposes a device for optimizing the process parameters of deep drawing forming of new energy power battery casings. The implementation of this device can be found in the implementation of the method described above; repeated details will not be repeated. Figure 11 As shown, the device 100 includes:

[0113] Orthogonal test module 101: used to design orthogonal tests with several sets of deep drawing parameters as the central composite design factor and the shell forming thinning rate corresponding to each set of deep drawing parameters as the response index, and to conduct several shell deep drawing forming tests under different process parameters;

[0114] Model building module 102: used to analyze the shell deep drawing test using the response surface methodology, and to establish mathematical models for the maximum forming thinning rate and the minimum forming thinning rate based on the test data;

[0115] Model Solving Module 103: Used to solve the optimal solution of process parameters using the established mathematical model as the objective function and the particle swarm optimization algorithm;

[0116] Numerical verification module 104: It is used to combine the optimal solution of process parameters with the monitoring of load on both left and right axes to achieve data fitting of the difference between the left and right load forces and calculate the stable output air pressure control value. Through the measurement and analysis of the verticality error of the left and right axes controlled by air pressure output, the deviation verification analysis of the multi-objective optimization values ​​of process parameters is realized.

[0117] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0118] like Figure 12 As shown, the device includes a central processing unit (CPU), which can perform various appropriate actions and processes based on computer program instructions stored in read-only memory (ROM) or loaded from storage units into random access memory (RAM). The RAM can also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0119] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0120] The processing unit executes the various methods and processes described above, such as method steps S01 to S04. For example, in some embodiments, method steps S01 to S04 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of method steps S01 to S04 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute method steps S01 to S04 by any other suitable means (e.g., by means of firmware).

[0121] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0122] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0123] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0124] Furthermore, although the operations are described in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the invention. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0125] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for optimizing process parameters of deep drawing forming of new energy power battery casings, characterized in that, The method includes: Step S01: Using several sets of deep drawing parameters as the central composite design factor, and using the shell forming thinning rate corresponding to each set of deep drawing parameters as the response index, design an orthogonal experiment, and conduct several shell deep drawing forming experiments under different process parameters. Step S02: The response surface methodology is used to analyze the shell deep drawing test, and mathematical models for the maximum forming thinning rate and the minimum forming thinning rate are established based on the test data; Step S03: Using the established mathematical model as the objective function, the particle swarm optimization algorithm is used to find the optimal solution of the process parameters, and the PID control algorithm is used to adjust the process parameters in real time. Step S04: Combining the optimal solution of process parameters with left and right dual-axis load monitoring, the data of the difference between left and right load forces is fitted and the stable output air pressure control value is calculated. Through the measurement and analysis of the left and right verticality error controlled by air pressure output, the deviation verification analysis of the multi-objective optimization value of process parameters is realized.

2. The method for optimizing the process parameters of deep drawing forming of new energy power battery casing according to claim 1, characterized in that, The process parameters mentioned in step S01 include: stamping speed, blank holder force, and die clearance.

3. The method for optimizing the process parameters of deep drawing forming of new energy power battery casing according to claim 1, characterized in that, The specific steps of step S02 are as follows: Step S021: Use a coordinate measuring machine, digital vernier caliper and electron microscope to measure the wall thickness of the processed shell and obtain the shell forming thinning rate corresponding to multiple sets of deep drawing parameters; Step S022: Using stamping speed, blank holder force, and die clearance as parameter variables, and the maximum and minimum forming thinning rates of the shell as response variables, the expected values ​​of the maximum and minimum forming thinning rates of the shell corresponding to multiple sets of deep drawing parameters are calculated using the response surface method, that is, the mathematical models of the maximum and minimum forming thinning rates of the shell are constructed.

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

5. The method for optimizing the process parameters of deep drawing forming of new energy power battery casing according to claim 4, characterized in that, The specific steps of step S032 are as follows: Step S0321: The dimensions of the mathematical models for the maximum and minimum forming thinning rates of the shell are standardized according to the following formula: In the formula, Ra (max) Ra represents the maximum value of the mathematical model for the maximum forming thinning rate of the shell; (min) This represents the minimum value of the mathematical model for shell forming thinning rate; FR (max) This represents the maximum value of the mathematical model for the minimum forming thinning rate of the shell; FR (min) This represents the minimum value of the mathematical model for the minimum forming thinning rate of the shell; Step S0322: Substitute the unified mathematical models of the maximum and minimum forming thinning rates of the shell into the single-objective function formula to obtain the dimensionally transformed single-objective function formula as follows: Step S0323: Calculate a set of random solutions to the optimization model, and continuously iterate hierarchically to search for the optimal solution of the population. During each iteration, the particle will track the optimal solution pBest it has found and the optimal solution qBest found by the entire population. By comparing the fitness value of the particle at this time with its historical optimal solution, it updates its position and velocity. The update formula for the historical optimal solution is: In the formula, i represents a particle; j represents the current iteration number; and f(.) represents the single objective function after dimension transformation. Position of particles in the population and speed The update formula is: In the formula, This represents the optimal solution found by the i-th particle on its own; This represents the optimal solution found in the entire population at present; r 1j r 2j c1 represents a random number in the interval [0,1]; c2 represents the acceleration coefficient for updating the particle's own optimal solution; c3 represents the acceleration coefficient for updating the population's optimal solution; t represents the t-dimensional search space; ω represents the inertia weight coefficient.

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

7. A device for optimizing the process parameters of deep drawing forming of new energy power battery casings, characterized in that, The device implements the method as described in any one of claims 1 to 6, comprising: Orthogonal test module: Used to design orthogonal tests with several sets of deep drawing parameters as the central composite design factor and the shell forming thinning rate corresponding to each set of deep drawing parameters as the response index, and to conduct several shell deep drawing forming tests under different process parameters; Model building module: used to analyze shell deep drawing tests using response surface methodology, and to build mathematical models for maximum forming thinning rate and minimum forming thinning rate based on experimental data; Model Solving Module: Used to solve for the optimal process parameters using the established mathematical model as the objective function and the particle swarm optimization algorithm. Numerical verification module: It is used to combine the optimal solution of process parameters with left and right dual-axis load monitoring to realize the data fitting of the difference between left and right load forces and calculate the stable output air pressure control value. Through the measurement and analysis of the left and right verticality error controlled by air pressure output, the deviation verification analysis of the multi-objective optimization values ​​of process parameters is realized.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 6.

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

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