A method for optimizing differential temperature stamping forming process of magnesium alloy 3C product cover

Abstract

The application discloses a kind of magnesium alloy 3C product covering piece difference temperature stamping forming process optimization method, comprising: using finite element numerical analysis model to the numerical simulation of difference temperature stamping forming of covering piece;Calibration is carried out to finite element numerical analysis model;The sample range and single-factor optimal value of each stamping forming process parameter are calculated;Key parameters are used as variables, non-key parameters are used as quantitative, determine optimization target and carry out finite element simulation orthogonal test, obtain multiple sets of test result samples;Using multiple sets of test result samples constructs quadratic polynomial response surface model, and obtains non-inferior solution set and optimal process parameter group using multi-objective particle swarm optimization algorithm.The application effectively solves the multi-parameter optimization problem of magnesium alloy sheet under complex temperature change condition, obtains the optimal process parameter group of magnesium alloy 3C product covering piece precision stamping forming, and helps to formulate and optimize multi-parameter difference temperature stamping process.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of metal plate stamping forming, and particularly relates to a magnesium alloy 3C product cover piece differential temperature stamping forming process optimization method. BACKGROUND

[0002] In recent years, lightweight has become a research hotspot and frontier in the field of 3C electronic product manufacturing. Magnesium alloy is the lightest metal structural material at present, has good heat dissipation, shock absorption, electromagnetic shielding and easy recycling, and is praised as "21st century green engineering material" and "best 3C product cover piece material selection". However, due to the close-packed hexagonal crystal structure of magnesium alloy, it is difficult to carry out stamping, forging and other large plastic deformation processing, and when magnesium alloy sheet is stamped and formed, cracking, wrinkling and instability and other problems are prone to occur, and the requirements for the process parameters of the stamping die are extremely harsh.

[0003] At present, for the precise stamping forming of magnesium alloy 3C product cover pieces, enterprises generally adopt a process method combining physical stamping trial and error method with finite element numerical simulation, and through repeated trial and error, correction, and gradual exploration of feasible magnesium alloy 3C product cover piece stamping forming process. But this method needs to process multiple sets of physical stamping dies, and relies on human experience to repeatedly try and error, repeatedly modify the mold and repeatedly verify, resulting in long test cycle, high cost and low efficiency. And the above process method can only get a feasible stamping process, but cannot get the optimal stamping process, so it cannot meet the production needs of enterprises for magnesium alloy 3C product cover pieces. Especially for differential temperature stamping forming under complex temperature change conditions, due to many influencing factors, mutual restriction between factors, and more stringent test conditions, there is currently no method that can effectively guide the development and optimization of magnesium alloy 3C product cover piece differential temperature stamping process. SUMMARY

[0004] The purpose of the present application is to provide a magnesium alloy 3C product cover piece differential temperature stamping forming process optimization method, which can solve at least one technical problem in the prior art.

[0005] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows:

[0006] In the first aspect, the present application provides a magnesium alloy 3C product cover piece differential temperature stamping forming process optimization method, comprising:

[0007] generating a finite element numerical analysis model according to the three-dimensional model of the magnesium alloy 3C product cover piece, and using the finite element numerical analysis model to numerically simulate the differential temperature stamping forming of the cover piece;

[0008] collecting entity stamping data in a differential temperature stamping forming test of the covering, and repeatedly calibrating the finite element numerical analysis model by using the entity stamping data until the finite element numerical analysis model meets the closed-loop analysis requirement, and stopping the calibration;

[0009] analyzing a plurality of stamping forming process parameters of the covering by using the calibrated finite element numerical analysis model, and calculating sample ranges and single-factor optimal values of each stamping forming process parameter;

[0010] extracting key parameters and non-key parameters from the plurality of stamping forming process parameters, taking the key parameters as to-be-solved variables, taking the non-key parameters as constants, determining an optimization target, and performing finite element simulation orthogonal test to obtain a plurality of test result samples, wherein the key parameters at least include a magnesium sheet temperature T d and a punch temperature T p ;

[0011] constructing a quadratic polynomial response surface model with cracking and wrinkling as objective functions by using the plurality of test result samples, and calculating the quadratic polynomial response surface model by using a multi-objective particle swarm optimization algorithm to obtain a non-inferior solution set and an optimal process parameter group.

[0012] In a possible design, before generating the finite element numerical analysis model according to the three-dimensional model of the magnesium alloy 3C product covering, the method further includes:

[0013] performing uniaxial mechanical tensile test on the magnesium alloy sheet at different deformation temperatures and different strain rates, measuring stress-strain curves of the magnesium alloy sheet, and obtaining a plurality of mechanical performance parameters based on the stress-strain curves;

[0014] establishing a material database for finite element analysis of the magnesium alloy sheet by using the plurality of mechanical performance parameters.

[0015] In a possible design, collecting entity stamping data in a differential temperature stamping forming test of the covering, and repeatedly calibrating the finite element numerical analysis model by using the entity stamping data until the finite element numerical analysis model meets the closed-loop analysis requirement, and stopping the calibration, includes:

[0016] performing a differential temperature stamping forming test by using a differential temperature stamping forming entity die matched with the three-dimensional model, and collecting entity stamping data obtained in the test;

[0017] taking maximum thinning rate Y1 and maximum thickening rate Y2 as target calibration points, and repeatedly calibrating the finite element numerical analysis model by using the entity stamping data;

[0018] When the matching degree of the target calibration point in the entity stamping data and the target calibration point in the simulation result exceeds a threshold value, it is determined that the finite element numerical analysis model meets the closed-loop analysis requirement, and calibration is stopped.

[0019] In a possible design, a plurality of stamping forming process parameters of the cover are analyzed by using the calibrated finite element numerical analysis model, and a sample range and a single-factor optimal value of each stamping forming process parameter are calculated, including:

[0020] Taking any stamping forming process parameter as a variable parameter and taking the remaining parameters as quantitative parameters, the maximum stamping forming depth H of the cover before rupture corresponding to each variable parameter is calculated by using the calibrated finite element numerical analysis model;

[0021] The sample range and the single-factor optimal value of each variable parameter are determined according to the value of the maximum stamping forming depth H.

[0022] In a possible design, the stamping forming process parameters at least further include a punch round corner radius R p , a die straight wall round corner radius R c , a punch-die gap Z, a blank holder force F, a stamping speed v, an equivalent drawbead resistance f and a friction coefficient μ.

[0023] In a possible design, key parameters and non-key parameters are extracted from the plurality of stamping forming process parameters, the key parameters are taken as to-be-solved variables, the non-key parameters are taken as quantifications, and an optimization target is determined to perform finite element simulation orthogonal test to obtain a plurality of test result samples, including:

[0024] A first key parameter is extracted from the plurality of stamping forming process parameters, and the remaining process parameters are taken as first non-key parameters, wherein the first key parameter at least includes a stamping speed v, a magnesium plate temperature T d , a punch temperature T p and a blank holder force F;

[0025] The first key parameters are taken as to-be-solved variables, the first non-key parameters are taken as quantifications, the maximum thinning rate Y1 and the maximum thickening rate Y2 are taken as optimization targets, finite element simulation orthogonal test is performed, and a plurality of test result samples are obtained.

[0026] In a possible design, before the first key parameter is extracted from the plurality of stamping forming process parameters and the remaining process parameters are taken as the first non-key parameters, the method further includes:

[0027] A second key parameter is extracted from the plurality of stamping forming process parameters, and the remaining process parameters are taken as second non-key parameters, wherein the second key parameter at least includes a punch round corner radius R p , a magnesium plate temperature Td , punch temperature T p and blank holder force F;

[0028] The second key parameter is taken as a variable to be solved, the second non-key parameter is taken as a quantitative value, the maximum forming depth H is taken as an optimization target, a finite element simulation orthogonal test is performed, and a group of parameters corresponding to the maximum value of the optimization target of the test result is taken as a first optimization parameter group;

[0029] The optimal values of the geometric parameters are subjected to a springback compensation cyclic iteration until the stamping springback meets the springback limit requirement of the 3D product cover, the optimized geometric parameters are fused with the first optimization parameter group to generate a second optimization parameter group, and the geometric parameters at least include punch fillet radius R p , die straight wall fillet radius R c and punch-die gap Z;

[0030] The geometric shape of the finite element numerical analysis model is corrected by using the second optimization parameter group.

[0031] In a possible design, a quadratic polynomial response surface model with cracking and wrinkling as objective functions is constructed by using multiple sets of test result samples, including:

[0032] A plurality of sets of test result samples are taken as training samples, a sample value range is set, and an index of a safe distance of cracking and wrinkling is taken as a weight to construct a cracking objective function R obj and a wrinkling objective function W obj , and the function expressions are as follows:

[0033]

[0034] wherein, represents a principal strain, represents a secondary strain, represents a cracking safety curve, n represents the number of units, and i represents the i th unit in the n units;

[0035]

[0036] wherein, η () represents a wrinkling safety curve;

[0037] According to the formula (1), the formula (2) and the sample value range, the quadratic polynomial response surface model is constructed.

[0038] In a possible design, the quadratic polynomial response surface model is calculated by using a multi-objective particle swarm optimization algorithm to obtain a non-inferior solution set and an optimal process parameter group, including:

[0039] The multi-objective particle swarm optimization algorithm is used to calculate the quadratic polynomial response surface model, and a Pareto non-inferior solution set is obtained.

[0040] The minimum distance selection method is used to select an optimal solution particle P from the Pareto non-inferior solution set, which is closest to a defect-free ideal particle P0 m The optimal solution particle P m is a particle in which the cracking target function R obj and the wrinkling target function W obj reach the minimum values at the same time.

[0041] The optimal solution particle P m corresponding to the optimal process parameter set is selected as the optimal process parameter set.

[0042] In a possible design, the finite element numerical analysis model is established by means of the interlaced analysis of the DYNAFORM software and the DEFORM software.

[0043] In a second aspect, the present application provides a magnesium alloy 3C product cover difference temperature stamping forming process optimization device, comprising:

[0044] A numerical simulation module is configured to generate a finite element numerical analysis model according to a three-dimensional model of a magnesium alloy 3C product cover, and to perform numerical simulation on the difference temperature stamping forming of the cover by using the finite element numerical analysis model.

[0045] A model calibration module is configured to collect entity stamping data during the difference temperature stamping forming test of the cover, and to repeatedly calibrate the finite element numerical analysis model by using the entity stamping data until the finite element numerical analysis model meets the closed-loop analysis requirement, and then stop the calibration.

[0046] A parameter value determination module is configured to analyze a plurality of stamping forming process parameters of the cover by using the calibrated finite element numerical analysis model, and to calculate the sample range and the single-factor optimal value of each stamping forming process parameter.

[0047] A sample acquisition module is configured to extract key parameters and non-key parameters from the plurality of stamping forming process parameters, to take the key parameters as to-be-solved variables, to take the non-key parameters as constants, to determine an optimization target, and to perform finite element simulation orthogonal test to obtain a plurality of test result samples, wherein the key parameters at least include a magnesium sheet temperature T d and a punch temperature T p .

[0048] An optimal parameter obtaining module is configured to construct a quadratic polynomial response surface model with cracking and wrinkling as target functions by using multiple sets of test result samples, and calculate the quadratic polynomial response surface model by using a multi-objective particle swarm optimization algorithm to obtain a non-inferior solution set and an optimal process parameter set.

[0049] In a third aspect, the present application provides a computer device, comprising a memory, a processor and a transceiver connected in sequence and in communication, wherein the memory is configured to store a computer program, the transceiver is configured to transceive messages, and the processor is configured to read the computer program and execute the magnesium alloy 3C product cover piece differential temperature stamping forming process optimization method according to any one of the possible designs in the first aspect.

[0050] In a fourth aspect, the present application provides a computer readable storage medium having instructions stored thereon, wherein when the instructions are executed on a computer, the magnesium alloy 3C product cover piece differential temperature stamping forming process optimization method according to any one of the possible designs in the first aspect is executed.

[0051] In a fifth aspect, the present application provides a computer program product comprising instructions, wherein when the instructions are executed on a computer, the computer is caused to execute the magnesium alloy 3C product cover piece differential temperature stamping forming process optimization method according to any one of the possible designs in the first aspect.

[0052] Beneficial effects:

[0053] 1. The present application combines finite element simulation orthogonal test and multi-objective particle swarm optimization algorithm, introduces magnesium alloy sheet temperature T d and punch temperature T p as orthogonal test variable parameters and multi-objective particle swarm optimization operator into finite element numerical analysis and optimization calculation; on the basis of finite element simulation orthogonal test, a quadratic polynomial response surface model with cracking and wrinkling as target functions is constructed, and multiple sets of result samples obtained by orthogonal test are used as calculation samples of multi-objective particle swarm optimization to calculate a non-inferior solution set, and the optimal process parameter set of magnesium alloy 3C product cover piece differential temperature stamping forming under complex temperature change condition is obtained according to the non-inferior solution set. The multi-parameter optimization problem of magnesium alloy sheet under complex temperature change condition is effectively solved, and the optimal process parameter set of magnesium alloy 3C product cover piece precision stamping forming is obtained, which is helpful for the formulation and optimization of multi-parameter differential temperature stamping process.

[0054] 2. The present application determines multiple influence parameters affecting the differential temperature stamping forming performance of the cover piece, extracts key parameters from the multiple influence parameters, and performs finite element simulation orthogonal test analysis by taking the key parameters as variables, so as to reduce the calculation amount under the premise of ensuring the accuracy of test results.

[0055] 3.The method combines the DYNAFORM software of the dynamic explicit algorithm and the DEFORM software of the static implicit algorithm, obtains an optimal finite element numerical analysis model through staggered analysis, verification feedback and calibration correction and the like, thereby overcoming the defects of the DYNAFORM software in the simulation calculation under the condition of temperature difference and the defects of the DEFORM software in the simulation calculation of sheet stamping, expanding the application scenarios of the finite element numerical simulation calculation in the nonlinear and large deformation stamping forming, and especially in the high-temperature magnesium alloy sheet stamping forming under complex temperature change conditions, when the matching degree of the target calibration point of the finite element calculation simulation result and the physical stamping forming result exceeds a threshold value, the finite element numerical analysis model is calibrated to be optimal. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 A flowchart of the magnesium alloy 3C product cover piece temperature difference stamping forming process optimization method in the embodiment;

[0057] Figure 2 A solving schematic diagram of the optimal solution particle P m in the embodiment. DETAILED DESCRIPTION

[0058] To make the objectives, technical solutions and advantages of the embodiments of the present specification clearer, the technical solutions in the embodiments of the present specification will be described clearly and completely below with reference to the drawings in the embodiments of the present specification. Obviously, the described embodiments are part of the embodiments of the present specification, rather than all the embodiments. Based on the embodiments in the present specification, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0059] EMBODIMENT

[0060] The magnesium alloy sheet has a close-packed hexagonal crystal structure, and the plastic deformation performance is poor at room temperature, is significantly improved at high temperature, and is further improved under complex temperature change conditions than under constant high temperature. However, the existing multi-factor stamping process technology is only limited to the multi-parameter optimization of the magnesium alloy sheet under constant high temperature, and there is no effective solution to the process parameter optimization of the multi-parameter temperature difference stamping forming under complex temperature change conditions. In order to solve the above problems, the embodiment provides a magnesium alloy 3C product cover piece temperature difference stamping forming process optimization method, which can effectively solve the multi-parameter optimization problem of the magnesium alloy sheet under complex temperature change conditions, obtain the optimal process parameter group of the magnesium alloy 3C product cover piece precision stamping forming, and help to formulate and optimize the multi-parameter temperature difference stamping process. The embodiment will be described in detail below.

[0061] As Figure 1 and Figure 2As shown in the first aspect, the embodiment provides a differential temperature stamping forming process optimization method for a magnesium alloy 3C product cover, which is realized by steps S1-S5 and specifically as follows:

[0062] Step S1. A finite element numerical analysis model is generated according to a three-dimensional model of the magnesium alloy 3C product cover, and the differential temperature stamping forming of the cover is numerically simulated by using the finite element numerical analysis model.

[0063] In step S1, the 3C product includes but is not limited to a mobile phone, a notebook computer, a tablet computer, a digital camera, etc., which are not limited here; the three-dimensional model of the magnesium alloy 3C product cover is constructed based on existing three-dimensional modeling software, such as UG, CAD, solidworks, etc., which are not limited here; when the three-dimensional model is imported into DYNAFORM and DEFORM software, the file format of the model needs to be converted into IGES format and STL format respectively, DEFORM software is used to generate a DEFORM 1:1 finite element analysis model, finite element pre-processing, FEM solver calculation, finite element post-processing, DYNAFORM software is used to generate a DYNAFORM 1:1 finite element analysis model, set process parameters, LS-DYNA solver calculation and output simulation calculation results, so as to perform staggered analysis based on DEFORM software and DYNAFORM software, and numerically simulate the differential temperature stamping forming of the cover.

[0064] Preferably, before step S1, the method further includes:

[0065] Step a. Uniaxial mechanical tensile test is performed on the magnesium alloy sheet at different deformation temperatures and different strain rates, the stress-strain curve of the magnesium alloy sheet is measured, and a plurality of mechanical property parameters are obtained based on the stress-strain curve.

[0066] The mechanical property parameters include but are not limited to tensile strength σ b , yield strength σ s , maximum elongation A, plastic strain ratio r, reduction of area ψ, deformation hardening index n, strain rate sensitivity index m and elastic modulus E.

[0067] Step b. A material database for finite element analysis of the magnesium alloy sheet is established by using the plurality of mechanical property parameters.

[0068] Specifically, the plurality of measured mechanical property parameters are input into DYNAFORM and DEFORM finite element simulation calculation software, and a material database for finite element analysis of the magnesium alloy sheet is established by using the software function, which can be used for subsequent numerical simulation calculation of the finite element numerical analysis model.

[0069] Step S2. Collecting entity stamping data in the differential temperature stamping forming test of the cover, and repeatedly calibrating the finite element numerical analysis model by using the entity stamping data until the finite element numerical analysis model meets the closed-loop analysis requirement, and stopping the calibration;

[0070] Before step S2, a differential temperature stamping forming entity mold matched with the three-dimensional model 1:1 needs to be customized by a mold manufacturing company, and then a physical test is carried out by using the differential temperature stamping forming entity mold.

[0071] Specifically, step S2 includes the following steps:

[0072] Step S21. Carrying out a differential temperature stamping forming test by using a differential temperature stamping forming entity mold matched with the three-dimensional model, and collecting entity stamping data obtained in the test; wherein the entity stamping data at least includes a maximum thinning rate Y1 and a maximum thickening rate Y2.

[0073] Step S22. Taking the maximum thinning rate Y1 and the maximum thickening rate Y2 as target calibration points, and repeatedly calibrating the finite element numerical analysis model by using the entity stamping data.

[0074] Specifically, according to the set target calibration points, the target calibration points in the entity stamping data are compared with the target calibration points in the simulation results by using DYNAFORM and DEFORM finite element software, so as to realize the repeated interlaced analysis, verification feedback and calibration correction of the finite element numerical analysis model.

[0075] Step S23. When the matching degree of the target calibration points in the entity stamping data and the target calibration points in the simulation results exceeds a threshold value, it is determined that the finite element numerical analysis model meets the closed-loop analysis requirement, and the calibration is stopped.

[0076] Specifically, when the matching degree of the maximum thinning rate Y1 in the entity stamping data and the maximum thinning rate Y1 in the simulation results, and the matching degree of the maximum thickening rate Y2 in the entity stamping data and the maximum thickening rate Y2 in the simulation results all exceed the threshold value, preferably the threshold value is 97.5%, then the finite element numerical analysis model calibration is completed; of course, it can be understood that the matching degree threshold value is not limited to the above value, but also can be 97%, 98%, 98.5%, etc., which is not limited here.

[0077] Step S3. Analyzing a plurality of stamping forming process parameters of the cover by using the calibrated finite element numerical analysis model, and calculating a sample range and a single factor optimal value of each stamping forming process parameter;

[0078] In step S3, the plurality of stamping forming process parameters at least includes a punch round corner radius R p , a die straight wall round corner radius R cDie-punch clearance Z, magnesium plate temperature T d Punch temperature T p Nine single factors were considered, including blank holder force F, stamping speed v, equivalent drawbead resistance f, and friction coefficient μ. While it's understandable that many factors influence the stamping forming of body panels, designing too many optimization parameters or excessively high constraint dimensions not only requires a large number of experimental samples but also reduces the accuracy of the subsequently established response surface, interfering with the optimization process. Therefore, this embodiment preferably uses nine single factors in subsequent finite element simulation orthogonal experiments and algorithm calculations to ensure the accuracy of the results.

[0079] Specifically, step S3 includes:

[0080] Step S31. Take any stamping process parameter as a variable parameter and the remaining parameters as quantitative parameters, and use the calibrated finite element numerical analysis model to calculate the maximum stamping depth H of the cover before the failure of each variable parameter.

[0081] Preferably, one of the stamping process parameters (e.g., the punch fillet radius R) is used. p ) as variable parameters, and use the remaining parameters (e.g., the radius of the die straight wall fillet R) as variable parameters. c Die-punch clearance Z, magnesium plate temperature T d Punch temperature T p Using blank holder force F, stamping speed v, equivalent drawbead resistance f and friction coefficient μ as quantitative parameters, the maximum stamping depth H corresponding to each single factor is simulated and calculated using a calibrated finite element numerical analysis model.

[0082] Step S32. Determine the sample range and single-factor optimal value for each variable parameter based on the value of the maximum stamping depth H.

[0083] Specifically, the larger the value of the maximum forming depth H, the better the current single factor value is, thus determining the sample range and optimal value of each stamping forming process parameter, as shown in Table 1:

[0084] Table 1. Calculation Results of Single Factors in Stamping Forming

[0085] No. Influencing factor Optimum value of single factor Sample selection range 1 Punch corner radius R p ]]> 10 mm 1-20 mm 2 Die straight wall fillet radius R c ]]> 4 mm 1-10 mm 3 Clearance between punch and die Z 1.1t 1.0～1.5t 4 Magnesium sheet temperature T d ]]> 270℃ 150～300℃ 5 Core temperature T p ]]> 65℃ 20～150℃ 6 Blank holder force F 9800N 4900～29400N 7 Punching speed v 0.7 mm / s 0.1-5.0 mm / s 8 Equivalent resistance of drawbead f 230 N / mm 10-2000 N / mm 9 Friction coefficient μ 0.12 0.12～1.0

[0086] Step S4. Extract key and non-key parameters from multiple stamping process parameters. The key parameters are those that significantly affect the stamping performance of magnesium alloy sheets. Use these key parameters as variables to be solved, and the non-key parameters as quantifiers. Determine the optimization objective and conduct finite element simulation orthogonal experiments to obtain multiple sets of experimental results. The key parameters must include at least the magnesium plate temperature T, which has the greatest impact on the stamping performance of magnesium alloy sheets. dand die temperature T p ;

[0087] wherein, the step S4 specifically comprises:

[0088] Step S41. extracting a first key parameter from the plurality of stamping forming process parameters, and taking the remaining process parameters as first non-key parameters, wherein the first key parameter at least includes stamping speed v, magnesium plate temperature T d , punch temperature T p and blank holder force F;

[0089] For example: when 9 factor process parameters are selected, the first non-key parameters include punch fillet radius R p , concave die straight wall fillet radius R c , punch and die gap Z, equivalent draw bead resistance f and friction coefficient μ.

[0090] Before step S41, the method further comprises:

[0091] Step S4a. extracting a second key parameter from the plurality of stamping forming process parameters, and taking the remaining process parameters as second non-key parameters, wherein the second key parameter at least includes punch fillet radius R p , magnesium plate temperature T d , punch temperature T p and blank holder force F;

[0092] Preferably, the punch fillet radius R p , magnesium plate temperature T d , punch temperature T p and blank holder force F are taken as the second key parameters, and the stamping speed v, concave die straight wall fillet radius R c , punch and die gap Z, equivalent draw bead resistance f and friction coefficient μ are taken as the second non-key parameters.

[0093] Step S4b. taking the second key parameters as variables to be solved, taking the second non-key parameters as constants, taking the maximum forming depth H as the optimization target, performing finite element simulation orthogonal test, and taking the group of parameters corresponding to the maximum value of the optimization target of the test results as a group of primary optimization parameters;

[0094] For example, after taking the second key parameters as variables to be solved, the value range of the variables is determined, such as the value range of the variables in Table 1 (punch fillet radius R p = 1-20mm, magnesium plate temperature T d = 150-300℃, punch temperature T p= 20-150℃ and blank holder force F = 4900-29400 N); after quantifying and simplifying the first non-key factor, the optimal values in Table 1 are selected (punch straight wall fillet radius R c = 4 mm, punch-die gap Z = 1.1t, t is the thickness of the plate, stamping speed v = 0.7 mm / s, equivalent drawbead resistance f = 230 N / mm, friction coefficient μ = 0.12), taking the maximum forming depth H as the optimization goal, using finite element numerical analysis to conduct a 4-factor 4-level orthogonal test, and selecting a group of process parameters with the maximum stamping forming depth H from the calculation results (such as multi-factor combination: punch fillet radius Rp = 8 mm; magnesium plate temperature T d = 250℃; punch temperature T p = 50℃; blank holder force F = 19600 N) is the optimal one-time optimization parameter group for the difference temperature stamping forming effect.

[0095] Step S4c. The optimal values of the geometric parameters are iterated in a springback compensation cycle until the stamping springback amount meets the springback limit requirements of the 3D product cover, the optimized geometric parameters are fused with the one-time optimization parameter group, and a secondary optimization parameter group is generated, wherein the geometric parameters at least include punch fillet radius R p , punch straight wall fillet radius R c , and punch-die gap Z.

[0096] Specifically, the optimal values of the geometric parameters (such as punch straight wall fillet radius R c = 4 mm, punch fillet radius R p = 8 mm, and punch-die gap Z = 1.1t) are iterated in a springback compensation cycle, the springback defect amount is offset by a springback shape size compensation method, until the stamping springback amount meets the specific springback limit requirements of the 3C product cover (such as springback angle ΔT≤1-3°), the optimized geometric parameters are used to replace the geometric parameters in the one-time optimization parameter group, thereby generating a secondary optimization parameter group.

[0097] Step S4d. The geometric shape of the finite element numerical analysis model is modified by using the secondary optimization parameter group.

[0098] Step S42. The first key parameter is taken as a to-be-solved variable, the first non-key parameter is taken as a quantification, the maximum thinning rate Y1 and the maximum thickening rate Y2 are taken as optimization goals, a finite element simulation orthogonal test is conducted, and a plurality of groups of test result samples are obtained.

[0099] For example, after taking the first key parameter as a variable to be solved, the value range of the variable is determined, such as the value range of the variable in Table 1 (punching speed v = 0.1-5.0 mm / s, magnesium plate temperature T d = 150-300℃, punch temperature T p = 20-150℃, and blank holder force F = 4900-29400 N); after quantifying the first non-key factor and simplifying, the optimal value of the quantification in Table 1 is selected (concave mold straight wall fillet radius R c = 4 mm, concave-convex mold gap Z = 1.1t, t is the thickness of the plate, convex mold fillet radius R p = 10 mm, equivalent drawing rib resistance f = 230 N / mm, and friction coefficient μ = 0.12); taking the maximum thinning rate Y1 and the maximum thickening rate Y2 as the optimization objectives, a plurality of test result samples can be obtained by using a 4-factor 4-level orthogonal test of finite element simulation of stamping forming, as shown in Table 2:

[0100] Table 2: Orthogonal test simulation result sample

[0101] Test v / mm / s T d / ℃]]> ​ T p / ℃]]> ​ F / N [Y1 / %] [Y2 / %] 1 0.1 150 20 4900 30.01 8.83 2 0.1 200 50 9800 28.60 8.05 3 0.1 250 100 19600 27.08 7.50 4 0.1 300 150 29400 25.30 6.84 5 0.5 150 20 19600 26.75 7.37 6 0.5 200 50 29400 25.04 6.69 7 0.5 250 150 4900 24.57 8.12 8 0.5 300 100 9800 23.78 7.15 9 2 150 100 29400 25.03 6.92 10 2 200 150 19600 23.77 7.03 11 2 250 20 9800 21.57 7.01 12 2 300 50 4900 21.87 10.69 13 5 150 150 9800 24.30 6.88 14 5 200 100 4900 20.61 10.03 15 5 250 50 19600 18.52 6.32 16 5 300 20 29400 19.94 6.67

[0102] Step S5. Constructing a quadratic polynomial response surface model with cracking and wrinkling as objective functions by using a plurality of test result samples, and calculating the quadratic polynomial response surface model by using a multi-objective particle swarm optimization algorithm to obtain a non-inferior solution set and an optimal process parameter group.

[0103] In step S5, a quadratic polynomial response surface model with cracking and wrinkling as objective functions is constructed by using a plurality of test result samples, including:

[0104] Taking the plurality of test result samples as training samples, setting the sample value range, and taking the exponent of the safety distance of cracking and wrinkling as the weight, a cracking objective function R obj and a wrinkling objective function W obj are constructed, and the function expressions are as follows:

[0105]

[0106] wherein, represents the principal strain, represents the secondary strain, represents the cracking safety curve, n represents the number of units, and i represents the i th unit in the n units;

[0107]

[0108] wherein, η() represents the wrinkling safety curve;

[0109] According to the formula (1), the formula (2) and the sample value range, a quadratic polynomial response surface model is constructed.

[0110] It should be noted that, since the plurality of sets of test result samples obtained in S42 are with the stamping speed v, the magnesium plate temperature T d , the punch temperature T p and the blank holder force F as variables, the above sample value range is only required to set the value range of the variables, and the optimal value is quantitatively obtained, wherein the value range of each variable is as follows:

[0111] min(R obj ,W obj )

[0112] s.t.0.1≤v≤5;

[0113] 150≤T d ≤300;

[0114] 20≤T p ≤150;

[0115] 9800≤F≤29400;

[0116] Then, based on the formula (1), the formula (2) and the sample value range, the expression of the quadratic polynomial response surface model is as follows:

[0117]

[0118]

[0119] In step S5, the quadratic polynomial response surface model is calculated by using a multi-objective particle swarm optimization algorithm to obtain a non-inferior solution set and an optimal process parameter group, including:

[0120] The quadratic polynomial response surface model is calculated by using a multi-objective particle swarm optimization algorithm to obtain a Pareto non-inferior solution set, including:

[0121] 1) Initialize the particle swarm POP: for i = 0 to MAX / / MAX is the population number, randomly generate the position POP[i] of each particle;

[0122] 2) Initialize the speed of each particle: VEL[i] = 0;

[0123] 3) Calculate the target vector corresponding to each particle, and evaluate each particle in POP;

[0124] 4) Store the non-inferior solution in the particle swarm in the archive REP;

[0125] 5) increment the loop above to the maximum number of iterations M, end the current process.

[0126] The optimal solution particle P is selected from the Pareto non-inferior solution set by using the minimum distance solution method, which is closest to the defect-free ideal particle P0 m , wherein the optimal solution particle P m is the particle whose cracking target function R obj value and wrinkle target function W obj value reach the lowest at the same time.

[0127] The optimal solution particle P m corresponding to the group of process parameters is taken as the optimal process parameter.

[0128] For example, based on the above, the optimal process parameter group ultimately obtained by the embodiment includes a punch speed v = 4.10 mm / s, a magnesium plate temperature T d = 276.33 DEG C, a punch temperature T p = 44.69 DEG C, a blank holder force F = 20604.14 N, a punch fillet radius R p = 10 mm, a die straight wall fillet radius R c = 4 mm, a punch-die gap Z = 1.1t, an equivalent drawbead resistance f = 230 N / mm, and a friction coefficient mu = 0.12.

[0129] Based on the above disclosure, the beneficial effects of the embodiment are:

[0130] 1. By combining finite element simulation orthogonal test and multi-objective particle swarm optimization algorithm, the magnesium alloy sheet temperature T d and the punch temperature T p are taken as orthogonal test variable parameters and multi-objective particle swarm optimization operators and introduced into finite element numerical analysis and optimization calculation; on the basis of finite element simulation orthogonal test, a quadratic polynomial response surface model with cracking and wrinkling as target functions is constructed, and the multiple result samples obtained by orthogonal test are taken as calculation samples of multi-objective particle swarm optimization, and a non-inferior solution set is calculated, and the optimal process parameter group of magnesium alloy 3C product cover part differential temperature stamping forming under complex temperature change condition is obtained according to the non-cracking set. The multi-parameter optimization problem of magnesium alloy sheet under complex temperature change condition is effectively solved, and the optimal process parameter group of magnesium alloy 3C product cover part precision stamping forming is obtained, which is helpful for the formulation and optimization of multi-parameter differential temperature stamping process.

[0131] 2. The present application determines multiple process parameters affecting the performance of cover part differential temperature stamping forming, extracts key parameters from the multiple process parameters, and performs finite element simulation orthogonal test analysis by taking the key parameters as variables, thereby reducing the calculation amount under the premise of ensuring the accuracy of test results.

[0132] 3. The application combines the DYNAFORM software of the dynamic explicit algorithm and the DEFORM software of the static implicit algorithm, and through methods such as staggered analysis, verification feedback and calibration correction, an optimal finite element numerical analysis model is obtained, thereby overcoming the defects of the DYNAFORM software in the simulation calculation of temperature difference and the defects of the DEFORM software in the simulation calculation of thin plate stamping, expanding the application scenarios of finite element numerical simulation calculation in nonlinear and large deformation stamping forming, especially in high-temperature magnesium alloy thin plate stamping forming under complex temperature change conditions, the embodiment can effectively reduce the trial and error times of solid die repeated stamping, reduce material and die loss, avoid a large amount of repetitive labor and repetitive test consumption, reduce the dependence on human experience in die testing, and further reduce product development cost.

[0133] In a second aspect, the application provides a magnesium alloy 3C product cover difference temperature stamping forming process optimization device, comprising:

[0134] A numerical simulation module is configured to generate a finite element numerical analysis model according to a three-dimensional model of a magnesium alloy 3C product cover, and to perform numerical simulation on the difference temperature stamping forming of the cover by using the finite element numerical analysis model.

[0135] A model calibration module is configured to collect solid stamping data during the difference temperature stamping forming test of the cover, and to repeatedly calibrate the finite element numerical analysis model by using the solid stamping data until the finite element numerical analysis model meets the closed-loop analysis requirements, and then stop calibration.

[0136] A parameter value determination module is configured to analyze a plurality of stamping forming process parameters of the cover by using the calibrated finite element numerical analysis model, and to calculate a sample range and a single-factor optimal value of each stamping forming process parameter.

[0137] A sample acquisition module is configured to extract key parameters and non-key parameters from the plurality of stamping forming process parameters, take the key parameters as variables, take the non-key parameters as constants, determine an optimization target, and perform finite element simulation orthogonal test to obtain a plurality of test result samples, wherein the key parameters at least include a magnesium plate temperature T d and a die temperature T p ;

[0138] An optimal parameter acquisition module is configured to construct a quadratic polynomial response surface model with cracking and wrinkling as objective functions by using the plurality of test result samples, and to calculate the quadratic polynomial response surface model by using a multi-objective particle swarm optimization algorithm to obtain a non-inferior solution set and an optimal process parameter group.

[0139] In a third aspect, the present application provides a computer device, comprising a memory, a processor and a transceiver connected in sequence and in communication, wherein the memory is configured to store a computer program, the transceiver is configured to transceive messages, and the processor is configured to read the computer program and execute the magnesium alloy 3C product cover differential temperature stamping forming process optimization method according to any possible design in the first aspect.

[0140] In a fourth aspect, the present application provides a computer readable storage medium, wherein instructions are stored on the computer readable storage medium, and when the instructions are executed on a computer, the magnesium alloy 3C product cover differential temperature stamping forming process optimization method according to any possible design in the first aspect is executed.

[0141] In a fifth aspect, the present application provides a computer program product comprising instructions, and when the instructions are executed on a computer, the computer is caused to execute the magnesium alloy 3C product cover differential temperature stamping forming process optimization method according to any possible design in the first aspect.

[0142] Finally, it should be noted that: the above only describes the preferred embodiments of the present application and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. An optimized method for differential temperature stamping forming of magnesium alloy 3C product cover parts, characterized in that, include: A finite element numerical analysis model is generated based on the three-dimensional model of the magnesium alloy 3C product cover, and the differential temperature stamping forming of the cover is numerically simulated using the finite element numerical analysis model. Collect solid stamping data during the differential temperature stamping forming test of the cover part, and use the solid stamping data to repeatedly calibrate the finite element numerical analysis model until the finite element numerical analysis model meets the closed-loop analysis requirements, and then stop the calibration. The calibrated finite element numerical analysis model was used to analyze multiple stamping process parameters of the cover, and the sample range and single-factor optimal value of each stamping process parameter were calculated. Key and non-key parameters were extracted from multiple stamping process parameters. The key parameters were used as variables to be solved, and the non-key parameters were used as quantifiers. Finite element simulation orthogonal experiments were conducted to determine the optimization objective and obtain multiple sets of experimental results. Among them, the key parameters included at least the magnesium plate temperature T, which has the greatest impact on the stamping performance of magnesium alloy sheets. d and punch temperature T p ; A quadratic polynomial response surface model with cracking and wrinkling as objective functions is constructed using multiple sets of experimental results. The quadratic polynomial response surface model is then calculated using a multi-objective particle swarm optimization algorithm to obtain a non-dominated solution set and an optimal set of process parameters.

2. The optimized method for differential temperature stamping forming process of magnesium alloy 3C product cover parts according to claim 1, characterized in that, Before generating the finite element numerical analysis model based on the three-dimensional model of the magnesium alloy 3C product cover, the method further includes: Uniaxial mechanical tensile tests were conducted on magnesium alloy sheets at different deformation temperatures and strain rates to obtain stress-strain curves of the magnesium alloy sheets, and several mechanical property parameters were obtained based on the stress-strain curves. A material database for finite element analysis of magnesium alloy thin plates was established using several mechanical property parameters.

3. The optimized method for differential temperature stamping forming process of magnesium alloy 3C product cover parts according to claim 1, characterized in that, Collect solid stamping data during the differential temperature stamping forming test of the cover part, and use the solid stamping data to repeatedly calibrate the finite element numerical analysis model until the finite element numerical analysis model meets the closed-loop analysis requirements, at which point the calibration stops, including: A differential temperature stamping forming experiment was conducted using a differential temperature stamping forming solid mold that is matched with the three-dimensional model, and the solid stamping data obtained from the experiment was collected. Using the maximum thinning rate Y1 and the maximum thickening rate Y2 as target calibration points, the finite element numerical analysis model is repeatedly calibrated using the solid stamping data. When the matching degree between the target calibration point in the physical stamping data and the target calibration point in the simulation results exceeds the threshold, it is determined that the finite element numerical analysis model meets the closed-loop analysis requirements, and calibration is stopped.

4. The optimized method for differential temperature stamping forming process of magnesium alloy 3C product cover parts according to claim 1, characterized in that, The calibrated finite element numerical analysis model was used to analyze multiple stamping process parameters of the body panel, and the sample range and single-factor optimal value of each stamping process parameter were calculated, including: Using any stamping process parameter as a variable parameter and the remaining parameters as quantitative parameters, the maximum stamping depth H of the cover before fracture is calculated using the calibrated finite element numerical analysis model for each variable parameter. The sample range and single-factor optimal value of each variable parameter are determined based on the value of the maximum stamping depth H.

5. The optimized method for differential temperature stamping forming process of magnesium alloy 3C product cover parts according to claim 1, characterized in that, The stamping process parameters also include at least the punch fillet radius R. p The radius of the fillet on the straight wall of the die is R. c The parameters are: punch-die clearance Z, blank holder force F, stamping speed v, equivalent draw bead resistance f, and friction coefficient μ.

6. The optimized method for differential temperature stamping forming process of magnesium alloy 3C product cover parts according to claim 5, characterized in that, Key and non-key parameters are extracted from multiple stamping process parameters. The key parameters are used as variables to be solved, and the non-key parameters are used as quantifiers. Finite element simulation orthogonal experiments are conducted to determine the optimization objective and obtain multiple sets of experimental results samples, including: A first critical parameter is extracted from multiple stamping process parameters, and the remaining process parameters are designated as the first non-critical parameters. The first critical parameter includes at least the stamping speed v and the magnesium plate temperature T. d Punch temperature T p and blank holder force F; Using the first key parameter as the variable to be solved and the first non-key parameter as the quantitative parameter, and taking the maximum thinning rate Y1 and the maximum thickening rate Y2 as the optimization objectives, a finite element simulation orthogonal experiment was conducted to obtain multiple sets of experimental result samples.

7. The optimized method for differential temperature stamping forming process of magnesium alloy 3C product cover parts according to claim 6, characterized in that, Before extracting the first critical parameter from multiple stamping process parameters and using the remaining process parameters as the first non-critical parameters, the method further includes: A second critical parameter is extracted from multiple stamping process parameters, and the remaining process parameters are designated as second non-critical parameters. The second critical parameter includes at least the punch fillet radius R. p Magnesium plate temperature T d Punch temperature T p and blank holder force F; The second key parameter is used as the variable to be solved, the second non-key parameter is used as the quantitative parameter, and the maximum forming depth H is used as the optimization target. Finite element simulation orthogonal test is carried out, and the set of parameters corresponding to the maximum value of the optimization target in the test results is used as the first optimization parameter set. The optimal values ​​of the geometric parameters are iteratively adjusted for springback compensation until the springback amount meets the springback limit requirement of the 3D product cover. Then, the optimized geometric parameters are merged with the first-order optimized parameter set to generate a second-order optimized parameter set. The geometric parameters include at least the punch fillet radius R. p The radius of the fillet on the straight wall of the die is R. c And the gap Z between the punch and die; The geometric shape of the finite element numerical analysis model is corrected using the aforementioned secondary optimization parameter set.

8. The optimized method for differential temperature stamping forming process of magnesium alloy 3C product cover parts according to claim 1, characterized in that, A quadratic polynomial response surface model with cracking and wrinkling as objective functions was constructed using multiple sets of experimental results, including: Multiple sets of experimental results were used as training samples. A range of sample values ​​was defined, and the exponent of the safety distance for cracking and wrinkling was used as the weight to construct an objective function R describing the cracking of differential temperature stamping. obj And the wrinkling objective function W obj Its function expression is as follows: in, Indicates the principal strain. Indicates secondary strain. This represents the tensile strength safety curve, where n represents the number of elements, and i represents the i-th element out of n elements; Where η() represents the wrinkling safety curve; Based on formulas (1) and (2) and the range of sample values, a quadratic polynomial response surface model is constructed.

9. The optimized method for differential temperature stamping forming process of magnesium alloy 3C product cover parts according to claim 8, characterized in that, The quadratic polynomial response surface model is calculated using a multi-objective particle swarm optimization algorithm to obtain a non-dominated solution set and an optimal set of process parameters, including: The quadratic polynomial response surface model was calculated using a multi-objective particle swarm optimization algorithm to obtain the Pareto non-dominated solution set; Using the minimum distance selection method, the optimal solution particle P0, which is closest to the defect-free ideal particle P0, is selected from the Pareto non-dominated solution set. m Wherein, the optimal solution particle P m The cracking objective function R obj Value and wrinkling objective function W obj The particle whose value simultaneously reaches its lowest level; The optimal solution particle P m The corresponding set of process parameters is taken as the optimal set of process parameters.

10. The optimized method for differential temperature stamping forming process of magnesium alloy 3C product cover parts according to claim 1, characterized in that, The finite element numerical analysis model was established through cross-analysis using DYNAFORM and DEFORM software.

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