A composite material step-by-step electromagnetic forming method and system

Abstract

The present invention discloses a composite material step-by-step electromagnetic forming method and system. The method includes finite element simulation of the electromagnetic forming process of the workpiece to obtain multiple sets of simulation data; obtaining similar simulation data based on the multiple sets of simulation data; splicing the simulation data and the similar simulation data and performing data format processing to obtain training samples; using the training samples to train the third network model, and predicting the adjustment parameters and maximum stress of the workpiece under certain conditions; judging whether it meets the processing permission range of the workpiece based on the maximum stress; if it meets the requirements, controlling the screw lifter to adjust the distance from the discharge coil to the workpiece; repeating multiple times until the workpiece reaches the bottom of the base mold. The solution provided by the present invention establishes the relationship between multiple parameters through finite element simulation and neural network architecture, realizes intelligent control of the distance from the coil to the workpiece and stress prediction, and realizes multiple step-by-step forming through multiple adjustments.

Description

Technical Field

[0001] The present invention relates to the technical field of electromagnetic forming and intelligent manufacturing, and in particular to a composite material step-by-step electromagnetic forming method and system. Background Art

[0002] Electromagnetic forming (EMF) is a processing technology that uses pulsed electromagnetic force. It has attracted widespread attention from industry and academia due to its advantages such as extremely fast workpiece forming speed, high surface quality of workpieces, and greatly improved material forming limit. Electromagnetic forming technology is currently widely used in the processing of lightweight sheets. Studies have shown that electromagnetic forming can improve the forming limit of AA1060-H24 sheets by up to 68.3%. Some scholars have studied the electromagnetic forming of metal polymer composites, which also performs well in improving the forming limit of materials. However, the current research on electromagnetic forming of metal composites is still very limited. In the new generation of aerospace technology, metal composites are gradually replacing single metal materials. In other fields, the demand for metal composites is also increasing. For example, multilayer Al-Ti plates have the characteristics of strong ductility and high specific strength, and are widely used in the automotive industry and aerospace. Al / Mg composites effectively combine the advantages of Mg's high specific strength, light weight and Al's ductility, and their performance is much better than that of single metals. Therefore, it has become an important engineering issue to study the electromagnetic forming technology of composite materials, make electromagnetic forming technology more compatible with the processing of composite materials, and improve the performance of composite forming.

[0003] There are three major problems in the electromagnetic forming of metal composites: (1) The simulation and calculation efficiency of electromagnetic forming is low, which involves multiple physical fields such as structure and electromagnetics. Improving the calculation efficiency of electromagnetic forming has become a key issue in the industry; (2) The ductility of different parts of the composite material is different, and process control is required during forming to reduce problems such as material separation and deformation during the forming process; (3) Due to the coupling of multiple physical fields, there are many parameters, such as electromagnetic parameters such as current, workpiece parameters such as width and thickness, and process parameters such as friction and pressure. Controlling and optimizing multiple parameters has become a major difficulty in electromagnetic forming. Summary of the invention

[0004] In order to solve the technical problems of low calculation efficiency, difficulty in parameter optimization and difficulty in process control in existing electromagnetic forming solutions, an embodiment of the present invention provides a step-by-step electromagnetic forming method and system for composite materials.

[0005] The technical solution of the embodiment of the present invention is achieved as follows:

[0006] An embodiment of the present invention provides a method for step-by-step electromagnetic forming of composite materials, which is applied to a step-by-step electromagnetic forming system for composite materials, wherein the step-by-step electromagnetic forming system for composite materials comprises a movable platform and a composite network model; the movable platform comprises a substrate mold, a discharge coil and a screw lift; the substrate mold is used to provide a forming shape for a workpiece; the discharge coil is used to generate an electromagnetic force so that the workpiece is formed under the action of the electromagnetic force; the screw lift is used to adjust the distance from the discharge coil to the workpiece; the composite network model comprises a first network model, a second network model and a third network model; the method comprises: using the first network model to perform finite element simulation on the electromagnetic forming process of the workpiece to obtain multiple sets of simulation data; each set of simulation data comprises electromagnetic parameters, workpiece parameters, process parameters, adjustment parameters, and the corresponding maximum deformation and maximum stress of the workpiece; the multiple sets of simulation data are input into the second network model to obtain the second network model generated similar simulation data; splicing the simulation data and the similar simulation data and performing data format processing to obtain training samples; using the training samples to train the third network model, and using the trained third network model to predict the adjustment parameters and maximum stress of the processed workpiece under the conditions of electromagnetic parameters, workpiece parameters, process parameters and maximum deformation; the trained third network model contains the mapping relationship between electromagnetic parameters, workpiece parameters, process parameters, maximum deformation and adjustment parameters and the mapping relationship between electromagnetic parameters, workpiece parameters, process parameters, maximum deformation and maximum stress; judging whether it meets the processing permission range of the processed workpiece according to the maximum stress; if it meets the processing permission range of the processed workpiece, controlling the screw lift to adjust the distance from the discharge coil to the processed workpiece so that the distance from the discharge coil to the processed workpiece is the adjustment parameter; repeating the above operation multiple times until the processed workpiece reaches the bottom of the base mold, completing the electromagnetic forming of the processed workpiece.

[0007] In one embodiment, the first network model includes a first script and a first solver; the first network model is used to perform finite element simulation on the electromagnetic forming process of the workpiece to obtain multiple sets of simulation data, including: establishing an initial simulation process through a first software to obtain a first script; running the first script in the first solver, setting parameters and loop conditions, inputting multiple sets of electromagnetic parameters, workpiece parameters, process parameters and adjustment parameters, and obtaining the corresponding output maximum stress and maximum deformation; based on the multiple sets of electromagnetic parameters, workpiece parameters, process parameters, adjustment parameters, corresponding maximum stress and maximum deformation, multiple sets of simulation data are obtained.

[0008] In one embodiment, the second network model includes an encoder, a generator and a discriminator, and the multiple groups of simulation data are input into the second network model to obtain similar simulation data generated by the second network model, including: inputting the multiple groups of simulation data into the encoder to obtain intermediate variables output by the encoder; inputting the intermediate variables into the generator to obtain similar simulation data output by the generator; inputting the simulation data and the similar simulation data into the discriminator to obtain the identification probability output by the discriminator; the identification probability is used to identify whether the similar simulation data meets the preset requirements, and outputting the similar simulation data that meets the preset requirements.

[0009] In one embodiment, the encoder, the generator and the discriminator are all deep neural networks, the input layer of the encoder includes 8 neurons or 6 neurons, and the output layer of the encoder includes 2 neurons; the input layer of the generator includes 2 neurons, and the output layer of the generator includes 8 neurons or 6 neurons; the input layer of the discriminator includes 16 neurons or 12 neurons, and the output layer of the discriminator includes one neuron; the number of hidden layers and neurons in the hidden layers of the encoder, the generator and the discriminator is positively correlated with the amount of simulation data.

[0010] In one embodiment, the loss function during training of the second network model includes a first reconstruction loss part and a second part loss:

[0011] The first reconstruction loss part includes:

[0012] Loss reconstruction →||Output Generator -G(E(Input Encoder ))||

[0013] Among them, G() is the generator, E() is the encoder, Output Generator is the output of the generator, Input Encoder is the input of the encoder;

[0014] The second part of the loss includes:

[0015] Loss GAN →log(D(Input Encoder ))+log(1-D(G(E(Output Encoder ))))+log(1-D(G(Z p1 ,Z p2 )))

[0017] Among them, D() is the discriminator, Z p1 ,Zp2 is an intermediate variable, obtained by resampling in the normal distribution space, Input Encoder is the input of the encoder, Output Encoder is the output of the encoder, G() is the generator, and E() is the encoder.

[0018] In one embodiment, the simulation data and the similar simulation data are spliced ​​and then data formatted to obtain training samples, including: splicing the simulation data and the similar simulation data row by row to obtain spliced ​​data; adjusting the input-output relationship of the spliced ​​data to obtain training samples; the training samples include a first training sample and a second training sample; the input items of the first training sample are electromagnetic parameters, workpiece parameters, process parameters and maximum deformation, and the output item of the first training sample is an adjustment parameter; the input items of the second training sample are electromagnetic parameters, workpiece parameters, process parameters and maximum deformation, and the output item of the second training sample is maximum stress.

[0019] In one embodiment, the third network model includes a first sub-model and a second sub-model; training the third network model using the training samples includes: training the first sub-model using the first training samples; training the second sub-model using the second training samples; the trained first sub-model includes a mapping relationship between electromagnetic parameters, workpiece parameters, process parameters, and maximum deformation to adjustment parameters; the trained second sub-model includes a mapping relationship between electromagnetic parameters, workpiece parameters, process parameters, and maximum deformation to maximum stress.

[0020] In one embodiment, the loss function during training of the first sub-model is:

[0021]

[0022] Among them, H i To adjust the parameters, that is, the distance from the coil to the workpiece (the distance from the coil to the lowest point of the mold), H' i Generate parameters for the corresponding generator, H″ i is the prediction parameter of LSTM1, and m is the amount of data of LSTM;

[0023] The loss function of the second sub-model during training is:

[0024]

[0025] Among them, SMAX i is the maximum stress of the workpiece, SMAX′ i Generate stress for generator, SMAX″ i The stress prediction of LSTM2 is m, and the amount of data of LSTM.

[0026] In one embodiment, controlling the screw lifter to adjust the distance from the discharge coil to the workpiece includes:

[0027] The linear quadratic regulator is used to control the screw lift to adjust the distance from the discharge coil to the workpiece.

[0028] An embodiment of the present invention also provides a composite material step-by-step electromagnetic forming system, which includes a movable platform, a composite network model and a processor; the movable platform includes a base mold, a discharge coil and a screw lift; the base mold is used to place a workpiece for processing; the discharge coil is used to generate electromagnetic force so that the workpiece is formed under the action of the electromagnetic force; the screw lift is used to adjust the distance from the discharge coil to the workpiece; the composite network model includes a first network model, a second network model and a third network model; the processor is used to execute the steps of any of the above methods.

[0029] The composite material step-by-step electromagnetic forming method and system provided by the embodiment of the present invention utilizes the first network model to perform finite element simulation on the electromagnetic forming process of the processed workpiece to obtain multiple sets of simulation data; each set of simulation data includes electromagnetic parameters, workpiece parameters, process parameters, adjustment parameters, and the corresponding maximum deformation and maximum stress of the processed workpiece; the multiple sets of simulation data are input into the second network model to obtain similar simulation data generated by the second network model; the simulation data and the similar simulation data are spliced ​​and then processed in data format to obtain training samples; the training samples are used to train the third network model, and the trained third network model is used to predict the electromagnetic parameters, workpiece parameters, process parameters, adjustment parameters, and the maximum deformation and maximum stress of the processed workpiece. The third network model after training includes the mapping relationship between electromagnetic parameters, workpiece parameters, process parameters, maximum deformation and adjustment parameters, and the mapping relationship between electromagnetic parameters, workpiece parameters, process parameters, maximum deformation and maximum stress; judging whether it meets the processing permission range of the workpiece according to the maximum stress; if it meets the processing permission range of the workpiece, controlling the screw lift to adjust the distance from the discharge coil to the workpiece, so that the distance from the discharge coil to the workpiece is the adjustment parameter; repeating the above operation multiple times until the workpiece reaches the bottom of the base mold, completing the electromagnetic forming of the workpiece. The scheme provided by the present invention establishes the relationship between multiple parameters through finite element simulation and neural network architecture, realizes intelligent control of the distance from the coil to the workpiece and stress prediction, and realizes multiple step-by-step forming through multiple adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1It is a schematic diagram of the process of the step-by-step electromagnetic forming method of the composite material according to an embodiment of the present invention;

[0031] Figure 2 A schematic diagram of the structure of a movable platform according to an embodiment of the present invention;

[0032] Figure 3 It is a schematic diagram of the technical implementation process of the composite material step-by-step electromagnetic forming technology based on the movable platform and MC-VAE-GAN-LSTM in an embodiment of the present invention;

[0033] Figure 4 This is a partial mesh schematic diagram of a COMSOL composite plate according to an embodiment of the present invention;

[0034] Figure 5 This is a schematic diagram of electromagnetic forming in steps according to an embodiment of the present invention;

[0035] Figure 6 This is a schematic diagram of the network architecture of the MC-VAE-GAN-LSTM model according to an embodiment of the present invention;

[0036] Figure 7 1 is a diagram showing the internal structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

[0038] An embodiment of the present invention provides a composite material step-by-step electromagnetic forming method, the method is applied to a composite material step-by-step electromagnetic forming system, the composite material step-by-step electromagnetic forming system comprises a movable platform and a composite network model; the movable platform comprises a substrate mold, a discharge coil and a screw lift; the substrate mold is used to provide a forming shape for a workpiece; the discharge coil is used to generate an electromagnetic force so that the workpiece is formed under the action of the electromagnetic force; the screw lift is used to adjust the distance from the discharge coil to the workpiece; the composite network model comprises a first network model, a second network model and a third network model; Figure 1 As shown, the method includes:

[0039] Step 101: using the first network model to perform finite element simulation on the electromagnetic forming process of the workpiece to obtain multiple sets of simulation data; each set of simulation data includes electromagnetic parameters, workpiece parameters, process parameters, adjustment parameters, and the corresponding maximum deformation and maximum stress of the workpiece;

[0040] Step 102: inputting the plurality of sets of simulation data into the second network model to obtain similar simulation data generated by the second network model;

[0041] Step 103: splicing the simulation data and the similar simulation data and performing data format processing to obtain training samples;

[0042] Step 104: using the training samples to train the third network model, and using the trained third network model to predict the adjustment parameters and maximum stress of the processed workpiece under the conditions of electromagnetic parameters, workpiece parameters, process parameters and maximum deformation; the trained third network model includes a mapping relationship between electromagnetic parameters, workpiece parameters, process parameters, maximum deformation and adjustment parameters, and a mapping relationship between electromagnetic parameters, workpiece parameters, process parameters, maximum deformation and maximum stress;

[0043] Step 105: judging whether the maximum stress meets the processing allowable range of the workpiece according to the maximum stress; if the maximum stress meets the processing allowable range of the workpiece, controlling the screw lifter to adjust the distance from the discharge coil to the workpiece so that the distance from the discharge coil to the workpiece is the adjustment parameter;

[0044] Step 106: Repeat the above operation multiple times until the workpiece reaches the bottom of the base mold, completing the electromagnetic forming of the workpiece.

[0045] The method of this embodiment realizes multiple step-by-step forming through a mobile platform with adjustable distance from coil to workpiece. Based on the composite network model (i.e., MC-VAE-GAN-LSTM model), through automated finite element simulation and neural network architecture, the relationship between multiple parameters is established to realize intelligent control of the distance from coil to workpiece and stress prediction of workpiece.

[0046] Specifically, see Figure 2 , the movable platform in this embodiment includes the following components:

[0047] Processing part:

[0048] 1-Matrix mold: composite material forming mold, the matrix shape is the forming shape;

[0049] 2-Mold base: Place the base mold and connect it to the 7-screw lift;

[0050] 3-Clamp: Apply force F to clamp the two ends of the composite material;

[0051] 4-Discharge coil: generates electromagnetic force;

[0052] 5-Coil fixing bracket: fix 4-discharge coil;

[0053] 6- Composite sheet: A composite material sheet that needs to be processed and formed, consisting of different metal material layers 1 and metal material 2.

[0054] Adjustment part:

[0055] 7-Screw lift: realize the adjustment of the distance H from the coil to the workpiece (the distance from the coil to the lowest point of the mold);

[0056] 8-Motor: realizes the rotation movement of 7-screw lift;

[0057] 9- Spring damping support: supports the 2- mold base and absorbs vibration when the 7- screw lift is working.

[0058] 10-Lifter base: fix 7-screw lift, 8-motor and 9-spring damping support.

[0059] Specifically, the composite network model in this embodiment, namely the MC-VAE-GAN-LSTM model, includes a first network model MC model, a second network model VAE-GAN model and a third network model LSTM model.

[0060] Further, see Figure 3 , the MC-VAE-GAN-LSTM model in this embodiment includes the following steps:

[0061] S1: Build the MC model, which includes the first script, i.e., the MATLAB script, and the first solver, i.e., the COMSOL solver. The COMSOL structural and electromagnetic multi-physics field loop simulation scripts are established through MATLAB, and real-time communication with COMSOL is realized. The maximum deformation and maximum stress of the corresponding composite material plate under multiple sets of different electromagnetic parameters, workpiece parameters, process parameters, and adjustment parameters (H) are obtained, and the results are automatically written into the text file ".txt".

[0062] S2: Build and train the VAE-GAN model, which is a deep neural network. The VAE-GAN model includes a deep encoder, a deep generator, and a deep discriminator. The input of the VAE-GAN network is multiple sets of different electromagnetic parameters, workpiece parameters, process parameters, and adjustment parameters (H), and the maximum deformation and maximum stress of the composite material sheet corresponding to the parameters. The output is a set of electromagnetic parameters, workpiece parameters, process parameters, adjustment parameters (H), maximum deformation and maximum stress of the composite material sheet generated by the deep generator.

[0063] S3: Use Python Script to reorganize the data obtained in S1 and S2. The data of S1 and S2 are spliced ​​row by row, and the electromagnetic parameters, workpiece parameters, process parameters, and maximum deformation of the composite material plate are used as input columns, and the adjustment parameters (H) and maximum stress are used as output columns.

[0064] S4: Build and train LSTM. LSTM1 establishes the mapping relationship from electromagnetic parameters, workpiece parameters, process parameters, maximum deformation of composite material plate to adjustment parameters (H), and LSTM2 establishes the mapping relationship from electromagnetic parameters, workpiece parameters, process parameters, maximum deformation of composite material plate to maximum stress. Both LSTM1 and LSTM2 need to pass through the fully connected layer to get the output.

[0065] Among them, the specific implementation steps of S1 are:

[0066] S11: First, the entire solution process is established through the first software, namely COMSOL, that is, the initial simulation process is established, including structural dimensions, structural-electromagnetic physical fields, geometric (workpiece) parameter settings, solution domain and deformation domain selection, structural boundary conditions, material definitions, electromagnetic parameter settings, meshing and solver settings. And record and save it as a basic MATLAB script. Then the input of MATLAB is:

[0067] Input MATLAB =[A i ,T i ,W i ,F i ,f i ,H i ](i=1,2,...,n)

[0068] Among them, A i is the current (electromagnetic parameter), T i is the thickness of the workpiece (workpiece parameter), W i is the width of the workpiece (workpiece parameter), F i is the clamping force of the fixture (process parameter), f i is the friction coefficient (process parameter), H i is the adjustment parameter, i.e. the distance from the coil to the workpiece (represented by the coil to the lowest point of the mold), and n is the data volume. (To simplify the solution, the process parameters can be simplified to fixed supports in structural mechanics).

[0069] S12: Then, modify the basic MATLAB script in the MATLAB software, parameterize the electromagnetic parameters, workpiece parameters (geometric parameters), and process parameters (structural boundary conditions), and establish the For loop conditions. Obtain the maximum stress and maximum deformation through the mphmax function, define the 'solid.mises' parameter in the mphmax function, and obtain the maximum stress of the workpiece. Define the 'solid.disp' parameter in the mphmax function to obtain the maximum deformation of the workpiece. Save the results line by line to the ".txt" file through the fprintf function. Then the MATLAB output is:

[0070] OutputMATLAB =[DMAX i ,SMAX i ](i=1,2,...,n)

[0071] Among them, DMAX i is the maximum deformation, SMAX i is the maximum stress.

[0072] S13: Finally, solve the problem. Run MATLAB software and call the COMSOL solver in real time to obtain the input [A i ,T i ,W i ,F i ,f i ,H i ]The corresponding output result [DMAX i ,SMAX i ].

[0073] Furthermore, the specific implementation steps of S2 are:

[0074] S21: Building VAE-GAN, see Figure 4 , VAE-GAN contains an encoder, a generator, and a discriminator, where the input of the encoder is:

[0075] Input Encoder =[A i ,T i ,W i ,F i ,f i ,H i ,DMAX i ,SMAX i ]

[0076] The output of the encoder is:

[0077] Output Encoder =[Z 1i ,Z 2i ]

[0078] Where Z 1i ,Z 2i is an intermediate variable.

[0079] The input to the generator is:

[0080] Input Generator =[Z 1i ,Z 2i ]

[0081] The output of the generator is:

[0082] OutputGenerator =[A' i ,T' i ,W' i ,F' i ,f' i ,H' i ,DMAX' i ,SMAX' i ]

[0083] Where A' i ,T' i ,W' i ,F' i ,f' i ,H' i ,DMAX' i ,SMAX' i Similar to A i ,T i ,W i ,F i ,f i ,H i ,DMAX i ,SMAX i A new set of parameters.

[0084] The input of the discriminator is divided into two parts, one comes from the input of the encoder and the other comes from the output of the generator:

[0085] Input Discriminator-Encoder =[A i ,T i ,W i ,F i ,f i ,H i ,DMAX i ,SMAX i ]

[0086] Input Discriminator-Generator =[A' i ,T' i ,W' i ,F' i ,f' i ,H' i ,DMAX' i ,SMAX' i ]

[0087] The output of the discriminator is the probability of identifying the encoder input data, which is between 0 and 1.

[0088] The encoder, generator, and discriminator are all deep neural networks. The input layer of the encoder contains 8 neurons (or 6 neurons when F i ,fi When simplified to fixed support), the output layer contains 2 neurons. The input layer of the generator contains 2 neurons, and the output layer contains 8 neurons (or 6 neurons). The input layer of the discriminator contains 16 neurons (or 12 neurons), and the output layer contains one neuron. The number of hidden layers and hidden layer neurons of the encoder, generator and discriminator depends on the amount of data n.

[0089] S22: Train VAE-GAN. The VAE reconstruction loss part of the loss function of VAE-GAM is:

[0090] Loss reconstruction →||Output Generator -G(E(Input Encoder ))||

[0091] Where G() is the generator, E() is the encoder, Output Generator is the output of the generator, Input Encoder is the input of the encoder.

[0092] In the second part, the GAN part loss of the loss function of VAE-GAN is:

[0093]

[0094] Where D() is the discriminator, Z p1 ,Z p2 It is also an intermediate variable, obtained by resampling in the normal distribution space. Input Encoder is the input of the encoder, Output Encoder is the output of the encoder, G() is the generator, and E() is the encoder.

[0095] S23: Finally, the trained VAE-GAN obtains the output result [A' i ,T' i ,W' i ,F' i ,f' i ,H' i ,DMAX' i ,SMAX' i ].

[0096] Furthermore, the specific implementation steps of S3 are:

[0097] S31: The input [A i ,T i ,W i ,F i ,f i ,H i] and output [DMAX i ,SMAX i ] data and the output data [A' i ,T' i ,W' i ,F' i ,f' i ,H' i ,DMAX' i ,SMAX' i ] to splice and get:

[0098] [[A i ,T i ,W i ,F i ,f i ,H i ,DMAX i ,SMAX i ],...,[A' i ,T' i ,W' i ,F' i ,f' i ,H' i ,DMAX' i ,SMAX' i ]]

[0099] S32: Adjust the input-output relationship and divide the data into two groups, LSTM1 and LSTM2, namely the first training sample and the second training sample, which are used for the subsequent training of LSTM1 and LSTM2 networks respectively:

[0100] LSTM1:[[A i ,T i ,W i ,F i ,f i ,DMAX i ,H i ],...,[A' i ,T' i ,W' i ,F' i ,f' i ,DMAX' i ,H' i ]]

[0101] LSTM2:[[A i ,T i ,W i ,F i ,f i ,DMAX i ,SMAX i],...,[A' i ,T' i ,W' i ,F' i ,f' i ,DMAX' i ,SMAX' i ]]

[0102] The last column is used as the output data of LSTM, and the remaining columns are the input data.

[0103] Furthermore, the specific implementation steps of S4 are:

[0104] S41: Construct LSTM1 and LSTM2 (i.e., the first sub-model and the second sub-model). Both networks complete the output after passing through a fully connected network. LSTM1 realizes the prediction based on electromagnetic parameters A, workpiece parameters T, W, process parameters F, f and composite material maximum deformation DMAX to adjustment parameter H. The input data of LSTM1 is:

[0105] [[A i ,T i ,W i ,F i ,f i ,DMAX i ],...,[A' i ,T' i ,W' i ,F' i ,f' i ,DMAX' i ]]

[0106] The output data is:

[0107] [[H i ],...,[H' i ]]

[0108] LSTM2 implements the prediction based on electromagnetic parameters A, workpiece parameters T, W, process parameters F, f and the maximum deformation DMAX to the maximum stress SMAX of the composite material. The input data of LSTM2 is:

[0109] [[A i ,T i ,W i ,F i ,f i ,DMAX i ],...,[A' i ,T' i ,W' i ,F' i ,f' i ,DMAX'i ]]

[0110] The output data is:

[0111] [[SMAX i ],...,[SMAX' i ]]

[0112] S42: Train LSTM1 and LSTM2. The loss functions of LSTM1 and LSTM2 are both MSE loss:

[0113]

[0114] Among them, H i To adjust the parameters, that is, the distance from the coil to the workpiece (the distance from the coil to the lowest point of the mold), H' i Generate parameters for the corresponding generator, H″ i is the prediction parameter of LSTM1, and m is the amount of data of LSTM.

[0115]

[0116] Among them, SMAX i is the maximum stress of the workpiece, SMAX′ i Generate stress for generator, SMAX″ i The stress prediction of LSTM2 is m, and the amount of data of LSTM.

[0117] Based on the above steps of the MC-VAE-GAN-LSTM model, see 5 and Figure 6 , then the step-by-step electromagnetic forming process of the composite material includes the following:

[0118] S1: Input electromagnetic parameters A, workpiece parameters T (thickness), W (width), process parameters F (clamp clamping force), f (friction force), and the current actual measured maximum deformation of the workpiece DMAX into the MC-VAE-GAN-LSTM model to obtain the predicted H (height adjustment parameter, the distance from the coil to the processed workpiece, represented by the coil to the lowest point of the mold) and the maximum stress SMAX of the workpiece.

[0119] S2: Evaluate whether the maximum stress SMAX of the workpiece is within the permitted range of composite material processing. If it is, the obtained H is input into the motor controller to control the screw lift to move the height of H to achieve one-time processing.

[0120] S3: The process of S1-S2 is cycled to realize the next processing and forming until the composite sheet is formed (reaches the bottom of the mold). And the real-time storage and interaction of data are realized through the database.

[0121] Furthermore, the specific implementation steps of S2 are:

[0122] S21: Controlled by a linear quadratic regulator LQR. Define the state equation of the system, which includes a dynamic model of the gravity and damping effects on the screw lift. The current height value is predicted by MC-VAE-GAN-LSTM: H", and the target height value is defined:

[0123] H target =H”

[0124] The dynamic behavior of the screw lift is described by establishing the following state space model:

[0125]

[0126] Where H(t) is the height of the screw lift at time t, represents the rate of change of height, u(t) is the control input, A is the state matrix, and B is the input matrix.

[0127] Considering the gravity and damping effects, the system is considered as an approximate second-order system:

[0128]

[0129] Where m is the mass of the platform, δ is the damping coefficient, and k is the spring stiffness.

[0130] Then, the second-order differential equation is converted to state-space form:

[0131]

[0132] Define the state vector:

[0133]

[0134] S22: Design the LQR controller. The control objective of the system is to design a linear feedback controller u(t)=-Kx(t) with a gain matrix K so that the system state x(t) reaches the target state x Target , or stabilize at the target state x Target Nearby. x Target for:

[0135]

[0136] The cost function of LQR control is:

[0137] ∫0 ∞ (x T Qx+u T Ru)dt

[0138] Where Q and R are diagonal matrices.

[0139] S23: Solve the gain matrix K. First solve the ARE equation:

[0140] A T P+PA-PBR -1 B T P+Q=0

[0141] The auxiliary matrix P is obtained by the ARE equation, and K is obtained by solving the specified A, B, Q, and R:

[0142] K=R -1 B T P

[0143] S24: The height value is controlled to reach the ideal H obtained by the MC-VAE-GAN-LSTM neural network model. The relationship between the magnitude of the electromagnetic force and H is generally:

[0144]

[0145] Therefore, when the distance H between the composite material workpiece and the discharge coil changes, the electromagnetic force on the workpiece changes, so that the electromagnetic force can be controlled step by step.

[0146] The magnitude of the electromagnetic force is obtained from the following relationship:

[0147] F=J×B

[0148] Where J is the induced eddy current and B is the magnetic flux density.

[0149] The composite material step-by-step electromagnetic forming method provided by the embodiment of the present invention uses the first network model to perform finite element simulation on the electromagnetic forming process of the workpiece to obtain multiple sets of simulation data; each set of simulation data includes electromagnetic parameters, workpiece parameters, process parameters, adjustment parameters, and the corresponding maximum deformation and maximum stress of the workpiece; the multiple sets of simulation data are input into the second network model to obtain similar simulation data generated by the second network model; the simulation data and the similar simulation data are spliced ​​and then processed in data format to obtain training samples; the training samples are used to train the third network model, and the trained third network model is used to predict the electromagnetic parameters, workpiece parameters, process parameters, and maximum deformation and maximum stress of the workpiece. parameters and maximum deformation conditions; the trained third network model includes electromagnetic parameters, workpiece parameters, process parameters, maximum deformation to adjustment parameters and electromagnetic parameters, workpiece parameters, process parameters, maximum deformation to maximum stress mapping relationship; according to the maximum stress, it is judged whether it meets the processing permission range of the workpiece; if it meets the processing permission range of the workpiece, the screw elevator is controlled to adjust the distance from the discharge coil to the workpiece, so that the distance from the discharge coil to the workpiece is the adjustment parameter; repeat the above operation multiple times until the workpiece reaches the bottom of the base mold, and the electromagnetic forming of the workpiece is completed. The solution provided by the present invention establishes the relationship between multiple parameters through finite element simulation and neural network architecture, realizes intelligent control of the distance from the coil to the workpiece and stress prediction, and realizes multiple step-by-step forming through multiple adjustments.

[0150] In order to implement the method of an embodiment of the present invention, an embodiment of the present invention further provides a composite material step-by-step electromagnetic forming system, wherein the composite material step-by-step electromagnetic forming system comprises a movable platform, a composite network model and a processor; the movable platform comprises a base mold, a discharge coil and a screw lift; the base mold is used to place a workpiece for processing; the discharge coil is used to generate an electromagnetic force so that the workpiece is formed under the action of the electromagnetic force; the screw lift is used to adjust the distance from the discharge coil to the workpiece for processing; the composite network model comprises a first network model, a second network model and a third network model; the processor is used to execute the steps of any of the above methods.

[0151] In order to implement the method of the embodiment of the present invention, the embodiment of the present invention also provides a computer program product, which includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps of the above method.

[0152] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiment of the present invention, the embodiment of the present invention also provides an electronic device (computer device). Specifically, in one embodiment, the computer device can be a terminal, and its internal structure diagram can be as follows: Figure 7 As shown. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05 and a memory (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor A01, the method of any one of the above embodiments is implemented. The display screen A04 of the computer device can be a liquid crystal display or an electronic ink display, and the input device A05 of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0153] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0154] The device provided by the embodiment of the present invention includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the method of any one of the above embodiments is implemented.

[0155] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0156] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0157] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0158] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0159] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0160] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0161] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0162] It can be understood that the memory of the embodiment of the present invention can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAM bus random access memory (DRRAM, Direct Rambus Random Access Memory).The memories described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memories.

[0163] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0164] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A composite material step-by-step electromagnetic forming method, characterized in that: The method is applied to a composite material step-by-step electromagnetic forming system, which comprises a movable platform and a composite network model; the movable platform comprises a base mold, a discharge coil and a screw lifter; the base mold is used to provide a forming shape for a workpiece; the discharge coil is used to generate an electromagnetic force so that the workpiece is formed under the action of the electromagnetic force; The screw lift is used to adjust the distance between the discharge coil and the lowest point of the base mold; The composite network model includes a first network model, a second network model and a third network model; the method includes: Using the first network model, finite element simulation is performed on the electromagnetic forming process of the workpiece to obtain multiple sets of simulation data; each set of simulation data includes electromagnetic parameters, workpiece parameters, process parameters, adjustment parameters, and the corresponding maximum deformation and maximum stress of the workpiece; Inputting the plurality of sets of simulation data into the second network model to obtain similar simulation data generated by the second network model; The simulation data and the similar simulation data are spliced ​​and then processed in data format to obtain training samples; The third network model is trained by using the training samples, and the adjusted parameters and the maximum stress of the processed workpiece under the conditions of electromagnetic parameters, workpiece parameters, process parameters and maximum deformation are predicted by using the trained third network model; the trained third network model includes a mapping relationship between electromagnetic parameters, workpiece parameters, process parameters, maximum deformation and adjusted parameters, and a mapping relationship between electromagnetic parameters, workpiece parameters, process parameters, maximum deformation and maximum stress; Judging whether the maximum stress meets the processing permission range of the workpiece according to the maximum stress; if the maximum stress meets the processing permission range of the workpiece, controlling the screw lifter to adjust the distance between the discharge coil and the lowest point of the base mold so that the distance between the discharge coil and the lowest point of the base mold is the adjustment parameter; The above operation is repeated for multiple times until the workpiece reaches the bottom of the base mold, and the electromagnetic forming of the workpiece is completed.

2. The composite material step-by-step electromagnetic forming method according to claim 1, characterized in that: The first network model includes a first script and a first solver; the first network model is used to perform finite element simulation on the electromagnetic forming process of the workpiece to obtain multiple sets of simulation data, including: Establishing an initial simulation process through the first software to obtain a first script; Running the first script in the first solver, setting parameters and cycle conditions, inputting multiple sets of electromagnetic parameters, workpiece parameters, process parameters and adjustment parameters, and obtaining the corresponding output maximum stress and maximum deformation; Based on multiple sets of electromagnetic parameters, workpiece parameters, process parameters, adjustment parameters, corresponding maximum stresses and maximum deformations, multiple sets of simulation data are obtained.

3. The composite material step-by-step electromagnetic forming method according to claim 1, characterized in that: The second network model includes an encoder, a generator, and a discriminator. The plurality of sets of simulation data are input into the second network model to obtain similar simulation data generated by the second network model, including: Inputting the plurality of sets of simulation data into the encoder to obtain intermediate variables output by the encoder; Input the intermediate variable into the generator to obtain similar simulation data output by the generator; The simulation data and the similar simulation data are input into the discriminator to obtain the identification probability output by the discriminator; the identification probability is used to identify whether the similar simulation data meets the preset requirements, and the similar simulation data that meets the preset requirements is output.

4. The composite material step-by-step electromagnetic forming method according to claim 3, characterized in that: The encoder, the generator and the discriminator are all deep neural networks, the input layer of the encoder contains 8 neurons or 6 neurons, and the output layer of the encoder contains 2 neurons; the input layer of the generator contains 2 neurons, and the output layer of the generator contains 8 neurons or 6 neurons; the input layer of the discriminator contains 16 neurons or 12 neurons, and the output layer of the discriminator contains one neuron; the hidden layers of the encoder, the generator and the discriminator and the number of neurons in the hidden layers are positively correlated with the data volume of the simulation data.

5. The composite material step-by-step electromagnetic forming method according to claim 3, characterized in that: The loss function of the second network model during training includes a first reconstruction loss part and a second part loss: The first reconstruction loss part includes: in, For the generator, For the encoder, is the output of the generator, is the input of the encoder; represents the Euclidean distance, represents the first reconstruction loss part; The second part of the loss includes: in, is the discriminator, is the intermediate variable, which is obtained by resampling in the normal distribution space. is the input of the encoder, is the output of the encoder, For the generator, For the encoder, Indicates the second part of the loss.

6. The composite material step-by-step electromagnetic forming method according to claim 1, characterized in that: The simulation data and the similar simulation data are spliced ​​and then processed in data format to obtain training samples, including: splicing the simulation data and the similar simulation data row by row to obtain spliced ​​data; The input-output relationship of the spliced ​​data is adjusted to obtain a training sample; the training sample includes a first training sample and a second training sample; the input items of the first training sample are electromagnetic parameters, workpiece parameters, process parameters and maximum deformation, and the output item of the first training sample is an adjustment parameter; the input items of the second training sample are electromagnetic parameters, workpiece parameters, process parameters and maximum deformation, and the output item of the second training sample is maximum stress.

7. The composite material step-by-step electromagnetic forming method according to claim 6, characterized in that: The third network model includes a first sub-model and a second sub-model; and using the training sample to train the third network model includes: Training the first sub-model using the first training sample; The second sub-model is trained using the second training sample; the trained first sub-model includes a mapping relationship between electromagnetic parameters, workpiece parameters, process parameters, and maximum deformation to adjustment parameters; the trained second sub-model includes a mapping relationship between electromagnetic parameters, workpiece parameters, process parameters, and maximum deformation to maximum stress.

8. The composite material step-by-step electromagnetic forming method according to claim 7, characterized in that: The loss function of the first sub-model during training is: in, To adjust the parameter, that is, the distance between the coil and the lowest point of the substrate mold, Generate parameters for the corresponding generator, The first submodel Prediction parameters, m is the data volume of LSTM, LSTM is the model; The loss function of the second sub-model during training is: in, is the maximum stress of the workpiece, Generate stresses for the generator, Predict stress for the second sub-model LSTM2, m is the data volume of LSTM, and LSTM is the model.

9. The composite material step-by-step electromagnetic forming method according to claim 1, characterized in that: Controlling the screw lifter to adjust the distance between the discharge coil and the lowest point of the base mold includes: The linear quadratic regulator is used to control the screw lift to adjust the distance between the discharge coil and the lowest point of the base mold.

10. A composite material step-by-step electromagnetic forming system, characterized in that: The composite material step-by-step electromagnetic forming system comprises a movable platform, a composite network model and a processor; the movable platform comprises a base mold, a discharge coil and a screw lifter; the base mold is used to place a workpiece for processing; the discharge coil is used to generate electromagnetic force, so that the workpiece is formed under the action of the electromagnetic force; The screw lift is used to adjust the distance between the discharge coil and the lowest point of the base mold; the composite network model includes a first network model, a second network model and a third network model; the processor is used to execute the steps of the method described in any one of claims 1 to 9.

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