A method for processing laminated boards and a production line

Abstract

The present invention discloses a method and a production line for laminar board processing, which relate to the technical field of laminar board processing. The method includes: matching the unwinding control parameters of the corresponding unwinding equipment based on the material parameters and processing attributes of each metal sheet to transfer each metal sheet into a polishing machine; determining the corresponding polishing information based on the roughness analysis and polishing influence analysis of each metal sheet, and the polishing machine polishes each metal sheet based on the polishing information; a first heating device preheats each polished metal sheet based on a first adaptive controller; a rolling device rolls each preheated metal sheet to obtain an initial laminar board; a second heating device reheats the initial laminar board based on a second adaptive controller; a shearing mechanism shears the initial laminar board after the reheating process to obtain a target laminar board, and a winding machine winds the target laminar board. The present invention forms a complete process for laminar board processing, making the quality of the obtained target laminar board more reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of laminate processing, and in particular, to a laminate processing method and production line. Background Art

[0002] A laminate is a structure formed by pressing multiple metal sheets, which is widely used in fields such as equipment manufacturing. The processing quality of the laminate affects the reliability of equipment use. In this regard, how to improve the processing quality of the laminate has become the research focus of various enterprises. In the current laminate processing methods, usually, the polishing information of the metal sheets is determined through manual analysis. However, this method is too dependent on the professional qualities of relevant personnel and cannot ensure the reliability of the obtained polishing information, affecting the polishing quality. At the same time, currently, during the heating process of the metal sheets, there is a lack of adaptive control during the heating process, resulting in a large deviation between the heat treatment of the metal sheets and the expected effect, making it impossible for each metal sheet to form the required laminate well, seriously affecting the processing quality of the laminate. At the same time, how to form a complete and comprehensive process for the processing of the laminate is also an issue that needs to be considered. Only in this way can the processing reliability of the laminate be improved. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art. The present invention provides a laminate processing method and production line, forming a complete process for laminate processing, making the quality of the obtained target laminate more reliable.

[0004] To solve the above technical problems, the present invention provides a laminate processing method, which is applied to a laminate processing production line. The laminate processing production line includes a plurality of unwinding devices, a polishing machine, a plurality of heating devices, a rolling device, a shearing mechanism, and a winding machine. The method includes:

[0005] Based on the material parameters and processing attributes of each metal sheet, matching the unwinding control parameters of the corresponding unwinding device, and each unwinding device transports each metal sheet into the polishing machine based on the corresponding unwinding control parameters;

[0006] Based on the roughness analysis and polishing influence analysis of each metal sheet, determining the corresponding polishing information, and the polishing machine performs polishing treatment on each metal sheet based on the corresponding polishing information, and transports the polished metal sheets into the first heating device;

[0007] The first heating device performs preheating treatment on the polished metal sheets based on the first adaptive controller, and transports the preheated metal sheets into the rolling device;

[0008] The rolling device performs rolling treatment on the preheated metal sheets to obtain an initial laminate;

[0009] The second heating device reheats the initial laminate based on the second adaptive controller to obtain the reheated initial laminate;

[0010] The shearing mechanism shears the reheated initial laminate to obtain the target laminate, and the coiler winds up the target laminate.

[0011] Optionally, the unwind control parameters corresponding to the unwind devices are matched based on the material parameters and processing attributes of each metal sheet, including:

[0012] Determine the starting rotation speeds of the corresponding unwind devices based on the material parameters of each metal sheet combined with the coil diameter data;

[0013] Match the operating rotation speeds of the corresponding unwind devices based on the processing attributes of each metal sheet, and use the starting rotation speed and the operating rotation speed as the unwind control parameters of the corresponding unwind devices.

[0014] Optionally, the corresponding polishing information is determined based on the roughness analysis and polishing influence analysis of each metal sheet, including:

[0015] Perform apparent feature value analysis on the apparent images of each metal sheet to obtain the corresponding apparent feature values, and determine the first roughness data of each metal sheet based on the apparent feature values;

[0016] Input the apparent images of each metal sheet into the roughness analysis model to obtain the second roughness data, where the roughness analysis model includes a feature extraction network, a cross-mixed attention module, and a graph convolutional channel attention module;

[0017] Determine the target roughness data based on the first roughness data and the second roughness data;

[0018] Perform material polishing influence analysis and flat polishing influence analysis based on the material parameters and flatness of each metal sheet to obtain the first polishing influence data and the second polishing influence data;

[0019] Perform environmental polishing influence analysis based on the topological network using real-time environmental information to obtain the third polishing influence data;

[0020] Determine the corresponding polishing information based on the target roughness data, the first polishing influence data, the second polishing influence data, and the third polishing influence data.

[0021] Optionally, the first heating device preheats each polished metal sheet based on the first adaptive controller, including:

[0022] Construct the first adaptive controller based on the PID controller and the state error system;

[0023] During the preheating process of each polished metal sheet by the first heating device, calculate the first deviation value between the current temperature of each polished metal sheet and the first preset target temperature;

[0024] Obtain the temperature change curve of the preheating of past metal sheets under PID parameter control, and establish fuzzy rules based on the temperature change curve;

[0025] Based on the first deviation value, use the first adaptive controller combined with fuzzy rules to generate the adjustment control parameters of the first heating device, and optimize the adjustment control parameters based on the whale optimization algorithm to obtain the optimized adjustment control parameters;

[0026] The first heating device adjusts the temperature during the preheating process based on the optimized adjustment control parameters.

[0027] Optionally, the construction of the first adaptive controller based on the PID controller and the state error system includes:

[0028] Establish an object model for each polished metal sheet, construct a fractional-order complex network system based on the object model, and construct a state error system based on the fractional-order complex network system;

[0029] Calculate the transfer function based on the preset proportional coefficient, preset integral coefficient, and preset differential coefficient, and generate a conventional PID controller based on the transfer function;

[0030] Tune the parameters of the conventional PID controller to obtain the PID controller with tuned parameters;

[0031] Construct the first adaptive controller based on the PID controller with tuned parameters and the state error system.

[0032] Optionally, the rolling device performs rolling on each preheated metal sheet to obtain an initial laminate, including:

[0033] Determine the control parameters of the rolling device based on the viscoelastic model and material parameters of each preheated metal sheet in combination with the preset desired laminate thickness;

[0034] The rolling device performs rolling on each preheated metal sheet based on the control parameters to obtain an initial laminate.

[0035] Optionally, the second heating device performs reheating on the initial laminate based on the second adaptive controller, including:

[0036] During the reheating process of the initial laminate, detect the real-time temperature of the initial laminate during reheating, and calculate the second deviation value between the real-time temperature and the second preset target temperature;

[0037] The second adaptive controller generates an adjustment parameter by using the second deviation value, and adjusts the control parameter of the second heating device in real time based on the adjustment parameter.

[0038] Optionally, the shearing mechanism shears the initial laminate after reheating treatment to obtain a target laminate, including:

[0039] Cool the initial laminate after reheating treatment to obtain a cooled initial laminate;

[0040] Perform shear parameter analysis on the cooled initial laminate to obtain shear control parameters, and perform laminate shear simulation based on the shear control parameters to obtain laminate shear simulation results;

[0041] Perform shear quality evaluation based on the laminate shear simulation results to obtain a shear quality evaluation coefficient;

[0042] Optimize the shear control parameters by using the optimization space based on the shear quality evaluation coefficient to obtain optimized shear control parameters;

[0043] The shearing mechanism shears the cooled initial laminate based on the optimized shear control parameters to obtain a target laminate.

[0044] Optionally, the coiling machine performs coiling treatment on the target laminate, including:

[0045] Determine the coiling control parameter of the coiling machine based on the thickness data and material parameters of the target laminate in combination with the processing attributes, and the coiling machine performs coiling treatment on the target laminate based on the coiling control parameter.

[0046] In addition, the present invention also provides a laminate processing production line, which includes a plurality of uncoiling devices, a polishing machine, a plurality of heating devices, a rolling device, a shearing mechanism and a coiling machine, and the laminate processing production line is configured to execute the above laminate processing method.

[0047] In the embodiments of the present invention, the unwinding control parameters of the corresponding unwinding equipment are matched based on the material parameters and processing attributes of each metal sheet, so that the matched unwinding control parameters are more in line with the actual situation of each metal sheet. Based on the roughness analysis and polishing influence analysis of each metal sheet, the corresponding polishing information is determined, and the polishing machine polishes each metal sheet based on the corresponding polishing information, which can ensure the reliability of the polishing information analysis and effectively improve the polishing quality of each metal sheet. The first heating device preheats each metal sheet after polishing treatment based on the first adaptive controller, which can effectively regulate the preheating process of each metal sheet and avoid a large deviation between the actual preheating effect and the expected effect. Based on the viscoelastic model and material parameters of each metal sheet after preheating treatment and in combination with the preset expected laminate thickness, the control parameters of the rolling device are determined to roll each metal sheet after preheating treatment to obtain an initial laminate, so that the thickness of the obtained initial laminate reaches the expected thickness. The second heating device reheats the initial laminate based on the second adaptive controller, the shearing mechanism shears the initial laminate after reheating treatment to obtain a target laminate, and the winding machine winds the target laminate, forming a complete process for laminate processing and making the quality of the obtained target laminate more reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a schematic flowchart of the laminate processing method in the embodiments of the present invention;

[0049] Figure 2 is a schematic structural diagram of the laminate processing production line in the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] Embodiment 1:

[0052] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of the laminate processing method in the embodiments of the present invention. The method is applied to a laminate processing production line, and the laminate processing production line includes a plurality of unwinding devices, a polishing machine, a plurality of heating devices, a rolling device, a shearing mechanism, and a winding machine. The method includes:

[0053] S11: Match the unwinding control parameters of the corresponding unwinding equipment based on the material parameters and processing attributes of each metal sheet. Each unwinding equipment transports each metal sheet into the polishing machine based on the corresponding unwinding control parameters.

[0054] In the specific implementation process of the present invention, the matching of the unwinding control parameters of the corresponding unwinding equipment based on the material parameters and processing attributes of each metal sheet includes: determining the starting rotation speed of the corresponding unwinding equipment based on the material parameters of each metal sheet combined with the coil diameter data; matching the operating rotation speed of the corresponding unwinding equipment based on the processing attributes of each metal sheet, and taking the starting rotation speed and the operating rotation speed as the unwinding control parameters of the corresponding unwinding equipment.

[0055] Specifically, determine the starting rotation speed of the corresponding unwinding equipment based on the material parameters of each metal sheet combined with the coil diameter data. The material parameters include the material type, tensile properties, thickness, etc. of the metal sheet, and the coil diameter data includes the winding radius of the metal sheet and the unwinding parameters of the unwinding equipment, etc. Match the starting rotation speed of the corresponding unwinding equipment in the database through the material parameters of each metal sheet combined with the coil diameter data. Match the operating rotation speed of the corresponding unwinding equipment based on the processing attributes of each metal sheet, and take the starting rotation speed and the operating rotation speed as the unwinding control parameters of the corresponding unwinding equipment. The processing attribute refers to the processing method and process of the metal sheet. Determine the intermittent stop parameters for each metal sheet according to the processing attributes of each metal sheet, such as the intermittent stop duration and the intermittent stop distance, etc. Determine the operating rotation speed of each unwinding equipment according to the intermittent stop parameters, and take the starting rotation speed and the operating rotation speed as the unwinding control parameters of the corresponding unwinding equipment. Different metal sheets are unwound by different unwinding equipment. At the same time, in order to make the subsequent processing proceed synchronously, the starting rotation speed and the operating rotation speed can be adjusted so that each metal sheet can be transported into the polishing machine at the same time. Each unwinding equipment transports each metal sheet into the polishing machine based on the corresponding unwinding control parameters.

[0056] S12: Determine the corresponding polishing information based on the roughness analysis and polishing influence analysis of each metal sheet. The polishing machine polishes each metal sheet based on the corresponding polishing information and transports the polished metal sheets into the first heating device.

[0057] In the specific implementation process of the present invention, determining the corresponding polishing information based on the roughness analysis and polishing influence analysis of each metal sheet includes: performing apparent feature value analysis on the apparent images of each metal sheet to obtain corresponding apparent feature values, and determining the first roughness data of each metal sheet based on the apparent feature values; inputting the apparent images of each metal sheet into a roughness analysis model to obtain second roughness data, where the roughness analysis model includes a feature extraction network, a cross-mixed attention module, and a graph convolutional channel attention module; determining target roughness data based on the first roughness data and the second roughness data; performing material polishing influence analysis and flat polishing influence analysis based on the material parameters and flatness of each metal sheet to obtain first polishing influence data and second polishing influence data; performing environmental polishing influence analysis using real-time environmental information based on a topological network to obtain third polishing influence data; and determining the corresponding polishing information based on the target roughness data, the first polishing influence data, the second polishing influence data, and the third polishing influence data.

[0058] Specifically, the roughness of the metal sheet is usually not recorded in its parameter table, but its roughness is one of the factors determining the polishing information. Therefore, it is necessary to analyze the roughness of each metal sheet. Analyze the apparent feature values of the apparent images of each metal sheet. Collect the apparent images of each metal sheet through the corresponding imaging device. Divide the apparent images into grids to obtain a set of apparent image grids. Perform discrete cosine transform on each apparent image grid to obtain discrete cosine coefficients. Extract the direct current coefficient and the alternating current coefficient according to the discrete cosine coefficients. Calculate the feature values based on the direct current coefficient and the alternating current coefficient. Calculate the average value of the feature values of each grid to obtain the corresponding apparent feature value. Determine the first roughness data of each metal sheet based on the apparent feature value, that is, match the first roughness of each metal sheet according to the apparent feature value. Input the apparent images of each metal sheet into the roughness analysis model to obtain the second roughness data. The roughness analysis model includes a feature extraction network, a cross-mixed attention module, and a graph convolutional channel attention module. Collect the apparent sample images of the metal sheet samples at the same position by configuring image acquisition devices with different light sources as the training image set. The cross-mixed attention module calculates the self-attention mechanism using the features of different rows and columns. It extracts the feature information of different light sources and performs spatial feature fusion on the feature information. The graph convolutional channel attention module calculates the correlation between each channel between different light sources in a self-attention manner as the information of the edges in the graph convolution and splices them in the channel dimension to obtain the mixed channel feature information. The feature extraction network is a multi-branch structure. Train the initial roughness analysis model through the training image set to obtain a trained roughness analysis model. Training the model with the apparent sample images of metal sheets with different light sources can avoid the influence of the apparent images of metal sheets on the roughness analysis of the model. At the same time, the cross-mixed attention module and the graph convolutional channel attention module are introduced, enabling the model to have stronger feature learning ability, improving the analysis efficiency and accuracy, and finally outputting the second roughness through the roughness analysis model. Determine the target roughness data based on the first roughness data and the second roughness data. The target roughness can be calculated through the first roughness data, the second roughness data, and their corresponding weight coefficients. Calculating the target roughness data through the first roughness data and the second roughness data can avoid the one-sidedness and limitations brought by single roughness analysis.Based on the material parameters and flatness of each metal sheet, conduct material polishing impact analysis and flatness polishing impact analysis. Different material parameters and flatness of metal sheets will affect polishing. The flatness can be the difference between the highest point and the lowest point between the surface of each metal sheet and a preset reference plane. Input the material parameters of each metal sheet into the material polishing impact analysis model to obtain the first polishing impact data, that is, the impact degree of different materials on polishing. Input the flatness of each metal sheet into the flatness polishing impact analysis model. The material polishing impact analysis model and the flatness polishing impact analysis model can adopt deep convolutional neural networks to obtain the second polishing impact data, that is, the impact degree of different flatness on polishing. Based on the topological network, use real-time environmental information to conduct environmental polishing impact analysis, detect the real-time environmental information of the current environment. The real-time environmental information includes air dust particles, temperature, humidity, etc. For example, if there are too many air dust particles, they will adhere to the surface of the metal sheet, resulting in scratches during the polishing process. If the temperature is too high, it will accelerate the volatilization of the polishing machine, etc. Construct a feature data matrix through the polishing state environmental impact record data set. According to the feature data matrix, generate several environmental impact factors, evaluate the several environmental impact factors to obtain the corresponding environmental impact characteristic values, construct a topological network through the environmental impact factors and the corresponding environmental impact characteristic values, input the real-time environmental information into the topological network to obtain the impact degree of the environmental information on polishing, that is, obtain the third polishing impact data. Determine the corresponding polishing information based on the target roughness data, the first polishing impact data, the second polishing impact data, and the third polishing impact data. Determine the target polishing impact data according to the first polishing impact data, the second polishing impact data, and the third polishing impact data. The target polishing impact data can be determined through weight coefficients. Determine the corresponding polishing information for each metal sheet according to the target roughness data and the target polishing impact data. The polishing machine polishes each metal sheet based on the corresponding polishing information to obtain the polished metal sheets, and transmits the polished metal sheets to the first heating device.

[0059] S13: The first heating device preheats the polished metal sheets based on the first adaptive controller and transmits the preheated metal sheets to the rolling device;

[0060] In the specific implementation process of the present invention, the first heating device preheats each polished metal sheet based on a first adaptive controller, including: constructing a first adaptive controller based on a PID controller and a state error system; calculating a first deviation value between the current temperature of each polished metal sheet and a first preset target temperature during the preheating process of each polished metal sheet by the first heating device; obtaining the temperature change curve of the preheating of past metal sheets under PID parameter control, and establishing fuzzy rules based on the temperature change curve; generating adjustment control parameters of the first heating device by combining the first deviation value with the first adaptive controller and the fuzzy rules, and optimizing the adjustment control parameters based on the whale optimization algorithm to obtain optimized adjustment control parameters; the first heating device adjusts the temperature during the preheating process based on the optimized adjustment control parameters.

[0061] Further, constructing the first adaptive controller based on the PID controller and the state error system includes: establishing an object model of each polished metal sheet, constructing a fractional-order complex network system based on the object model, and constructing a state error system based on the fractional-order complex network system; calculating a transfer function based on a preset proportional coefficient, a preset integral coefficient, and a preset differential coefficient, and generating a conventional PID controller based on the transfer function; performing parameter tuning on the conventional PID controller to obtain a PID controller after parameter tuning; constructing a first adaptive controller based on the PID controller after parameter tuning and the state error system.

[0062] Specifically, each metal sheet after polishing is transferred to the first heating device for preheating in order to make each metal sheet sticky after preheating so that each metal sheet can be subsequently rolled. During the preheating process, the heating temperature of the metal sheet will inevitably deviate. Therefore, the temperature needs to be adjusted in time according to the deviation to avoid affecting the subsequent processing. An object model of each metal sheet after polishing is established. The object model is an abstraction of the entity of each metal sheet. The object is taken as a controlled object. A preset level of abstraction is made through the controlled object and the corresponding function to form a corresponding object model. A fractional-order complex network system is constructed based on the object model. A differential equation corresponding to the object model is established. Fractional orders are taken through fractional derivatives. The fractional derivatives are fractional derivatives in the sense of Caputo. A fractional-order complex network system is constructed by using the corresponding differential equation through fractional orders. A state error system is constructed based on the fractional-order complex network system. Considering the existence of uncertain and unpredictable nonlinear states, non-periodic orbits and equivalent disturbances for parameters corresponding to nonlinear states are introduced to construct a target state equation. A state error system between the object model and the fractional-order complex network system is constructed based on the target state equation. A transfer function is calculated based on a preset proportional coefficient, a preset integral coefficient and a preset differential coefficient, and a conventional proportional-integral-differential (PID) controller is generated based on the transfer function. The conventional PID controller is parameterized, equivalent processing is performed based on the object model to obtain an equivalent model, and the equivalent model is decomposed and calculated to obtain the first-order derivative and the second-order derivative; the first-order inertia transfer function is generated based on the first-order derivative and the second-order derivative combined with the preset filter constant; the first-order inertia transfer function is decomposed to obtain the target parameter, and the conventional PID controller is parameterized based on the target parameter to obtain the PID controller after parameter tuning. A first adaptive controller is constructed based on the parameter-tuned PID controller and the state error system, and the state error system is subjected to an observation system equivalent disturbance by an extended state observer to obtain an extended state vector, and an extended state feedback compensation is obtained by using the extended state vector and the preset bandwidth parameter, and a first adaptive controller is constructed based on the extended state feedback compensation and the PID controller after parameter tuning. During the preheating process of each metal sheet after polishing by the first heating device, the current temperature of each metal sheet is detected by the temperature detector built into the first heating device, and the first deviation value between the current temperature of each metal sheet after polishing and the first preset target temperature is calculated.Obtain the temperature change curve of the preheating of past metal sheets under PID parameter control. By using PID parameters to simulate the preheating process of past metal sheets, corresponding data is obtained. According to the corresponding data, the temperature change curve is plotted, and then the temperature change curve is stored in the corresponding database. Through the corresponding database, the temperature change curve can be quickly obtained, and fuzzy rules are established based on the temperature change curve. The dynamic characteristics of temperature change are extracted from the temperature change curve. Through the dynamic characteristics, an adjustment speed rule, a static error elimination rule, and a controller oscillation error elimination rule are set. The adjustment speed rule, the static error elimination rule, and the controller oscillation error elimination rule constitute the fuzzy rules. The adjustment speed rule is that when the current real-time temperature is much lower than the preset target temperature, the adjustment speed should be increased to reach the preset target temperature as soon as possible. When the current real-time temperature is lower than the preset target temperature, but the difference between it and the preset target temperature is not large, the adjustment speed can be appropriately slowed down. The static error elimination rule is that in order to make the temperature of the heating device reach the preset target temperature value as soon as possible and be stable, the integral action should be increased in the adjacent area of the preset target temperature. The controller oscillation error elimination rule is to introduce an early differential control signal to quickly correct the deviation generated by the controller and reduce the possible overshoot. Based on the first deviation value, the first adaptive controller combines the fuzzy rules to generate the adjustment control parameters of the first heating device. The first deviation value is input into the first adaptive controller through the fuzzy rules for operation to obtain the adjustment control parameters, which are the adjustment coefficients for the temperature parameters of the first heating device. And the adjustment control parameters are optimized based on the whale optimization algorithm. The adjustment control parameters are optimized in the optimization space according to the whale optimization algorithm until the preset number of iterations. When the optimization iteration ends, the optimized adjustment control parameters are obtained. The first heating device adjusts the temperature during the preheating process based on the optimized adjustment control parameters. During the preheating process, the temperature deviation value is continuously detected to adjust the temperature through the first adaptive controller until the preheating process ends, so that the first heating device can correct the deviation in time. Then, each preheated metal sheet is transferred to the rolling device.

[0063] S14: The rolling device performs rolling processing on each preheated metal sheet to obtain an initial laminate;

[0064] In the specific implementation process of the present invention, the rolling device performs rolling processing on each preheated metal sheet to obtain an initial laminate, including: determining the control parameters of the rolling device based on the viscoelastic model and material parameters of each preheated metal sheet in combination with the preset expected laminate thickness; the rolling device performs rolling processing on each preheated metal sheet based on the control parameters to obtain an initial laminate.

[0065] Specifically, based on the viscoelastic models and material parameters of the preheated metal sheets, the control parameters of the rolling device are determined in combination with the preset expected laminate thickness. The viscoelastic models corresponding to the preheated metal sheets are constructed through the viscoelastic stress, pure viscous stress components, and strain rate tensors of the preheated metal sheets. The preset expected laminate thickness is the expected thickness after the laminate is formed by rolling. The corresponding control parameters are matched according to the viscoelastic models and material parameters in combination with the preset expected laminate thickness. The control parameters include driving power, rolling accuracy, etc. The rolling device performs rolling treatment on the preheated metal sheets based on the control parameters to obtain an initial laminate, and then transfers the initial laminate to the second heating device.

[0066] S15: The second heating device performs reheating treatment on the initial laminate based on the second adaptive controller to obtain the reheated initial laminate;

[0067] In the specific implementation process of the present invention, the second heating device performing reheating treatment on the initial laminate based on the second adaptive controller includes: during the reheating treatment of the initial laminate, detecting the real-time temperature of the initial laminate during the reheating process, and calculating the second deviation value between the real-time temperature and the second preset target temperature; generating an adjustment parameter based on the second deviation value by using the second adaptive controller, and adjusting the control parameters of the second heating device in real time based on the adjustment parameter.

[0068] Specifically, the second heating device performs reheating treatment on the initial laminate to further better bond the metal sheets in the initial laminate together. During the reheating treatment of the initial laminate, detecting the real-time temperature of the initial laminate during the reheating process, and calculating the second deviation value between the real-time temperature and the second preset target temperature. Generating an adjustment parameter based on the second deviation value by using the second adaptive controller. The construction steps of the second adaptive controller are the same as those of the first adaptive controller, replacing the object model with the object model of the initial laminate, and then the remaining steps are the same. And adjusting the control parameters of the second heating device in real time based on the adjustment parameter. The second heating device adjusts the temperature during the reheating process according to the adjusted control parameters to avoid temperature deviation until the initial laminate reaches the preset temperature of the reheating process, and then transfers the target laminate after reheating treatment to the shearing mechanism.

[0069] S16: The shearing mechanism shears the initial laminate after reheating treatment to obtain the target laminate, and the winder winds up the target laminate.

[0070] In the specific implementation process of the present invention, the shearing mechanism shears the initial laminate after reheating treatment to obtain a target laminate, including: cooling the initial laminate after reheating treatment to obtain the cooled initial laminate; analyzing the shearing parameters of the cooled initial laminate to obtain shearing control parameters, and performing laminate shearing simulation based on the shearing control parameters to obtain a laminate shearing simulation result; evaluating the shearing quality based on the laminate shearing simulation result to obtain a shearing quality evaluation coefficient; optimizing the shearing control parameters using the optimization space based on the shearing quality evaluation coefficient to obtain optimized shearing control parameters; and the shearing mechanism shears the cooled initial laminate based on the optimized shearing control parameters to obtain a target laminate.

[0071] Further, the winding machine winds the target laminate, including: determining the winding control parameters of the winding machine based on the thickness data and material parameters of the target laminate in combination with the processing attributes, and the winding machine winds the target laminate based on the winding control parameters.

[0072] Specifically, to cool the initial laminate after reheating treatment, a cooling device is provided in the shearing mechanism, and the initial laminate is cooled by the cold air outlet of the cooling device to obtain the cooled initial laminate. Analyzing the shearing parameters of the cooled initial laminate, that is, analyzing the shearing control parameters of the shearing mechanism for the cooled initial laminate. The shearing control parameters include the power of the shearing mechanism, the shearing width, etc. Based on the shearing control parameters, a laminate shearing simulation is performed, and the shearing control parameters are input into the simulation software for laminate shearing simulation to obtain a laminate shearing simulation result. Evaluating the shearing quality based on the laminate shearing simulation result, and performing quality evaluations such as shearing accuracy and shearing surface roughness on the laminate shearing simulation result according to the shearing quality evaluation model. The shearing quality evaluation model is constructed by multiple linear regression and random forest models to obtain a shearing quality evaluation coefficient. Optimizing the shearing control parameters using the optimization space based on the shearing quality evaluation coefficient, and the shearing control parameters can be optimized using the shearing quality evaluation coefficient through an optimization algorithm based on the optimization space to obtain optimized shearing control parameters. The shearing mechanism shears the cooled initial laminate based on the optimized shearing control parameters to obtain a target laminate. Determining the winding control parameters of the winding machine based on the thickness data and material parameters of the target laminate in combination with the processing attributes, and the winding machine winds the target laminate based on the winding control parameters. After completing the winding process of the target laminate, the overall processing process of the laminate is completed.

[0073] In the embodiments of the present invention, the unwinding control parameters of the corresponding unwinding equipment are matched based on the material parameters and processing attributes of each metal sheet, so that the matched unwinding control parameters are more in line with the actual situation of each metal sheet. Based on the roughness analysis and polishing influence analysis of each metal sheet, the corresponding polishing information is determined. The polishing machine polishes each metal sheet based on the corresponding polishing information, which can ensure the reliability of the polishing information analysis and effectively improve the polishing quality of each metal sheet. The first heating device preheats each metal sheet after polishing treatment based on the first adaptive controller, which can effectively regulate the preheating process of each metal sheet and avoid a large deviation between the actual preheating effect and the expected effect. Based on the viscoelastic model and material parameters of each metal sheet after preheating treatment and combined with the preset expected laminate thickness, the control parameters of the rolling device are determined to roll each metal sheet after preheating treatment to obtain an initial laminate, so that the thickness of the obtained initial laminate reaches the expected thickness. The second heating device reheats the initial laminate based on the second adaptive controller, the shearing mechanism shears the initial laminate after reheating treatment to obtain the target laminate, and the winding machine winds the target laminate, forming a complete process for laminate processing, making the quality of the obtained target laminate more reliable.

[0074] Embodiment 2:

[0075] Please refer to Figure 2 , Figure 2 which is a schematic structural diagram of the laminate processing production line in the embodiments of the present invention.

[0076] As Figure 2 shown, the laminate processing production line includes a plurality of unwinding devices 1, a polishing machine, a plurality of heating devices, a rolling device 3, a shearing mechanism 5, and a winding machine 6, so that the laminate processing production line executes the laminate processing method in the above embodiments.

[0077] In the specific implementation process of the present invention, the unwinding device 1 is used to unwind each metal sheet, the polishing machine is used to polish each metal sheet, the heating devices include a first heating device 2 and a second heating device 4. The first heating device 2 is used to preheat each metal sheet, the second heating device 4 is used for reheating treatment, the rolling device 3 is used to roll each metal sheet to form an initial laminate, the shearing mechanism 5 is used to perform cooling and shearing treatment on the initial laminate, and the winding machine 6 is used to wind the laminate. Figure 2 The shown laminate processing production line does not limit all components, and may include more or fewer components than shown, or combine certain components. The specific implementation manner of the laminate processing production line can refer to the above embodiments and will not be elaborated here.

[0078] In the embodiments of the present invention, the unwinding control parameters of the corresponding unwinding equipment are matched based on the material parameters and processing attributes of each metal sheet, so that the matched unwinding control parameters are more in line with the actual situation of each metal sheet. The corresponding polishing information is determined based on the roughness analysis and polishing influence analysis of each metal sheet. The polishing machine polishes each metal sheet based on the corresponding polishing information, which can ensure the reliability of the polishing information analysis and effectively improve the polishing quality of each metal sheet. The first heating device 2 preheats each metal sheet after polishing based on the first adaptive controller, which can effectively regulate the preheating process of each metal sheet and avoid a large deviation between the actual preheating effect and the expected effect. The control parameters of the rolling device are determined based on the viscoelastic model and material parameters of each metal sheet after preheating and in combination with the preset expected laminate thickness, so as to roll each metal sheet after preheating to obtain an initial laminate, and make the thickness of the obtained initial laminate reach the expected thickness. The second heating device 4 reheats the initial laminate based on the second adaptive controller, the shearing mechanism 5 shears the initial laminate after reheating to obtain the target laminate, and the winding machine 6 winds the target laminate, forming a complete process for laminate processing and making the quality of the obtained target laminate more reliable.

[0079] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.

[0080] In addition, the above has introduced in detail a laminate processing method and production line provided by the embodiments of the present invention. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for processing a laminated board, characterized in that, Applied to a laminated board processing production line, the laminated board processing production line includes several unwinding devices, a polishing machine, several heating devices, a rolling device, a shearing mechanism, and a winding machine; the method includes: Based on the material parameters and processing attributes of each metal sheet, matching the unwinding control parameters of the corresponding unwinding device, and each unwinding device transports each metal sheet into the polishing machine based on the corresponding unwinding control parameters; Based on the roughness analysis and polishing influence analysis of each metal sheet, determining the corresponding polishing information, and the polishing machine performs polishing treatment on each metal sheet based on the corresponding polishing information, and transports each polished metal sheet into the first heating device; The first heating device performs preheating treatment on each polished metal sheet based on the first adaptive controller, and transports each preheated metal sheet into the rolling device; The rolling device performs rolling treatment on each preheated metal sheet to obtain an initial laminated board; The second heating device performs reheating treatment on the initial laminated board based on the second adaptive controller to obtain the initial laminated board after reheating treatment; The shearing mechanism shears the initial laminated board after reheating treatment to obtain a target laminated board, and the winding machine winds the target laminated board; Among them, the determining the corresponding polishing information based on the roughness analysis and polishing influence analysis of each metal sheet includes: performing apparent feature value analysis on the apparent image of each metal sheet to obtain the corresponding apparent feature value, and determining the first roughness data of each metal sheet based on the apparent feature value; inputting the apparent image of each metal sheet into a roughness analysis model to obtain second roughness data, and the roughness analysis model includes a feature extraction network, a cross-mixed attention module, and a graph convolutional channel attention module; determining the target roughness data based on the first roughness data and the second roughness data; performing material polishing influence analysis and flatness polishing influence analysis based on the material parameters and flatness of each metal sheet to obtain first polishing influence data and second polishing influence data; performing environmental polishing influence analysis based on the topological network using real-time environmental information to obtain third polishing influence data; determining the corresponding polishing information based on the target roughness data, the first polishing influence data, the second polishing influence data, and the third polishing influence data.

2. The panel processing method according to claim 1, characterized in that The matching the unwinding control parameters of the corresponding unwinding device based on the material parameters and processing attributes of each metal sheet includes: Based on the material parameters of each metal sheet combined with the coil diameter data, determining the starting rotation speed of the corresponding unwinding device; Based on the processing attributes of each metal sheet, matching the operating rotation speed of the corresponding unwinding device, and taking the starting rotation speed and the operating rotation speed as the unwinding control parameters of the corresponding unwinding device.

3. The panel processing method according to claim 1, characterized in that, The first heating device performs preheating treatment on each polished metal sheet based on the first adaptive controller, including: Constructing the first adaptive controller based on a PID controller and a state error system; During the process of the first heating device performing preheating treatment on each polished metal sheet, calculating the first deviation value between the current temperature of each polished metal sheet and the first preset target temperature; Obtain the temperature change curve of the preheating of past metal sheets under PID parameter control, and establish fuzzy rules based on the temperature change curve; Generate the adjustment control parameters of the first heating device based on the first deviation value using the first adaptive controller in combination with the fuzzy rules, and optimize the adjustment control parameters based on the whale optimization algorithm to obtain the optimized adjustment control parameters; The first heating device adjusts the temperature during the preheating process based on the optimized adjustment control parameters.

4. The method for processing a laminated board according to claim 3, characterized in that, The construction of the first adaptive controller based on the PID controller and the state error system includes: Establish an object model for each metal sheet after polishing treatment, construct a fractional-order complex network system based on the object model, and construct a state error system based on the fractional-order complex network system; Calculate the transfer function based on the preset proportional coefficient, preset integral coefficient, and preset differential coefficient, and generate a conventional PID controller based on the transfer function; Tune the parameters of the conventional PID controller to obtain the PID controller with tuned parameters; Construct the first adaptive controller based on the PID controller with tuned parameters and the state error system.

5. The veneer processing method according to claim 1, characterized in that, The rolling device performs rolling treatment on each preheated metal sheet to obtain an initial laminate, including: Determine the control parameters of the rolling device based on the viscoelastic model and material parameters of each preheated metal sheet in combination with the preset desired laminate thickness; The rolling device performs rolling treatment on each preheated metal sheet based on the control parameters to obtain an initial laminate.

6. The method for processing the laminate according to claim 1, characterized in that, The second heating device performs reheating treatment on the initial laminate based on the second adaptive controller, including: During the reheating process of the initial laminate, detect the real-time temperature of the initial laminate during reheating, and calculate the second deviation value between the real-time temperature and the second preset target temperature; Generate adjustment parameters based on the second deviation value using the second adaptive controller, and adjust the control parameters of the second heating device in real time based on the adjustment parameters.

7. The method for processing a laminated board according to claim 1, characterized in that, The shearing mechanism shears the initial laminate after reheating treatment to obtain the target laminate, including: Cool the initial laminate after reheating treatment to obtain the cooled initial laminate; Analyze the shearing parameters of the cooled initial laminate to obtain the shearing control parameters, perform laminate shearing simulation based on the shearing control parameters, and obtain the laminate shearing simulation results; Perform shearing quality evaluation based on the laminate shearing simulation results to obtain the shearing quality evaluation coefficient; Optimize the shearing control parameters using the optimization space based on the shearing quality evaluation coefficient to obtain the optimized shearing control parameters; The shearing mechanism shears the cooled initial laminate based on the optimized shearing control parameters to obtain the target laminate.

8. The panel processing method according to claim 1, wherein The winding machine winds the target laminate, including: Determine the winding control parameters of the winding machine based on the thickness data and material parameters of the target laminate in combination with the processing attributes, and the winding machine winds the target laminate based on the winding control parameters.

9. A laminated board processing production line, characterized in that, The laminate processing production line includes a plurality of unwinding devices, a polishing machine, a plurality of heating devices, a rolling device, a shearing mechanism, and a winding machine, and the laminate processing production line is configured to perform the laminate processing method according to any one of claims 1 to 8.

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