Method and system for welding different materials based on hot plate mold

Abstract

The invention discloses a different material welding method and system based on a hot plate mold. The method comprises the steps that the pressure of a preset area of a hot plate is monitored through a force sensor, the temperature of the preset area of the hot plate is monitored through a temperature sensor, and the thickness of a material layer on the hot plate is monitored through a thickness sensor; identifying pre-input material characteristic information of the material layer, and determining a welding pressure range according to a preset pressure-temperature model; training a neural network model according to the historical experiment data set; the welding pressure range and pressure distribution information, temperature information and thickness information in the welding process are input into the neural network model, and optimal distribution pressure information is obtained; and the pressure applied to the preset area of the hot plate by the pressure adjusting device is adjusted in a self-adaptive mode, so that the optimal distribution pressure is achieved till welding is completed. According to the technical scheme, the contact uniformity and the welding quality of two different materials are improved, and the welding defects in a traditional welding method are eliminated.

Description

Technical Field

[0001] The invention relates to the technical field of welding, and in particular to a method for welding different materials based on a hot plate mold and a system for welding different materials based on a hot plate mold. Background Art

[0002] In the hot plate welding process, the control of welding pressure directly affects the welding quality, especially when welding multiple materials. Different materials' elastic modulus, hardness, thickness and other characteristics may lead to uneven contact, which in turn affects the heat conduction effect and welding strength.

[0003] Traditional pressure control methods often make adjustments based on set fixed values ​​instead of real-time adjustments, which can result in poor welding results in certain areas. Summary of the invention

[0004] In response to the above problems, the present invention provides a method and system for welding different materials based on a hot plate mold. By combining the real-time monitoring data of force sensors, temperature sensors and thickness sensors, the welding pressure in the welding process is adaptively and dynamically adjusted according to the optimal distribution pressure information predicted by the neural network model, thereby improving the contact uniformity and welding quality of two different materials. Through precise pressure adjustment and multi-dimensional feedback adjustment, the welding defects in traditional welding methods are eliminated, and ultimately high-quality welding effects are achieved.

[0005] To achieve the above object, the present invention provides a method for welding different materials based on a hot plate mold, comprising:

[0006] The pressure of the preset area of ​​the hot plate is monitored by a force sensor on the surface of the hot plate, the temperature of the preset area of ​​the hot plate is monitored by a temperature sensor, and the thickness of the material layer on the hot plate is monitored by a thickness sensor;

[0007] Identify the pre-input material property information of the material layer, and determine the welding pressure range of the corresponding material according to a predetermined pressure-temperature model;

[0008] A neural network model is trained based on a historical experimental data set mapping material, thickness, and temperature inputs to pressure distribution outputs;

[0009] Inputting the welding pressure range and pressure distribution information, temperature information and thickness information during the welding process into the neural network model to obtain optimal distribution pressure information;

[0010] The pressure applied by the pressure regulating device to the preset area of ​​the hot plate is adaptively adjusted to achieve the optimal distribution pressure until welding is completed.

[0011] In the above technical solution, preferably, the force sensors are respectively installed in different areas of the surface of the hot plate, and the thickness sensor is arranged on the contact surface between the material layers to monitor the contact state between the material layers in real time.

[0012] In the above technical solution, preferably, the identifying of the pre-input material property information of the material layer and determining the welding pressure range of the corresponding material according to a predetermined pressure-temperature model specifically includes:

[0013] Determining the material type of the required welding material layer clamped between the hot plates, and determining the material characteristic information of the material layer according to the pre-input physical properties of different materials;

[0014] According to the pressure-temperature model of different material types, the ideal welding pressure range of the required welding material layer is determined, so that effective welding can be performed with the ideal welding pressure at the preset temperature.

[0015] In the above technical solution, preferably, the neural network model is trained according to the historical experimental data set mapped with the material, thickness and temperature input and the pressure distribution output, and the specific process includes:

[0016] The material type, material layer thickness, and welding temperature are used as inputs, and the pressure distribution data is used as output. The neural network model is trained using historical experimental data as a training set, so that the neural network model can predict the optimal pressure distribution result that best suits the required welding material based on the input data.

[0017] In the above technical solution, preferably, the adaptive adjustment of the pressure applied by the pressure regulating device to the preset area of ​​the hot plate to achieve the optimal distribution pressure includes:

[0018] According to the optimal distribution pressure information, controlling the pressure regulating device to apply pressure to different areas of the hot plate respectively;

[0019] When the temperature of the material layer is above a preset first temperature at the initial stage of welding, applying a pressure within a preset first pressure range;

[0020] During the welding process, the pressure is gradually increased to a preset second pressure range to ensure that the material layers are in good contact;

[0021] When the temperature of the material layer drops below the preset second temperature at the later stage of welding, the pressure is restored to a preset third pressure range to ensure the strength of the weld joint during the cooling and solidification process of the material.

[0022] In the above technical solution, preferably, the adaptively adjusting pressure applied by the pressure regulating device to the preset area of ​​the hot plate to achieve the optimal distributed pressure, the specific process also includes:

[0023] The heating temperature of the material layer and the change of the contact surface thickness during the welding process are monitored in real time according to the temperature sensor and the thickness sensor, and the monitoring result is used as the feedback input of the neural network model;

[0024] The pressure of the material layer during welding is monitored in real time according to the force sensor, and the monitoring result is used as feedback input of the neural network model;

[0025] The neural network model performs feedback regulation control on the pressure applied to the preset area of ​​the hot plate according to the test result data input by feedback.

[0026] In the above technical solution, preferably, the method for welding different materials based on the hot plate mold also includes:

[0027] The displacement of the hot plate is monitored by a motion sensor, images of the welded joints of the material layer are collected, and welding defects of the welded joints are detected by using a pre-trained machine vision detection model;

[0028] The welding pressure is adjusted by taking into account the welding defect problem and the pressure distribution information, temperature information, thickness information and displacement information during the welding process.

[0029] The present invention further proposes a hot plate mold-based welding system for different materials, which uses the hot plate mold-based welding method for different materials disclosed in any one of the above technical solutions, including:

[0030] A parameter acquisition module, used to monitor the pressure of a preset area of ​​the hot plate through a force sensor on the surface of the hot plate, monitor the temperature of the preset area of ​​the hot plate through a temperature sensor, and monitor the thickness of the material layer on the hot plate through a thickness sensor;

[0031] A material determination module, used to identify the pre-input material property information of the material layer, and determine the welding pressure range of the corresponding material according to a predetermined pressure-temperature model;

[0032] A model training module for training a neural network model based on a historical experimental data set mapping material, thickness and temperature inputs to pressure distribution outputs;

[0033] A pressure analysis module, used to input the welding pressure range and pressure distribution information, temperature information and thickness information during the welding process into the neural network model to obtain optimal distribution pressure information;

[0034] The pressure regulating module is used to adaptively adjust the pressure applied by the pressure regulating device to the preset area of ​​the hot plate to achieve the optimal distribution pressure until welding is completed.

[0035] In the above technical solution, preferably, the pressure regulating module is specifically used for:

[0036] According to the optimal distribution pressure information, controlling the pressure regulating device to apply pressure to different areas of the hot plate respectively;

[0037] When the temperature of the material layer is above a preset first temperature at the initial stage of welding, applying a pressure within a preset first pressure range;

[0038] During the welding process, the pressure is gradually increased to a preset second pressure range to ensure that the material layers are in good contact;

[0039] When the temperature of the material layer drops below the preset second temperature at the later stage of welding, the pressure is restored to a preset third pressure range to ensure the strength of the weld joint during the cooling and solidification process of the material.

[0040] In the above technical solution, preferably, the hot plate mold-based different material welding system also includes a visual feedback module, which is specifically used for:

[0041] The displacement of the hot plate is monitored by a motion sensor, images of the welded joints of the material layer are collected, and welding defects of the welded joints are detected by using a pre-trained machine vision detection model;

[0042] The welding pressure is adjusted by taking into account the welding defect problem and the pressure distribution information, temperature information, thickness information and displacement information during the welding process.

[0043] Compared with the prior art, the beneficial effects of the present invention are: by combining the real-time monitoring data of force sensors, temperature sensors and thickness sensors, the welding pressure in the welding process is adaptively and dynamically adjusted according to the optimal distribution pressure information predicted by the neural network model, thereby improving the contact uniformity and welding quality of two different materials, and eliminating welding defects in traditional welding methods through precise pressure regulation and multi-dimensional feedback regulation, ultimately achieving high-quality welding effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic flow chart of a method for welding different materials based on a hot plate mold disclosed in an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of a module of a hot plate mold-based welding system for different materials disclosed in an embodiment of the present invention.

[0046] In the figure, the corresponding relationship between each component and the reference numeral is as follows:

[0047] 1. Parameter acquisition module, 2. Material determination module, 3. Model training module, 4. Pressure analysis module, 5. Pressure regulation module, 6. Visual feedback module. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0050] like Figure 1 As shown, a method for welding different materials based on a hot plate mold according to the present invention comprises:

[0051] The pressure of the preset area of ​​the hot plate is monitored by a force sensor on the surface of the hot plate, the temperature of the preset area of ​​the hot plate is monitored by a temperature sensor, and the thickness of the material layer on the hot plate is monitored by a thickness sensor;

[0052] Identify the material property information of the pre-input material layer, and determine the welding pressure range of the corresponding material according to the predetermined pressure-temperature model;

[0053] A neural network model is trained based on a historical experimental data set mapping material, thickness, and temperature inputs to pressure distribution outputs;

[0054] The welding pressure range and pressure distribution information, temperature information and thickness information during the welding process are input into the neural network model to obtain the optimal distribution pressure information;

[0055] The pressure regulator adaptively adjusts the pressure applied to the preset area of ​​the hot plate to achieve the optimal distributed pressure until welding is completed.

[0056] In this embodiment, by combining the real-time monitoring data of the force sensor, temperature sensor and thickness sensor, the welding pressure in the welding process is adaptively and dynamically adjusted according to the optimal distribution pressure information predicted by the neural network model, thereby improving the contact uniformity and welding quality of the two different materials. Through precise pressure regulation and multi-dimensional feedback regulation, the welding defects in the traditional welding method are eliminated, and ultimately a high-quality welding effect is achieved.

[0057] Among them, the force sensor is used to monitor the contact pressure between the hot plate and the material layer in real time, the temperature sensor is used to detect the heating state of the material during welding to ensure the synchronous control of pressure and temperature, and the thickness sensor is used to monitor the contact state of the two materials in real time.

[0058] According to the force sensor data during the welding process, the system will obtain the pressure distribution information in real time and determine the pressure status of different areas. The temperature sensor and thickness sensor provide the heating conditions of the material and the change of the contact surface during the welding process as feedback input. The entire pressure control process is a closed-loop system. The pressure sensor, temperature sensor and thickness sensor work together to form a monitoring and adjustment mechanism. The real-time data of each round of welding (including temperature, pressure, mechanical feedback, etc.) will be calculated and analyzed in real time by the main control system, and the pressure output will be continuously adjusted to ensure efficient and stable welding.

[0059] In the above embodiment, preferably, force sensors are installed in different areas of the hot plate surface respectively, and thickness sensors are arranged on the contact surface between the material layers for real-time monitoring of the contact state between the material layers.

[0060] The force sensor is installed on the surface of the hot plate to monitor the contact force in real time. Each hot plate can be equipped with multiple sensors to ensure accurate measurement of pressure.

[0061] In the above embodiment, preferably, the material property information of the pre-input material layer is identified, and the welding pressure range of the corresponding material is determined according to a predetermined pressure-temperature model. The specific process includes:

[0062] Determine the material type of the required welding material layer clamped between the hot plates, and determine the material characteristic information of the material layer based on the pre-input physical properties of different materials;

[0063] According to the pressure-temperature model of different material types, the ideal welding pressure range of the required welding material layer is determined, so that effective welding can be performed with the ideal welding pressure at the preset temperature.

[0064] During the implementation process, the system first identifies the material information that has been input in advance and analyzes it based on its physical properties (such as elastic modulus, thermal expansion coefficient, hardness, etc.). Based on the characteristics of different materials, the system determines the preliminary welding pressure range.

[0065] Among them, the hardness and elastic modulus of the material: for materials with higher hardness or greater elasticity, apply lower initial pressure to avoid damage.

[0066] Thickness difference of material layers: If the thickness of the welding material varies greatly, the system will prioritize the pressure distribution in the thin material area.

[0067] During the implementation process, when the control system receives real-time data on pressure and temperature, it determines whether the current pressure is within the ideal welding pressure range based on the predetermined pressure-temperature model. If the pressure is too high or too low, the system will send an adjustment signal to control the actuator to increase or decrease the pressure.

[0068] In the above embodiment, preferably, the neural network model is trained according to the historical experimental data set mapping the material, thickness and temperature input with the pressure distribution output, and the specific process includes:

[0069] Taking material type, material layer thickness, and welding temperature as input and pressure distribution data as output, the neural network model is trained using historical experimental data as a training set, so that the neural network model can predict the optimal pressure distribution result that best suits the required welding material based on the input data.

[0070] In the implementation process, the training process of the neural network model is common knowledge. The training data set and objective function are constructed according to the above input, output and target to complete the training of the neural network model.

[0071] In the above embodiment, preferably, the pressure applied by the pressure regulating device to the preset area of ​​the hot plate is adaptively adjusted to achieve the optimal distribution pressure, and the specific process includes:

[0072] According to the optimal distribution pressure information, the pressure regulating device is controlled to apply pressure to different areas of the hot plate respectively;

[0073] When the temperature of the material layer is above a preset first temperature at the initial stage of welding, applying a pressure within a preset first pressure range;

[0074] During the welding process, the pressure is gradually increased to a preset second pressure range to ensure good contact between the material layers;

[0075] When the temperature of the material layer drops below the preset second temperature in the later stage of welding, the pressure is restored to the preset third pressure range to ensure the strength of the weld joint during the cooling and solidification process of the material.

[0076] In the above embodiment, preferably, the pressure applied by the pressure regulating device to the preset area of ​​the hot plate is adaptively adjusted to achieve the optimal distribution pressure, and the specific process also includes:

[0077] The heating temperature of the material layer and the change of the contact surface thickness during the welding process are monitored in real time by the temperature sensor and the thickness sensor, and the monitoring results are used as the feedback input of the neural network model;

[0078] The pressure of the material layer during welding is monitored in real time by the force sensor, and the monitoring results are used as feedback input of the neural network model;

[0079] The neural network model performs feedback regulation and control on the pressure applied to the preset area of ​​the hot plate according to the test result data input by feedback.

[0080] During the implementation process, the system will use the thickness sensor to determine the uniformity of the contact surface. For areas with lower pressure, the system will increase the pressure, and vice versa.

[0081] Judgment logic: Multiple thickness sensors are installed at different positions of the welding workpiece, and the sensors are used to monitor the thickness changes of the contact surface during the welding process in real time.

[0082] Judgment process: Continuously collect data from different positions of the contact surface, and calculate the difference between the maximum and minimum thickness on the contact surface. If the difference is large, it means that the contact is uneven. According to the calculation results, the pressure is adjusted in real time. If the contact surface is uneven, the system can adjust the pressure to ensure the welding quality.

[0083] In addition, based on the data from the force sensor and temperature sensor, the system determines the thermal expansion and elastic recovery of the material to ensure the continuity and balance of pressure. During the welding stage, the material expands and generates internal stress under the heating state; during the cooling stage, the stress is gradually released. The deformation and compression degree are determined by real-time monitoring of the temperature of the material in the welding area and the thickness sensor detecting the dimensional changes during the heating and cooling process.

[0084] During the implementation process, the contact part between the hot plate and the welding mold will be equipped with an electric pressure regulating device. According to the feedback signal, the pressure regulating device will drive the vertical movement of the hot plate to adjust the pressure distribution. Among them, the pressure regulating device can be fine-tuned by the electric servo control system to make the pressure distribution accurate and real-time.

[0085] In the above embodiment, preferably, the method for welding different materials based on a hot plate mold further includes:

[0086] The displacement of the hot plate is monitored by motion sensors, images of the welded joints of the material layers are collected, and the welded joints are detected with a pre-trained machine vision inspection model.

[0087] The welding defect problem as well as the pressure distribution information, temperature information, thickness information and displacement information during the welding process are comprehensively considered to provide feedback and adjust the welding pressure.

[0088] In this embodiment, the quality of the weld joint is checked by using a machine vision system after welding (the camera captures the image and analyzes it through an image processing algorithm (such as edge detection)). The system can confirm the contact uniformity and weld strength by processing the data of these images through the machine vision detection model. If the visual feedback results show that there is a problem at the joint, the system will automatically adjust the welding pressure.

[0089] During the specific implementation process, the visual system collects images and temperature data of the welding process in real time, processes the collected image data, analyzes defects, temperature, pressure and other information in the welding process, and judges whether there are abnormalities and non-compliance with standards by comparing standards and thresholds. The detection results are fed back to the control system, and the control system adjusts the welding parameters according to the feedback information. The adjusted welding process continues to be monitored by the visual system to verify the adjustment effect and perform necessary optimization according to real-time data to ensure welding quality. Based on the actual welding effect, the system automatically optimizes the welding pressure parameters and continuously optimizes the pressure control strategy through an iterative process during the welding process.

[0090] like Figure 2 As shown, the present invention also proposes a different material welding system based on a hot plate mold, using the different material welding method based on a hot plate mold disclosed in any one of the above embodiments, including:

[0091] The parameter acquisition module 1 is used to monitor the pressure of the preset area of ​​the hot plate through the force sensor on the surface of the hot plate, monitor the temperature of the preset area of ​​the hot plate through the temperature sensor, and monitor the thickness of the material layer on the hot plate through the thickness sensor;

[0092] Material determination module 2, used to identify the material property information of the pre-input material layer, and determine the welding pressure range of the corresponding material according to a predetermined pressure-temperature model;

[0093] Model training module 3, for training a neural network model based on a historical experimental data set mapping material, thickness and temperature inputs with pressure distribution outputs;

[0094] The pressure analysis module 4 is used to input the welding pressure range and the pressure distribution information, temperature information and thickness information during the welding process into the neural network model to obtain the optimal distribution pressure information;

[0095] The pressure regulating module 5 is used to adaptively adjust the pressure applied by the pressure regulating device to the preset area of ​​the hot plate to achieve the optimal distribution pressure until the welding is completed.

[0096] In the above embodiment, preferably, the pressure regulating module 5 is specifically used for:

[0097] According to the optimal distribution pressure information, the pressure regulating device is controlled to apply pressure to different areas of the hot plate respectively;

[0098] When the temperature of the material layer is above a preset first temperature at the initial stage of welding, applying a pressure within a preset first pressure range;

[0099] During the welding process, the pressure is gradually increased to a preset second pressure range to ensure good contact between the material layers;

[0100] When the temperature of the material layer drops below the preset second temperature in the later stage of welding, the pressure is restored to the preset third pressure range to ensure the strength of the weld joint during the cooling and solidification process of the material.

[0101] In the above embodiment, preferably, the hot plate mold-based different material welding system further includes a visual feedback module 6, which is specifically used for:

[0102] The displacement of the hot plate is monitored by motion sensors, images of the welded joints of the material layers are collected, and the welded joints are detected with a pre-trained machine vision inspection model.

[0103] The welding defect problem as well as the pressure distribution information, temperature information, thickness information and displacement information during the welding process are comprehensively considered to provide feedback and adjust the welding pressure.

[0104] According to the different material welding system based on hot plate mold disclosed in the above embodiment, the functions to be implemented by each module thereof correspond to the various steps of the different material welding method based on hot plate mold disclosed in the above embodiment. During the implementation process, the operations are performed with reference to the above embodiment, which will not be repeated here.

[0105] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for welding different materials based on a hot plate mold, characterized in that: include: The pressure of the preset area of ​​the hot plate is monitored by a force sensor on the surface of the hot plate, the temperature of the preset area of ​​the hot plate is monitored by a temperature sensor, and the thickness of the material layer on the hot plate is monitored by a thickness sensor; Identify the pre-input material property information of the material layer, and determine the welding pressure range of the corresponding material according to a predetermined pressure-temperature model; A neural network model is trained based on a historical experimental data set mapping material, thickness, and temperature inputs to pressure distribution outputs; Inputting the welding pressure range and pressure distribution information, temperature information and thickness information during the welding process into the neural network model to obtain optimal distribution pressure information; The pressure applied by the pressure regulating device to the preset area of ​​the hot plate is adaptively adjusted to achieve the optimal distribution pressure until welding is completed.

2. The method for welding different materials based on hot plate mold according to claim 1, characterized in that: The force sensors are installed in different areas of the surface of the hot plate respectively, and the thickness sensor is arranged on the contact surface between the material layers to monitor the contact state between the material layers in real time.

3. The method for welding different materials based on hot plate mold according to claim 2, characterized in that: The identifying of the pre-input material property information of the material layer and determining the welding pressure range of the corresponding material according to a predetermined pressure-temperature model specifically includes: Determining the material type of the required welding material layer clamped between the hot plates, and determining the material characteristic information of the material layer according to the pre-input physical properties of different materials; According to the pressure-temperature model of different material types, the ideal welding pressure range of the required welding material layer is determined, so that effective welding can be performed with the ideal welding pressure at the preset temperature.

4. The method for welding different materials based on hot plate mold according to claim 3 is characterized in that: The neural network model is trained based on the historical experimental data set mapped with the material, thickness and temperature input and the pressure distribution output, and the specific process includes: The material type, material layer thickness, and welding temperature are used as inputs, and the pressure distribution data is used as output. The neural network model is trained using historical experimental data as a training set, so that the neural network model can predict the optimal pressure distribution result that best suits the required welding material based on the input data.

5. The method for welding different materials based on hot plate mold according to claim 4, characterized in that: The adaptive adjustment of the pressure regulating device applied to the preset area of ​​the hot plate to achieve the optimal distribution pressure includes: According to the optimal distribution pressure information, controlling the pressure regulating device to apply pressure to different areas of the hot plate respectively; When the temperature of the material layer is above a preset first temperature at the initial stage of welding, applying a pressure within a preset first pressure range; During the welding process, the pressure is gradually increased to a preset second pressure range to ensure that the material layers are in good contact; When the temperature of the material layer drops below the preset second temperature at the later stage of welding, the pressure is restored to a preset third pressure range to ensure the strength of the weld joint during the cooling and solidification process of the material.

6. The method for welding different materials based on hot plate mold according to claim 5, characterized in that: The adaptively adjusting pressure regulating device applies pressure to the preset area of ​​the hot plate to achieve the optimal distribution pressure, and the specific process also includes: The heating temperature of the material layer and the change of the contact surface thickness during the welding process are monitored in real time according to the temperature sensor and the thickness sensor, and the monitoring result is used as the feedback input of the neural network model; The pressure of the material layer during welding is monitored in real time according to the force sensor, and the monitoring result is used as feedback input of the neural network model; The neural network model performs feedback regulation control on the pressure applied to the preset area of ​​the hot plate according to the test result data input by feedback.

7. The method for welding different materials based on hot plate mold according to any one of claims 1 to 6, characterized in that: Also includes: The displacement of the hot plate is monitored by a motion sensor, images of the welded joints of the material layer are collected, and welding defects of the welded joints are detected by using a pre-trained machine vision detection model; The welding pressure is adjusted by taking into account the welding defect problem and the pressure distribution information, temperature information, thickness information and displacement information during the welding process.

8. A hot plate mold based welding system for different materials, characterized in that: The method for welding different materials based on a hot plate mold as claimed in any one of claims 1 to 7 comprises: A parameter acquisition module, used to monitor the pressure of a preset area of ​​the hot plate through a force sensor on the surface of the hot plate, monitor the temperature of the preset area of ​​the hot plate through a temperature sensor, and monitor the thickness of the material layer on the hot plate through a thickness sensor; A material determination module, used to identify the pre-input material property information of the material layer, and determine the welding pressure range of the corresponding material according to a predetermined pressure-temperature model; A model training module for training a neural network model based on a historical experimental data set mapping material, thickness and temperature inputs to pressure distribution outputs; A pressure analysis module, used to input the welding pressure range and pressure distribution information, temperature information and thickness information during the welding process into the neural network model to obtain optimal distribution pressure information; The pressure regulating module is used to adaptively adjust the pressure applied by the pressure regulating device to the preset area of ​​the hot plate to achieve the optimal distribution pressure until welding is completed.

9. The hot plate mold-based different material welding system according to claim 8, characterized in that: The pressure regulating module is specifically used for: According to the optimal distribution pressure information, controlling the pressure regulating device to apply pressure to different areas of the hot plate respectively; When the temperature of the material layer is above a preset first temperature at the initial stage of welding, applying a pressure within a preset first pressure range; During the welding process, the pressure is gradually increased to a preset second pressure range to ensure that the material layers are in good contact; When the temperature of the material layer drops below the preset second temperature at the later stage of welding, the pressure is restored to a preset third pressure range to ensure the strength of the weld joint during the cooling and solidification process of the material.

10. The hot plate mold-based different material welding system according to claim 9, characterized in that: Also includes a visual feedback module specifically for: The displacement of the hot plate is monitored by a motion sensor, images of the welded joints of the material layer are collected, and welding defects of the welded joints are detected by using a pre-trained machine vision detection model; The welding pressure is adjusted by taking into account the welding defect problem and the pressure distribution information, temperature information, thickness information and displacement information during the welding process.

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