Intelligent thermal control method and system in sheet metal welding process

Abstract

The invention provides an intelligent thermal control method and system in the sheet metal welding process, and relates to the technical field of sheet metal welding. The intelligent thermal control method comprises the steps that a welding area of a to-be-welded sheet metal material piece is obtained, a spot welding device is connected, the spot welding imaging granularity of a welding head element in the welding area is recognized, and pixelate processing is conducted on the welding area; and a welding area pixel coordinate set is output, a spot welding path optimizer carries out path optimization on the welding area pixel coordinate set according to the welding heat identification model, a spot welding control path is output, and the spot welding control path is sent to a control terminal of the spot welding device. And controlling the welding head element to perform spot welding on the sheet metal material piece according to the spot welding control path. The technical problem that in the metal plate welding process in the prior art, due to the fact that welding points are too close, heat is concentrated, and the welding quality is reduced is solved. The technical effects of reasonably distributing welding heat input and ensuring welding quality and workpiece stability are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of sheet metal welding, and in particular to an intelligent heat control method and system in a sheet metal welding process. Background Art

[0002] In the existing electric tricycle manufacturing process, sheet metal welding is a key process. However, since the tricycle sheet metal welding is thin plate welding, the material has the characteristics of strong thermal conductivity and thin thickness, and the welding process is easily affected by heat input control. In the prior art, a common problem is that the welding points are too close, resulting in the occurrence of heat concentration during the welding process. This phenomenon is mainly reflected in the heat accumulation effect between adjacent welding points. The heat cannot be diffused in time, causing the local temperature of the welding area to be too high. Local overheating can cause deformation and welding defects in the welding area, which directly affects the welding quality and structural strength.

[0003] In the prior art, there is a technical problem in the sheet metal welding process that the heat concentration caused by the welding points being too close leads to a decrease in welding quality. Summary of the invention

[0004] The present application provides an intelligent heat control method and system in a sheet metal welding process, which is used to solve the technical problem in the prior art that in a sheet metal welding process, heat concentration results from welding points being too close, which leads to a decrease in welding quality.

[0005] In view of the above problems, the present application provides an intelligent thermal control method and system during sheet metal welding.

[0006] In a first aspect of the present application, an intelligent thermal control method is provided during sheet metal welding, the method comprising: obtaining a welding area of ​​a sheet metal material to be welded; connecting a spot welding device, obtaining a welding head element of the spot welding device, and identifying a spot welding imaging granularity of the welding head element in the welding area; pixelating the welding area according to the spot welding imaging granularity, and outputting a pixel coordinate set of the welding area; constructing a spot welding path optimizer, the spot welding path optimizer having a welding heat recognition model embedded therein, the spot welding path optimizer performing path optimization on the pixel coordinate set of the welding area according to the welding heat recognition model, and outputting a spot welding control path; and sending the spot welding control path to a control terminal of the spot welding device to control the welding head element to spot weld the sheet metal material according to the spot welding control path.

[0007] In a second aspect of the present application, an intelligent thermal control system is provided during sheet metal welding, the system comprising: a welding area acquisition module, used to acquire the welding area of ​​the sheet metal material to be welded; a spot welding imaging granularity recognition module, used to connect a spot welding device, acquire the welding head element of the spot welding device, and identify the spot welding imaging granularity of the welding head element in the welding area; a welding area processing module, used to pixelate the welding area according to the spot welding imaging granularity, and output a welding area pixel coordinate set; a path optimization module, used to construct a spot welding path optimizer, the spot welding path optimizer is embedded with a welding heat recognition model, the spot welding path optimizer performs path optimization on the welding area pixel coordinate set according to the welding heat recognition model, and outputs a spot welding control path; a path control module, used to send the spot welding control path to the control terminal of the spot welding device, so as to control the welding head element to spot weld the sheet metal material according to the spot welding control path.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: The method provided in the embodiment of the present application is to obtain the welding area of ​​the sheet metal material to be welded, connect the spot welding device, obtain the welding head element of the spot welding device, identify the spot welding imaging granularity of the welding head element in the welding area, pixelate the welding area according to the spot welding imaging granularity, output the welding area pixel coordinate set, and construct a spot welding path optimizer, the spot welding path optimizer is embedded with a welding heat recognition model, the spot welding path optimizer optimizes the path of the welding area pixel coordinate set according to the welding heat recognition model, outputs the spot welding control path, and sends the spot welding control path to the control terminal of the spot welding device to control the welding head element to spot weld the sheet metal material according to the spot welding control path. The technical effect of reasonably allocating welding heat input and ensuring welding quality and workpiece stability is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic flow chart of the intelligent heat control method in the sheet metal welding process provided in this application.

[0011] Figure 2 This is a schematic diagram of the structure of the intelligent thermal control system in the sheet metal welding process provided in this application.

[0012] Explanation of reference numerals: welding area acquisition module 11 , spot welding imaging granularity recognition module 12 , welding area processing module 13 , path optimization module 14 , path control module 15 . DETAILED DESCRIPTION

[0013] The present application provides an intelligent heat control method and system in the sheet metal welding process, which is used to solve the technical problem in the prior art that the heat concentration caused by the close welding points in the sheet metal welding process leads to the decline of welding quality. The technical effect of reasonably allocating welding heat input and ensuring welding quality and workpiece stability is achieved.

[0014] Below, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments described herein. 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. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all of them.

[0015] Embodiment 1, as Figure 1 As shown, the present application provides an intelligent heat control method in a sheet metal welding process, the method comprising: Get the welding area of ​​the sheet metal material to be welded.

[0016] Specifically, the welding area is determined by the geometric shape and design drawings of the sheet metal material to be welded, and the welding area includes locations such as welding points, welds and welding joints. While determining the welding area based on the geometric shape and design drawings, due to the different thermal conductivity of different sheet metal materials, the temperature distribution of the welding area is also different, and the characteristics of the sheet metal material to be welded, such as the thickness, hardness, surface finish, etc. of the material are obtained. The welding area of ​​the sheet metal material to be welded is obtained based on the geometric area combined with the physical properties of the sheet metal material. The welding area of ​​the sheet metal material to be welded refers to the part of the sheet metal material that needs to be welded to form a connection or reinforcement. By determining the welding area, it is ensured that the subsequent welding path optimization and thermal control can be performed in an accurate area, thereby improving the welding quality and efficiency.

[0017] A spot welding device is connected, a welding head element of the spot welding device is obtained, and the spot welding imaging granularity of the welding head element in the welding area is identified.

[0018] Specifically, the spot welding device is a welding device that uses the principle of resistance heat to apply pressure to the workpiece through the electrode and energize it, so that the metal at the contact point melts to form a weld, including components such as a welding head, a welding power supply, and a control system. The spot welding device is connected to the power supply through a cable or other connection means to ensure that the device can start and operate normally. According to the connected spot welding device, the welding head element of the spot welding device is obtained, and the welding head element refers to the part of the spot welding device that directly contacts the welding area and transfers heat energy during the spot welding process. The spot welding imaging granularity refers to the heat-affected range in the welding area after the welding head element contacts the sheet metal material and is energized during the welding process, which is expressed as the pixel size of the welding head element affecting the welding area. The spot welding imaging granularity determines the influence range of each spot welding image, that is, the area covered by each welding point in the welding area, and is obtained according to the physical characteristics of the welding head element, that is, the welding head diameter, the contact point size, and the performance parameters of the welding head element. By accurately identifying the spot welding imaging granularity of the welding head in the welding area, it can provide support for subsequent heat control and welding path optimization, thereby improving welding quality and efficiency.

[0019] Furthermore, the spot welding imaging granularity of the welding head element in the welding area is identified, and the method includes: obtaining the physical properties of the welding head element, including the welding head diameter and the welding contact point size; obtaining the performance parameters of the welding head element, including the welding current range, the welding voltage range and the welding power; and determining the spot welding imaging granularity by analyzing the physical properties and the performance parameters, wherein the spot welding imaging granularity is the influence range of the welding head element on the welding area.

[0020] Specifically, the physical characteristics and performance parameters of the welding element are obtained according to the equipment specification of the spot welding device. The physical characteristics of the welding head element include the welding head diameter and the size of the welding contact point. The welding head diameter is the geometric size of the welding head, which directly affects the heat conduction range during the welding process. The larger the welding head diameter, the wider the heat influence range of the welding area, and vice versa. The size of the welding contact point determines the contact area of ​​the welding point during the welding process, which further affects the heat transfer efficiency. The performance parameters of the welding head element include the welding current range, the welding voltage range and the welding power, wherein the welding current range determines the fluctuation range of the current during the welding process and affects the heating degree of the welding point; the welding voltage range is related to the welding current, which affects the voltage change between the electrodes, thereby affecting the current distribution and heating effect of the welding point; the welding power refers to the total amount of heat transferred to the welding area during the welding process. The greater the power, the faster the temperature of the welding area rises. Furthermore, the physical characteristics and the performance parameters of the welding head element are comprehensively analyzed, and a physical model is established to calculate the spot welding imaging granularity, which is the influence range of the welding head element on the welding area. For example, the spot welding imaging granularity G = welding head diameter D + heat affected radius R, where R can be estimated approximately by current density, R = k × , J=I / S, I is the welding current, S is the size of the welding contact point, current density J is the distribution of current in the welding area, k is a constant, usually 𝑘=0.01. By acquiring and analyzing the physical characteristics and performance parameters of the welding head components, the spot welding imaging granularity of the welding head in the welding area is identified, providing support for subsequent heat control and welding path optimization, thereby ensuring accurate heat distribution during the welding process and improving welding quality and efficiency.

[0021] The welding area is pixelated according to the spot welding imaging granularity, and a welding area pixel coordinate set is output.

[0022] Specifically, after the spot welding imaging granularity is used to calculate the heat affected range of the welding area, the welding area is pixelated, that is, the welding area is divided into a series of small pixel units, each pixel unit represents a small area on the welding area. After the segmentation is completed, the pixel coordinate set of the welding area is output. The pixel coordinate set contains the position information of each pixel unit in the welding area. By pixelizing the welding area, the pixel coordinate set of the welding area is obtained, which can accurately guide the planning of the spot welding path and improve the quality of sheet metal welding.

[0023] Furthermore, the welding area is pixelated according to the spot welding imaging granularity, and the method includes: establishing an initial pixelated model of the welding area, wherein the initial pixelated model is constructed by an edge coordinate set, and the edge coordinate set is obtained by pixel coordinate conversion of an edge data set of the welding area; performing two-dimensional grid division on the welding area based on the initial pixelated model according to the spot welding imaging granularity, and outputting a first welding point coordinate set; performing effective welding point identification on the first welding point coordinate set, and outputting a second welding point coordinate set; and outputting the second welding point coordinate set as the welding area pixel coordinate set.

[0024] Specifically, the edge coordinate set refers to the contour coordinates of the welding area, which are obtained by various measurement methods, such as image processing, laser scanning, CAD data, etc. Then, the actually obtained contour coordinates are converted into pixel coordinates in the computer image. The pixel coordinates refer to the position of each boundary point of the welding area in the two-dimensional coordinate system in the digital image. Through the conversion, the edge information of the welding area is digitized into a pixel form suitable for subsequent processing, and the initial pixel model of the welding area is obtained. Then, according to the initial pixel model, the welding area is divided into two-dimensional grids based on the spot welding imaging granularity. In the two-dimensional grid division process, the initial pixel model of the welding area is divided into uniform grid units according to the size of the spot welding imaging granularity. Each grid unit represents a small welding heat affected zone, that is, each cell corresponds to a certain heat affected zone. After completing the two-dimensional grid division, the first welding point coordinate set is output, which contains the center point coordinates of all grid units, that is, all the initially defined welding point coordinates. After obtaining the first welding point coordinate set, valid welding point identification is performed on these coordinates, usually boundary judgment, that is, by setting the geometric boundary of the welding area, it is judged whether each welding point is within the effective range of the welding area, and each point in the first welding point coordinate set is traversed to determine whether it is within the boundary of the welding area. The welding points that meet the conditions will be identified as valid welding points, and the valid welding points in the welding area are screened out, and the invalid points or points that do not meet the welding requirements are eliminated to obtain the second welding coordinate set, which contains all the screened valid welding point coordinates. The valid welding coordinate points represent the positions where spot welding operations need to be performed in the welding area, and these positions meet the welding quality requirements. Finally, the second welding point coordinate set is used as the output result as the pixel coordinate set of the welding area. Through pixel coordinate conversion, two-dimensional grid division and effective welding point identification, the pixel coordinate set of the welding area is finally generated, which not only defines the position of the welding point, but also ensures that the heat distribution of the welding area is accurate and effective, thereby improving the welding quality.

[0025] Furthermore, valid welding points are identified for the first welding point coordinate set, and the method includes: traversing the first welding point coordinate set to perform boundary judgment on each welding point coordinate; identifying the welding point coordinates that meet the boundary of the welding area as valid welding points, and outputting a second welding point coordinate set.

[0026] Specifically, the first welding point coordinate set is generated after preliminary grid division, and contains the center point coordinates of all grid cells in the welding area. These coordinates are the initially defined welding points, which may include some points located at the edge of the welding area or not meeting the welding requirements, so further screening is required. The welding area has a clear geometric boundary, which can be defined in a variety of ways, such as a rectangular boundary, a circular boundary or a complex polygonal boundary. The geometric boundary can be determined by the CAD data of the welding area or the actual measurement data. Each welding point coordinate in the first welding point coordinate set is traversed to determine whether each welding point is within the valid range of the welding area. For example, by coordinate calculation, it is determined whether each welding point is within the valid range of the welding area. If the boundary of the welding area is rectangular, it can be determined whether the point is valid by determining whether the horizontal coordinate and the vertical coordinate of the welding point are within the range of the rectangular boundary. If the boundary shape of the welding area is more complex, such as a polygon or an irregular shape, it can be determined whether the welding point is within the area by using an algorithm for the point inside the polygon, such as the ray method or the angle method. When a welding point is determined to be within the valid range of the welding area, the point is identified as a valid welding point. A valid welding point refers to a point that meets the welding requirements and is located within the welding area. After completing the boundary judgment and identifying the valid welding points, a second welding point coordinate set is generated, that is, a coordinate set containing all valid welding points. Each point coordinate in the second welding point coordinate set corresponds to a specific position that needs to be spot welded during the actual welding process. By accurately identifying the valid welding points, unnecessary heat waste can be avoided, ensuring that the heat of the welding head is concentrated in the effective area, thereby improving the welding quality.

[0027] Furthermore, the welding area is divided into two-dimensional grids according to the spot welding imaging granularity based on the initial pixelation model. The method also includes: judging whether the welding area is a two-dimensional area, if the welding area is a two-dimensional area, dividing the welding area into two-dimensional grids according to the spot welding imaging granularity based on the initial pixelation model; if the welding area is a three-dimensional area, splitting the welding area into two-dimensional areas, and dividing the split multiple welding areas into two-dimensional grids according to the spot welding imaging granularity based on the initial pixelation model.

[0028] Specifically, in the pixelation processing stage during the welding process, it is first determined whether the welding area is a two-dimensional area or a three-dimensional area. A two-dimensional area refers to an area with a flat surface, no bends or protrusions, and a welding area with a clear plane geometry. For example, welding areas of rectangles, circles or other plane figures can be classified as two-dimensional areas. If the welding area is judged to be a two-dimensional area, the welding area is divided into two-dimensional grids according to the spot welding imaging granularity. The two-dimensional grid division is to divide the welding area into uniform small cells, each cell represents a heat-affected zone in the welding process, and the size of the grid is determined by the spot welding imaging granularity. By meshing the welding area according to the spot welding imaging granularity, the welding area is divided into multiple small cells, and corresponding coordinate information is assigned to each cell. When the welding area is judged to be a three-dimensional area, such as a sheet metal part with a curved surface, due to the height change in the three-dimensional area, the shape and surface of the welding area are not simple planes, so the two-dimensional area is first split, that is, the three-dimensional area is decomposed into multiple two-dimensional plane areas. The three-dimensional area can be split by geometric modeling technology, through surface modeling and segmentation, the welding area can be split into multiple plane areas, and the welding area can be divided into multiple areas with different geometric shapes according to the contact angle between the welding head and the welding surface. Each small area after splitting can be regarded as an independent two-dimensional area, and each split two-dimensional area is divided into two-dimensional grids according to the spot welding imaging granularity to ensure that the heat distribution in each split area is accurately controllable. After processing all the split areas, the grid information of each split area is integrated to obtain a complete welding area grid division. By accurately dividing the welding point position according to the actual shape and size of the welding area, it is possible to ensure accurate control of heat input during welding, thereby improving welding quality.

[0029] A spot welding path optimizer is constructed, wherein the spot welding path optimizer is embedded with a welding heat recognition model, and the spot welding path optimizer performs path optimization on the welding area pixel coordinate set according to the welding heat recognition model, and outputs a spot welding control path.

[0030] Specifically, a spot welding path optimizer is constructed. The spot welding path optimizer is a computing module used to optimize the spot welding path and heat distribution. It mainly optimizes the welding path based on the pixel coordinate set of the welding area to ensure that the heat input of each welding point meets the predetermined heat requirement. In this module, a welding heat recognition model is embedded, which is used to evaluate the temperature and heat distribution of each welding point. The welding heat recognition model is based on sample data and machine learning algorithms and prediction models. It can quickly and effectively predict the temperature changes of each point in the welding process according to the characteristics and conditions of the welding area, and provide a basis for path optimization. The spot welding path optimizer optimizes the path of the pixel coordinate set of the welding area according to the prediction results of the welding heat recognition model, determines an optimal spot welding path, achieves a reasonable distribution of welding heat input, effectively controls thermal deformation, stress concentration and welding defects in the welding process of sheet metal materials, ensures welding quality and workpiece stability, and improves welding quality.

[0031] Furthermore, the spot welding path optimizer performs path optimization on the welding area pixel coordinate set according to the welding heat recognition model and outputs a spot welding control path. The method includes: obtaining an initial control path; predicting the temperature value of each welding coordinate point on the initial control path according to the welding heat recognition model to obtain a predicted temperature value set; calculating the heat accumulation effect index of adjacent welding coordinate points in the initial control path according to the predicted temperature value set; optimizing the initial control path based on the difference between a preset heat accumulation effect index and the heat accumulation effect index, and outputting the spot welding control path.

[0032] Specifically, the initial control path is first obtained. The initial control path refers to a basic welding point sequence generated based on a set of pixel coordinates of the welding area, according to the geometric distribution of welding points or fixed rules, such as row-by-row or spiral arrangement. Then, the embedded welding heat recognition model is called to predict the temperature value of each welding point by inputting the welding parameters and the coordinate information of the welding points on the initial control path, and a set of predicted temperature values ​​is generated. Then, the heat accumulation effect index of each pair of adjacent welding points in the initial control path is calculated based on the predicted temperature value set. The heat accumulation effect index is used to quantify the heat conduction and influence between adjacent welding points. According to the heat accumulation effect index, it can be evaluated whether there is a problem of uneven heat in the path. The calculation formula of the heat accumulation effect index is related to the theory of heat conduction, for example: , is the heat accumulation effect index of welding points i and j, are the predicted temperature values ​​of welding point i and welding point j respectively, is the coordinate distance between welding points i and j, is a weight coefficient used to balance the influence of temperature value and distance. After obtaining the heat accumulation effect index, it is compared with the preset heat accumulation effect index. The preset heat accumulation effect index is pre-set based on experimental data or welding process requirements, and represents the optimal heat transfer between adjacent points in the path. By comparing the difference between the actual index and the preset index, the spot welding path optimizer is used to optimize the initial control path, that is, through genetic algorithm, particle swarm optimization algorithm or simulated annealing algorithm, the goal is to minimize the difference between the actual heat accumulation effect index and the preset index. The connection order of the welding points is adjusted through multiple iterations to output the optimal spot welding control path. The optimized path has the following characteristics: the heat input of each welding point is evenly distributed; the moving path of the welding head is the shortest, which improves the welding efficiency; the heat accumulation effect is effectively controlled to avoid heat concentration caused by continuous spot welding, reduce thermal deformation and welding defects, and achieve reasonable distribution of welding heat input to ensure welding quality and workpiece stability. It is particularly suitable for the scene of precise thermal control of thin plate welding.

[0033] Furthermore, the method for training the welding heat recognition model includes: obtaining a heat influence weight matrix, which is used to describe the heat conduction relationship between each welding coordinate point and its adjacent welding coordinate points; obtaining multiple groups of data samples, the multiple groups of data samples include an input data group and a label data group, the input data group includes welding head parameters, initial welding temperature and welding area pixel coordinate set, the label data group includes predicted temperature values ​​of each welding coordinate point; training is performed according to the heat influence weight matrix and the multiple groups of data samples, and the welding heat recognition model is output.

[0034] Specifically, the heat influence weight matrix is ​​obtained. The heat influence weight matrix is ​​used to describe the heat conduction relationship between each welding coordinate point and the adjacent welding coordinate point. During the welding process, heat is transferred from one welding point to the points around it. This conduction relationship is affected by factors such as the thermal conductivity of the welding material, the welding head parameters, and the distance between welding points. Each element of the heat influence weight matrix represents the weight value of the heat conduction of a welding point to the adjacent point. For example, the matrix element 𝑊ij represents the heat transfer intensity of welding point i to welding point j, and the formula is , k is the thermal conductivity of the material, is the coordinate distance between welding points i and j. Multiple groups of data samples are obtained, and the multiple groups of data samples include an input data group and a label data group. The input data group includes welding head parameters, initial welding temperature and welding area pixel coordinate set, and the label data group includes the predicted temperature value of each welding coordinate point. By collecting a large amount of real or simulated welding sample data, sufficient training samples can be provided for the model to ensure the accuracy of its prediction. Then, according to the heat influence weight matrix and multiple groups of data samples, the input data group and the label data group are input into the model. Based on the heat influence weight matrix, the model simulates the heat conduction process of each welding point and predicts its temperature value. The difference between the temperature value predicted by the model and the actual temperature value in the label data is calculated, and the mean square error is used as the loss function. The parameters of the model are adjusted using the gradient descent method so that the loss function is gradually reduced, and the model gradually fits the heat conduction law of the data. The performance of the model is tested using an independent validation data set to ensure that the predicted temperature value of the welding point has high accuracy and generalization ability. After multiple rounds of iterative training and verification, the trained welding heat recognition model is output. The welding heat recognition model can quickly predict the temperature change of each welding point based on the input welding parameters and welding point coordinates, provide data support for welding path optimization and heat control, ensure the reasonable distribution of welding heat input during the welding process, and thus effectively improve the welding quality and workpiece stability.

[0035] The spot welding control path is sent to a control terminal of the spot welding device to control the welding head element to spot weld the sheet metal material piece according to the spot welding control path.

[0036] Specifically, after optimization by the spot welding path optimizer, an optimized spot welding control path is generated, and the spot welding control path is represented in the form of a coordinate sequence, and each coordinate point corresponds to the position of an effective welding point in the welding area. The spot welding control path is sent to the control terminal of the spot welding device. After receiving the spot welding control path, the control terminal will parse the path coordinates into a series of action instructions, and drive the welding head point by point according to the instructions in the path to perform welding operations to spot weld the sheet metal material, that is, the control terminal drives the welding head to move to the coordinate position of the first welding point in the path. After the welding head reaches the specified position, the control terminal triggers the spot welding operation, and completes the heat input of the welding point by controlling the welding current and voltage. After the welding is completed, the control terminal drives the welding head to move to the next welding point, and repeats the above steps until the operation of all welding points is completed. During the welding process, the position and welding parameters of the welding head are monitored in real time, and the data is fed back to the system to ensure the stability and consistency of the welding process. By sending the spot welding control path to the control terminal for spot welding, a complete closed loop from optimization to actual execution of the welding path is realized, ensuring uniform heat distribution in the welding area, improving welding quality and workpiece stability.

[0037] Embodiment 2, based on the same inventive concept as the intelligent heat control method in the sheet metal welding process in the above embodiment, Figure 2 As shown, the present application provides an intelligent thermal control system in a sheet metal welding process, wherein the system comprises: A welding area acquisition module 11 is used to acquire the welding area of ​​the sheet metal material to be welded; a spot welding imaging granularity recognition module 12 is used to connect a spot welding device, acquire the welding head element of the spot welding device, and identify the spot welding imaging granularity of the welding head element in the welding area; a welding area processing module 13 is used to pixelate the welding area according to the spot welding imaging granularity, and output a set of pixel coordinates of the welding area; a path optimization module 14 is used to construct a spot welding path optimizer, the spot welding path optimizer is embedded with a welding heat recognition model, the spot welding path optimizer optimizes the path of the welding area pixel coordinate set according to the welding heat recognition model, and outputs a spot welding control path; a path control module 15 is used to send the spot welding control path to the control terminal of the spot welding device to control the welding head element to spot weld the sheet metal material according to the spot welding control path.

[0038] Furthermore, the spot welding imaging granularity recognition module 12 is also used to perform the following steps: obtaining the physical properties of the welding head element, including the welding head diameter and the welding contact point size; obtaining the performance parameters of the welding head element, including the welding current range, the welding voltage range and the welding power; and determining the spot welding imaging granularity based on the analysis of the physical properties and the performance parameters, wherein the spot welding imaging granularity is the influence range of the welding head element on the welding area.

[0039] Furthermore, the welding area processing module 13 is also used to perform the following steps: establish an initial pixelated model of the welding area, wherein the initial pixelated model is constructed by an edge coordinate set, and the edge coordinate set performs pixel coordinate conversion to obtain the edge data set of the welding area; divide the welding area into two-dimensional grids based on the initial pixelated model according to the spot welding imaging granularity, and output a first welding point coordinate set; identify effective welding points on the first welding point coordinate set, and output a second welding point coordinate set; output the second welding point coordinate set as the welding area pixel coordinate set.

[0040] Furthermore, the welding area processing module 13 is also used to perform the following steps: traverse the first welding point coordinate set to perform boundary judgment on each welding point coordinate; identify the welding point coordinates that meet the welding area boundary as valid welding points, and output a second welding point coordinate set.

[0041] Furthermore, the welding area processing module 13 is also used to perform the following steps: determine whether the welding area is a two-dimensional area. If the welding area is a two-dimensional area, divide the welding area into two-dimensional grids according to the spot welding imaging granularity based on the initial pixelation model; if the welding area is a three-dimensional area, split the welding area into two-dimensional areas, and divide the multiple split welding areas into two-dimensional grids according to the spot welding imaging granularity based on the initial pixelation model.

[0042] Furthermore, the path optimization module 14 is also used to perform the following steps: obtain an initial control path; predict the temperature value of each welding coordinate point on the initial control path according to the welding heat recognition model to obtain a set of predicted temperature values; calculate the heat accumulation effect index of adjacent welding coordinate points in the initial control path according to the predicted temperature value set; optimize the initial control path based on the difference between a preset heat accumulation effect index and the heat accumulation effect index, and output a spot welding control path.

[0043] Furthermore, the path optimization module 14 is also used to perform the following steps: obtain a thermal influence weight matrix, which is used to describe the heat conduction relationship between each welding coordinate point and its adjacent welding coordinate points; obtain multiple groups of data samples, the multiple groups of data samples include an input data group and a label data group, the input data group includes welding head parameters, initial welding temperature and welding area pixel coordinate set, the label data group includes predicted temperature values ​​of each welding coordinate point; train according to the thermal influence weight matrix and the multiple groups of data samples, and output the welding heat recognition model.

[0044] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0045] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. Intelligent heat control method in sheet metal welding process, characterized in that: The method comprises: Obtaining a welding area of ​​a sheet metal material to be welded; Connecting a spot welding device, acquiring a welding head element of the spot welding device, and identifying a spot welding imaging granularity of the welding head element in the welding area; Perform pixel processing on the welding area according to the spot welding imaging granularity, and output a set of pixel coordinates of the welding area; Constructing a spot welding path optimizer, wherein the spot welding path optimizer is embedded with a welding heat recognition model, and the spot welding path optimizer performs path optimization on the welding area pixel coordinate set according to the welding heat recognition model, and outputs a spot welding control path; The spot welding control path is sent to a control terminal of the spot welding device to control the welding head element to spot weld the sheet metal material piece according to the spot welding control path.

2. The intelligent heat control method in the sheet metal welding process according to claim 1, characterized in that: Identifying the spot welding imaging granularity of the welding head element in the welding area, the method comprises: Obtaining physical properties of the welding head element, including welding head diameter and welding contact point size; Obtaining performance parameters of the welding head element, including welding current range, welding voltage range and welding power; The spot welding imaging granularity is determined by analyzing the physical characteristics and the performance parameters, wherein the spot welding imaging granularity is the influence range of the welding head element on the welding area.

3. The intelligent heat control method in the sheet metal welding process according to claim 1, characterized in that: The welding area is pixelated according to the spot welding imaging granularity, and the method includes: Establishing an initial pixelated model of the welding area, wherein the initial pixelated model is constructed by an edge coordinate set, and the edge coordinate set is obtained by performing pixel coordinate transformation on an edge data set of the welding area; Performing two-dimensional grid division on the welding area according to the spot welding imaging granularity based on the initial pixelation model, and outputting a first welding point coordinate set; Performing effective welding point identification on the first welding point coordinate set, and outputting a second welding point coordinate set; The second welding point coordinate set is output as the welding area pixel coordinate set.

4. The intelligent heat control method in the sheet metal welding process according to claim 3, characterized in that: Performing effective welding point identification on the first welding point coordinate set, the method comprising: Traversing the first welding point coordinate set and performing boundary judgment on each welding point coordinate; The welding point coordinates that meet the boundary of the welding area are identified as valid welding points, and a second welding point coordinate set is output.

5. The intelligent heat control method in the sheet metal welding process according to claim 3, characterized in that: The welding area is divided into two-dimensional grids based on the initial pixelation model according to the spot welding imaging granularity, and the method further includes: Determining whether the welding area is a two-dimensional area, if the welding area is a two-dimensional area, dividing the welding area into two-dimensional grids based on the initial pixelation model according to the spot welding imaging granularity; If the welding area is a three-dimensional area, the welding area is split into two-dimensional areas, and two-dimensional grids are formed on the split multiple welding areas based on the initial pixelation model according to the spot welding imaging granularity.

6. The intelligent heat control method in the sheet metal welding process according to claim 1, characterized in that: The spot welding path optimizer performs path optimization on the welding area pixel coordinate set according to the welding heat recognition model and outputs a spot welding control path, and the method includes: Get the initial control path; Predicting the temperature value of each welding coordinate point on the initial control path according to the welding heat recognition model to obtain a set of predicted temperature values; Calculating a heat accumulation effect index of adjacent welding coordinate points in the initial control path according to the predicted temperature value set; The initial control path is optimized based on the difference between a preset heat accumulation effect index and the heat accumulation effect index, and a spot welding control path is output.

7. The intelligent heat control method in the sheet metal welding process according to claim 6, characterized in that: Methods for training welding heat recognition models include: Obtaining a heat influence weight matrix, where the heat influence weight matrix is ​​used to describe the heat conduction relationship between each welding coordinate point and its adjacent welding coordinate points; Acquire multiple sets of data samples, the multiple sets of data samples include an input data set and a label data set, the input data set includes welding head parameters, initial welding temperature and welding area pixel coordinate set, the label data set includes predicted temperature values ​​of each welding coordinate point; Training is performed based on the heat impact weight matrix and the multiple groups of data samples to output the welding heat recognition model.

8. Intelligent thermal control system during sheet metal welding, characterized in that: The steps for implementing the intelligent heat control method in the sheet metal welding process according to any one of claims 1 to 7 include: A welding area acquisition module is used to acquire the welding area of ​​the sheet metal material to be welded; A spot welding imaging granularity recognition module is used to connect a spot welding device, obtain a welding head element of the spot welding device, and recognize the spot welding imaging granularity of the welding head element in the welding area; A welding area processing module, used for performing pixel processing on the welding area according to the spot welding imaging granularity, and outputting a welding area pixel coordinate set; A path optimization module is used to construct a spot welding path optimizer, wherein the spot welding path optimizer is embedded with a welding heat recognition model, and the spot welding path optimizer performs path optimization on the welding area pixel coordinate set according to the welding heat recognition model and outputs a spot welding control path; The path control module is used to send the spot welding control path to the control terminal of the spot welding device to control the welding head element to spot weld the sheet metal material according to the spot welding control path.

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