Intelligent Thermal Control Method and System during Sheet Metal Welding

Abstract

The present invention provides an intelligent thermal control method and system during sheet metal welding, which relates to the technical field of sheet metal welding. By obtaining the welding area of the sheet metal material to be welded, connecting the spot welding device, identifying the spot welding imaging granularity of the welding head element in the welding area, pixelating the welding area, and outputting the welding area pixel coordinate set, 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 perform spot welding on the sheet metal material according to the spot welding control path. It solves the technical problem in the prior art that during the sheet metal welding process, the heat concentration caused by too close welding points leads to the decline of welding quality. It achieves the technical effect of reasonably distributing the welding heat input and ensuring the welding quality and workpiece stability.

Description

Technical Field

[0001] The present invention relates to the technical field of sheet metal welding, and particularly to an intelligent thermal control method and system during sheet metal welding. Background Art

[0002] During the manufacturing process of existing electric tricycles, sheet metal welding is a key process. However, since the sheet metal welding of tricycles is thin plate welding, its materials have the characteristics of strong thermal conductivity and thin thickness, and its 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 heat concentration during the welding process. This phenomenon is mainly reflected in the heat accumulation effect between adjacent welding points, where the heat cannot be dissipated in time, resulting in too high local temperature in the welding area. Local overheating will cause problems such as deformation and welding defects in the welding area, directly affecting the welding quality and structural strength.

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

[0004] This application provides an intelligent thermal control method and system during sheet metal welding, which is used to solve the technical problem in the prior art that in the sheet metal welding process, heat concentration caused by too close welding points leads to a decline in welding quality.

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

[0006] In the first aspect of this application, an intelligent thermal control method during sheet metal welding is provided. The method includes: obtaining the welding area of the sheet metal material piece to be welded; connecting the spot welding device, obtaining the welding head element of the spot welding device, and identifying the spot welding imaging granularity of the welding head element in the welding area; performing pixelization processing on the welding area according to the spot welding imaging granularity, and outputting a welding area pixel coordinate set; constructing a spot welding path optimizer, which is embedded with a welding heat recognition model, and the spot welding path optimizer optimizes the path according to the welding heat recognition model for the welding area pixel coordinate set, and outputs a spot welding control path; sending the spot welding control path to the control terminal of the spot welding device to control the welding head element to perform spot welding on the sheet metal material piece according to the spot welding control path.

[0007] In the second aspect of the present application, an intelligent thermal control system during sheet metal welding is provided. The system includes: a welding area acquisition module for acquiring the welding area of the sheet metal material to be welded; a spot welding imaging granularity recognition module for connecting a spot welding device, acquiring the welding head element of the spot welding device, and recognizing the spot welding imaging granularity of the welding head element in the welding area; a welding area processing module for pixelating the welding area according to the spot welding imaging granularity and outputting a set of welding area pixel coordinates; a path optimization module for constructing a spot welding path optimizer, which is embedded with a welding heat recognition model, and the spot welding path optimizer optimizes the path of the set of welding area pixel coordinates according to the welding heat recognition model and outputs a spot welding control path; and a path control module for sending the spot welding control path to the control terminal of the spot welding device to control the welding head element to perform spot welding on the sheet metal material according to the spot welding control path.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0009] The method provided in the embodiment of the present application acquires the welding area of the sheet metal material to be welded, connects a spot welding device, acquires the welding head element of the spot welding device, recognizes the spot welding imaging granularity of the welding head element in the welding area, pixelates the welding area according to the spot welding imaging granularity, outputs a set of welding area pixel coordinates, constructs a spot welding path optimizer, which is embedded with a welding heat recognition model, the spot welding path optimizer optimizes the path of the set of welding area pixel coordinates according to the welding heat recognition model, outputs a 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 perform spot welding on the sheet metal material according to the spot welding control path. It achieves the technical effect of reasonably distributing the welding heat input and ensuring the welding quality and workpiece stability. Description of the Drawings

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0011] Figure 1 It is a schematic flowchart of the intelligent thermal control method during sheet metal welding provided by the present application.

[0012] Figure 2 It is a schematic structural diagram of the intelligent thermal control system during sheet metal welding provided by the present application.

[0013] Description of the reference numerals: Welding area acquisition module 11, spot welding imaging particle size recognition module 12, welding area processing module 13, path optimization module 14, path control module 15. Detailed implementation manners

[0014] This application provides an intelligent thermal control method and system during sheet metal welding, which is used to solve the technical problem in the prior art that during the sheet metal welding process, the heat concentration caused by too close welding points leads to the decline of welding quality. It achieves the technical effect of reasonably distributing the welding heat input and ensuring the welding quality and workpiece stability.

[0015] Next, 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 a part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited by the exemplary embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention. In addition, it should be noted that for the sake of description, only the parts related to the present invention are shown in the accompanying drawings rather than all.

[0016] Embodiment 1, as Figure 1 shown, this application provides an intelligent thermal control method during sheet metal welding, and the method includes:

[0017] Obtain the welding area of the sheet metal material piece to be welded.

[0018] Specifically, determine the welding area through the geometric shape and design drawings of the sheet metal material piece to be welded. The welding area includes positions such as welding points, weld seams, and welding joints. While determining the welding area according to the geometric shape and design drawings, due to different sheet metal materials and different thermal conductivities, the temperature distribution in the welding area also varies. Obtain the characteristics of the sheet metal material to be welded, such as the thickness, hardness, surface finish, etc. of the material. Obtain the welding area of the sheet metal material piece to be welded according to the geometric area combined with the physical characteristics of the sheet metal material piece. The welding area of the sheet metal material piece to be welded refers to the part on the sheet metal material piece where welding operations need to be performed to form connections or reinforcements. By determining the welding area, it is ensured that subsequent welding path optimization and thermal control can be carried out within the accurate area, thereby improving the welding quality and efficiency.

[0019] Connect the spot welding device, obtain the welding head element of the spot welding device, and identify the spot welding imaging particle size of the welding head element in the welding area.

[0020] Specifically, a spot welding device is a welding equipment that utilizes the principle of resistance heat. By applying pressure and passing an electric current through the electrodes to the workpiece, the metal at the contact point melts to form a solder joint. It includes components such as a welding head, a welding power source, and a control system. Connect the spot welding device to the power source through a cable or other connection means to ensure that the device can be started and operated normally. According to the connected spot welding device, obtain the welding head element of the spot welding device. The welding head element refers to the part in 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 and is energized with the sheet metal material during the welding process, which is manifested as the pixel size affected by the welding head element in 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 by identifying according to the physical characteristics of the welding head element, namely 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, improving the welding quality and efficiency.

[0021] Further, to identify the spot welding imaging granularity of the welding head element in the welding area, the method includes: obtaining the physical characteristics 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; analyzing according to the physical characteristics and the performance parameters to determine the spot welding imaging granularity, where the spot welding imaging granularity is the influence range of the welding head element in the welding area.

[0022] Specifically, according to the equipment specification manual of the spot welding device, the physical characteristics and performance parameters of the welding element are obtained. 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 dimension 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 affected zone 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, further affecting 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. Among them, the welding current range determines the fluctuation range of the current during the welding process, affecting the heating degree of the welding point; the welding voltage range is related to the welding current, affecting the voltage change between the electrodes, thereby affecting the current distribution and heating effect at 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, by comprehensively analyzing the physical characteristics and the performance parameters of the welding head element, a physical model is established to calculate the spot welding imaging granularity, where the spot welding imaging granularity 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 approximately estimated using the current density, R = k × , J = I / S, I is the welding current, S is the size of the welding contact point, the current density J represents the distribution of the current in the welding area, and k is a constant, usually taken as 𝑘 = 0.01. By obtaining and analyzing the physical characteristics and performance parameters of the welding head element, 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 precise heat distribution during the welding process and improving the welding quality and efficiency.

[0023] The welding area is pixelated according to the spot welding imaging granularity, and a set of pixel coordinates of the welding area is output.

[0024] Specifically, after the heat affected range of the welding area is calculated by the spot welding imaging granularity, the welding area is pixelated, that is, the welding area is divided into a series of small pixel units, and each pixel unit represents a small area on the welding area. After the division is completed, a set of pixel coordinates of the welding area is output. The set of pixel coordinates contains the position information of each pixel unit in the welding area. By pixelating the welding area and obtaining the set of pixel coordinates of the welding area, the planning of the spot welding path can be accurately guided, improving the sheet metal welding quality.

[0025] Further, pixelate the welding area according to the spot welding imaging granularity. The method includes: establishing an initial pixelation model of the welding area, where the initial pixelation model is constructed through an edge coordinate set, and the edge coordinate set is obtained by performing pixel coordinate conversion on the edge data set of the welding area; performing two-dimensional grid division on the welding area based on the initial pixelation model according to the spot welding imaging granularity, and outputting a first set of welding point coordinates; performing identification of valid welding points on the first set of welding point coordinates, and outputting a second set of welding point coordinates; and outputting the second set of welding point coordinates as the pixel coordinate set of the welding area.

[0026] Specifically, the edge coordinate set refers to the contour coordinates of the welding area, which are obtained through various measurement methods, such as image processing, laser scanning, CAD data, etc. Then, the actually obtained contour coordinates are converted into pixel coordinates in a computer image. Pixel coordinates refer to the positions of each boundary point of the welding area in a two-dimensional coordinate system in a digital image. Through the conversion, the edge information of the welding area is digitized into a pixel form suitable for subsequent processing, and an initial pixelation model of the welding area is obtained. Then, based on the initial pixelation model, perform two-dimensional grid division on the welding area according to the spot welding imaging granularity. During the two-dimensional grid division process, according to the size of the spot welding imaging granularity, the initial pixelation model of the welding area is divided into uniform grid cells. Each grid cell represents a small welding heat-affected area, that is, each cell corresponds to a certain heat-affected area. After completing the two-dimensional grid division, output a first set of welding point coordinates. The first set of welding point coordinates contains the center point coordinates of all grid cells, that is, the coordinates of all initially defined welding points. After obtaining the first set of welding point coordinates, perform identification of valid welding points on these coordinates. Usually, boundary judgment is performed, that is, by setting the geometric boundary of the welding area, judge whether each welding point is within the effective range of the welding area. Traverse each point in the first set of welding point coordinates to judge whether it is within the boundary of the welding area. The welding points that meet the conditions will be marked as valid welding points, and the valid welding points within the welding area are selected, and the invalid or non-compliant welding points are eliminated to obtain a second set of welding coordinates. The second set of welding coordinates contains the coordinates of all screened valid welding points. The valid welding coordinate points represent the positions where spot welding operations need to be performed in the welding area, and these positions all meet the welding quality requirements. Finally, take the second set of welding point coordinates as the output result and use it as the pixel coordinate set of the welding area. Through pixel coordinate conversion, two-dimensional grid division, and identification of valid welding points, the pixel coordinate set of the welding area is finally generated, which not only defines the positions of the welding points but also ensures the accurate and effective heat distribution in the welding area, thereby improving the welding quality.

[0027] Further, perform effective welding point identification on the set of first welding point coordinates. The method includes: traversing the set of first welding point coordinates to perform boundary judgment on each welding point coordinate; identifying the welding point coordinates within the boundary of the welding area as effective welding points, and outputting a set of second welding point coordinates.

[0028] Specifically, the set of first welding point coordinates is generated after preliminary grid division and contains the central point coordinates of all grid cells within the welding area. These coordinates are initially defined welding points and may include some points located at the edge of the welding area or not meeting the welding requirements. Therefore, further screening is required. The welding area has a clear geometric boundary, which can be defined in various ways, such as a rectangular boundary, a circular boundary, or a complex polygon boundary. The geometric boundary can be determined through CAD data or actual measurement data of the welding area. Traverse each welding point coordinate in the set of first welding point coordinates to determine whether each welding point is within the effective range of the welding area. For example, through coordinate calculation, determine whether each welding point is within the effective range of the welding area. If the boundary of the welding area is rectangular, it can be confirmed whether the point is valid by judging whether the abscissa and ordinate 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, the algorithm for determining whether a point is inside a polygon, such as the ray method or the angle method, can be used to judge whether the welding point is within the area. When it is determined that a certain welding point is within the effective range of the welding area, the point is identified as an effective welding point. An effective welding point refers to a point that meets the welding requirements and is within the welding area. After completing the boundary judgment and identifying the effective welding points, a set of second welding point coordinates is generated, that is, a set of coordinates containing all effective welding points. Each point coordinate in the set of second welding point coordinates corresponds to the specific position that needs to be spot-welded during the actual welding process. By accurately identifying the effective welding points, unnecessary heat waste can be avoided, ensuring that the heat of the welding head is concentrated within the effective area, thereby improving the welding quality.

[0029] Further, perform two-dimensional grid division on the welding area based on the initial pixelation model according to the spot welding imaging granularity. The method further includes: judging whether the welding area is a two-dimensional area. If the welding area is a two-dimensional area, perform two-dimensional grid division on the welding area based on the initial pixelation model according to the spot welding imaging granularity; if the welding area is a three-dimensional area, split the welding area into two-dimensional areas, and perform two-dimensional grid division on the split multiple welding areas based on the initial pixelation model according to the spot welding imaging granularity.

[0030] Specifically, during the pixelation process in the welding operation, first, it is 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, without bends or protrusions, and has a welding area with a definite planar geometric shape. For example, welding areas in the shape of rectangles, circles, or other planar figures can be classified as two-dimensional areas. If the welding area is determined to be a two-dimensional area, a two-dimensional grid division is performed on the welding area according to the spot welding imaging granularity. The two-dimensional grid division is to divide the welding area into uniform small cells, and each cell represents a heat-affected area during the welding process. The size of the grid is determined by the spot welding imaging granularity. By performing grid division on the welding area according to the spot welding imaging granularity, the welding area is divided into multiple small units, and corresponding coordinate information is assigned to each unit. When the welding area is determined to be a three-dimensional area, such as a sheet metal part with a curved surface, due to the height variation in the three-dimensional area, the shape and surface of the welding area are not simply planar. Therefore, it is first split into two-dimensional areas, that is, the three-dimensional area is decomposed into multiple two-dimensional planar areas. The splitting of the three-dimensional area can be achieved through geometric modeling techniques. By surface modeling and segmentation, the welding area is split into multiple planar areas. According to the angle of contact between the welding head and the welding surface, the welding area is divided into multiple areas with different geometric shapes. Each small area after splitting can be regarded as an independent two-dimensional area, and each split two-dimensional area is subjected to two-dimensional grid division according to the spot welding imaging granularity to ensure precise control of the heat distribution within each split area. After processing all the split areas, the grid information of each split area is integrated to obtain a complete grid division of the welding area. By accurately dividing the positions of the welding points according to the actual shape and size of the welding area, precise control of the heat input during the welding process can be ensured, thereby improving the welding quality.

[0031] Construct a spot welding path optimizer, which is embedded with a welding heat recognition model. The spot welding path optimizer optimizes the path for the set of pixel coordinates of the welding area according to the welding heat recognition model and outputs a spot welding control path.

[0032] Specifically, a spot welding path optimizer is constructed. The spot welding path optimizer is a calculation module used to optimize the spot welding path and heat distribution. It mainly optimizes the welding path based on the set of pixel coordinates in the welding area to ensure that the heat input at each welding point meets the predetermined heat requirements. In this module, a welding heat recognition model is embedded. The welding heat recognition model is used to evaluate the temperature and heat distribution at each welding point. Based on sample data, machine learning algorithms, and prediction models, the welding heat recognition model can quickly and effectively predict the temperature change at each point during 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 set of pixel coordinates in 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 during the welding process of sheet metal materials, ensures welding quality and workpiece stability, and improves welding quality.

[0033] Further, the spot welding path optimizer optimizes the set of pixel coordinates in the welding area 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 values 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 the heat accumulation effect index of adjacent welding coordinate points in the initial control path according to the set of predicted temperature values; and optimizing the initial control path with the difference between the preset heat accumulation effect index and the heat accumulation effect index, and outputting a spot welding control path.

[0034] Specifically, first, an initial control path is obtained. The initial control path refers to a basic welding point sequence generated based on the set of pixel coordinates in the welding area according to the geometric distribution or fixed rules of the welding points, 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 coordinate information of the welding points on the initial control path, and a set of predicted temperature values is generated. Furthermore, the heat accumulation effect index of each pair of adjacent welding points in the initial control path is calculated according to the set of predicted temperature values. The heat accumulation effect index is used to quantify the heat conduction and influence between adjacent welding points, and based on 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 heat conduction theory. 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 effects 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, representing the optimal heat transfer situation between adjacent points in the path. By comparing the difference between the actual index and the preset index, a 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, with the goal of minimizing the difference between the actual heat accumulation effect index and the preset index, the connection order of welding points is adjusted through multiple iterations, and the optimal spot welding control path is output. 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, improving the welding efficiency; the heat accumulation effect is effectively controlled, avoiding heat concentration caused by continuous spot welding, reducing thermal deformation and welding defects, achieving reasonable distribution of welding heat input, ensuring welding quality and workpiece stability, and is particularly suitable for the scenario of precise thermal control in thin plate welding.

[0035] Further, 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 including an input data group and a label data group, the input data group including welding head parameters, initial welding temperature and a set of welding area pixel coordinates, and the label data group including the predicted temperature values of each welding coordinate point; training according to the heat influence weight matrix and the multiple groups of data samples, and outputting the welding heat recognition model.

[0036] Specifically, a 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 its adjacent welding coordinate points. During the welding process, heat is transferred from one welding point to its surrounding points, and this conduction relationship is affected by factors such as the thermal conductivity of the welding material, welding head parameters and the distance between welding points. Each element of the heat influence weight matrix represents the weight value of a welding point's heat conduction to adjacent points. For example, the matrix element 𝑊ij represents the heat transfer intensity of welding point i to welding point j, and the formula is , where k is the thermal conductivity of the material, It is the coordinate distance between welding points i and j. Multiple sets of data samples are obtained. The multiple sets of data samples include two parts: an input data set and a label data set. The input data set includes welding head parameters, the initial welding temperature, and a set of pixel coordinates of the welding area. The label data set includes the predicted temperature values of each welding coordinate point. By collecting a large number of real or simulated welding sample data, sufficient training samples can be provided for the model to ensure the accuracy of its prediction. Then, based on the thermal influence weight matrix and multiple sets of data samples, training is carried out. The input data set and the label data set are input into the model. Based on the thermal influence weight matrix, the model simulates the heat conduction process of each welding point and predicts its temperature value. Calculate the difference between the temperature value predicted by the model and the real temperature value in the label data. Use the mean square error as the loss function and use the gradient descent method to adjust the parameters of the model to gradually reduce the loss function and make the model gradually fit the heat conduction law of the data. Use an independent validation data set to test the performance of the model to ensure that the predicted temperature values of the welding points have high accuracy and generalization ability. After multiple rounds of iterative training and validation, 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, providing data support for welding path optimization and heat control, ensuring the reasonable distribution of welding heat input during the welding process, and thus effectively improving the welding quality and workpiece stability.

[0037] Send the spot welding control path to the control terminal of the spot welding device to control the welding head component to perform spot welding on the sheet metal part according to the spot welding control path.

[0038] Specifically, after being optimized by the spot welding path optimizer, an optimized spot welding control path is generated. The spot welding control path is represented in the form of a coordinate sequence, and each coordinate point corresponds to the position of a valid welding point within the welding area. Send the spot welding control path 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 to perform welding operations on the sheet metal part point by point according to the instructions in the path, 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 all welding points are operated. 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 achieved, ensuring uniform heat distribution within the welding area, improving the welding quality and workpiece stability.

[0039] Embodiment 2. Based on the same inventive concept as the intelligent thermal control method in the sheet metal welding process in the foregoing embodiment, as Figure 2 shown, the present application provides an intelligent thermal control system for the sheet metal welding process. Among them, the system includes:

[0040] A welding area acquisition module 11, configured to acquire the welding area of the sheet metal material to be welded; a spot welding imaging granularity recognition module 12, configured to connect to a spot welding device, acquire the 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 13, configured to perform pixelization processing on the welding area according to the spot welding imaging granularity, and output a welding area pixel coordinate set; a path optimization module 14, configured to construct a spot welding path optimizer, which 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; a path control module 15, configured 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 perform spot welding on the sheet metal material according to the spot welding control path.

[0041] Further, the spot welding imaging granularity recognition module 12 is further configured to perform the following steps: acquire the physical characteristics of the welding head element, including the welding head diameter and the size of the welding contact point; acquire the performance parameters of the welding head element, including the welding current range, the welding voltage range, and the welding power; analyze according to the physical characteristics and the performance parameters to determine the spot welding imaging granularity, where the spot welding imaging granularity is the influence range of the welding head element in the welding area.

[0042] Further, the welding area processing module 13 is further configured to perform the following steps: establish an initial pixelization model of the welding area, where the initial pixelization model is constructed through an edge coordinate set, and the edge coordinate set is obtained by performing pixel coordinate conversion on the edge data set of the welding area; perform two-dimensional grid division on the welding area based on the initial pixelization model according to the spot welding imaging granularity, and output a first welding point coordinate set; perform effective welding point recognition 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.

[0043] Further, the welding area processing module 13 is further configured to perform the following steps: traverse the first welding point coordinate set to perform boundary judgment on each welding point coordinate; mark the welding point coordinates that satisfy the boundary of the welding area as effective welding points, and output a second welding point coordinate set.

[0044] Further, the welding area processing module 13 is further configured to perform the following steps: determine whether the welding area is a two-dimensional area. If the welding area is a two-dimensional area, perform two-dimensional grid division on the welding area based on the initial pixelation model according to the spot welding imaging granularity; if the welding area is a three-dimensional area, split the welding area into two-dimensional areas, and perform two-dimensional grid division on the split multiple welding areas based on the initial pixelation model according to the spot welding imaging granularity.

[0045] Further, the path optimization module 14 is further configured to perform the following steps: obtain an initial control path; predict the temperature values 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 set of predicted temperature values; optimize the initial control path with the difference between the preset heat accumulation effect index and the heat accumulation effect index, and output the spot welding control path.

[0046] Further, the path optimization module 14 is further configured to perform the following steps: obtain 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; obtain multiple groups of data samples, where the multiple groups of data samples include an input data group and a label data group, the input data group includes welding head parameters, an initial welding temperature, and a set of welding area pixel coordinates, and the label data group includes the predicted temperature values of each welding coordinate point; train according to the heat influence weight matrix and the multiple groups of data samples, and output the welding heat recognition model.

[0047] The foregoing are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

[0048] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and deformations to the present application without departing from the scope of the present application. Thus, if these modifications and deformations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and deformations.

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; 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 piece according to the spot welding control path; 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; Analyze according to the physical characteristics and the performance parameters to determine the spot welding imaging granularity, wherein the spot welding imaging granularity is the influence range of the welding head element on the welding area; 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.

2. The intelligent heat control method in the sheet metal welding process according to claim 1, 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.

3. The intelligent heat control method in the sheet metal welding process according to claim 1, 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.

4. 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.

5. The intelligent heat control method in the sheet metal welding process according to claim 4, 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.

6. 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 5 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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