Automatic optimization control method for resistance spot welding

Abstract

In the field of resistance spot welding technology, we provide an automatic optimization control method for resistance spot welding. [Solution] By collecting parameters and material information of the welded object, the appropriate electrode cap is selected, the ideal welding current value is calculated, a historical database and predictive model are established to predict the optimal current and pressure combination state, the welding process is optimized, the ideal parameters are integrated into the control parameters, the automatic completion of spot welding work by spot welding equipment is controlled, alignment checks are performed before welding to avoid abnormal welding conditions, and the present invention automatically adjusts welding parameters during resistance spot welding using intelligent algorithms and historical data analysis, improving efficiency and stability, reducing defective products, saving energy, and achieving higher quality welding and higher production efficiency.

Description

[Technical Field]

[0001] The present invention relates to the field of resistance spot welding, and more particularly to an automatic optimization control method for resistance spot welding. [Background technology]

[0002] Resistance spot welding is a metal welding process that joins metal parts by applying pressure and passing an electric current between the two metal parts, creating localized resistance heating between them, which then melts or deforms them. Because this process enables fast and efficient metal joining, it is commonly used in fields such as automotive manufacturing, home appliance manufacturing, and metal product manufacturing.

[0003] In conventional resistance spot welding control, the setting of welding parameters, such as current, pressure, and welding time, generally relies on manual experience. This method has the drawback that setting welding parameters depends on the operator's experience and skill, leading to inconsistencies in parameter settings and human errors, which affect the stability of weld quality. When welding different materials or changing welding conditions, welding parameters must be adjusted manually, a time-consuming process that may result in the loss of optimal parameters. Inaccurate parameter settings can lead to inconsistent weld quality between different batches of welded products, potentially impacting product consistency and reliability. Although a large amount of data is generated during the welding process, conventional techniques often fail to effectively utilize this data to optimize the welding process, thus underutilizing its value. Conventional resistance spot welding control methods generally lack intelligence, failing to automatically identify and optimize problems during welding, thus limiting the development of automation and intelligence in welding technology.

[0004] Therefore, providing an automated optimization control method for resistance spot welding in order to improve the degree of automation in the welding process and the quality of the welds is an urgent issue that needs to be resolved in this field. [Overview of the project] [Problems that the invention aims to solve]

[0005] The objective of this invention is to achieve higher quality welding and higher production efficiency by automatically adjusting welding parameters during resistance spot welding using intelligent algorithms and historical data analysis. [Means for solving the problem]

[0006] To achieve the above objectives, the present invention provides the following solutions.

[0007] An automatic optimization control method for resistance spot welding, To collect the thickness of the welding material, the initial temperature, and various material parameters, Install multiple types of electrode caps in advance, The predicted welding temperature is calculated based on the collected thickness, initial temperature, and various material parameters of the welding material. The process involves selecting a corresponding type of electrode cap based on the predicted welding temperature, and simultaneously recording the shape factor of the selected electrode cap. The ideal welding time is set in advance, and the ideal output current value of the spot welding equipment is calculated by linking the predicted welding temperature with various material parameters. To establish a historical database for classifying and recording current values, pressure values, and corresponding welding results in spot welding of various types of welding materials, The process involves establishing a range prediction model, training it based on historical data in the historical database, and outputting multiple range prediction models after training is complete. The current ideal output current value and the pressure intensity range of the spot welding equipment are substituted into the corresponding range prediction model to output the ideal pressure intensity range. The ideal pressure range is calculated by linking the ideal compression range with the shape factor, and the median of the pressure values ​​within the said ideal pressure range is taken as the ideal pressure value. Integrating the ideal output current value, ideal pressure value, and ideal welding time into control parameters; Controlling the automatic completion of spot welding operations by a spot welding device based on the control parameters.

[0008] Preferably, in the above automatic optimization control method for resistance spot welding, the various material parameters include the quality of the welding material, heat capacity value, heat efficiency, maximum thickness and minimum thickness of the welding material in the historical spot welding process, and thermal conductivity coefficient; Pre-setting the plurality of types of electrode caps includes classifying the types of electrode caps based on material and structure, where the structural category is divided into a protrusion type, a concave type, and a flat head type; On the contact surface between the protrusion type electrode cap and the welding material, multi-layer annular ridges, spiral ridges or radial ridges are installed; On the contact surface between the concave type electrode cap and the welding material, a welding surface, an annular ridge, a circumference and a concave groove are installed. The concave groove is located in the central region of the contact surface. The upper edge of the concave groove is transitionally connected to the welding surface through a smooth fillet. The circumference is the outer diameter of the welding surface. The shape of the concave groove is spherical, and the annular ridge is installed on the welding surface; The flat head type electrode cap includes a welding contact segment, a consumption segment, and a mounting segment. The contact segment adopts a taper structure, and the contact surface at the top of the contact segment is a plane. The angle formed between the contact segment and the consumption segment is 120° - 145°.

[0009] Preferably, in the above automatic optimization control method for resistance spot welding, calculating the predicted welding temperature based on the collected thickness, initial temperature and various material parameters of the welding material, and selecting the corresponding type of electrode cap based on the predicted welding temperature, and at the same time recording the shape coefficient of the selected electrode cap specifically includes Calculating the predicted temperature, where T y = T0 + [(k - T0) × (D0 - D min ) / (Dmax establishing a calculation model for the predicted temperature, which is -D0), wherein: Here, T y is the predicted temperature value, representing the temperature required for welding; k is the thermal conductivity coefficient; T0 is the initial temperature of the welding material; D0 is the thickness of the welding material; D max and D min are respectively the maximum thickness and the minimum thickness of this welding material in the process of historical spot welding; substituting the collected thermal conductivity coefficient k, the initial temperature T0 of the welding material, the thickness D0 of the welding material, the maximum thickness and the minimum thickness D max and D min of this welding material in the process of historical spot welding into the calculation model of the predicted temperature to obtain the predicted temperature value T y ; selecting the material category by matching the predicted temperature value T y with the spot welding temperature ranges corresponding to various electrode cap materials to obtain the electrode cap that can achieve the predicted temperature value T y ; selecting the structure category by selecting the electrode cap of the corresponding structure based on the thickness of the welding material, the spot welding requirement and the predicted temperature value T y ; providing a first thickness threshold and a first temperature threshold applied to the flat - head electrode cap, a second temperature threshold applied to the protruding electrode cap, and a third temperature threshold applied to the concave electrode cap; when the thickness of the welding material is greater than the first thickness threshold, the predicted temperature value T y is lower than the first temperature threshold, and the spot welding requirement is uniform welding, selecting the flat - head electrode cap; when the predicted temperature value T y of the welding material is higher than the second temperature threshold and the spot welding requirement is deep penetration welding, selecting the protruding electrode cap; when the predicted temperature value T yIf the temperature is higher than the third temperature threshold and the spot welding demand is fine welding, the step is to select the concave electrode cap, The process includes determining and using an electrode cap in this category based on the material and structure of the selected electrode cap, and recording the shape factor Ca of the electrode cap.

[0010] Preferably, in the above-described automatic optimization control method for resistance spot welding, the ideal welding time is set in advance, and the ideal output current value of the spot welding equipment is calculated by linking the expected welding temperature with various material parameters, specifically, I = √([(η × m × c × (T y Using the formula -T0) / (R×t))+E, Here, I is the ideal output current value, η is the thermal efficiency of the welding material, m is the quality of the welding material, c is the heat capacity value of the welding material, and (T y -T0) is the temperature change required for welding, R is the resistance of the welding material, t is the preset ideal welding time, and E is the error bias term, which is set empirically. y T0 is the predicted temperature value, representing the temperature required for welding, while T0 is the initial temperature of the welding material.

[0011] Preferably, in the above-described automatic optimization control method for resistance spot welding, establishing the range prediction model, training it based on historical data in the historical database, and outputting multiple range prediction models after training completion, specifically, f(x) = sign[Σ] i N A step to establish the range prediction model ai × yi × (xi·x) + b, Here, x is the input vector, i.e., the current value and pressure value for each set in the history database, f(x) is the analysis result, i.e., the corresponding welding result, where if the welding result is successful, f(x)>0, and a decimal number belonging to the interval (0,1) is assigned to f(x) based on the welding effect, and if the welding result is unsuccessful, f(x)<0, and a decimal number belonging to the interval (-1,0) is assigned to f(x) based on the welding effect, xi is the support vector, yi is the category tag corresponding to each support vector, where yi=+1 represents a successful weld, and yi=-1 represents an unsuccessful weld, ai is the Lagrange multiplier of each support vector xi, b is the bias term, and the sign function is a step used by the model to convert the distance from the sample point x to the decision boundary into a category tag and to determine which side of the decision boundary the sample point is on. The historical data for each set in the aforementioned history database is then input to the vector. The steps include: substituting JPEG2026085267000002.jpg33 and the analysis result f(x) into the range prediction model for training, and generating support vectors xi, category tags yi, Lagrange multipliers ai, and bias term b in the range prediction model corresponding to each type of welding material; The process includes the step of outputting a plurality of range prediction models trained based on support vectors xi, category tags yi, Lagrange multipliers ai, and bias terms b in the range prediction model corresponding to each type of welding material.

[0012] Preferably, in the above-described automatic optimization control method for resistance spot welding, the current ideal output current value and the pressure range of the spot welding equipment are substituted into the corresponding range prediction model to output the ideal pressure range, specifically, The steps include organizing the pressure values ​​provided by the spot welding equipment during the historical spot welding process in the historical database and integrating each of the pressure values ​​into a pressure range set, The steps include: sequentially substituting the ideal output current value and each pressure data in the pressure range set as input vector x into the range prediction model corresponding to the welding material to generate a corresponding number of analysis results f(x); The process includes selecting each analysis result f(x), retaining each pressure intensity value for f(x)>0, and integrating the said pressure intensity values ​​into the ideal pressure intensity range by arranging them by numerical magnitude.

[0013] Preferably, in the above-described automatic optimization control method for resistance spot welding, the ideal pressure range is calculated by linking the ideal compression range with the shape factor, and the median of the pressure values ​​in the ideal pressure range is set as the ideal pressure value. Specifically, A step of establishing a pressure estimation model and determining the pressure to be applied to a spot welding machine based on the pressure estimation model Fi = Pi × (Ai × Cai), wherein Pi is the i-th pressure value in the ideal pressure range, Ai is the original contact area of ​​the electrode cap corresponding to this pressure value Pi, Cai is the shape factor of the electrode cap, (Ai × Cai) is the actual contact area of ​​the electrode cap, and Fi is the pressure value corresponding to this pressure value Pi, i.e., the pressure to be applied to the spot welding machine. The steps include: linking each pressure value in the aforementioned ideal pressure range to the actual contact area of ​​the selected electrode cap, substituting these values ​​into the pressure estimation model, outputting each corresponding pressure value Fi, and integrating them into the aforementioned ideal pressure range; The process includes the step of sorting the data in the aforementioned ideal pressure range in descending or ascending order, and taking the median value as the ideal pressure value.

[0014] Preferably, the above-described automatic optimization control method for resistance spot welding further includes detecting the alignment checkability of the set of electrode caps by an alignment check detection device between the steps of "selecting a corresponding type of electrode cap based on the predicted welding temperature and simultaneously recording the shape factor of the selected electrode cap" and "presetting an ideal welding time and calculating an ideal output current value for the spot welding equipment by linking the predicted welding temperature with various material parameters". The alignment check detection device is A spot weld test specimen comprising an upper end test specimen and a lower end test specimen, wherein the upper end test specimen and the lower end test specimen are of the same dimensions, and the upper end test specimen and the lower end test specimen are aligned and overlapping spot weld test specimens, A pressure sensor is installed between the upper end test specimen and the lower end test specimen to collect the pressure value and deformation state acting on the upper end test specimen and the lower end test specimen, and to convert the pressure value and deformation state into an electronic image using a computer. A guide component connected to the spot welding equipment, which guides the spot welding equipment to grip and apply pressure to the spot welding test piece, The system includes a computer for analyzing electronic images of the upper and lower end test specimens, determining the pressure difference, deformation difference, and same heart rate experienced by the upper and lower end test specimens, pre-setting pressure difference thresholds, deformation difference thresholds, and same heart rate difference thresholds, and comparing the corresponding pressure difference, deformation difference, and same heart rate with the pressure difference thresholds, deformation difference thresholds, and same heart rate difference thresholds to determine the alignment checkability of the electrode caps.

[0015] Preferably, in the above-described automatic optimization control method for resistance spot welding, the alignment checkability of the electrode cap is detected by the alignment check detection device. The spot welding equipment is controlled to apply pressure to the spot welding test piece at an ideal pressure value. The method further includes analyzing the difference between the actual pressure value and the ideal pressure value applied to the spot welding test piece, obtaining the analysis results, and determining the abnormal condition of the spot welding equipment and electrode cap based on the analysis results.

[0016] Preferably, the automatic optimization control method for resistance spot welding described above includes the step of "controlling the automatic completion of spot welding operations by spot welding equipment based on the control parameters," After the spot welding is completed, the ideal output current value, ideal pressure value, and spot welding effect of the spot welding are stored as historical data in the historical database, and the range prediction model is updated with this data. [Effects of the Invention]

[0017] Based on specific embodiments of the present invention, the present invention discloses the following technical effects.

[0018] 1. This invention aims to improve welding efficiency and reduce energy consumption of materials by predicting the temperature required during welding, thereby selecting an appropriate electrode cap, avoiding unnecessary losses due to temperatures being too high or too low, and preventing unnecessary losses.

[0019] 2. This invention meets different welding requirements by allowing the selection of different types of electrode caps, reduces debugging and adjustment time during welding, and improves production efficiency and work efficiency.

[0020] 3. This invention maintains consistency and stability during welding by controlling the current parameters during welding by calculating the ideal output current value, thereby meeting ideal requirements, avoiding energy consumption due to excessively high or low current, and achieving energy optimization of the welding process.

[0021] 4. This invention predicts the optimal current and pressure combination through analysis of a historical database and a training range prediction model, which helps improve welding quality and stability, reduce the occurrence of defects and faulty products, make the welding process more efficient and stable, reduce adjustment time, improve production efficiency, and ensure the stable operation of spot welding equipment and extend the service life of the equipment by estimating appropriate pressure values.

[0022] 5. The proposed solution can be integrated into an automated system, making the spot welding process more intelligent and automated, reducing human intervention, and improving the degree of automation in the production line. [Brief explanation of the drawing]

[0023] To more clearly illustrate embodiments of the present invention or technical concepts in the prior art, the following briefly introduces the accompanying drawings that may be used in the embodiments. Obviously, the accompanying drawings in the following description represent only a few embodiments of the present invention, and those skilled in the art can obtain other accompanying drawings based on these without expending any creative effort. [Figure 1] This is a flowchart of the automatic optimization control method for resistance spot welding according to the present invention. [Figure 2] This is a schematic diagram of the structure of an annular projection electrode cap that is cut with three blades. [Figure 3] This is a schematic diagram of the structure of an annular projection-type electrode cap that is divided into a cross shape. [Figure 4] This is a schematic diagram of the structure of a spiral-shaped electrode cap. [Figure 5] This is a schematic diagram of the structure of a concave electrode cap. [Figure 6] This is a schematic diagram of the structure of a flat-head electrode cap. [Figure 7] This is a schematic diagram of the alignment check detection device. [Modes for carrying out the invention]

[0024] The following clearly and completely describes the technical concepts in the embodiments of the present invention, linking them to the accompanying drawings. Clearly, the described embodiments represent only a portion of the embodiments of the present invention, not all of them. All other embodiments obtained by those skilled in the art without any creative effort based on the embodiments of the present invention are all within the scope of protection of the present invention.

[0025] The objective of this invention is to achieve higher quality welding and higher production efficiency by automatically adjusting welding parameters during resistance spot welding using intelligent algorithms and historical data analysis.

[0026] To make the above-mentioned objectives, features, and advantages of the present invention clearer and easier to understand, the present invention will be described in more detail below, linking the attached drawings with specific embodiments.

[0027] (Example 1) In one embodiment, referring to Figures 1 to 6, an automatic optimization control method for resistance spot welding includes the following steps:

[0028] Step 1 involves collecting data on the welding material's thickness, initial temperature, and various material parameters.

[0029] Here, the various material parameters include the quality of the welding material, its heat capacity, thermal efficiency, the maximum and minimum thickness of the welding material during the hysteretic spot welding process, and its thermal conductivity coefficient.

[0030] The various material parameters collected in this embodiment include the quality of the welding material, heat capacity value, thermal efficiency, thickness change during hysteretic spot welding, and thermal conductivity coefficient. The collection method is a conventional technical method and will not be described in this embodiment. These parameters are used to analyze and control heat transfer and material performance changes during welding. Here, the thermal conductivity coefficient is obtained based on the thermal conductivity of the material. The thermal conductivity of each material is converted to a thermal conductivity coefficient using a normalization method. Assuming the welding material is magnesium, the thermal conductivity of magnesium is 160 W / (m × K), and the thermal conductivity of copper is 401 W / (m × K). Assuming the highest thermal conductivity in the hysteretic spot welding process is the thermal conductivity of copper and the lowest thermal conductivity is the thermal conductivity of magnesium, Normalized thermal conductivity of copper = (401 - minimum value) / (maximum value - minimum value) = (401 - 160) / ((401 - 160) = 241 / 241 = 1 The calculation process for the thermal conductivity coefficient of other materials is similar. By collecting various material parameters, it is possible to improve welding quality and efficiency by more precisely controlling heat transfer during welding.

[0031] Overall, Step 1 aims to achieve a more accurate, efficient, and flexible resistance spot welding process by meticulously collecting material parameters.

[0032] In step 2, multiple types of electrode caps are pre-installed.

[0033] As shown in Figures 2 to 6, pre-installing multiple types of electrode caps involves classifying the electrode cap types based on material and structure, where the structural categories are divided into protruding, concave, and flat-head types. The protruding electrode cap has a multilayer annular ridge 1, a spiral ridge 2, or a radial ridge on its contact surface with the welding material. The concave electrode cap has a welding surface 6, an annular ridge 4, a circumference 5, and a recessed groove 3 on its contact surface with the welding material. The recessed groove 3 is located in the central region of the contact surface, and the upper edge of the recessed groove 3 is transiently connected to the welding surface via a smooth fillet 6. The circumference 5 is the outer diameter of the welding surface 6, the shape of the recessed groove 3 is spherical, and the annular ridge 4 is positioned on the welding surface. The flat-head electrode cap includes a welding contact segment 7, a consumption segment 8, and a mounting segment 9. The contact segment 7 employs a tapered structure, and the contact surface at the top of the contact segment 7 is flat. The angle between the contact segment 7 and the consumption segment 8 is 120° to 145°.

[0034] Furthermore, different types of electrode cap structures (protruding, concave, and flat-headed) offer different contact methods and heat transfer characteristics to meet different welding needs.

[0035] Electrode caps with ridges and protrusions are always used to improve welding performance and extend electrode life. The protrusion electrode caps of this embodiment include, but are not limited to, multilayer annular ridges 1, spiral ridges 2, or radial ridge structures, and further include several types as described below.

[0036] The tapered ridge, where the top of the electrode cap is tapered, can concentrate the electric arc more effectively and improve welding quality.

[0037] The cylindrical ridge, with the top of the electrode cap exhibiting a cylindrical shape, is applied to several special welding tasks and welding applications with relatively high material requirements.

[0038] The angular ridges and the angular shape of the electrode cap's top provide a larger electric shock area, increasing the contact area with the workpiece and improving heat transfer efficiency.

[0039] The V-shaped ridge, with the top of the electrode cap exhibiting a V-shape, makes it easier to concentrate the electric arc on the weld bead, thereby improving welding quality.

[0040] Furthermore, the specific ridge structure to be selected during application depends on the requirements of the welding task and the characteristics of the workpiece; therefore, in actual applications, it is necessary to select according to the specific situation. At the same time, electrode caps from different manufacturers and brands may have different ridge structures to meet different needs.

[0041] For multi-layered annular ridges 1, it is necessary to cross-split or triple-cut them. Cutting the multi-layered annular ridges can improve the effectiveness of resistance spot welding to some extent. Cross-splitting or triple-cutting increases the contactable area of ​​the weld bead edge, which helps improve welding quality, and reduces welding spatter, thereby improving welding efficiency and quality.

[0042] Specifically, flat-head electrode caps have a relatively large contact area, which allows them to provide a relatively uniform welding temperature distribution. This makes them suitable for welding thick plates or workpieces requiring relatively high welding strength. Flat-head electrode caps are generally used to stabilize the welding process.

[0043] The electrode cap with a ridge structure (protruding type) concentrates the current over a relatively small area, resulting in a relatively high welding area temperature. This may be used when welding thin-walled workpieces or when deep-solution welding is required. However, excessively high temperatures can overheat the material around the welding point, potentially affecting the quality of the weld and the dimensional accuracy of the workpiece.

[0044] A recessed electrode cap creates a relatively low-temperature region during welding, which helps avoid overheating and reduces deformation around the weld point. Such a structure is suitable for welding heat-sensitive materials or especially for fine welding processes.

[0045] Different types of electrode cap structures offer more flexible choices and can improve welding quality and stability by adapting to welding materials of different shapes and materials.

[0046] In step 3, the predicted welding temperature is calculated based on the collected welding material thickness, initial temperature, and various material parameters.

[0047] Step 3 specifically includes the following steps: This is a calculation of the predicted temperature, T y =T0+[(k-T0)×(D0-D min ) / (D max A calculation model for the predicted temperature (-D0) was established, Here, T y is the predicted temperature value, representing the temperature required for welding, k is the thermal conductivity coefficient, T0 is the initial temperature of the welding material, D0 is the thickness of the welding material, and D max and D min These are the maximum and minimum thicknesses of the weld material during the hysteresis spot welding process, respectively. The collected thermal conductivity coefficient k, initial temperature T0 of the welding material, thickness D0 of the welding material, and maximum and minimum thickness D of this welding material during the hysteretic spot welding process. max and D min Substitute this into the calculation model for the predicted temperature, and the predicted temperature value T y Obtained, The predicted temperature calculation model uses historical data to determine the maximum and minimum thickness (d) of the welding material. max and d min The rate of change in thickness ((d0-d)) and the current thickness (d0) are obtained. min ) / (d max Calculate -d0)), and this ratio reflects the change in the current thickness relative to the hysteretic thickness. Multiply the difference between the thermal conductivity coefficient (k) and the initial temperature (T0) (k-T0) by the rate of thickness change to obtain a correction amount related to the material's thermal conductivity and temperature difference. Adding this correction amount to the initial temperature (T0) gives the expected welding temperature (T y ) obtain.

[0048] The purpose of this model is to compensate for the effect of changes in welding material thickness on welding temperature, ensuring that high-quality welds can be achieved even with different thicknesses. This method improves welding quality by enhancing the stability and consistency of the welding process.

[0049] This step utilizes scientific calculations and intelligent selection to optimize the automated welding parameters, contributing to improved efficiency and reliability in the resistance spot welding process.

[0050] In step 4, the corresponding type of electrode cap is selected based on the expected welding temperature, and the shape factor of the selected electrode cap is recorded.

[0051] Step 4 includes the following specific steps:

[0052] Selection of material categories, with an expected temperature value T y The spot welding temperature range corresponding to each type of electrode cap material is matched, and the predicted temperature value T is obtained. y Obtain an electrode cap that can achieve this, Selection of structural categories, based on welding material thickness, spot welding demand, and expected temperature value T. y Select an electrode cap with a corresponding structure based on the above. A first thickness threshold and a first temperature threshold are provided for flat-head electrode caps, a second temperature threshold is provided for protruding electrode caps, and a third temperature threshold is provided for concave electrode caps. The thickness of the welding material is greater than the first thickness threshold, and the expected temperature value T y If the temperature is lower than the first temperature threshold and the spot welding demand is for uniform welding, select a flat-head electrode cap. Predicted temperature T of welding material y If the temperature is higher than the second temperature threshold and the spot welding demand is for deep fusion welding, select a protruding electrode cap. Predicted temperature T of welding material y If the temperature is higher than the third temperature threshold and the spot welding demand is for fine welding, select a concave electrode cap. Based on the material and structure of the selected electrode cap, determine and use an electrode cap from this category, and record the shape factor Ca of the electrode cap.

[0053] The shape factor Ca represents the ratio of the actual weld area of ​​the electrode cap to the original contact area during the spot welding process. It is modified based on the structural category of the electrode cap and is obtained from the hysteretic spot welding process.

[0054] In step 5, the ideal welding time is predetermined, and the ideal output current value for the spot welding equipment is calculated by linking it with the expected welding temperature and various material parameters.

[0055] Step 5, "calculating the ideal output current value of the spot welding equipment by linking the expected welding temperature with various material parameters," specifically means: I = √([(η × m × c × (T y The expression includes -T0) / (R×t))+E, Here, I is the ideal output current value, η is the thermal efficiency of the welding material, m is the quality of the welding material, c is the heat capacity value of the welding material, and (T y -T0) is the temperature change required for welding, R is the resistance of the welding material, t is the preset ideal welding time, and E is the error bias term, which is set empirically. y T0 is the predicted temperature value, representing the temperature required for welding, while T0 is the initial temperature of the welding material.

[0056] The current calculation model is based on the principles of energy conservation and Ohm's law to calculate the ideal output current value. The quality of the welding material, its heat capacity and thermal efficiency, and the temperature change required for welding (T y -T0) affects the heat required during welding. Resistance (R) is one of the welding material properties and affects the voltage drop when the current passes through the welding material. The pre-set ideal welding time (t) is used to determine and define the time scale of the welding process.

[0057] By calculating the ideal output current value, the current controller or resistance welding machine can be adjusted according to the actual situation to achieve the desired welding effect. By considering factors such as the thermal efficiency, quality, heat capacity, and temperature changes of the welding material, the heat required during the welding process can be estimated more accurately, and the current control can be adjusted accordingly to improve welding quality and efficiency.

[0058] An error bias term (E) may be used to correct for differences between the theoretical model and the actual situation in order to improve the accuracy of the calculation results.

[0059] Step 6 involves establishing a historical database to classify and record current values, pressure values, and corresponding welding results in historical spot welding of each type of welding material.

[0060] Several methods can be used to monitor the compressive load on a metal, and the following are some of the most common methods.

[0061] The strain gauge method involves attaching a strain gauge to a metal surface. When the metal is subjected to pressure, deformation occurs, causing a change in the resistance value within the strain gauge. By measuring this change in resistance, the stress and compressive strength acting on the metal can be determined.

[0062] This sound wave detection method utilizes ultrasonic technology to transmit high-frequency sound waves to a metal surface. When the sound waves encounter defects or deformations within the metal, they are reflected, and the internal stress state of the metal can be determined by measuring the intensity of the reflected signal.

[0063] Optical microscopy allows for the observation of features such as texture, cracks, and deformation of metal surfaces using an optical microscope, and by linking these findings to the principles of material mechanics, it is possible to estimate the stress and compressive strength of the metal.

[0064] This simulation method utilizes finite element analysis software to obtain the stress and compression distribution of a metal under different loads through modeling and simulation calculations of the metal material.

[0065] It is necessary to select an appropriate monitoring method based on the specific application scenario and requirements to ensure the accuracy and reliability of the monitoring results.

[0066] Step 7 involves establishing a range prediction model, training it based on historical data in the historical database, and outputting multiple range prediction models after training is complete.

[0067] Step 7 specifically involves: f(x) = sign[Σ] i N The step of establishing a range prediction model for ai × yi × (xi·x) + b, Here, x is the input vector, i.e., the current value and pressure value for each pair in the history database; f(x) is the analysis result, i.e., the corresponding welding result; if the welding result is successful, f(x)>0, and a decimal number belonging to the interval (0,1) is assigned to f(x) based on the welding effect; if the welding result is unsuccessful, f(x)<0, and a decimal number belonging to the interval (-1,0) is assigned to f(x) based on the welding effect; xi is the support vector; yi is the category tag corresponding to each support vector, where yi=+1 represents a successful weld and yi=-1 represents an unsuccessful weld; ai is the Lagrange multiplier for each support vector xi; b is the bias term; and the sign function is a step used by the model to convert the distance from the sample point x to the decision boundary into a category tag and to determine which side of the decision boundary the sample point is on. The historical data for each set in the historical database is then input to the vector. The steps include: substituting JPEG2026085267000003.jpg33 and the analysis result f(x) into a range prediction model for training, and generating support vectors xi, category tags yi, Lagrange multipliers ai, and bias term b in the range prediction model corresponding to each type of welding material; The process includes the step of outputting multiple range prediction models trained based on support vectors xi, category tags yi, Lagrange multipliers ai, and bias term b in range prediction models corresponding to each type of welding material.

[0068] In this system, f(x) represents the analysis result, i.e., the corresponding welding result. If the welding result is satisfactory, f(x) > 0, and a decimal number belonging to the interval (0,1) is assigned to f(x) based on the welding effect. If the welding result is unsatisfactory, f(x) < 0, and a decimal number belonging to the interval (-1,0) is assigned to f(x) based on the welding effect. The principle for assigning these numerical values ​​is based on the actual welding effect. In some embodiments, the effect is determined by visual inspection by an engineer, and a numerical value is assigned. In other embodiments, images of the welded area are collected using image recognition technology, and the images are analyzed in conjunction with a trained deep learning algorithm (such as a cyclic neural network model), and a numerical value is assigned.

[0069] The range prediction model is based on the Support Vector Machine algorithm, where the Support Vector Machine (SVM) describes an SVM classifier based on a kernel function. The role of the kernel function is to allow the SVM to search for the optimal classification hyperplane in this high-dimensional space without explicitly mapping the data to this high-dimensional space. The kernel function parameters may be determined by methods such as cross-validation.

[0070] In step 8, the current ideal output current value and the pressure range of the spot welding equipment are substituted into the corresponding range prediction model to output the ideal pressure range.

[0071] Step 8 specifically involves: The steps include organizing the pressure values ​​provided by the spot welding equipment during the historical spot welding process in the historical database, and integrating each pressure value into a pressure range set, The ideal output current value and each pressure data in the pressure range set are input vectors. The steps include: sequentially substituting the data as JPEG2026085267000004.jpg33 into the range prediction model corresponding to the welding material, and generating the corresponding number of analysis results f(x); The process includes the steps of selecting each analysis result f(x), retaining each compression value for f(x)>0, and integrating each compression value into an ideal compression range by sorting them by numerical magnitude.

[0072] Suppose we have a historical database that records current values, pressure values, and corresponding welding results (i.e., effectiveness coefficients) during spot welding of different materials. To simplify the analysis, we will perform calculations using a specific welding material as an example. [Table 1] The data from the historical database mentioned above is substituted into the range prediction model for training, and various parameters for the model are obtained. Here, the method for obtaining the parameters is a conventional technique (e.g., cross-validation, least squared method, etc.), which is not described in the examples. The trained range prediction model is then output. Now, we make a prediction. We assume that given a specific current value and an ideal set of pressure ranges as input, the model will predict the corresponding analytical results.

[0073] The input vector, with a current value of 10KA, and an ideal pressure range set to [50N / cm²]. 2 , 60 N / cm 2 70 N / cm 2 Let's assume we choose to do this.

[0074] This is a calculation of a predictive model, in which the above input vectors are substituted into a support vector machine model to generate the corresponding number of analysis results f(x).

[0075] This involves selecting the analysis results, retaining each compression value for f(x)>0, and then arranging them by numerical magnitude to integrate them into an ideal compression range.

[0076] The beneficial effects of this step include predicting the optimal current and pressure combination through analysis of the historical database and training models, which helps improve welding quality and stability, and reduces the occurrence of defects and faulty products. It makes the welding process more efficient and stable, reduces adjustment time, and improves production efficiency. It lowers production costs by avoiding excessive use of resources or unnecessary equipment adjustments. Estimating appropriate pressure values ​​based on data acquired in the historical database helps ensure the stable operation of spot welding equipment and extends the service life of the equipment.

[0077] In step 9, the ideal pressure range is calculated by linking the ideal compression range with the shape factor, and the median of the pressure values ​​within the ideal pressure range is taken as the ideal pressure value.

[0078] Step 9 specifically involves: The step of establishing a pressure estimation model and determining the pressure that needs to be applied to the spot welding equipment based on the pressure estimation model Fi = Pi × (Ai × Cai), where Pi is the i-th pressure value in the ideal pressure range and Ai is this pressure value Step 47 is the original contact area of ​​the electrode cap corresponding to JPEG2026085267000006.jpg47, Cai is the shape factor of this electrode cap, (Ai × Cai) is the actual contact area of ​​this electrode cap, and Fi is the pressure value corresponding to this pressure strength value Pi, i.e., the pressure that needs to be applied to the spot welding equipment. The process involves the steps of: linking each pressure value within the ideal pressure range to the actual contact area of ​​the selected electrode cap, substituting these values ​​into a pressure estimation model, outputting each corresponding pressure value Fi, and integrating them into the ideal pressure range; This includes the step of sorting the data within the ideal pressure range in descending or ascending order, and taking the median as the ideal pressure value.

[0079] Furthermore, the median has a relatively small impact on outliers and can reflect the data trend better than the mean. Within the obtained range data, there may be some extreme values ​​that deviate from the normal range, and these values ​​can have a relatively large impact on the calculated mean, but the median can better resist such influences. The median is unaffected by the distribution shape; whether the data is normally distributed, skewed, or in other distribution forms, the median can provide a relatively reasonable estimate. Therefore, when selecting an ideal pressure value, adopting the median can be applied to various different types of welding materials and process parameters.

[0080] Because the median is relatively stable compared to extreme values, adopting the median as the ideal value can reduce fluctuations caused by noise or small outliers in historical data. In this way, it is possible to ensure that the set pressure value remains relatively stable in the actual spot welding process, improving the reliability and consistency of production.

[0081] In some embodiments, the median has limitations as an ideal value, and in some cases, if the dataset has specific distributional characteristics or biases, other statistical indicators, such as the mean or weighted mean, may be more suitable as the ideal value. Therefore, in specific applications, an appropriate ideal value can be selected based on actual demand and data characteristics.

[0082] In step 10, the ideal output current value, ideal pressure value, and ideal welding time are integrated into the control parameters.

[0083] Step 11 controls the automatic completion of spot welding operations by the spot welding equipment based on control parameters.

[0084] The principle of the above embodiment is that the automatic optimization control method according to this embodiment is an optimized design for the resistance spot welding process, achieving the optimal spot welding effect based on the characteristics and material parameters of the welding material. The principle is to collect information on the welding material, select an appropriate electrode cap, calculate ideal welding parameters, link a historical database and a range prediction model to make adjustments in real time, and finally achieve the objective of automatically controlling the spot welding equipment to obtain optimal welding quality.

[0085] The beneficial effects of the above embodiment are as follows:

[0086] To improve welding quality, it is possible to enhance welding quality and stability by selecting appropriate electrode caps, ideal output current values, and pressure ranges based on the characteristics and parameters of the welding material, thereby ensuring that the parameters during welding are in an optimal state.

[0087] This reduces human error by automating the control of spot welding equipment, thereby reducing errors and subjective factors during human operation and improving the consistency and repeatability of welds.

[0088] By predicting the application of models and historical databases, it is possible to quickly determine optimal welding parameters, saving time and material waste, thereby reducing production costs.

[0089] This improves production efficiency by making the welding process more intelligent and efficient through an automatic optimization control method, thereby improving the production efficiency and utilization rate of production capacity on the production line.

[0090] (Example 2) In one embodiment, referring to Figure 7, an automatic optimization control method for resistance spot welding is provided, further comprising detecting the alignment checkability of the set of electrode caps by an alignment check detection device between step 4, which is to "select a corresponding type of electrode cap based on the expected welding temperature and record the shape coefficient of the selected electrode cap," and step 5, which is to "preset an ideal welding time and calculate an ideal output current value for the spot welding equipment by linking the expected welding temperature with various material parameters." The alignment check detection device is A spot-welded test specimen comprising an upper end specimen 12 and a lower end specimen 13, wherein the upper end specimen 12 and the lower end specimen 13 are of the same dimensions (without specifying or mentioning the material), and the upper end specimen 12 and the lower end specimen 13 are aligned and overlapping spot-welded test specimens. A pressure sensor 14 is installed between the upper end test specimen 12 and the lower end test specimen 13 to collect the pressure value and deformation state applied to the upper end test specimen 12 and the lower end test specimen 13, and to convert the pressure value and deformation state into an electronic image by a computer. A guide component connected to the spot welding equipment 11, which allows the spot welding equipment 11 to clamp and apply pressure to the spot welding test piece, The system includes a computer for analyzing electronic images of the upper end specimen 12 and the lower end specimen 13, determining the pressure difference, deformation difference, and same heart rate, pressure difference threshold, deformation difference threshold, and same heart rate, pre-setting difference thresholds, and comparing the corresponding pressure difference, deformation difference, and same heart rate with the pressure difference threshold, deformation difference threshold, and same heart rate difference threshold to determine the alignment checkability of the electrode caps.

[0091] As shown in Figure 5, the alignment check detection device works by having the spot weld test piece, pressure sensor, and guide component work together to detect the alignment of the electrode cap 10. Specifically, when the upper end test piece 12 and lower end test piece 13 are subjected to pressure, the pressure sensor 14 collects their pressure values ​​and deformation state, converts this data into an electronic image, and transmits it to a computer for analysis. The guide component is responsible for guiding the spot welding machine 11 to hold the electrode cap 10, clamp the spot weld test piece, and apply pressure, thereby ensuring that the test piece is in the correct position.

[0092] The computer analyzes electronic images of the upper end specimen 12 and the lower end specimen 13 to determine parameters such as the pressure difference, deformation difference, and heart rate between them. A preset difference threshold is used to determine the degree of alignment check of the electrode caps by comparing it with the analysis results. The advantages of this method include the following:

[0093] This automated system uses computers to analyze data and set thresholds to automatically detect electrode cap alignment issues, thereby improving production efficiency.

[0094] By utilizing pressure sensor and deformation state data, the alignment status of the electrode cap can be evaluated more accurately, thus avoiding human error.

[0095] This system allows for immediate adjustment; if the computer detects that the electrode cap is not properly aligned, it will immediately adjust, issue a warning, and ensure welding quality.

[0096] To further optimize the above embodiment, detecting the alignment of this electrode cap using an alignment check detection device is possible. The spot welding equipment 11 is controlled to apply pressure to the spot welding test piece at an ideal pressure value, The method further includes analyzing the difference between the actual pressure value and the ideal pressure value applied to the spot welding test piece, obtaining the analysis results, and determining the abnormal condition of the spot welding equipment 11 and electrode cap 10 based on the analysis results.

[0097] The principle of this embodiment is to determine whether the electrode cap 10 is functioning correctly by comparing the actual pressure value applied to the spot weld test piece with a preset ideal pressure value. The specific steps are as follows.

[0098] 1. The spot welding equipment 11 is controlled to apply pressure to the spot welding test piece according to a preset ideal pressure value.

[0099] 2. Monitor the actual pressure applied to the spot weld test specimen in real time and compare it to the ideal pressure value.

[0100] 3. By analyzing the difference between the actual pressure value and the ideal pressure value, it is determined whether there are any abnormal conditions in the electrode cap 10, such as pressure instability or offset.

[0101] This embodiment allows for the timely detection of abnormal conditions in the electrode cap 10 by monitoring the actual pressure value in real time, thereby preventing defective welding. Potential abnormalities in the electrode cap 10 can be detected early, allowing for corrective action to prevent failures and reduce downtime.

[0102] To further optimize the above embodiment, after the spot welding is completed, following step 11, which involves "controlling the automatic completion of the spot welding operation by the spot welding equipment based on the control parameters", After the spot welding is completed, the ideal output current value, ideal pressure value, and spot welding effect of the spot welding are stored as historical data in a historical database, and the range prediction model is updated using this data as training data.

[0103] Furthermore, after each spot weld is completed, the ideal output current value, ideal pressure value, and spot welding effect are recorded and stored in a history database. By using this history data to update and optimize the predictive model, it becomes easier to better control welding parameters during future spot welds and improve welding quality.

[0104] This embodiment can improve the level of automation and quality control capabilities of the spot welding process by continuously collecting historical data to train a predictive model, thereby improving the accuracy and adaptability of the model. Furthermore, by constantly updating the predictive model, it is possible to achieve continuous optimization of welding parameters and improve production efficiency and product quality.

[0105] Each of the technical features of the above embodiments can be combined in any way, and for the sake of brevity, not all possible combinations of each technical feature in the above embodiments have been described. However, as long as there are no inconsistencies in these combinations of technical features, they should be considered to fall within the scope described herein.

[0106] This specification has described the principles and embodiments of the present invention using specific examples. However, the above descriptions of examples are merely intended to aid in understanding the methods and core ideas of the present invention, and those skilled in the art will know that specific embodiments and scope of application can be modified based on the ideas of the present invention. For these reasons, the contents of this specification should not be understood as limitations on the present invention. [Explanation of Symbols]

[0107] 1. Ring ridge, 2 spiral ridges, 3. Concave groove, 4 ring ridges, 5. Circumference, 6. Welding surface, 7 contact segments, 8 Consumption segments, 9 mounting segments, 10 electrode caps, 11 Spot welding equipment, 12 Upper end test specimen, 13 Lower end test specimen, 14. Pressure Sensor

Claims

1. An automatic optimization control method for resistance spot welding, To collect the thickness of the welding material, the initial temperature, and various material parameters, Install multiple types of electrode caps in advance, The predicted welding temperature is calculated based on the collected thickness, initial temperature, and various material parameters of the welding material. The process involves selecting a corresponding type of electrode cap based on the predicted welding temperature, and simultaneously recording the shape factor of the selected electrode cap. The ideal welding time is set in advance, and the ideal output current value of the spot welding equipment is calculated by linking the predicted welding temperature with various material parameters. To establish a historical database for classifying and recording current values, pressure values, and corresponding welding results in spot welding of various types of welding materials, The process involves establishing a range prediction model, training it based on historical data in the historical database, and outputting multiple range prediction models after training is complete. The current ideal output current value and the pressure intensity range of the spot welding equipment are substituted into the corresponding range prediction model to output the ideal pressure intensity range. The ideal pressure range is calculated by linking the ideal compression range with the shape factor, and the median of the pressure values ​​within the said ideal pressure range is taken as the ideal pressure value. The ideal output current value, ideal pressure value, and ideal welding time are integrated into the control parameters, An automatic optimization control method characterized by including control of the automatic completion of spot welding work by spot welding equipment based on the control parameters.

2. The aforementioned material parameters are: This includes the quality of the welding material, its heat capacity value, thermal efficiency, the maximum and minimum thickness of the welding material during the hysteretic spot welding process, and its thermal conductivity coefficient. The pre-installation of the aforementioned multiple types of electrode caps includes classifying the electrode cap types based on material and structure, where the structural categories are divided into protruding, concave, and flat-headed types. The contact surface between the protruding electrode cap and the welding material is provided with multilayer annular ridges, spiral ridges, or radial ridges. The contact surface between the concave electrode cap and the welding material is provided with a welding surface, an annular ridge, a circumference, and a groove located in the central region of the contact surface. The upper edge of the groove is transiently connected to the welding surface via a smooth fillet. The circumference is the outer diameter of the welding surface, the groove is spherical in shape, and the annular ridge is provided on the welding surface. The automatic optimization control method for resistance spot welding according to claim 1, characterized in that the flat-head electrode cap includes a welding contact segment, a consumption segment, and a mounting segment, wherein the contact segment employs a tapered structure, the contact surface at the top of the contact segment is flat, and the angle between the contact segment and the consumption segment is 120° to 145°.

3. Specifically, calculating the predicted welding temperature based on the thickness, initial temperature, and various material parameters of the collected welding material, selecting the corresponding type of electrode cap based on the predicted welding temperature, and simultaneously recording the shape factor of the selected electrode cap, is performed as follows: This is a calculation of the predicted temperature, T ｙ = T ０ +[(k-T ０ ) × (D ０ -D ｍｉｎ ) / (D ｍａｘ -D ０ The step of establishing a calculation model for the predicted temperature, Here, T ｙ is the predicted temperature value, representing the temperature required for welding, k is the thermal conductivity coefficient, and T ０ is the initial temperature of the welding material, D ０ is the thickness of the welding material, and D ｍａｘ and D ｍｉｎ are the maximum thickness and minimum thickness of this welding material in the history spot welding process respectively, and The collected thermal conductivity coefficient k and the initial temperature T of the welding material. ０ Thickness D of the welding material ０ , the maximum and minimum thickness D of this weld material during the hysteresis spot welding process ｍａｘ and D ｍｉｎ Substitute this into the calculation model for the predicted temperature, and the predicted temperature value T ｙ Steps to obtain, Selection of material categories, with a predicted temperature value T ｙ The predicted temperature value T is matched with the spot welding temperature range corresponding to each type of electrode cap material. ｙ The steps include obtaining the electrode cap that can achieve the above, Selection of structural categories, based on welding material thickness, spot welding demand, and expected temperature value T. ｙ The steps include selecting an electrode cap of the corresponding structure based on the above, The steps include providing a first thickness threshold and a first temperature threshold applicable to the flat-head electrode cap, providing a second temperature threshold applicable to the protruding electrode cap, and providing a third temperature threshold applicable to the concave electrode cap, The thickness of the welding material is greater than the first thickness threshold, and the expected temperature value T ｙ If the temperature is lower than the first temperature threshold and the spot welding demand is for uniform welding, the step is to select the flat-head electrode cap. Predicted temperature T of welding material ｙ If the temperature is higher than the second temperature threshold and the spot welding demand is deep fusion welding, the step is to select the protruding electrode cap, Predicted temperature T of welding material ｙ If the temperature is higher than the third temperature threshold and the spot welding demand is fine welding, the step is to select the concave electrode cap, An automatic optimization control method for resistance spot welding according to claim 2, comprising the steps of determining and using an electrode cap in this category based on the material and structure of the selected electrode cap, and recording the shape factor Ca of the electrode cap.

4. To pre-set the ideal welding time and calculate the ideal output current value of the spot welding equipment by linking the predicted welding temperature with various material parameters, specifically, I=√([(η×m×c×(T ｙ -T ０ Using the formula ) ] / (R × t) + E, Here, I is the ideal output current value, η is the thermal efficiency of the welding material, m is the quality of the welding material, c is the heat capacity value of the welding material, and (T ｙ -T ０ ) is the temperature change required for welding, R is the resistance of the welding material, and t is the preset ideal welding time. The error bias term is set based on experience, T ｙ This is the predicted temperature value, representing the temperature required for welding. ０ The automatic optimization control method for resistance spot welding according to claim 1, characterized in that is the initial temperature of the welding material.

5. Establishing the range prediction model, training it based on historical data in the historical database, and outputting multiple range prediction models after training completion specifically means: f(x)=sign[Σ ｉ Ｎ A step to establish the range prediction model ai × yi × (xi・x) + b, Here, x is the input vector, i.e., the current value and pressure value for each set in the history database, f(x) is the analysis result, i.e., the corresponding welding result, where if the welding result is successful, f(x) > 0, and a decimal number belonging to the interval (0, 1) is assigned to f(x) based on the welding effect, where if the welding result is unsuccessful, f(x) < 0, and a decimal number belonging to the interval (-1, 0) is assigned to f(x) based on the welding effect, xi is the support vector, yi is the category tag corresponding to each support vector, where yi = +1 represents a successful weld, and yi = -1 represents an unsuccessful weld, ai is the Lagrange multiplier of each support vector xi, b is the bias term, and the sign function is a step used by the model to convert the distance from the sample point x to the decision boundary into a category tag and to determine which side of the decision boundary the sample point is on. The steps include sequentially substituting each set of historical data from the historical database as input vector x and analysis result f(x) into the range prediction model for training, and generating support vectors xi, category tags yi, Lagrange multipliers ai, and bias term b in the range prediction model corresponding to each type of welding material, An automatic optimization control method for resistance spot welding according to claim 1, comprising the step of outputting a plurality of range prediction models trained based on support vectors xi, category tags yi, Lagrange multipliers ai and bias term b in the range prediction model corresponding to each type of welding material.

6. Substituting the current ideal output current value and the pressure intensity range of the spot welding equipment into the corresponding range prediction model to output the ideal pressure intensity range specifically means, The steps include organizing the pressure values ​​provided by the spot welding equipment during the historical spot welding process in the historical database and integrating each of the pressure values ​​into a pressure range set, The steps include: sequentially substituting the ideal output current value and each pressure data in the pressure range set as input vector x into the range prediction model corresponding to the welding material to generate a corresponding number of analysis results f(x); The automatic optimization control method for resistance spot welding according to claim 5, comprising the steps of selecting each analysis result f(x), retaining each pressure value for f(x) > 0, and integrating the pressure values ​​into the ideal pressure range by arranging them by numerical magnitude.

7. Specifically, calculating the ideal pressure range by linking it with the shape factor, and taking the median of the pressure values ​​within that ideal pressure range as the ideal pressure value, means: A step of establishing a pressure estimation model where Fi = Pi × (Ai × Cai), and determining the pressure to be applied to the spot welding equipment based on the pressure estimation model, wherein Pi is the i-th pressure value in the ideal pressure range, Ai is the original contact area of ​​the electrode cap corresponding to this pressure value Pi, Cai is the shape factor of the electrode cap, (Ai × Cai) is the actual contact area of ​​the electrode cap, and Fi is the pressure value corresponding to this pressure value Pi, i.e., the pressure to be applied to the spot welding equipment. The steps include: assigning each pressure value in the aforementioned ideal pressure range to the actual contact area of ​​the selected electrode cap, substituting it into the pressure estimation model, outputting each corresponding pressure value Fi, and integrating it into the aforementioned ideal pressure range; The automatic optimization control method for resistance spot welding according to claim 1, characterized by comprising the step of sorting the data in the aforementioned ideal pressure range in descending or ascending order, and setting the median value as the ideal pressure value.

8. Between the steps of "selecting the corresponding type of electrode cap based on the predicted welding temperature and simultaneously recording the shape factor of the selected electrode cap" and "pre-setting the ideal welding time and calculating the ideal output current value of the spot welding equipment by linking the predicted welding temperature with various material parameters," the alignment check detection device further includes detecting the alignment checkability of this set of electrode caps. The alignment check detection device is A spot weld test specimen comprising an upper end test specimen and a lower end test specimen, wherein the upper end test specimen and the lower end test specimen are of the same dimensions, and the upper end test specimen and the lower end test specimen are aligned and overlapping spot weld test specimens, A pressure sensor is installed between the upper end test specimen and the lower end test specimen to collect the pressure value and deformation state acting on the upper end test specimen and the lower end test specimen, and to convert the pressure value and deformation state into an electronic image using a computer. A guide component connected to the spot welding equipment, which guides the spot welding equipment to grip and apply pressure to the spot welding test piece, An automatic optimization control method for resistance spot welding according to claim 1, comprising: a computer for analyzing electronic images of the upper and lower end test specimens to determine the pressure difference, deformation difference, and same heart rate experienced by the upper and lower end test specimens; presetting pressure difference thresholds, deformation difference thresholds, and same heart rate difference thresholds; and comparing the corresponding pressure difference, deformation difference, and same heart rate with the pressure difference thresholds, deformation difference thresholds, and same heart rate difference thresholds to determine the alignment checkability of the electrode cap.

9. The alignment check detection device described above detects the alignment checkability of this electrode cap, The spot welding equipment is controlled to apply pressure to the spot welding test piece at an ideal pressure value. The automatic optimization control method for resistance spot welding according to claim 8, further comprising analyzing the difference between the actual pressure value and the ideal pressure value applied to the spot welding test piece, obtaining the analysis results, and determining the abnormal condition of the spot welding equipment and electrode cap based on the analysis results.

10. After the step of controlling the automatic completion of spot welding work by the spot welding equipment based on the aforementioned control parameters, The automatic optimization control method for resistance spot welding according to claim 1, further comprising, after the completion of the spot welding, storing the ideal output current value, ideal pressure value, and spot welding effect of the spot welding as historical data in the historical database and updating the range prediction model as training data.

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