A method for determining the resistance welding window

Abstract

This invention discloses a method for determining the resistance welding window, relating to the field of welding technology, including the following steps: S1, initial process principle anchoring and condition setting; S2, critical heat calibration test; S3, boundary model fitting and initial static window generation; S4, digital twin system construction; S5, dynamic iteration and window self-updating; S6, continuous verification and archiving of window effectiveness. This invention relies on a digital twin system to build a real-time "physical-virtual" mapping, overcoming the limitations of traditional static windows. Through full data acquisition and closed-loop iteration, it captures dynamic factors such as electrode wear, batch changes in base material, and current fluctuations in real time, automatically correcting model parameters and window boundaries. It can quickly respond to and adjust process parameters for sudden situations such as oxide layer regeneration and power grid fluctuations. It effectively solves the quality instability problem caused by operating condition drift in mass production, significantly improving the consistency of welding quality in complex scenarios.

Description

Technical Field

[0001] This invention relates to the field of welding technology, specifically a method for determining the resistance welding window. Background Technology

[0002] Resistance spot welding is a core joining process in automobile manufacturing, aerospace, and home appliance production, widely used for joining thin-plate components due to its high efficiency and reliability. The welding window, as the core basis for controlling key parameters such as welding current and time, directly determines the quality of the weld nugget, structural strength, and service life of the workpiece. It is a crucial prerequisite for ensuring consistent welding quality and workpiece safety in mass production.

[0003] However, existing methods are mostly based on static models, calibrating window boundaries through limited experiments, and only considering basic factors, making it difficult to cope with the many dynamic disturbances in production. Contact resistance and heat loss are easily affected by the oxide layer of the base material, electrode wear, shunt effects, etc., causing the static window to fail quickly and leading to defects such as cold solder joints and spatter. Moreover, when operating conditions change, a large number of calibration tests need to be carried out again, resulting in long adjustment cycles and failing to meet the flexible requirements of multi-variety mixed production lines. Summary of the Invention

[0004] The purpose of this invention is to provide a method for determining the resistance welding window to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for determining a resistance welding window, comprising the following steps: S1. Initial process principle anchoring and condition setting: Based on Joule's law, clarify the relationship between welding heat generation and effective heat, preset and fix the electrode force to determine the resistance reference value, and pre-treat the base material and surface condition; S2. Critical heat calibration test: Select 3 to 5 typical welding times, adjust the current step by step for each time point, calibrate the minimum current for weld nugget formation by metallographic detection, calibrate the maximum current for spatter failure by visual acquisition device, record the corresponding data and label the full working condition information to form a dataset with working condition labels. S3. Boundary model fitting and initial static window generation: Substitute the dataset into the two-parameter empirical model, and fit the model parameters of the weld nugget formation boundary and the spatter failure boundary respectively by the least squares method. Select parameter points for verification experiments and generate the initial static welding window. S4. Digital Twin System Construction: Deploy a data acquisition layer to collect process parameters, process status data and quality result data in real time, construct a twin model with an embedded dual-parameter model, establish multi-source data association and virtual-real linkage rules, and develop a visual interface; S5. Dynamic Iteration and Window Self-Update: Data is collected in real time through the twin platform. When changes in working conditions are detected, the model parameters are automatically corrected and the window boundaries are adjusted. Combined with defect monitoring, closed-loop feedback optimization is achieved. The initial window is quickly generated by matching similar working conditions based on the historical database. S6. Continuous Validation and Archiving of Window Validity: Periodically extract parameter points from the dynamic window to conduct destructive tests, compare the model prediction values ​​with the actual values ​​and analyze the deviations, archive all window parameters, validation results and process adjustment measures, and establish a related knowledge base.

[0006] Preferably, the preset and fixed electrode force in step S1 to determine the resistance reference value specifically includes: The electrode force is preset according to the thickness and properties of the base material. When the electrode force increases, the corresponding resistance decreases, and a larger current is matched to reach the critical effective heat. When the electrode force decreases, the corresponding resistance increases, requiring a smaller current to be matched, and the reference resistance value corresponding to the initial electrode force is recorded simultaneously.

[0007] Preferably, the preprocessing in step S1 specifically includes: For materials with high resistivity, a smaller electrode force is preset, while for aluminum alloys with good conductivity, a larger electrode force is preset, to match the initial electrode force value. The surface of the base material is polished and cleaned to remove the oxide layer and oil stains.

[0008] Preferably, the calibration test in step S2 also includes interference factor avoidance: optimizing the weld point arrangement to control the shunt effect when performing multi-point spot welding; and modifying the critical current judgment criterion when using copper-chromium alloy electrodes, taking into account their high heat loss characteristics.

[0009] Preferably, the two-parameter empirical model in step S3 is: ; Where t is time and I is current; a is a constant related to heat conduction loss, and the minimum welding time when the current is extremely high; b is a constant related to resistance R and critical heat, and the rate of change of time with current when the current is small; Adjust the values ​​of a and b according to the characteristics of the base material and the electrode.

[0010] Preferably, the process status data collected by the data acquisition layer in step S4 includes: temperature field of the welding zone, electrode wear, regeneration status of oxide layer on the base material surface, and weld point arrangement and diversion data.

[0011] Preferably, the multi-source data association and virtual-real linkage rules in step S4 are as follows: when the batch of parent material changes, the historical database parameter benchmark is called to correct a and b; Automatic compensation of the upper limit of current when the shunt effect occurs; The preset value offset of a is used to compensate for heat loss under the working conditions of copper-chromium alloy electrodes.

[0012] Preferably, the changes in operating conditions mentioned in step S5 include: electrode wear exceeding the threshold, batch replacement of the base material, fluctuation of the shunt coefficient, fluctuation of the power grid, and regeneration of the oxide layer on the surface of the base material.

[0013] Preferably, the rapid generation of the initial window in step S5 specifically involves: matching similar working conditions according to the type of base material, surface condition, electrode condition, and current distribution, and completing the debugging through 2-3 sets of verification tests.

[0014] Preferably, the destructive test in step S6 involves sampling 5-8 parameter points per month, simultaneously recording the electrode wear, surface cleanliness, and current shunting during the test, and optimizing the corresponding process based on the cause of the deviation.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention relies on a digital twin system to build a real-time "physical-virtual" mapping, breaking through the limitations of traditional static windows. Through full data acquisition and closed-loop iteration, it captures dynamic factors such as electrode wear, batch changes in base material, and current fluctuations in real time, automatically correcting model parameters and window boundaries. For unexpected situations such as oxide layer regeneration and power grid fluctuations, it can quickly respond and adjust process parameters to ensure that the welding window always covers the qualified range, effectively solving the quality instability problem caused by operating condition drift in mass production, and significantly improving the consistency of welding quality in complex scenarios.

[0016] 2. This invention controls data accuracy from the source, laying a solid foundation through multi-dimensional preprocessing and refined experimental calibration. In the initial stage, electrode force is pre-adjusted and the surface condition of the base material is optimized to avoid key interference factors. During critical heat calibration, metallographic inspection and visual monitoring technologies are combined to accurately locate the critical points of melt nugget formation and spatter failure, simultaneously labeling information under all operating conditions. The least squares method is used to fit the model, coupled with multiple sets of verification experiments to optimize parameters, effectively reducing misjudgments caused by factors such as current shunting and electrode heat dissipation. This provides a high-precision, traceable dataset for window generation, ensuring the accuracy of window boundaries. Attached Figure Description

[0017] Figure 1 This is a flowchart of a method for determining a resistance welding window according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example: Refer to Figure 1 As shown: A method for determining the resistance welding window, comprising the following steps: I. Initial Process Principle Anchoring and Condition Setting. The heat generation in resistance welding follows Joule's law (… Where Q is the total heat and I is the welding current. (where t is the dynamic contact resistance of the welding zone and t is the welding time). In actual welding, heat is lost through conduction to the electrodes and base material, and the effective heat used for weld nugget formation is relatively small. , This is due to heat loss. Contact resistance... Heat loss It is affected by a variety of factors, which directly determine the critical heat requirement.

[0020] The specific operating steps are as follows: (I) Electrode Force Setting and Control: The electrode force is preset and fixed according to the thickness and properties of the base material. Increased electrode force → increased contact area → increased resistance. Decrease → Requires a larger current to reach the critical point Electrode force decreases → resistance decreases Increasing the electrode force reduces the required current; therefore, the resistance corresponding to the initial electrode force needs to be recorded. Benchmark value.

[0021] (II) Pretreatment of key influencing factors: (1) Base material: Materials with high resistivity (such as stainless steel) have high resistance values, and the total heat is generated quickly under the same current, and the window is easy to shift to the left; materials with good conductivity (such as aluminum alloy) have low resistance values, and the window is easy to shift to the right, so the initial value of the electrode force needs to be matched in advance.

[0022] (2) Surface condition: The oxide layer and oil stains on the surface of the base material will increase. This causes the window to shift to the left, so the surface of the base material needs to be sanded and cleaned in advance to remove impurities and interference.

[0023] II. Critical Heat Calibration Test. The two boundaries of the welding window correspond to two critical effective heats: The nucleus formation boundary: the minimum effective heat required to form a qualified nucleus. .

[0024] Splash failure boundary: the maximum effective heat corresponding to the exact moment splashing occurs. .

[0025] When time is fixed, the current must satisfy ≤I≤ Only then can the effective heat fall into ~ Within the specified range. Furthermore, shunt current and electrode conditions can interfere with the critical current calibration.

[0026] The specific operating steps are as follows: (a) Select 3 to 5 typical welding times (e.g.) , , The current was adjusted gradually at each time point, and interference from influencing factors was avoided. (1) Lower critical point calibration: Gradually increase the current and find the minimum current that just forms a qualified melt nugget by metallographic testing. correspond For multi-point spot welding, it is necessary to control the current shunting effect (current shunting will reduce the effective current). The arrangement of the solder joints can be optimized to reduce current shunting and ensure calibration accuracy.

[0027] (2) Upper critical point calibration: Continue to increase the current, and use visual acquisition devices such as industrial cameras and high-speed imaging equipment deployed at the welding station to monitor and find the maximum current at which spatter just occurs by extracting visual feature data directly related to welding quality. correspond If copper-chromium alloy electrodes are used (fast heat dissipation, ... (For large electrodes, note that the critical current will be higher than that of ordinary electrodes to avoid misjudging the spatter critical point.)

[0028] (ii) Record the corresponding time point and Simultaneously label the surface condition of the base material, the degree of electrode wear, and the current shunting situation during the test to form a dataset with working condition annotations.

[0029] III. Boundary Model Fitting and Initial Static Window Generation. Under fixed electrode force, the resistance of the weld zone... Since the heat loss coefficient is relatively stable, the functional relationship between time and current can be derived from Joule's law formula: ; Further simplified to a two-parameter empirical model: ; Where a is a constant related to heat conduction loss (minimum welding time when the current is extremely high). For resistors The constants related to the critical heat (the rate of change of time with respect to current when the current is relatively small) and the properties of the base material and electrodes directly affect the model parameters. , The value of .

[0030] Detailed operation steps: (a) Data substitution and parameter fitting: Substitute the experimental data into the two-parameter empirical model, and fit the lower boundary (melt nucleus formation) parameters using the least squares method. , Parameters of the upper boundary (splash failure) , If the base material is aluminum alloy (low resistance, high thermal conductivity). The value may be too small, and more test points need to be added to optimize the fitting accuracy.

[0031] (II) Initial Window Verification: Select 3 to 5 parameter points within the window to conduct verification tests, checking the melt nugget size and tensile strength; simultaneously consider the influence of electrode shape (electrode wear will increase the contact area and reduce the...). During verification, use brand new or polished electrodes to avoid interference from the electrode condition in determining the validity of the window.

[0032] IV. Building a Digital Twin System. The core of a digital twin is establishing a real-time mapping between a "physical workstation and a virtual model," achieved through data collection and analysis. , It uses full-factor data to dynamically correct model parameters and achieves adaptive window adjustment, covering all key influencing factors such as base material, electrodes, and surface condition.

[0033] Detailed operation steps: (I) Data Acquisition Layer Deployment. Sensors and monitoring modules are installed at the welding station to collect data in real time. The data is as follows: Process parameters: fluctuations in welding current, time, and electrode force directly affect... .

[0034] Process status: temperature field of welding zone, electrode wear (core indicator of electrode status), surface condition of base material (real-time monitoring of oxide layer regeneration) and weld point layout and flow distribution data.

[0035] Quality results: weld nugget size, spatter identification, and solder joint strength reflect... Does it meet the standard?

[0036] (ii) Twin model construction.

[0037] (1) Core algorithm embedding: The two-parameter model is used as the core of the Siamese model to store the initial boundary parameters. , and , It also pre-sets the association rules between influencing factors and parameters.

[0038] (2) Multi-source data association: Establish a mapping relationship between input (process parameters, process status) → model parameters (a, b) → output (qualified current range): When the base material batch changes (such as switching between stainless steel and carbon steel), the parameter baseline of the corresponding material in the historical database is automatically called and the parameters a and b are corrected. When the current shunting effect occurs, the upper limit of the current is automatically compensated to avoid defects in the molten core due to insufficient effective current. When the electrode material is a copper-chromium alloy, a preset 'a' value offset is used to compensate for high heat loss.

[0039] (3) Definition of virtual-real linkage rule: Set a closed loop of "factor change - model response - parameter optimization", such as surface oxide layer regeneration trigger. As the value increases, the model automatically decreases the corresponding time. When electrode wear intensifies, the window boundary automatically shifts to the right.

[0040] (4) Visual interface development: Real-time display of dynamic window curves, trends of influencing factors (such as electrode wear rate and surface cleanliness), and model parameter iteration records, making it convenient for operators to intuitively control the influence of factors.

[0041] V. Dynamic Iteration and Window Self-Update. During the production process, factors such as electrode wear, power grid fluctuations, and batch changes in the base material will alter... and This leads to the critical heat. , Dynamic changes necessitate real-time data iteration to correct model parameters. , To maintain window validity.

[0042] Detailed operation steps: (i) Real-time parameter correction: The twin platform continuously collects data, and when it detects changes in factors (such as electrode wear exceeding the threshold, batch replacement of base material, and fluctuations in the shunt coefficient), it automatically calculates the correction parameters. and Impact, Update , The parameters are adjusted in real time to change the window boundaries; for example, when a new batch of aluminum alloys is launched, the model automatically matches the low resistance characteristics and moves the window to the right to meet the heat requirements.

[0043] (2) Closed-loop feedback optimization: After the visual acquisition device detects spatter and molten core defects, it traces back the influencing factors (such as whether the effective current is insufficient due to increased shunt current, or whether electrode wear causes this). (If the value is too small), simultaneously adjust the model parameters and process parameters to ensure that the window always covers the acceptable range.

[0044] (III) Predictive window matching: Combining historical databases, similar working conditions are matched according to the combination of "base material type + surface condition + electrode condition + current shunting situation" to quickly pre-generate the initial window. Only 2 to 3 sets of verification tests are needed to complete the debugging, which greatly shortens the changeover time.

[0045] VI. Continuous Validation and Archiving of Window Validity. In long-term production, the cumulative effect of influencing factors (such as continuous electrode wear and fluctuations in the surface treatment process of the base material) may lead to model deviations. It is necessary to periodically verify the consistency between the window and the actual quality, and at the same time archive factor data and window parameters to form a traceable process knowledge base.

[0046] Detailed operation steps: (a) Periodic verification test: each month, 5 to 8 parameter points are randomly selected from the dynamic window to conduct destructive tests to detect the size and strength of the melt core, and compare the model prediction value with the actual value; at the same time, record the status of all influencing factors during the test (such as electrode wear, surface cleanliness, and current shunting), and analyze the reasons for the deviation (if the deviation is due to inadequate surface treatment, the cleaning process needs to be optimized).

[0047] (ii) Full data archiving: Archive the window parameters, verification results, records of changes in influencing factors, and process adjustment measures for each iteration, and establish a knowledge base linking "influencing factors - window parameters - quality results" to provide data support for the generation of new working condition windows and the elimination of factor interference in the future.

[0048] This method relies on a digital twin system to build a real-time "physical-virtual" mapping, overcoming the limitations of traditional static windows. Through full data acquisition and closed-loop iteration, it captures dynamic factors such as electrode wear, batch changes in base material, and current fluctuations in real time, automatically correcting model parameters and window boundaries. For unexpected situations such as oxide layer regeneration and power grid fluctuations, it can quickly respond and adjust process parameters to ensure that the welding window always covers the acceptable range, effectively solving the quality instability problem caused by operating condition drift in mass production and significantly improving the consistency of welding quality in complex scenarios.

[0049] To ensure data accuracy from the outset, a solid foundation is laid through multi-dimensional preprocessing and refined experimental calibration. In the initial stage, electrode force is pre-adjusted and the surface condition of the base material is optimized to avoid key interference factors. During critical heat calibration, metallographic inspection and visual monitoring technologies are combined to accurately locate the critical points of melt nugget formation and spatter failure, while simultaneously labeling information under all operating conditions. A least squares fitting model is employed, coupled with multiple sets of verification experiments to optimize parameters, effectively reducing misjudgments caused by factors such as current shunting and electrode heat dissipation. This provides a high-precision, traceable dataset for window generation, ensuring the accuracy of window boundaries.

[0050] Balancing production efficiency and long-term application value, it possesses strong feasibility and scalability. By quickly matching similar operating conditions through a historical knowledge base, changeover and debugging can be completed with only a small number of verification tests, significantly shortening process changeover time. During long-term operation, regular verification and full data archiving form a complete process system, providing data support for the generation of new operating condition windows and troubleshooting. Simultaneously, the dynamic window can adapt to different materials, electrode types, and solder joint arrangements, eliminating the need for frequent process refactoring, reducing experimental consumables and labor costs, and adapting to the needs of multiple production categories.

[0051] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for determining a resistance welding window, characterized in that, Includes the following steps: S1. Initial process principle anchoring and condition setting: Based on Joule's law, clarify the relationship between welding heat generation and effective heat, preset and fix the electrode force to determine the resistance reference value, and pre-treat the base material and surface condition; S2. Critical heat calibration test: Select 3 to 5 typical welding times, adjust the current step by step for each time point, calibrate the minimum current for weld nugget formation by metallographic detection, calibrate the maximum current for spatter failure by visual acquisition device, record the corresponding data and label the full working condition information to form a dataset with working condition labels. S3. Boundary model fitting and initial static window generation: Substitute the dataset into the two-parameter empirical model, and fit the model parameters of the weld nugget formation boundary and the spatter failure boundary respectively by the least squares method. Select parameter points for verification experiments and generate the initial static welding window. S4. Digital Twin System Construction: Deploy a data acquisition layer to collect process parameters, process status data and quality result data in real time, construct a twin model with an embedded dual-parameter model, establish multi-source data association and virtual-real linkage rules, and develop a visual interface; S5. Dynamic Iteration and Window Self-Update: Data is collected in real time through the twin platform. When changes in working conditions are detected, the model parameters are automatically corrected and the window boundaries are adjusted. Combined with defect monitoring, closed-loop feedback optimization is achieved. The initial window is quickly generated by matching similar working conditions based on the historical database. S6. Continuous Validation and Archiving of Window Validity: Periodically extract parameter points from the dynamic window to conduct destructive tests, compare the model prediction values ​​with the actual values ​​and analyze the deviations, archive all window parameters, validation results and process adjustment measures, and establish a related knowledge base.

2. The method for determining a resistance welding window according to claim 1, characterized in that, The step S1 of presetting and fixing the electrode force to determine the resistance reference value specifically includes: The electrode force is preset according to the thickness and properties of the base material. When the electrode force increases, the corresponding resistance decreases, and a larger current is matched to reach the critical effective heat. When the electrode force decreases, the corresponding resistance increases, requiring a smaller current to be matched, and the reference resistance value corresponding to the initial electrode force is recorded simultaneously.

3. The method for determining a resistance welding window according to claim 1, characterized in that, The preprocessing described in step S1 specifically includes: For materials with high resistivity, a smaller electrode force is preset, while for aluminum alloys with good conductivity, a larger electrode force is preset, to match the initial electrode force value. The surface of the base material is polished and cleaned to remove the oxide layer and oil stains.

4. The method for determining the resistance welding window according to claim 1, characterized in that, The calibration test in step S2 also includes interference factor avoidance: optimizing the weld point layout to control the shunt effect when performing multi-point spot welding; and modifying the critical current judgment criteria when using copper-chromium alloy electrodes, taking into account their high heat loss characteristics.

5. The method for determining a resistance welding window according to claim 1, characterized in that, The two-parameter empirical model mentioned in step S3 is: ； Where t is time and I is current; a is a constant related to heat conduction loss, and the minimum welding time when the current is extremely high; b is a constant related to resistance R and critical heat, and the rate of change of time with current when the current is small; Adjust the values ​​of a and b according to the characteristics of the base material and the electrode.

6. The method for determining a resistance welding window according to claim 1, characterized in that, The process status data collected by the data acquisition layer in step S4 includes: temperature field of the welding zone, electrode wear, regeneration status of oxide layer on the base material surface, and weld point arrangement and diversion data.

7. The method for determining a resistance welding window according to claim 1, characterized in that, The multi-source data association and virtual-real linkage rules in step S4 are as follows: when the batch of parent material changes, the historical database parameter benchmark is called to correct a and b; Automatic compensation of the upper limit of current when the shunt effect occurs; The preset value offset of a is used to compensate for heat loss under the working conditions of copper-chromium alloy electrodes.

8. The method for determining a resistance welding window according to claim 1, characterized in that, The changes in operating conditions mentioned in step S5 include: electrode wear exceeding the threshold, batch replacement of the base material, fluctuation of the shunt coefficient, power grid fluctuation, and regeneration of the oxide layer on the surface of the base material.

9. The method for determining a resistance welding window according to claim 1, characterized in that, The rapid generation of the initial window in step S5 specifically involves: matching similar working conditions by combining the base material type, surface condition, electrode condition, and current distribution, and completing the debugging through 2-3 sets of verification tests.

10. The method for determining a resistance welding window according to claim 1, characterized in that, The destructive test mentioned in step S6 involves sampling 5-8 parameter points each month, simultaneously recording the electrode wear, surface cleanliness, and current shunting during the test, and optimizing the corresponding process based on the cause of the deviation.

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