Infrared welding quality evaluation method based on temperature physical field simulation
Through the temperature physical field simulation evaluation method, the problems of open-loop control and simulation model deviation in photovoltaic cell welding were solved, efficient welding quality evaluation and parameter optimization were achieved, and the machine adjustment efficiency and welding quality were improved.
Patent Information
- Application Number
- CN202510835600.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-20
- Publication Date
- 2025-09-19
AI Technical Summary
The existing photovoltaic cell welding technology has problems such as difficulty in dynamic parameter evaluation under open-loop control, insufficient real-time sensor data, large differences between simulation models and actual conditions, and single quality assessment, which leads to unstable welding quality and low machine adjustment efficiency.
Through the infrared welding quality assessment method based on temperature physical field simulation, including model correction, energy coefficient calibration, internal stress assessment and total energy assessment, a closed-loop process is established to achieve simulation pre-verification and replace manual trial and error.
It has increased machine adjustment efficiency by more than 50%, reduced material waste by 60%, accurately controlled welding quality, reduced defects, and improved production efficiency and product performance.
Smart Images
Figure CN120671403A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of photovoltaic cell welding, and in particular to an infrared welding quality assessment method based on temperature physical field simulation. Background Art
[0002] The photovoltaic cell string soldering process utilizes a light box composed of infrared lamps. During soldering, the welding power is set and instantaneous heating is activated. The string soldering machine used in this process consists of two main heating units: a welding light box and a preheating platform. The welding light box consists of infrared lamps, a light box, a fan, and an infrared sensor. The fan extracts air from the light box to prevent heat buildup from prolonged operation and dissipate heat. The infrared sensor provides auxiliary thermal field temperature feedback, typically providing two temperature readings per second. The preheating platform, constructed from multiple aluminum panels, contains heating wires and thermocouple sensors, and is constantly heated to maintain a constant preheat temperature.
[0003] In the above process, the preheating platform is controlled by closed-loop PID, which is simple to operate and provides relatively stable feedback data and control. However, it has the following defects: 1. Welding light box control defects: The welding light box adopts open-loop power control, lacks dynamic adjustment capabilities, and cannot optimize power output in real time according to the actual welding status, resulting in unstable welding quality, such as frequent battery cell cold welding, over-welding and other adverse phenomena.
[0004] 2. Shortcomings in sensor data: Infrared sensor data feedback is slow, and can only feedback 2 temperature data in 1 second. It is difficult to capture subtle temperature changes in the welding process in real time, and cannot provide sufficient and timely data support for the dynamic adjustment of the welding process. The real-time performance is seriously insufficient.
[0005] 3. Model deviation problem: There is a large difference between the theoretical simulation model and the actual welding thermal field, but there is a lack of effective model correction methods. As a result, the reference value of process analysis and parameter optimization based on the theoretical model is limited, and it is impossible to accurately guide actual welding production.
[0006] 4. Single quality assessment: Existing technologies mostly rely on power adjustment and post-processing to check welding quality, without integrating multi-dimensional indicators such as internal stress and total energy into comprehensive assessment. Moreover, when the mechanical structure parameters of the string welding machine (such as the thickness of the preheating platform and the number of lamps in the light box) change, engineers need to repeatedly adjust the parameters through trial and error, which results in extremely low machine adjustment efficiency and a large amount of material waste.
[0007] Although existing patents involve the optimization of string welding machine structure or process, none of them solves the problem of dynamic parameter evaluation under open-loop control, nor does it propose an effective method to achieve process pre-verification through simulation model correction.
[0008] Therefore, a new technical solution is urgently needed to solve the above technical problems. Summary of the Invention
[0009] The purpose of the present invention is to overcome the above-mentioned problems of the prior art and provide an infrared welding quality assessment method based on temperature physical field simulation to solve the technical problems in the prior art of difficulty in dynamic parameter assessment under open-loop control and the lack of an effective method for process pre-verification through simulation model correction.
[0010] The above objectives are achieved through the following technical solutions: A method for evaluating infrared welding quality based on temperature physical field simulation includes: Step (1) Model correction: Correct the infrared welding simulation model of the string welding machine by fixed spacing reference correction or variable spacing reference correction; Step (2) calibrating the energy coefficient a: Based on the law of conservation of heat, the loss parameter is inversely solved by the formula to determine the corresponding energy coefficient a of the welding power and the thermal radiation power; Step (3) Evaluate the quality of welding internal stress: simulate the temperature rise curve of the battery cell, extract the temperature change rate v, set the threshold v_max, and evaluate the internal stress risk; Step (4) Evaluate the total welding energy value: calculate the difference between the simulated energy Q_sim and the measured energy Q_actual to determine whether the welding parameters are qualified; Step (5) Applying cell welding: Apply the evaluated parameters to the welding equipment.
[0011] Furthermore, during the model correction process in step (1), temperature data collection needs to be carried out in a steady-state thermal field environment, and each power point needs to be measured repeatedly three times, and the average of multiple measurements is taken as the valid data for model correction.
[0012] Furthermore, the specific execution process of the fixed spacing benchmark correction in step (1) is as follows: Under the condition of distance h1, the temperature data T1, T2 and the corresponding power P1 and P2 are collected by sensors; Input P1 and P2 into the infrared welding simulation model of the string welding machine, adjust the model parameters of the heat conduction coefficient and the radiation loss coefficient, so that the error between the simulated temperature T1_sim and T2_sim and the collected measured temperature data is ≤3%; Under the condition of spacing h2, the same power P1 is input into the infrared welding simulation model of the stringer. If the deviation between the temperature data collected by the sensor and the temperature T3_sim and T4_sim output by the model simulation is greater than 5%, the model parameters are readjusted for correction.
[0013] Furthermore, the specific execution process of the variable spacing benchmark correction in step (1) is as follows: Under different conditions of spacing h1 and h2, the temperature data T1 and T3 corresponding to the single power P1 are collected respectively; Input P1 into the infrared welding simulation model of the stringer, and adjust the model parameters of the heat conduction coefficient and radiation loss coefficient so that the error between the simulated temperature T1_sim and T3_sim and the measured temperature data is ≤3%; Adjust the power to P2. If the error between the model simulation temperature and the sensor measured temperature at all intervals is ≤5%, the model is considered valid and the correction is complete.
[0014] Furthermore, when calibrating the energy coefficient a in step (2), the heat conservation formula is as follows: (1) Where Q represents heat; t0 and t1 represent the integration start time and integration end time; T represents the sensor measurement temperature; P 热 Indicates the radiation power, that is, the actual amount of heat received by the battery cell; P 灯 Indicates the set lamp power; a represents the energy coefficient, ranging from 0 to 1; the loss parameter is inversely solved through this formula to determine the corresponding relationship between welding power and thermal radiation power.
[0015] Furthermore, when evaluating the welding internal stress quality in step (3), the calculation formula of the temperature change rate v is as follows: v=ΔT / Δt (2) Where ΔT is the temperature change and Δt is the time change; Determining the Maximum Rate of Temperature Change through Simulation v_max , when the calculated v > v_max* When the value is 0.95, it is determined that there is an internal stress risk in welding.
[0016] Furthermore, when evaluating the total welding energy in step (4), the difference between the simulated energy Q_sim and the measured energy Q_actual is calculated. If |Q_sim-Q_actual| / Q_actual<5%, the welding parameters are determined to be qualified.
[0017] The present invention provides an infrared welding quality assessment method based on temperature physical field simulation. This method replaces manual trial and error with simulation pre-verification, improving machine adjustment efficiency by over 50%. When process changes occur to some of the on-site machine structures (such as the thickness of the preheating platform, the number of lamps in the light box, etc.), the model parameters can be adjusted directly through simulation, and the offline on-site equipment debugging is switched to the online platform to adjust the welding parameters. Combined with the total energy evaluation to ensure the welding parameters, the recommended parameters are obtained, and simple offline tuning can meet the standards, avoiding repeated processing and material waste. Specifically: Improved machine adjustment efficiency: By replacing manual trial and error with simulation pre-verification, the adjustment time of the string welding machine is shortened from the traditional month to a week or even shorter time, increasing the adjustment efficiency by more than 50% and significantly shortening the production preparation cycle.
[0018] Reduced material waste: Using simulation models to assess the impact of process parameters and mechanical structure changes in advance, we can avoid repeated welding and scrapping of battery cells due to inappropriate parameters. This can reduce material waste by about 60%, lowering production costs.
[0019] Accurate quality control: The multi-dimensional quality assessment system and precise model correction and parameter calibration methods make welding quality control more accurate, effectively reducing undesirable phenomena such as cold solder joints, over-soldering, and stress damage within the cell, thereby improving the production quality and product performance of photovoltaic cells. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Figure 1 This is a schematic diagram of a three-dimensional model of the welding heating part of a string welding machine in an infrared welding quality assessment method based on temperature physical field simulation according to the present invention; Figure 2 This is a structural analysis diagram of the welding heating part of the string welding machine in the infrared welding quality assessment method based on temperature physical field simulation described in the present invention; Figure 3 This is a diagram of a model correction experiment in an infrared welding quality assessment method based on temperature physical field simulation according to the present invention; Figure 4 This is the energy diagram in the infrared welding quality assessment method based on temperature physical field simulation described in the present invention; Figure 5 This is a flow chart of an infrared welding quality assessment method based on temperature physical field simulation according to the present invention; Figure 6 This is an energy coefficient diagram in the infrared welding quality assessment method based on temperature physical field simulation described in the present invention. DETAILED DESCRIPTION
[0021] The present invention will be further described in detail below with reference to the accompanying drawings and examples. The described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort shall fall within the scope of protection of the present invention.
[0022] This solution provides an infrared welding quality assessment method based on temperature physical field simulation. By establishing an infrared welding simulation model of a string welder and introducing a temperature physical field simulation method, the simulation model is corrected by matching field measured data with theoretical data. Temperature parameter operation is performed on the simulation platform. The impact of mechanical process changes on welding temperature distribution and welding quality can be evaluated in advance, effectively guiding the adjustment of the open-loop part of the welding process without frequent machine testing and waste of materials.
[0023] Among them, the simulation model of the string welding machine in this scheme is as follows Figure 1 As shown, there are 12 infrared lamps in the light box, with a single rated power of 2.2KW. The thermal field structure disassembly diagram is as follows Figure 2 As shown. Specifically: Figure 1 The 3D model shows the overall structure of the stringer's welding heating section, clearly illustrating the spatial layout of key components such as the infrared lamps, housing, and preheating platform within the light box. The infrared lamps, arranged in an orderly manner within the light box, are the primary source of welding heat. The housing acts as a heat insulator and heat collector, ensuring a stable thermal field. The preheating platform, located beneath the light box, provides a basic preheating environment for the solar cells. This 3D model provides a visual understanding of the structural composition of the stringer's welding heating section, providing a reference for subsequent simulation model development and mechanical parameter adjustments.
[0024] Figure 2 A detailed disassembly and analysis of the welding heating section of the stringer is performed, further detailing components such as the light box, infrared lamps, and preheating platform, with key dimensions, connections, and functional areas clearly marked. For example, the installation method and spacing of the infrared lamps within the light box, the distribution of the heating wires within the preheating platform, and the location of the thermocouple sensors are clearly shown. This diagram facilitates a deeper understanding of the role and mutual influence of each component in the welding heating process, providing a detailed structural basis for adjusting parameters such as the thermal conductivity coefficient and radiation loss coefficient during model modification, as well as analyzing the impact of changes in mechanical structural parameters on the welding thermal field.
[0025] like Figure 5 As shown, the various steps and their logical sequence of the evaluation method of the present invention are clearly presented, starting from model correction, sequentially through calibration of energy coefficient a, evaluation of welding internal stress quality, evaluation of total welding energy value, and finally to application of battery cell welding, forming a complete closed-loop process. The arrows between each step clearly show the execution order and logical progressive relationship of the method, making the entire evaluation method process clear at a glance, easy to understand and implement. This solution includes the following steps: Step (1) Model correction: Correct the infrared welding simulation model of the string welding machine by fixed spacing reference correction or variable spacing reference correction; Step (2) calibrating the energy coefficient a: Based on the law of conservation of heat, the loss parameter is inversely solved by the formula to determine the corresponding energy coefficient a of the welding power and the thermal radiation power; Step (3) Evaluate the quality of welding internal stress: simulate the temperature rise curve of the battery cell, extract the temperature change rate v, set the threshold v_max, and evaluate the internal stress risk; Step (4) Evaluate the total welding energy value: calculate the difference between the simulated energy Q_sim and the measured energy Q_actual to determine whether the welding parameters are qualified; Step (5) Applying cell welding: Apply the evaluated parameters to the welding equipment.
[0026] The model correction in step (1) in this embodiment is specifically as follows: The establishment process of the model in the thermal field software will not be described here. Due to the deviation between the theoretical model and the actual model, this step first corrects the model. The correction process needs to consider the joint verification under multiple coupling factors. This solution provides the following two correction methods, such as Figure 3 The figure shows the sensor layout and experimental setup for the model correction experiment. The figure clearly indicates the installation locations of sensors 1–4 at different spacings (h1 and h2). The sensors are used to collect temperature data at different locations and spacings, providing a basis for actual measurement for model correction. The figure also illustrates the process linking power input and data collection during the experiment, helping to understand the experimental logic of fixed-spacing and variable-spacing benchmark corrections and providing a visual representation of the implementation of the model correction method.
[0027] Data collection specifications: Temperature data collection must be carried out under a steady-state thermal field (such as after the light box has been running for 5 minutes), and each power point must be measured three times to obtain the average value.
[0028] Method 1: Fixed-interval benchmark correction (1) At the interval h1, collect the temperature data (T1, T2) and corresponding power (P1, P2) of sensors 1 and 2.
[0029] (2) Input P1 and P2 into the simulation model and adjust parameters such as thermal conductivity coefficient and radiation loss coefficient so that the error between the simulated temperature (T1_sim, T2_sim) and the measured data is ≤3%.
[0030] (3) Verification: Input the same power P1 at the spacing h2. If the simulated temperature of sensors 3 and 4 (T3_sim, T4_sim) deviates from the measured data by more than 5%, the model parameters are recalibrated.
[0031] Method 2: Variable spacing benchmark correction (1) Collect temperature data (T1, T3) corresponding to a single power P1 at intervals h1 and h2 respectively.
[0032] (2) Input P1 into the simulation model and adjust the parameters such as the thermal conductivity coefficient and the radiation loss coefficient so that the error between the simulated temperature (T1_sim, T3_sim) and the measured data is ≤3%.
[0033] (3) Verification: Adjust the power to P2. If the simulation temperature error is ≤5% at all spacings, the model is considered valid. The calibration energy coefficient a in step (2) of this embodiment is specifically: The purpose of this step is to confirm the correspondence between welding power and thermal radiation power. Since the lamp setting power and radiation power cannot be equated (that is, the physical power set by the lamp is not 100% used for heat transfer, and there is loss), how to determine the radiation power has always been a difficult problem. This patent provides the following formula, which uses the law of heat conservation to inversely solve the loss parameters, as follows: (1) Where Q represents heat; t0 and t1 represent the integration start time and integration end time; T represents the sensor measurement temperature; P 热 Indicates the radiation power, that is, the actual amount of heat received by the battery cell; P 灯 Indicates the set lamp power; a represents the energy coefficient, ranging from 0 to 1.
[0034] Since the physical power of the lamp is not 100% used for heat transfer, there is loss, so it is necessary to use the above formula to convert the radiation power. The conversion process is based on the law of conservation of heat. In addition, considering that the temperature data cannot be collected in real time during the later application, this part uses P 灯 *a method determines a, that is, the lamp power a used for heat transfer.
[0035] In this embodiment, the evaluation of welding internal stress quality in step (3) is specifically as follows: After the above steps (1) to (2), the model is established and the thermal field energy value is determined. The energy coefficient a is imported into the temperature simulation software to view the real-time welding quality of the battery cell.
[0036] This section evaluates the quality problems caused by internal stress. Figure 4 The figure shows the temperature change curve over time and the thermal energy of the welding light box, including preheat temperature information. The curve clearly shows the temperature rise of the cell from the preheat state to the welding time, reflecting key information such as the temperature change rate. The thermal energy situation shows the overall distribution and changes of the welding light box input energy, thermal radiation energy, and cell absorption energy. It provides energy data support for energy coefficient calibration and internal stress quality assessment, helping to understand the energy transfer and change patterns during the welding process.
[0037] Normal process flow: The cell enters the welding light box and then exits after welding. The corresponding energy diagram is slowly rising -> uniform -> slowly falling. Quality problems caused by internal stress often occur in the slow rising stage. It is necessary to use temperature field simulation software to determine the optimal energy climbing diagram. The specific process is: (1) Simulate the temperature rise curve of the battery cell in the simulation and extract ΔT / Δt.
[0038] v=ΔT / Δt (2) Where v is the rate of change of temperature, ΔT is the temperature change, and Δt is the time change.
[0039] (2) Determine the maximum temperature change rate through simulated welding v_max (e.g. 20℃ / s), evaluation criteria: probability of insufficient stress in the cell > 90%.
[0040] (3) Setting the threshold v_max ,like v > v_max* If the value is 0.95, it is determined that there is a risk of internal stress and the welding power needs to be reduced or the preheating time needs to be extended.
[0041] The total welding energy value evaluated in step (4) of this embodiment is specifically: After the above step (3), the temperature change rate is calculated by simulation, and the quality problems caused by internal stress are evaluated. This step is to evaluate the overall heat under welding and determine the energy required for welding. The energy formula is still as shown in formula (1-1). By matching the measured data with the simulation data, the optimal welding process flow can be obtained as follows: Figure 4 The complete temperature change diagram is shown (Note: due to the existence of preheating temperature, the heat of the preheating part is not discussed in this patent, only the heat introduced by the welding light box is discussed).
[0042] Evaluation scheme: Calculate the difference between the simulated energy Q_sim and the measured energy Q_actual using formula (1). If |Q_sim-Q_actual| / Q_actual<5%, the welding parameters are considered qualified and can be applied to the welding equipment.
[0043] The specific application of battery cell welding in step (5) of this embodiment is as follows: After the above steps (1) to (4), all kinds of parameters and limit qualities have been evaluated and the parameters are applied to the welding equipment.
[0044] The mechanical structure changes involved in this solution typically occur during the R&D phase of a new machine. When introducing a new process or to increase welding efficiency, the general practice is to directly modify the mechanical structure, rework it, and then manually debug it on-site to determine whether the optimization goal can be achieved. Efficiency Improvement Plan 1 for String Welders: Preheating Platform Optimization, Plan 2: Light Box Optimization. Preheating platform optimization primarily involves changing the preheating platform thickness, while light box optimization includes changes to the light box size, the total number of lamps, lamp rated power adjustments, and lamp spacing.
[0045] The above changes will not involve changes in material properties. Therefore, the evaluation scheme provided by this solution can directly modify the structural parameters of the three-dimensional model of the simulation platform, evaluate the welding effect through the simulation platform, and determine the effectiveness of the process change.
[0046] In order to verify the effect of this patent, the test is carried out according to the steps. Taking a certain company's string welding machine as an example, the string welding machine simulation model is as follows Figure 1 As shown, there are 12 infrared lamps in the light box, with a single rated power of 2.2KW. The thermal field structure disassembly diagram is as follows Figure 2 shown.
[0047] By inversely solving the loss parameters according to the heat conservation in step (2), the energy coefficient a is calculated as follows: Figure 6 As shown in the figure, a curve showing the relationship between energy coefficient a and lamp power was obtained by fitting experimental data. The horizontal axis represents lamp power, and the vertical axis represents energy coefficient a. The curve illustrates the changing trend of energy coefficient a under different lamp powers. This curve provides an intuitive understanding of the proportional relationship between welding power and the energy actually used for heat transfer, providing visual results for energy coefficient calibration and facilitating analysis of the impact of power variations on energy transfer efficiency. It is a key achievement in the energy coefficient calibration step. (Note: Since the process does not exceed 60% of the rated power, measurements are only made up to 60% of the rated power.) The specific operation is to set six power points at 10% intervals within the range of 10% to 60% of the rated power. The integrated temperature Q_actual is measured three times for each point, and the aP lamp curve is fitted using linear regression.
[0048] Determine v_max = 32°C / s through step (3).
[0049] The total welding energy estimated by step (4) is approximately 2.34 kJ.
[0050] By inferring from the above data, the parameters are set to 61% of the rated power. The temperature welding process needs to last for 2 seconds. After giving the recommended parameters, the actual parameters of the offline test are set to 65% of the rated power, and the difference with the simulation is within 6.1%. The machine adjustment time covers the process flow and is only one week, which is significantly shortened compared to the original one month.
[0051] The above description is only for explaining the embodiments of the present invention and is not intended to limit the present invention. For those skilled in the art, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.
Claims
1. A method for evaluating infrared welding quality based on temperature physical field simulation, characterized in that: include: Step (1) Model correction: Correct the infrared welding simulation model of the string welding machine by fixed spacing reference correction or variable spacing reference correction; Step (2) calibrating the energy coefficient a: Based on the law of conservation of heat, the loss parameter is inversely solved by the formula to determine the corresponding energy coefficient a of the welding power and the thermal radiation power; Step (3) Evaluate the quality of welding internal stress: simulate the temperature rise curve of the battery cell, extract the temperature change rate v, set the threshold v_max, and evaluate the internal stress risk; Step (4) Evaluate the total welding energy value: calculate the difference between the simulated energy Q_sim and the measured energy Q_actual to determine whether the welding parameters are qualified; Step (5) Applying cell welding: Apply the evaluated parameters to the welding equipment.
2. The infrared welding quality assessment method based on temperature physical field simulation according to claim 1 is characterized in that: During the model correction process in step (1), temperature data collection needs to be carried out in a steady-state thermal field environment. Each power point needs to be measured repeatedly three times, and the average of multiple measurements is taken as the valid data for model correction.
3. The infrared welding quality assessment method based on temperature physical field simulation according to claim 1 or 2, characterized in that: The specific execution process of the fixed spacing benchmark correction in step (1) is as follows: Under the condition of distance h1, the temperature data T1, T2 and the corresponding power P1 and P2 are collected by sensors; Input P1 and P2 into the infrared welding simulation model of the string welding machine, adjust the model parameters of the heat conduction coefficient and the radiation loss coefficient, so that the error between the simulated temperature T1_sim and T2_sim and the collected measured temperature data is ≤3%; Under the condition of spacing h2, the same power P1 is input into the infrared welding simulation model of the stringer. If the deviation between the temperature data collected by the sensor and the temperature T3_sim and T4_sim output by the model simulation is greater than 5%, the model parameters are readjusted for correction.
4. The infrared welding quality assessment method based on temperature physical field simulation according to claim 1 or 2, characterized in that: The specific execution process of the variable spacing benchmark correction in step (1) is as follows: Under different conditions of spacing h1 and h2, the temperature data T1 and T3 corresponding to the single power P1 are collected respectively; Input P1 into the infrared welding simulation model of the stringer, and adjust the model parameters of the heat conduction coefficient and radiation loss coefficient so that the error between the simulated temperature T1_sim and T3_sim and the measured temperature data is ≤3%; Adjust the power to P2. If the error between the model simulation temperature and the sensor measured temperature at all intervals is ≤5%, the model is considered valid and the correction is complete.
5. The infrared welding quality assessment method based on temperature physical field simulation according to claim 1 is characterized in that: When calibrating the energy coefficient a in step (2), the heat conservation formula is as follows: (1) Where Q represents heat; t0 and t1 represent the integration start time and integration end time; T represents the sensor measurement temperature; P 热 Indicates the radiation power, that is, the actual amount of heat received by the battery cell; P 灯 Indicates the set lamp power; a represents the energy coefficient, ranging from 0 to 1; the loss parameter is inversely solved through this formula to determine the corresponding relationship between welding power and thermal radiation power.
6. The infrared welding quality assessment method based on temperature physical field simulation according to claim 1 or 4, characterized in that: When evaluating the welding internal stress quality in step (3), the calculation formula of the temperature change rate v is as follows: v=ΔT / Δt (2) Where ΔT is the temperature change and Δt is the time change; Determining the Maximum Rate of Temperature Change through Simulation v_max , when the calculated v > v_max* When the value is 0.95, it is determined that there is an internal stress risk in welding.
7. The infrared welding quality assessment method based on temperature physical field simulation according to claim 6 is characterized in that: When evaluating the total welding energy in step (4), the difference between the simulated energy Q_sim and the measured energy Q_actual is calculated. If |Q_sim-Q_actual| / Q_actual<5%, the welding parameters are judged to be qualified.