Method and system for optimizing welding process parameters according to simulated temperature field

Abstract

The invention belongs to the technical field of computer-aided welding engineering, and particularly relates to a method for optimizing welding process parameters according to a simulated temperature field, which comprises the following steps of: constructing a physical model and a geometric model of a welding assembly, optimizing the geometric model through a probe temperature actually measured by the physical model, and simulating a welding process through the geometric model. According to the method, the welding research and development cost is greatly reduced, the production efficiency is improved, the welding quality and reliability are improved from the source, the high material cost, equipment machine hour and human resource investment in the parameter optimization process completed through a large number of experiments are avoided, the product research and development cycle is shortened, and the production efficiency is improved. The method is suitable for the welding process of pilot test and small-batch production of chips. The invention also provides a non-transitory readable recording medium storing the program of the method and a system comprising the medium, and the program can be called through a processing circuit to execute the method.

Description

Technical Field

[0001] This invention belongs to the field of computer-aided welding engineering technology, and discloses a method, recording medium and system for optimizing welding process parameters based on simulated temperature field. Background Technology

[0002] Since the beginning of the 21st century, artificial intelligence has developed rapidly, placing increasingly higher demands on computing power, particularly on chip computing power. Chips are essential internal components in everything from nanorobots to supercomputers, thus requiring increasingly efficient chip installation. Using lasers to solder chips to FR4 (circuit boards) can effectively meet these efficiency requirements. Furthermore, laser welding offers advantages such as a small heat-affected zone, localized and non-contact heating, rapid heating, and rapid cooling.

[0003] In the laser welding process connecting chips and motherboards, the control of laser power and welding time are key factors affecting welding performance. Excessive laser power can cause warping or even burn-through of the chip and FR4 motherboard, while insufficient laser power results in incomplete solder melting and poor chip-to-motherboard connection. Therefore, controlling the laser power for different materials and solders is particularly important. Current welding process testing requires multiple trials to determine suitable welding process parameters, which wastes significant material costs. To reduce costs, software developers have created various simulation software programs, but these are difficult to implement on the intricate chip welding techniques. Each batch of motherboard circuits is different, and even a tiny error in parameter settings can cause irreparable damage. Therefore, developing a simulation welding system that closely approximates the chip welding process and can be optimized in real-time using limited experimental data is a problem urgently needing to be solved by those skilled in the art. Summary of the Invention

[0004] To address the above problems, this invention provides a method for optimizing welding process parameters based on a simulated temperature field, comprising the following steps: S1. Construct a physical model of the welding assembly and a 1:1 geometric model in the software. The physical model is used to run the actual welding process, and the geometric model is used to simulate the welding process. Both models include: chips arranged according to the chip welding process, FR4 circuit boards, isolation metal sheets located between the chips and the circuit boards, and multiple solder joints arranged between the chips and the circuit boards. Probes for checking the temperature are arranged in the physical model, and the positions of the probes are marked on the geometric model. S2. Based on the laser welding path, a Gaussian surface moving heat source is set in the geometric model to simulate the welding gun laser head, which moves along the welding point distribution area at a constant scanning speed, and scans cyclically along the rectangular path according to the set welding process. S3. In both models, the power output of the laser heat source is adjusted in real time. The preset temperature is 220-280℃, the heating time is 4.5-10s, and the heating power is controlled at 60-70W; the power is reduced to 55-60W, and the time is controlled at 1-2s; the constant temperature power is controlled at 50-55W, and the time is controlled at 48-55.5s. S4. Assign the corresponding material's thermal sensitivity parameters to each part of the geometric model, including thermal conductivity, specific heat capacity, density, coefficient of thermal expansion, Young's modulus, and Poisson's ratio; set the heat convection and heat radiation heat dissipation formulas as boundary conditions; S5. Mesh geometric model, set the total time and calculation step size for transient study, execute simulation calculation, and obtain the dynamic temperature field distribution throughout the welding process; S6. Adjust the geometric model parameters based on the difference between the measured temperature at multiple probes and the simulated temperature at the corresponding probe marker position in the dynamic temperature field, so that the temperature field obtained by the geometric model is close to the measured temperature field of the physical model, thus completing the optimization of the geometric model; S7. Compare the simulation results under different dynamic power control strategies in the optimized geometric model. Based on the simulated temperature cloud map and the simulated temperature at the marked position, determine the preferred laser power range and welding time range for welding, and complete the optimization of welding process parameters.

[0005] Preferably, the thermal sensitivity parameters of the material are simulated using Jmatpro software and imported into the geometric model.

[0006] Preferably, there are 3 probes, which are fixed in the non-metallic area on the motherboard. One probe is arranged outside the solder pad on any side of the chip in the physical model, one probe is symmetrically arranged outside the solder pad on the opposite side of the chip, and the remaining probe is arranged outside the solder pad on the adjacent side of the chip.

[0007] Preferably, the method for adjusting the geometric model parameters includes the following steps: setting a threshold for adjustment based on the average temperature difference; adjusting the geometric model parameters begins when the average difference between the measured temperature of all probes and the simulated temperature at the corresponding probe mark position in the dynamic temperature field reaches or exceeds the threshold; otherwise, zero adjustment is performed.

[0008] Preferably, if the temperature difference between the two symmetrically arranged probes exceeds 30% of the threshold when the adjustment begins, the motherboard is divided into four regions along the extension of the chip's diagonal. The geometric model parameters of each region are reset according to the density of the metal lines in each region until the average difference is less than the threshold.

[0009] Preferably, if the temperature difference between the two symmetrically arranged probes is less than 30% of the threshold when the adjustment begins, the geometric model parameters related to heat transfer are directly adjusted based on the average difference until the average difference is less than the threshold.

[0010] Preferably, the mathematical model (1) of the traditional Gaussian surface moving heat source is adjusted to (2) according to the rectangular trajectory in the experiment: In the formula, Where is the heat flux density, and Q is the heat input, i.e., the laser power input. Here are the Gaussian heat source distribution parameters, and r is the distance from the center of the circle; In the formula, Where is heat flux density, and Flux is laser input power. Let X_laser be the laser absorption coefficient, X_laser be the position of the laser source center along the X-axis, and X_laser be the position of the laser source center along the Y-axis. These are the Gaussian heat source distribution parameters.

[0011] Another aspect of the present invention is to provide a non-transient readable recording medium for storing one or more programs containing multiple instructions, which, when executed, cause the processing circuit to perform the aforementioned method for optimizing welding process parameters based on a simulated temperature field.

[0012] Another aspect of the present invention provides a system for optimizing welding process parameters based on a simulated temperature field, comprising a processing circuit and a memory electrically coupled thereto, the memory being configured to store at least one program, the program containing multiple instructions, the processing circuit running the program being able to execute the aforementioned method for optimizing welding process parameters based on a simulated temperature field.

[0013] Compared with existing technologies, the method, recording medium, and system for optimizing welding process parameters based on simulated temperature fields provided by this invention have the following advantages: By dynamically adjusting laser power, the actual welding thermal process is reproduced and optimized in a virtual environment, providing a simulated and verified optimal power and time parameter window for real chip welding processes. This invention can significantly reduce welding R&D costs and improve production efficiency, enhancing welding quality and reliability from the source. It avoids the high material costs, equipment time, and human resource investment required for parameter optimization through extensive experiments, thus shortening the product development cycle. It also solves the problem that conventional experimental methods struggle to capture transient temperature field distribution and molten pool dynamics during welding, resulting in insufficient predictability and control over heat-related defects such as warpage and burn-through. Attached Figure Description

[0014] Figure 1 This is a flowchart of the simulation experiment and the actual experiment in the embodiments of the present invention; Figure 2 This is a graph showing the change of the thermal sensitivity parameters of the solder with temperature in an embodiment of the present invention; Figure 3 This is a heating rate curve of a preset peak temperature of 220℃ in an embodiment of the present invention; Figure 4 This is a cooling rate curve of a preset peak temperature of 220℃ in an embodiment of the present invention; Figure 5 This is a temperature distribution cloud map of a preset peak temperature of 220℃ in an embodiment of the present invention; Figure 6 This is a probe diagram of the actual temperature at a preset peak temperature of 220℃ in an embodiment of the present invention; Figure 7 This is a heating rate curve of a preset peak temperature of 240℃ in an embodiment of the present invention; Figure 8 This is a cooling rate curve of a preset peak temperature of 240℃ in an embodiment of the present invention; Figure 9 This is a temperature distribution cloud map of a preset peak temperature of 240℃ in an embodiment of the present invention; Figure 10 This is a probe diagram of the actual temperature at a preset peak temperature of 240℃ in an embodiment of the present invention; Figure 11 This is a heating rate curve of a preset peak temperature of 260°C in an embodiment of the present invention; Figure 12 This is a cooling rate curve of a preset peak temperature of 280℃ in an embodiment of the present invention; Figure 13 This is a temperature distribution cloud map of a preset peak temperature of 280℃ in an embodiment of the present invention; Figure 14 This is a probe diagram of the actual temperature at a preset peak temperature of 280℃ in an embodiment of the present invention; Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings. The described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without innovative effort are within the scope of protection of the present invention.

[0016] Please see Figure 1 This invention provides a method for optimizing welding process parameters based on a simulated temperature field, comprising the following steps: Step 1: Construct a three-dimensional geometric model of the welding assembly.

[0017] The three-dimensional geometric model includes: a chip, an FR4 circuit board, an insulating metal sheet located between the chip and the circuit board, and multiple solder joints arranged between the chip and the circuit board. Preferably, the solder joints are spherical and arranged in an array. The chip is made of silicon, and the insulating metal sheet is made of aluminum.

[0018] Step 2: Define the moving Gaussian surface heat source.

[0019] Based on the laser welding path, a moving heat source is set to scan along the area where the weld points are arranged. The heat source is a Gaussian surface heat source, moving at a constant scanning speed and cyclically scanning along a rectangular path. The laser scanning period, spot radius, and moving speed are set according to the actual process.

[0020] One of the common Gaussian surface heat source formulas is: In the formula, Where is the heat flux density, and Q is the heat input, i.e., the laser power input. Here are the Gaussian heat source distribution parameters, and r is the distance from the center of the circle.

[0021] In this experiment, since the laser processing is dynamic and the laser moves in a rectangular pattern along the welding point, the Gaussian surface heat source formula is adjusted as follows: In the formula, Where is heat flux density, and Flux is laser input power. Let X_laser be the laser absorption coefficient, X_laser be the position of the laser source center along the X-axis, and X_laser be the position of the laser source center along the Y-axis. The parameters are Gaussian heat source distribution parameters, where the movement patterns of X_laser and Y_laser are shown in Table 1.

[0022] Step 3: Set up temperature monitoring points.

[0023] Multiple temperature probes are placed at specific locations on the chip to monitor temperature changes at different locations in real time during the soldering process, thereby assessing the temperature gradient and thermal stress risk inside the chip. Preferably, the probes include at least three, located on the left and right sides and the top of the chip, respectively.

[0024] Step 4: Set the dynamic laser power control function.

[0025] By adjusting the power output of the laser heat source in real time during the welding process, the controllable laser power process is divided into three stages.

[0026] The first stage (rapid heating after preheating): During the laser power processing, the heating time is within 4.5-10 seconds, and the heating power is controlled at 60-70W according to the preset temperature of 220-280℃.

[0027] The second stage (slightly reducing power to lower thermal stress): the laser power reduction time is controlled at 1-2 seconds, and the power is controlled at 55-60W according to the preset temperature of 220-280℃. The third stage (constant temperature power heating of the welding joint): the laser constant temperature processing power time is controlled at 48-55.5 seconds, and the constant temperature processing power is controlled at 50-55W according to the preset temperature of 220-280℃.

[0028] Step 5: Assign material properties and set boundary conditions.

[0029] Each part of the geometric model is assigned corresponding material parameters, including thermal conductivity, specific heat capacity, and density. The specific heat capacity, a thermally sensitive parameter of the solder, changes with temperature. The specific changes are obtained by simulating the selected solder SAC305 using Jmatpro software. Figure 9 .

[0030] Then, boundary conditions are set, including applying convection cooling to the model surface and thermal radiation to the high-temperature surface. The formulas for thermal convection and thermal radiation are as follows: In the formula, The heat consumed by heat convection. Heat transfer coefficient, External temperature, Let be the actual temperature of the object. The heat transfer coefficient can be determined by the flow velocity of the fluid (i.e., air) and other heat dissipation methods. The total power equation for heat radiation is as follows: In the formula, Net radiative heat flow, To determine the emissivity of different materials from 0 to 1, Here, A is the Stefan-Boltzmann constant, A is the effective radiating area, and T is the actual temperature of the object. The ambient temperature.

[0031] Step 6: Perform mesh generation and transient simulation calculations.

[0032] There are 3 probes, which are fixed to the non-metallic area on the motherboard. One probe is placed outside the solder pad on any side of the chip in the physical model, one probe is symmetrically placed outside the solder pad on the opposite side of the chip, and the remaining probe is placed outside the solder pad on the adjacent side of the chip.

[0033] The threshold for adjusting based on the average temperature difference is set to 5℃. When the average difference between the measured temperature of all probes and the simulated temperature at the corresponding probe mark position in the dynamic temperature field reaches 5℃ or more, the geometric model parameters are adjusted; otherwise, there is no adjustment.

[0034] If the temperature difference between the two symmetrically arranged probes exceeds 0.9℃ when the adjustment begins, the motherboard is divided into 4 regions along the extension of the chip's diagonal. The geometric model parameters of each region are reset according to the density of the metal lines in that region until the average difference is less than the threshold.

[0035] If the temperature difference between two symmetrically arranged probes is less than 0.9℃, the geometric model parameters related to heat transfer are adjusted directly based on the average difference until the average difference is less than the threshold.

[0036] Since the preliminary test showed that the average difference between the measured temperature of all probes and the simulated temperature at the corresponding probe mark position in the dynamic temperature field was less than 5℃, it indicates that the initially designed parameters match the actual situation and the geometric model does not need to be adjusted.

[0037] The geometric model is meshed, with particular mesh refinement for the weld joint region. Then, the total time and computational step size for the transient study are set, and simulation calculations are performed to obtain the dynamic temperature field distribution throughout the welding process.

[0038] Step 7: Analyze the simulation results and determine the optimal process window.

[0039] Based on the simulated temperature cloud map and temperature probe data, the maximum temperature, temperature uniformity, and thermal cycling curves were analyzed. By comparing the simulation results under different dynamic power control strategies, the optimal laser power range and welding time range were determined to avoid damage to the chip and circuit board while ensuring sufficient melting of the solder joints.

[0040] This invention abandons traditional simulation and commonly used constant power processing modes, and utilizes a multi-stage dynamic laser power control method of rapid heating followed by isothermal maintenance to achieve precise control of the welding temperature field. It directly links numerical simulation with process optimization, replacing extensive experimental trial and error with finite element simulation, and quickly determining the optimal process range for different materials and temperature requirements.

[0041] Example 1 Modeling using COMSOL software: (1) Model establishment: The model is designed with the following dimensions: a cuboid 1 (silicon-like chip) measuring 5.59 mm × 7.74 mm × 0.15 mm; a hollow cuboid 2 (FR4 flexible circuit board) with an outer frame of 10.9 mm × 10.9 mm × 0.23 mm and an inner frame of 7 mm × 5.28 mm × 0.23 mm; an insulating metal sheet measuring 7 mm × 5.28 mm × 0.23 mm; and 50µm × 50µm spheres (solder joints) spaced 0.167 mm apart, arranged on both sides between the chip and the motherboard. A detailed schematic diagram is shown below. Figure 2 As shown.

[0042] (2) Heat source: The entire laser welding process involves a Gaussian heat source distributed in a rectangular pattern along the solder joints of the FR4 motherboard, with a constant sweep rate and controllable laser power. One laser sweep cycle is 0.001s. The radius of the Gaussian laser heat source is set to 1.5mm; the laser travels at a speed of 3000mm / s along the welding path.

[0043] (3) Temperature range probe: To monitor the temperature of various parts of the chip in real time and prevent excessive temperature differences from causing internal stress, three temperature probes are set on the left, right, and top of the chip to detect the chip temperature at different times. Point probes are created in the definition, and under the condition of expression T, the maximum, minimum, and average values ​​of the three points are taken to observe the welding temperature difference.

[0044] (4) Assign material parameters to each part of the model and set boundary conditions in sequence: By utilizing the parsing function in the component definition, the starting point of the rectangular loop route can be selected from any point within the rectangle, and the condition function is set as shown in Table 1. Table 1 Laser Rectangular Loop Path Setting After setting the rectangular loop route, select the interpolation function in the variables to adjust the laser power from 0 to 60 seconds. The specific laser power is shown in Table 2.

[0045] Table 2 Laser power at preset peak temperature 220℃ Subsequently, the materials were referenced from the FR4 motherboard material database, and the silicon material parameters were imported from the chip material library. Aluminum was chosen as the insulating metal sheet (low cost, high heat resistance). To improve the simulation results, the convective heat flux value was adjusted to address the thermal insulation issue in solid-state heat transfer. The heat transfer coefficient in the convective heat flux affects the overall heat dissipation efficiency of the model, so the convective heat transfer coefficient was determined through simulation of an example. A material surface emissivity of 0.7 was set for the high-temperature welding surface, and the heat flux was determined using the controllable laser power defined in the specifications.

[0046] (5) Calculate using a free tetrahedral mesh and set the overall study time: After adjusting the physical field control grid, the grid size is set to custom for the locations of solder joints on both sides of the chip, with the maximum grid size set to 0.01mm and the minimum size to 0.001mm. After entering the study, the calculation step size for transient study is set to 0.2s and the total time is 60s. The system checks for parameter setting errors. If no errors are found, the simulation calculation is executed. After the calculation is completed, the result data is analyzed and processed.

[0047] In the temperature field simulation obtained in this example, the heat transfer coefficient in thermal convection is tentatively set at 50 W / (m²). 2 The temperature difference threshold was set to 10℃. After 4.5 seconds of light emission, the chip reached its maximum temperature of 225℃, then slowly cooled down for approximately 1 second, ending the light emission cycle at approximately 123℃. This was observed on the actual point probe's broken line. Figure 6 In the middle, the temperature dropped by about 135℃, which shows that the heat transfer coefficient in heat convection and heat conduction is too low and needs to be adjusted in subsequent examples.

[0048] Example 2 A method for COMSOL dynamic simulation of the chip welding temperature field under adjustable laser power in isothermal heating mode includes the following steps: (1) Model establishment: The model is designed with the following dimensions: a cuboid 1 (silicon-like chip) measuring 5.59 mm × 7.74 mm × 0.15 mm; a hollow cuboid 2 (FR4 flexible circuit board) with an outer frame of 10.9 mm × 10.9 mm × 0.23 mm and an inner frame of 7 mm × 5.28 mm × 0.23 mm; an insulating metal sheet measuring 7 mm × 5.28 mm × 0.23 mm; and 50µm × 50µm spheres (solder joints) spaced 0.167 mm apart, arranged on both sides between the chip and the motherboard. A detailed schematic diagram is shown below. Figure 2 As shown.

[0049] (2) Heat source: The entire laser welding process involves a Gaussian heat source distributed in a rectangular pattern along the solder joints of the FR4 motherboard, with a constant sweep rate and controllable laser power. One laser sweep cycle is 0.001s. The radius of the Gaussian laser heat source is set to 1.5mm; the laser travels at a speed of 3000mm / s along the welding path.

[0050] (3) Temperature range probe: To monitor the temperature of various parts of the chip in real time and prevent excessive temperature differences from causing internal stress, three temperature probes are set on the left, right, and top of the chip to detect the chip temperature at different times. Point probes are created in the definition, and under the condition of expression T, the maximum, minimum, and average values ​​of the three points are taken to observe the welding temperature difference.

[0051] (4) Assign material parameters to each part of the model and set boundary conditions in sequence: By utilizing the parsing function in the variable of the component definition, the starting point of the rectangular loop route can be selected from any point of the rectangle. After setting the rectangular loop route through the condition function, the laser power can be adjusted from 0 to 60 seconds by selecting the interpolation function in the variable. The specific laser power is shown in Table 3.

[0052] Table 3 Laser power at preset peak temperature 240℃ Subsequently, the materials were referenced from the FR4 motherboard material database, and the silicon material parameters were imported from the chip material library. Aluminum was chosen as the insulating metal sheet (low cost, high heat resistance). To improve the simulation results, the convective heat flux value was adjusted to address the thermal insulation issue in solid-state heat transfer. The heat transfer coefficient in the convective heat flux affects the overall heat dissipation efficiency of the model, so the convective heat transfer coefficient was determined through simulation of an example. A material surface emissivity of 0.7 was set for the high-temperature welding surface, and the heat flux was determined using the controllable laser power defined in the specifications.

[0053] (5) Calculate using a free tetrahedral mesh and set the overall study time: After adjusting the physical field control grid, the grid size is set to custom for the locations of solder joints on both sides of the chip, with the maximum grid size set to 0.01mm and the minimum size to 0.001mm. After entering the study, the calculation step size for transient study is set to 1s and the total time to 60s. The system checks for parameter setting errors. If no errors are found, the simulation calculation is executed. After the calculation is completed, the result data is analyzed and processed.

[0054] In the temperature field simulation obtained in this example, the heat transfer coefficient in thermal convection is tentatively set at 60-80 W / (m²). 2The chip was debugged within the range of K, with a temperature difference threshold set to 10℃. After 4.5 seconds of light emission, the temperature rose to a maximum of 234℃, then remained elevated for approximately 1 second before slowly cooling down. After light emission ceased, the chip temperature dropped by approximately 133℃. This was observed on the actual point probe's broken line. Figure 10 During the process, the temperature dropped by approximately 138°C, which is within the threshold range. In this example, the heat transfer coefficient during thermal convection was determined to be 70 W / (m²). 2 ·K), with a surface emissivity of 0.7.

[0055] Example 3 A method for COMSOL dynamic simulation of the chip welding temperature field under adjustable laser power in isothermal heating mode includes the following steps: (1) Model establishment: The model is designed with the following dimensions: a cuboid 1 (silicon-like chip) measuring 5.59 mm × 7.74 mm × 0.15 mm; a hollow cuboid 2 (FR4 flexible circuit board) with an outer frame of 10.9 mm × 10.9 mm × 0.23 mm and an inner frame of 7 mm × 5.28 mm × 0.23 mm; an insulating metal sheet measuring 7 mm × 5.28 mm × 0.23 mm; and 50µm × 50µm spheres (solder joints) spaced 0.167 mm apart, arranged on both sides between the chip and the motherboard. A detailed schematic diagram is shown below. Figure 2 As shown.

[0056] (2) Heat source: The entire laser welding process involves a Gaussian heat source distributed in a rectangular pattern along the solder joints of the FR4 motherboard, with a constant sweep rate and controllable laser power. One laser sweep cycle is 0.001s. The radius of the Gaussian laser heat source is set to 1.5mm; the laser travels at a speed of 3000mm / s along the welding path.

[0057] (3) Temperature range probe: To monitor the temperature of various parts of the chip in real time and prevent excessive temperature differences from causing internal stress, three temperature probes are set on the left, right, and top of the chip to detect the chip temperature at different times. Point probes are created in the definition, and under the condition of expression T, the maximum, minimum, and average values ​​of the three points are taken to observe the welding temperature difference.

[0058] (4) Assign material parameters to each part of the model and set boundary conditions in sequence: By utilizing the parsing function in the variable of the component definition, the starting point of the rectangular loop route can be selected from any point of the rectangle. After setting the rectangular loop route through the condition function, the laser power can be adjusted from 0 to 60 seconds by selecting the interpolation function in the variable. The specific laser power is shown in Table 4.

[0059] Table 4 Laser power at preset peak temperature 280℃ Subsequently, the materials referenced FR4 motherboard materials from the materials database, and the chip incorporated silicon material parameters from the materials library. Aluminum sheets were chosen for the insulating metal sheet (low cost, high heat resistance). In solid-state heat transfer, thermal insulation was used to increase convective heat flux by 70 W / (m²). 2 To achieve better simulation results, the thermal emissivity of the material on the high-temperature welding surface is set to 0.7, and the heat flux is the controllable laser power set in the definition of the variable.

[0060] (5) Calculate using a free tetrahedral mesh and set the overall study time: After adjusting the physical field control grid, the grid size is set to custom for the locations of solder joints on both sides of the chip, with the maximum grid size set to 0.01mm and the minimum size to 0.001mm. After entering the study, the calculation step size for transient study is set to 5s and the total time to 60s. The system checks for parameter setting errors. If no errors are found, the simulation calculation is executed. After the calculation is completed, the result data is analyzed and processed.

[0061] In the temperature field simulation obtained in this example, when the highest temperature of the midpoint probe reached 245℃ at 9.5s, the overall laser heat source input and heat loss were basically balanced, the laser power remained at its maximum, the chip soldering temperature was relatively stable, and the simulated cooling rate curve was consistent with the actual probe temperature. Figure 14 The temperature was reduced by 3 seconds after the light emission ended, and the difference between the simulation and the actual results was controlled within 5℃, indicating that the simulation prediction was reasonable.

[0062] The three examples show that the higher the preset peak temperature, the higher the temperature difference between the three probe points, and the higher the thermal stress generated. The temperature difference between the probes on both sides is about 2℃, which is a reasonable temperature difference. On the cloud map, the temperature difference between the solder joints on both sides and the middle of the chip is about 15℃. The generated thermal stress is within a controllable range, and the simulation effect is good.

[0063] By combining experimental and welding simulation temperature field power adjustment, the trained model provides an optimal laser power range and welding time range for actual welding, significantly reducing the cost of finding the optimal welding parameter range through experiments and improving production efficiency.

[0064] Assembling the above methods and steps into a program and storing it on a hard disk or other non-transitory storage medium constitutes an embodiment of the present invention, "a non-transitory readable recording medium"; while connecting the storage medium electrically to a computer processor and enabling data processing to optimize welding process parameters based on a simulated temperature field constitutes an embodiment of the present invention, "a system for optimizing welding process parameters based on a simulated temperature field".

[0065] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computers or available storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0066] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0067] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0068] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0069] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. 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 optimizing welding process parameters based on a simulated temperature field, characterized in that, Includes the following steps: S1. Construct a physical model of the welding assembly and a 1:1 geometric model in the software. The physical model is used to run the actual welding process, and the geometric model is used to simulate the welding process. Both models include: chips arranged according to the chip welding process, FR4 circuit board, isolation metal sheet located between the chip and the circuit board, and multiple solder joints arranged between the chip and the circuit board. In the physical model, probes for measuring temperature are arranged, and the positions of the probes are marked on the geometric model; S2. Based on the laser welding path, a Gaussian surface moving heat source is set in the geometric model to simulate the welding gun laser head, which moves along the welding point distribution area at a constant scanning speed, and scans cyclically along the rectangular path according to the set welding process. S3. In both models, the power output of the laser heat source is adjusted in real time. The preset temperature is 220-280℃, the heating time is 4.5-10s, and the heating power is controlled at 60-70W; the power is reduced to 55-60W, and the time is controlled at 1-2s; the constant temperature power is controlled at 50-55W, and the time is controlled at 48-55.5s. S4. Assign the corresponding material's thermal sensitivity parameters to each part of the geometric model, including thermal conductivity, specific heat capacity, density, coefficient of thermal expansion, Young's modulus, and Poisson's ratio; set the heat convection and heat radiation heat dissipation formulas as boundary conditions; S5. Mesh geometric model, set the total time and calculation step size for transient study, execute simulation calculation, and obtain the dynamic temperature field distribution throughout the welding process; S6. Adjust the geometric model parameters based on the difference between the measured temperature at multiple probes and the simulated temperature at the corresponding probe marker position in the dynamic temperature field, so that the temperature field obtained by the geometric model is close to the measured temperature field of the physical model, thus completing the optimization of the geometric model; S7. Compare the simulation results under different dynamic power control strategies in the optimized geometric model. Based on the simulated temperature cloud map and the simulated temperature at the marked position, determine the preferred laser power range and welding time range for welding, and complete the optimization of welding process parameters.

2. The method for optimizing welding process parameters based on a simulated temperature field according to claim 1, characterized in that, The thermal sensitivity parameters of the material were simulated using Jmatpro software and imported into the geometric model.

3. The method for optimizing welding process parameters based on a simulated temperature field according to claim 2, characterized in that, There are 3 probes, which are fixed to the non-metallic area on the motherboard. One probe is placed outside the solder pad on any side of the chip in the physical model, one probe is symmetrically placed outside the solder pad on the opposite side of the chip, and the remaining probe is placed outside the solder pad on the adjacent side of the chip.

4. The method for optimizing welding process parameters based on a simulated temperature field according to claim 3, characterized in that, The method for adjusting the geometric model parameters includes the following steps: setting a threshold for adjustment based on the average temperature difference; starting to adjust the geometric model parameters when the average difference between the measured temperature of all probes and the simulated temperature at the corresponding probe mark position in the dynamic temperature field reaches or exceeds the threshold; otherwise, zero adjustment is performed.

5. The method for optimizing welding process parameters based on a simulated temperature field according to claim 4, characterized in that, If the temperature difference between two symmetrically arranged probes exceeds 30% of the threshold when the adjustment begins, the motherboard is divided into four regions along the extension of the chip's diagonal. The geometric model parameters of each region are reset according to the density of the metal lines in that region until the average difference is less than the threshold.

6. The method for optimizing welding process parameters based on a simulated temperature field according to claim 5, characterized in that, If the temperature difference between the two symmetrically arranged probes is less than 30% of the threshold when the adjustment begins, the geometric model parameters related to heat transfer are adjusted directly based on the average difference until the average difference is less than the threshold.

7. The method for optimizing welding process parameters based on a simulated temperature field according to claim 6, characterized in that, This also includes adjusting the traditional Gaussian surface moving heat source mathematical model (1) to (2) based on the rectangular trajectory in the experiment: In the formula, Where is the heat flux density, and Q is the heat input, i.e., the laser power input. Here are the Gaussian heat source distribution parameters, and r is the distance from the center of the circle; In the formula, Where is heat flux density, and Flux is laser input power. Let X_laser be the laser absorption coefficient, X_laser be the position of the laser source center along the X-axis, and X_laser be the position of the laser source center along the Y-axis. These are the Gaussian heat source distribution parameters.

8. A non-transitory readable recording medium for storing one or more programs containing multiple instructions, characterized in that, When the instruction is executed, the processing circuit will perform a method for optimizing welding process parameters based on a simulated temperature field, as described in any one of claims 1-7.

9. A system for optimizing welding process parameters based on a simulated temperature field, comprising a processing circuit and a memory electrically coupled thereto, characterized in that, The memory is configured to store at least one program, the program containing multiple instructions, and the processing circuit runs the program to perform a method for optimizing welding process parameters based on a simulated temperature field, as described in any one of claims 1-7.

Images