Laser Welding Control Methods and Systems for Metal Parts

Abstract

This application relates to the field of dissimilar metal welding technology, specifically to a method and system for controlling laser welding of metal parts. The method first identifies the same-metal and dissimilar-metal welding positions in the welding path; for the same-metal welding positions, it performs fixed-parameter welding under a preset first working condition to ensure efficiency and stability; for the dissimilar-metal welding positions, it acquires the equivalent welding thickness in real time and dynamically adjusts the laser power, spot diameter, and pulse parameters based on this thickness. For dissimilar metals with significant differences in physical properties, such as phosphor bronze and iron, this invention effectively solves problems such as energy matching failure, molten pool instability, and the formation of brittle intermetallic compounds caused by changes in assembly gaps through thickness-driven multi-parameter coupling adjustment. This invention significantly improves the joint quality and yield of dissimilar metal welding and achieves adaptive precision welding under complex working conditions.

Description

Technical Field

[0001] This application relates to the field of dissimilar metal welding technology, specifically to a laser welding control method and system for metal parts. Background Technology

[0002] Laser welding technology, with its significant advantages such as high energy density, large aspect ratio, and small heat-affected zone, has become a core joining process in the manufacturing of various electronic devices. In practical industrial applications, the workpieces to be welded often have complex structures, and the same weld pass may simultaneously contain welding zones of the same metal (such as copper-copper lap joints) and welding zones of dissimilar metals (such as the transition joint between tin-phosphor bronze and iron). However, existing laser welding control methods have significant limitations in adapting to these two types of welding zones with drastically different properties, making it difficult to balance welding efficiency and joint reliability.

[0003] First, for welding positions of the same metal, since the physicochemical properties of the base materials on both sides (such as melting point, thermal conductivity, specific heat capacity, and laser absorptivity) are completely identical, the heat conduction within the molten pool exhibits a symmetrical distribution, and the molten pool flow behavior is stable and predictable. In industrial production, assembly tolerances in such areas are typically small, and the heat input requirements are relatively constant. Therefore, existing technologies generally employ preset operating conditions (i.e., fixed laser power, spot diameter, and pulse parameters) for continuous welding. This fixed parameter strategy not only meets the uniformity requirements of heat input for the same metal, ensuring consistent weld formation, but also simplifies control logic to the greatest extent possible, thereby improving production efficiency.

[0004] However, the situation is extremely complex and challenging for dissimilar metal welds, particularly those involving typical combinations of tin-phosphorus bronzes (such as C5210). These two materials differ significantly in their physical and chemical properties: The difference in thermal conductivity is significant: phosphor bronze has a thermal conductivity 8–10 times that of ordinary iron. During welding, heat from the phosphor bronze side is rapidly dissipated into the base material, making it difficult for the molten pool to form stably or causing incomplete fusion defects; while heat accumulates on the iron side, easily causing localized overheating or even burn-through. The melting points are vastly different: phosphor bronze has a melting point of approximately 900–1000°C, while iron has a melting point as high as approximately 1535°C. This huge difference in melting points results in an extremely narrow welding window: if the heat input is insufficient to melt the iron, a weak weld will form; if the heat input is too high to melt the iron, the phosphor bronze side will have already been severely overheated, collapsed, or vaporized. Brittle intermetallic compounds (IMCs): During direct fusion welding, Fe-Cu and Fe-P systems readily form brittle intermetallic compounds (such as Fe-Cu eutectic) at the interface. These brittle phases significantly reduce the ductility and toughness of the joint, causing the weld to crack during cooling or subsequent stress, failing to meet the stringent requirements for sealing and high strength in precision instruments, refrigeration systems, and semiconductor fields.

[0005] Although laser welding is the preferred solution for addressing the aforementioned issues due to its precise temperature control capabilities, existing dissimilar metal welding control technologies still suffer from the following key shortcomings, particularly in handling variations in actual assembly thickness. Existing technologies rely on a single dimension for parameter adjustment, neglecting the decisive influence of equivalent weld thickness on multi-parameter coupling: For combinations of dissimilar metals with vastly different physical properties, such as phosphor bronze and iron, even slight changes in weld thickness not only alter the total heat required but also affect energy demand adaptation: 1. Energy density mismatch failure: When the weld thickness increases due to the increased gap, simply increasing the power without adjusting the spot diameter will result in excessively high power density, easily causing violent spattering and vaporization on the low-melting-point phosphor bronze side, while failing to effectively overcome heat loss due to high thermal conductivity; conversely, if the weld thickness decreases, fixed parameters will lead to excess energy, burning through the workpiece and promoting excessive growth of the brittle IMC layer; 2. Lack of multi-dimensional parameter coordination: Different weld thicknesses require matching specific "power-spot-pulse" combinations. Existing technologies often neglect the coordinated adjustment mechanism of spot diameter and pulse parameters with thickness changes, resulting in the inability to guarantee the quality of dissimilar metal weld joints when facing dynamically changing weld thicknesses.

[0006] In summary, there is an urgent need to develop a control method that can distinguish between welding scenarios of the same and dissimilar metals and dynamically adapt various laser parameters according to different welding thicknesses, in order to overcome welding defects caused by different welding thicknesses and differences in physical properties. Summary of the Invention

[0007] In view of this, this application provides a laser welding control method and system for metal parts, which can distinguish between welding scenarios of the same and different metals, and can dynamically adapt the control method of various laser parameters according to different welding thicknesses, so as to overcome welding defects caused by different welding thicknesses and differences in physical properties.

[0008] In a first aspect, this application provides a laser welding control method for metal parts, comprising: acquiring information about the part to be welded; fixing the part to be welded on a welding station; controlling a laser to move to the position of the part to be welded corresponding to the current welding sequence; obtaining, based on the information about the part to be welded, the same metal welding position, the dissimilar metal welding position, and the welding thickness of the dissimilar metal welding position of the current part to be welded; controlling the laser to switch to a first working condition to weld the same metal welding position; controlling the laser to switch to a second working condition corresponding to the welding thickness; and welding the dissimilar metal welding position based on the second working condition.

[0009] In conjunction with the first aspect, in one possible implementation, after controlling the laser to switch to the second working condition corresponding to the welding thickness, the method further includes: generating secondary confirmation information; acquiring first surface information and second surface information on both sides of the joint of the dissimilar metal welding position; comparing the first surface information and the second surface information to obtain a comprehensive judgment value; if the comprehensive judgment value is greater than a preset threshold, then performing welding on the dissimilar metal welding position based on the second working condition.

[0010] In conjunction with the first aspect, in one possible implementation, the acquisition of the first surface information and the second surface information on both sides of the weld joint of the dissimilar metals includes: acquiring surface image data and magnetic field strength data of the weld joint of the dissimilar metals respectively; identifying the weld joint based on the surface image data; separating the surface image data into the first surface and the second surface on both sides of the weld joint to obtain corresponding first image information and second image information respectively; and separating the magnetic field strength data into the first magnetic field and the second magnetic field on both sides of the weld joint to obtain corresponding first magnetic field information and second magnetic field information respectively.

[0011] In conjunction with the first aspect, in one possible implementation, the step of comparing the first surface information and the second surface information to obtain a comprehensive judgment value includes: comparing the first image information and the second image information to obtain a reflectivity difference value; comparing the first magnetic field information and the second magnetic field information to obtain a magnetic field difference value; calculating a first product of the visual weight coefficient and the reflectivity difference value, and a second product of the magnetic weight coefficient and the magnetic field difference value, based on preset visual weight coefficients and magnetic weight coefficients; and calculating the sum of the first product and the second product to obtain the comprehensive judgment value.

[0012] In conjunction with the first aspect, in one possible implementation, obtaining the same-metal welding position, dissimilar-metal welding position, and welding thickness of the dissimilar-metal welding position based on the information of the workpiece to be welded includes: obtaining first metal information and second metal information of the dissimilar-metal welding position based on the information of the workpiece to be welded; obtaining a first thickness and a second thickness respectively based on the first metal information and the second metal information; obtaining a tolerance gap; and obtaining the welding thickness based on the first thickness, the second thickness, and the tolerance gap.

[0013] In conjunction with the first aspect, in one possible implementation, controlling the laser to switch to the second operating condition corresponding to the welding thickness includes: obtaining the laser power, spot diameter, and pulse data corresponding to the laser based on the welding thickness; the pulse data includes pulse frequency, pulse width, pulse energy, duty cycle, and peak power.

[0014] In conjunction with the first aspect, in one possible implementation, after welding the dissimilar metal welding position based on the second working condition, the method further includes: acquiring weld image data; identifying weld uniformity based on the weld image data; if the weld uniformity meets a preset uniformity, generating welding qualification information; if the weld uniformity does not meet the preset uniformity, re-welding the dissimilar metal welding position based on a third working condition; the spot diameter corresponding to the third working condition is greater than the spot diameter corresponding to the second working condition.

[0015] In conjunction with the first aspect, in one possible implementation, identifying weld uniformity based on the weld image data includes: identifying the average width, maximum width, and minimum width of the weld based on the weld image data; calculating the width difference between the maximum width and the minimum width; calculating the measured ratio of the width difference to the average width; and determining if the weld uniformity does not conform to the preset uniformity includes: if the measured ratio is greater than the preset ratio, then the weld uniformity is deemed not to conform to the preset uniformity.

[0016] In conjunction with the first aspect, one possible implementation further includes: each time the secondary welding of the dissimilar metal welding position based on the third working condition is performed, the spot diameter corresponding to the third working condition is increased by a preset step size.

[0017] Secondly, this application provides a metal laser welding control system, comprising: a pre-configuration module configured to: acquire information about the workpiece to be welded; fix the workpiece to be welded on a welding station; and obtain, based on the information about the workpiece to be welded, the same-metal welding position, the dissimilar-metal welding position, and the welding thickness of the dissimilar-metal welding position; a movement control module communicatively connected to the pre-configuration module, the movement control module being configured to: control the laser to move to the position of the workpiece to be welded corresponding to the current welding sequence; and a welding control module communicatively connected to the pre-configuration module, the welding control module being configured to: control the laser to switch to a first working condition to weld the same-metal welding position; control the laser to switch to a second working condition corresponding to the welding thickness; and weld the dissimilar-metal welding position based on the second working condition.

[0018] This application achieves intelligent differential control for welding of similar and dissimilar metals. The method automatically acquires workpiece information and accurately identifies weld types. For similar metals with uniform thermal conductivity, a common first working condition is used for rapid welding. However, for dissimilar metals, the large difference in thermal conductivity leads to unstable molten pools, easy burn-through, or the formation of brittle phases. A second working condition, including power, spot size, and pulse parameters, is dynamically matched based on the weld thickness. Through classification and identification, and execution of different working conditions, heat input and loss on both sides of the weld are effectively balanced, avoiding burn-through in thin plates and ensuring penetration in thick plates, significantly suppressing the formation of brittle intermetallic compounds. This method significantly improves the welding yield, joint strength, and production automation level of dissimilar metal components, solving the technical bottleneck of traditional single-parameter methods that cannot adapt to complex material combinations. Attached Figure Description

[0019] Figure 1 The diagram shown is a schematic representation of the steps of a laser welding control method for metal parts according to an embodiment of this application.

[0020] Figure 2 The diagram shows a machine tool structure using the laser welding control method for metal parts according to this application.

[0021] Figure 3 The diagram shows the steps for secondary verification during the welding of dissimilar metals.

[0022] Figure 4 The diagram shows the steps involved in identifying dissimilar metals.

[0023] Figure 5 The diagram shows the steps involved in identifying dissimilar metals.

[0024] Figure 6 The diagram shows the steps involved in obtaining the weld thickness.

[0025] Figure 7 The diagram shows the steps involved in obtaining specific laser parameters based on the weld thickness.

[0026] Figure 8 The diagram shows the steps of performing secondary welding based on weld uniformity.

[0027] Figure 9 The diagram shows the steps involved in identifying weld uniformity.

[0028] Figure 10 The diagram shows the steps for adjusting parameters during multiple secondary welding operations.

[0029] Figure 11 The diagram shows the system structure of a laser welding control system for metal parts. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0031] Figure 1 The diagram shown is a schematic representation of the method steps of a laser welding control method for metal parts according to an embodiment of this application. This application provides a laser welding control method for metal parts; in one embodiment, as shown... Figure 1 As shown, the laser welding control method for metal parts includes: Step 110: Obtain information about the parts to be welded.

[0032] In this step, pre-stored production order data is read to obtain various information data about the parts to be welded. This information data includes: workpiece model, material combination (such as copper-iron, iron-iron), thickness of each part, and preset welding path coordinates, etc.

[0033] Step 120: Fix the part to be welded on the welding station.

[0034] In this step, a special fixture is used to precisely position and lock the workpiece to be welded at the welding station.

[0035] Step 130: Control the laser to move to the position of the workpiece to be welded corresponding to the current welding sequence.

[0036] In this step, the laser is moved by a robotic arm and a slide rail. The welding sequence of the welding stations on the welding platform is preset, and the laser is moved to the current welding sequence position to prepare for laser welding of the workpiece.

[0037] Step 140: Obtain the same metal welding position, dissimilar metal welding position, and welding thickness of the dissimilar metal welding position of the current part to be welded based on the information of the part to be welded.

[0038] In this step, the three data points can be set during the pre-assembly stage of the parts to be welded. When the laser reaches above the parts to be welded, the three data points are obtained again based on the information of the parts to be welded, so as to determine the location where welding needs to be performed. Furthermore, it distinguishes between welding positions of the same metal and welding positions of dissimilar metals, thus performing welding under different working conditions.

[0039] Step 150: Control the laser to switch to the first working condition to weld the same type of metal at the welding position.

[0040] In this step, since the thermal conductivity of the same metal is the same, the heat loss rate on both sides of the weld is the same, thus a stable molten pool can be formed, and welding is carried out using the first working condition that is usually set.

[0041] Step 160: Control the laser to switch to the second working condition corresponding to the welding thickness.

[0042] In this step, the significant difference in thermal conductivity between dissimilar metals leads to markedly different heat dissipation rates, making it difficult for the molten pool to form stably. Furthermore, brittle intermetallic compounds are prone to form during welding, causing the joint to become brittle and prone to cracking. Therefore, a second working condition, more precisely matched to the weld thickness, is needed to adapt to dissimilar metal welding, ensuring weld strength while preventing breakdown. The second working condition includes adjustable parameters such as laser power, spot diameter, pulse data, pulse frequency, pulse width, pulse energy, duty cycle, and peak power.

[0043] Step 170: Weld the dissimilar metal welding position based on the second working condition.

[0044] In this step, welding the dissimilar metal welding position through a specific matching second working condition can improve the adaptability of the welding process to the dissimilar metal welding position and ensure the welding reliability and firmness of the dissimilar metal welding position.

[0045] In this embodiment, intelligent differential control is achieved for welding of similar and dissimilar metals. This method automatically acquires workpiece information and accurately identifies the weld type. For similar metals with uniform thermal conductivity, a common first working condition is used for rapid welding. However, for dissimilar metals, the large difference in thermal conductivity leads to unstable molten pools, easy burn-through, or the formation of brittle phases. A second working condition, including power, spot size, and pulse parameters, is dynamically matched based on the weld thickness. Through classification and identification, and execution of different working conditions, the heat input and loss on both sides of the weld are effectively balanced, avoiding burn-through in thin plates and ensuring penetration in thick plates, significantly suppressing the formation of brittle intermetallic compounds. This method significantly improves the welding yield, joint strength, and production automation level of dissimilar metal components, solving the technical bottleneck of traditional single-parameter methods being unable to adapt to complex material combinations.

[0046] Figure 2 The diagram shows a machine tool structure applying the laser welding control method for metal parts according to this application. In one embodiment, as shown... Figure 2 As shown, the first track 101 is fixed on the machine tool 104, and the second track 102 is slidably connected to the first track 101. The laser 100 is mounted on the second track 102 by the robotic arm 200. The laser 100 is translated by the first track 101 and the second track 102. The machine tool 104 is provided with a workpiece tray 105, which has multiple welding stations. Multiple parts 103 to be welded are embedded in the welding stations.

[0047] Figure 3 The diagram illustrates the steps of a secondary verification process during dissimilar metal welding. In one embodiment, as shown... Figure 3 As shown, after step 160, the method further includes: Step 161: Generate secondary confirmation information.

[0048] In this step, after the system completes the working condition switch but before the laser emits light, a logic interrupt is triggered. This step calls the internal verification program, and the laser enters a standby state, ready to enter the secondary detection mode, to prevent expensive dissimilar metal workpieces from being scrapped due to errors in the preceding data (such as incorrect model reading or fixture positioning deviation).

[0049] Step 162: Collect the first surface information and the second surface information on both sides of the joint of the dissimilar metal welding position.

[0050] In this step, a high-resolution industrial camera and a magnetic field detector are used to perform real-time imaging and magnetic field data acquisition on both sides of the dissimilar metal joint to be welded. The first / second surface information includes: material color, reflectivity, texture features (to distinguish copper / iron), surface roughness, magnetic field strength data, etc.

[0051] Step 163: Compare the information of the first surface and the information of the second surface to obtain a comprehensive judgment value.

[0052] Step 164: If the comprehensive judgment value is greater than the preset threshold, then perform welding on the dissimilar metal welding position based on the second working condition.

[0053] This embodiment implements a closed-loop verification mechanism, which makes up for the open-loop defect in the original process that only relies on pre-stored information of the parts to be welded. Through the logic of actual measurement-comparison-decision, it ensures the absolute controllability of the high-risk process of dissimilar metal welding, thereby achieving a more reliable welding operation.

[0054] Figure 4 The diagram illustrates the steps involved in identifying dissimilar metals. Specifically, as shown... Figure 4 As shown, step 162 includes: Step 1621: Collect surface image data and magnetic field strength data of the dissimilar metal welding site respectively.

[0055] In this step, an industrial camera is used to acquire features such as reflectivity, texture, and color of the surface of the workpiece to be welded. At the same time, a magnetic sensor array (such as a Hall / GMR / TMR sensor) is used to acquire the magnetic field strength of the workpiece to be welded.

[0056] Step 1622: Identify welded seams based on surface image data.

[0057] In this step, image processing algorithms (such as edge detection Canny, Hough transform, or deep learning semantic segmentation) are used to locate the physical boundary between the two metals to be welded in the image.

[0058] Step 1623: Separate the surface image data into the first surface and the second surface on both sides of the weld joint to obtain the corresponding first image information and second image information.

[0059] In this step, the image ROI (Region of Interest) is divided into two independent regions, using the weld seam identified in step 1622 as the boundary, thus obtaining the first image information and the second image information.

[0060] Step 1624: Separate the magnetic field strength data into the first magnetic field and the second magnetic field on both sides of the weld joint to obtain the corresponding first magnetic field information and second magnetic field information.

[0061] In this step, the continuous magnetic field scanning data is divided into two groups, with the weld seam as the boundary, thus obtaining the first magnetic field information and the second magnetic field information.

[0062] Figure 5 The diagram illustrates the steps of a method for identifying dissimilar metals. Specifically, in one embodiment, as shown... Figure 5 As shown, step 163 includes: Step 1631: Compare the first image information and the second image information to obtain the reflectance difference value.

[0063] Step 1632: Compare the first magnetic field information and the second magnetic field information to obtain the magnetic field difference value.

[0064] Step 1633: Based on the preset visual weight coefficient and magnetic weight coefficient, calculate the first product of the visual weight coefficient and the reflectivity difference value, and the second product of the magnetic weight coefficient and the magnetic field difference value.

[0065] Step 1634: Calculate the sum of the first product and the second product to obtain the comprehensive judgment value.

[0066] In this embodiment, relying solely on visual perception is susceptible to misjudgment due to surface oxidation, oil stains, or coating interference; relying solely on magnetism sometimes makes it difficult to distinguish between two dissimilar metals with similar magnetic properties. By integrating differences in reflectivity (characterizing surface optical properties) and differences in magnetic fields (characterizing internal magnetic properties), dual verification is achieved. Even if the workpiece surface has slight corrosion or uneven reflectivity, magnetic data can still definitively confirm the material; conversely, if the magnetic signal is weak, visual data can provide auxiliary judgment. This complementarity improves the accuracy of dissimilar metal identification to over 99%, completely eliminating batch scrap due to material confusion.

[0067] Step 1633 introduces visual and magnetic weight coefficients, allowing the system to dynamically adjust the emphasis of judgment criteria based on the characteristics of different material combinations. For "iron-copper" welding (with significant magnetic differences), the magnetic weight can be increased, ignoring surface color interference; for "aluminum-magnesium" welding (both are non-magnetic but have different reflectivities), the visual weight is increased. This avoids the failure problem of fixed threshold algorithms under complex working conditions, enabling the same equipment to be compatible with various complex dissimilar metal combinations and adapt to new production lines without frequent hardware replacements.

[0068] Step 1634 normalizes two physical quantities with different dimensions (reflectivity % and magnetic field mT) into a unified comprehensive judgment value through weighted summation. The downstream control system only needs to compare a simple value with a preset threshold to make a binary decision of "allow welding" or "alarm shutdown," which significantly reduces the computational complexity and response latency of real-time control. This comprehensive judgment value can be recorded and archived as a quality fingerprint of each process, providing accurate data support for subsequent quality traceability and process optimization.

[0069] The following are some specific implementation examples: Example 1: Typical ferromagnetic vs. nonferromagnetic combination (iron-phosphorus bronze): Scenario Description: The workpieces to be welded are cold-rolled iron sheet (ST14, ferromagnetic) and ultra-thin phosphor bronze corrugated pipe (C5210, antimagnetic). This is the core scenario for welding dissimilar metals, as they have significant differences in magnetic properties and visual appearance. Preset parameters: Visual weight (w1): 0.4 (since the magnetic difference is already significant enough, visual verification is only used as an auxiliary method to verify the surface condition). Magnetic weight (w2): 0.6 (primary criterion); Acceptable threshold (T): 0.65; Actual measurement data: Image data: First surface (iron): reflectivity R1 = 35% (dark); Second surface (copper): reflectivity R2 = 78% (purple-red bright); Reflectance difference value (V) diff ): After normalization, it is 0.85 (the difference is extremely large); Magnetic field data: First surface (iron): Magnetic induction intensity B1 = 450mT; Second surface (copper): Magnetic induction intensity B2≈0mT; Magnetic field difference value (M) diff ): After normalization, it is 0.98 (close to the theoretical maximum value); Calculation process: First product (visual term): 0.4 × 0.85 = 0.34; Second product (magnetic term): 0.6 × 0.98 = 0.588; The overall judgment value is 0.34 + 0.588 = 0.928, which is greater than 0.65. The judgment is passed. The system confirms that it is a typical dissimilar metal (iron-copper) and the surface condition is good, so the second welding condition can be performed.

[0070] Example 2: Weakly magnetic / non-magnetic combination (iron-aluminum alloy): Scenario Description: The workpieces to be welded are iron (weakly magnetic / paramagnetic) and aluminum alloy (paramagnetic). The difference in their magnetic properties is minimal, and they are mainly distinguished by visual inspection. Preset parameters: Visual weight (w1): 0.75 (magnetic reference is not very meaningful, mainly based on vision); Magnetic weight (w2): 0.25 (only used to exclude mistakenly inserted iron parts); Pass threshold (T): 0.55 (Since it mainly relies on a single feature, the threshold is appropriately lowered to prevent missed detections); Actual measurement data: Image data: First surface (iron): reflectivity R1 = 60% (silver-white brushed); Second surface (aluminum alloy): reflectivity R2 = 75% (silver-white matte); Reflectance difference value (V) diff ): After normalization, it is 0.45 (the difference is moderate, mainly due to the enhancement of texture algorithms, which is simplified to the difference in reflectivity here). Magnetic field data: First surface (iron): B1 = 5mT; Second surface (aluminum alloy): B2=1mT; Magnetic field difference value (M) diff ): After normalization, it is 0.08 (the difference is minimal); Calculation process: First product (visual term): 0.75 × 0.45 = 0.3375; Second product (magnetic term): 0.25 × 0.08 = 0.02; The overall judgment value is 0.3375 + 0.02 = 0.3575, which is less than 0.55. Therefore, the judgment fails and manual review or machine shutdown may be triggered to check for errors in metal assembly.

[0071] Example 3: Abnormal operating condition detection (mixing error: iron-iron): Scenario Description: A production order requires welding "iron-copper", but a worker mistakenly inserts two iron parts (of the same metal). The system needs to identify that this is not the expected "dissimilar metal" combination, thereby preventing the incorrect process parameters (second working condition) from being applied. Preset parameters: Visual weight (w1): 0.4; Magnetic weight (w2): 0.6; Dissimilar metal detection threshold (T): 0.65; Actual measurement data: Image data: First surface (iron): reflectivity R1 = 36%; Second surface (iron): reflectivity R2 = 35%; Reflectance difference value (V) diff ): After normalization, it is 0.02 (almost no difference); Magnetic field data: First surface (iron): B1 = 440mT; Second surface (iron): B2 = 438mT; Magnetic field difference value (M) diff ): After normalization, it is 0.03 (almost no difference); Calculation process: First product (visual term): 0.4 × 0.02 = 0.008; Second product (magnetic term): 0.6 × 0.03 = 0.018; The overall judgment value is 0.008 + 0.018 = 0.026, which is less than 0.65, indicating that it is not a dissimilar metal (or the material is incorrect). This triggers manual review or a shutdown to check for metal assembly errors. This prevents process parameter mismatches caused by incorrect material feeding (using a low-power dissimilar metal to weld two thick iron plates would result in incomplete melting), thus avoiding a batch quality incident.

[0072] Figure 6 The diagram illustrates the steps involved in obtaining the weld thickness. In one embodiment, as shown... Figure 6 As shown, step 140 includes: Step 141: Obtain the first metal information and the second metal information of the dissimilar metal welding position based on the information of the parts to be welded.

[0073] Step 142: Based on the first metal information and the second metal information, obtain the corresponding first thickness and second thickness respectively.

[0074] In this step, the first thickness can be obtained directly from the pre-stored parameters of the first metal information, and the second thickness can be obtained directly from the pre-stored parameters of the second metal information. Alternatively, based on the first metal information, the first thickness can be obtained by visual detection after identifying the first metal; and based on the second metal information, the second thickness can be obtained by visual detection after identifying the second metal.

[0075] Step 143: Obtain the tolerance gap.

[0076] Step 144: Obtain the welding thickness based on the first thickness, the second thickness, and the tolerance gap.

[0077] In this embodiment, traditional methods only set parameters based on the nominal thickness. When the actual gap is too large, heat conduction is often obstructed, leading to incomplete fusion or undercut. This embodiment incorporates the tolerance gap into the calculation, allowing the set welding thickness to reflect the actual assembly state of the workpiece in real time. The system can automatically compensate for heat input accordingly, such as increasing laser power when the gap is large, significantly improving the process's tolerance to incoming material tolerances and clamping errors. This enables the subsequent generated second working condition to accurately match the actual heat capacity requirements, effectively suppressing the growth of brittle intermetallic compounds (IMCs) while ensuring that the thin side does not burn through and the thick side is fully melted. Even if the tolerance gap between the two workpieces to be welded fluctuates in the same batch of production, the system can dynamically adjust the target thickness through this step, thereby outputting the most suitable process parameters, ensuring consistency in batch production and a high first-pass yield.

[0078] Figure 7 The diagram illustrates the steps of obtaining specific laser parameters based on the weld thickness. Specifically, in one embodiment, as shown... Figure 7 As shown, step 160 includes: Step 1601: Obtain the laser power, spot diameter, and pulse data corresponding to the laser based on the welding thickness; the pulse data includes pulse frequency, pulse width, pulse energy, duty cycle, and peak power.

[0079] In this embodiment, laser parameters corresponding to various welding thicknesses are preset. These parameters can be dynamically adjusted based on changes in thickness and gap, suppressing defects such as brittle phase formation, burn-through, or lack of fusion in dissimilar metal welding. This significantly reduces reliance on manual experience and achieves automation and standardization of high-quality welding. For example, a thicker welding thickness corresponds to an increased laser spot diameter to reduce power density, thereby widening the heating zone to balance the temperature difference between the copper and iron sides. Simultaneously, a specific pulse duty cycle is used to control the cooling rate and suppress brittle phase formation. Conversely, a thinner welding thickness results in a reduced laser spot diameter, appropriately increasing energy density for rapid fusion, and a specific pulse duty cycle is used to control the cooling rate.

[0080] Figure 8The diagram illustrates the steps of a method for performing secondary welding based on weld uniformity. In one embodiment, as shown... Figure 8 As shown, after step 170, the laser welding control method for metal parts further includes: Step 171: Obtain weld image data.

[0081] Step 172: Identify weld uniformity based on weld image data.

[0082] Step 173: If the weld uniformity meets the preset uniformity, then the welding qualification information is generated.

[0083] Step 174: If the weld uniformity does not meet the preset uniformity, then the dissimilar metal welding position is re-welded based on the third working condition; the spot diameter corresponding to the third working condition is greater than the spot diameter corresponding to the second working condition.

[0084] Figure 9 The diagram illustrates the steps involved in identifying weld uniformity. Specifically, as shown... Figure 9 As shown, step 172 includes: Step 1721: Based on the weld image data, identify the average width, maximum width, and minimum width of the weld.

[0085] Step 1722: Calculate the width difference between the maximum width and the minimum width.

[0086] Step 1723: Calculate the measured ratio of the width difference to the average width.

[0087] The phrase "if the weld uniformity does not meet the preset uniformity" in step 174 includes: Step 1741: If the measured ratio is greater than the preset ratio, it is determined that the weld uniformity does not meet the preset uniformity.

[0088] In this embodiment, a post-weld visual closed-loop feedback and adaptive repair mechanism is constructed. The system extracts the maximum, minimum, and average width of the weld seam using image algorithms, and calculates the ratio of the width difference to the average width as a quantitative indicator of weld seam uniformity. Once this ratio exceeds the standard (i.e., the weld seam width is inconsistent), the system immediately triggers a third working condition for secondary welding. Its core repair logic lies in increasing the beam diameter: using a wider beam to cover the original weld seam and heat-affected zone, and by reducing energy density and expanding the heating range, remelting and filling local unfused or excessively narrow areas, and utilizing the surface tension effect of liquid metal to improve the forming. This achieves closed-loop control for weld seam quality optimization, transforming quality inspection from post-inspection rejection to online repair, automatically intercepting and correcting uneven weld seams, and significantly improving the first-pass yield of the final product. For dissimilar metals, a large beam is used for secondary welding, avoiding the secondary burn-through or exacerbation of brittle phases that may be caused by the high energy density of a small beam. The weld seam is smoothed by a "gentle remelting" method, which not only solves the problem of poor forming but also protects the properties of the base material. Uniformity is objectively determined by comparing the measured ratio with the preset ratio, replacing the subjectivity of manual visual inspection and ensuring the consistency and traceability of quality judgment standards.

[0089] Figure 10 The diagram illustrates the steps of parameter adjustment during multiple secondary welding operations. In one embodiment, as shown... Figure 10 As shown, the laser welding control method for metal parts also includes: Step 180: Each time a secondary welding operation is performed on the dissimilar metal welding position based on the third working condition, the spot diameter corresponding to the third working condition is increased by a preset step size.

[0090] In this embodiment, based on gradient spot diameter adjustment, the system does not use fixed parameters during secondary welding (repair welding), but rather adopts an incremental strategy: the spot diameter is linearly increased by a preset step size with each repair welding operation. According to the laser power density formula (energy density = power / spot area), with constant power, increasing the spot area results in a decrease in energy density. This design aims to gradually reduce the thermal shock to the workpiece as the number of repair welding operations increases, gradually transitioning the action mode from "local remelting" to "large-area heat release," and using a wide spot to homogenize the temperature field. The gradient increase in spot diameter according to the number of secondary welding operations can prevent burn-through and damage caused by multiple repair welding operations: for ultra-thin dissimilar metals, repeated use of small spot high-energy-density repair welding can easily cause cumulative overheating and burn-through. Gradually expanding the spot size can effectively disperse heat input, ensuring that even after multiple repairs, the heat received per unit area remains within a safe threshold, protecting the base material from damage. As the laser spot size increases, the fluidity of the molten pool improves and the cooling rate slows down. This allows the liquid metal to automatically fill pits and eliminate undercut under the influence of surface tension, resulting in a smoother and more rounded weld transition and significantly improved appearance quality. The low-energy-density, wide-spot heating reduces the peak intensity during high-temperature dwell time, preventing excessive thickening of brittle intermetallic compounds (IMCs) at the dissimilar metal interface due to repeated high-temperature thermal cycling. This ensures the mechanical properties and toughness of the repaired joint.

[0091] Figure 11 The diagram shows a schematic of the system structure of a laser welding control system for metal parts. Secondly, this application provides a laser welding control system for metal parts, in one embodiment, as shown... Figure 11 As shown, the system includes: a pre-configuration module 1101, a movement control module 1102, and a welding control module 1103.

[0092] The pre-configuration module 1101 is configured to: acquire information about the workpiece to be welded; fix the workpiece to be welded on the welding station; and obtain the same metal welding position, dissimilar metal welding position, and welding thickness of the dissimilar metal welding position of the current workpiece to be welded based on the information about the workpiece to be welded.

[0093] The motion control module 1102 is communicatively connected to the pre-configuration module 1101. The motion control module 1102 is configured to control the laser to move to the position of the workpiece to be welded corresponding to the current welding sequence.

[0094] The welding control module 1103 is communicatively connected to the pre-configuration module 1101. The welding control module 1103 is configured to: control the laser to switch to the first working condition to weld the same type of metal at the welding position; control the laser to switch to the second working condition corresponding to the welding thickness; and weld the dissimilar metal welding position based on the second working condition.

[0095] In this embodiment, intelligent differential control is achieved for welding of similar and dissimilar metals. This method automatically acquires workpiece information and accurately identifies the weld type. For similar metals with uniform thermal conductivity, a common first working condition is used for rapid welding. However, for dissimilar metals, the large difference in thermal conductivity leads to unstable molten pools, easy burn-through, or the formation of brittle phases. A second working condition, including power, spot size, and pulse parameters, is dynamically matched based on the weld thickness. Through classification and identification, and execution of different working conditions, the heat input and loss on both sides of the weld are effectively balanced, avoiding burn-through in thin plates and ensuring penetration in thick plates, significantly suppressing the formation of brittle intermetallic compounds. This method significantly improves the welding yield, joint strength, and production automation level of dissimilar metal components, solving the technical bottleneck of traditional single-parameter methods being unable to adapt to complex material combinations.

[0096] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0097] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0098] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0099] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features of the invention herein.

[0100] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications or equivalent substitutions made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for controlling laser welding of metal parts, characterized in that, include: Obtain information about the parts to be welded; Fix the workpiece to be welded at the welding station; Control the laser to move to the position of the workpiece to be welded corresponding to the current welding sequence; Based on the information of the workpiece to be welded, the same metal welding position, dissimilar metal welding position, and welding thickness of the dissimilar metal welding position of the workpiece to be welded are obtained. The laser is controlled to switch to the first working condition to perform welding on the same type of metal welding position; Control the laser to switch to the second working condition corresponding to the welding thickness; Welding is performed on the dissimilar metal welding position based on the second working condition.

2. The method for controlling laser welding of metal parts according to claim 1, characterized in that, After controlling the laser to switch to the second operating condition corresponding to the welding thickness, the method further includes: Generate secondary confirmation information; Collect information on the first and second surfaces on both sides of the joint at the dissimilar metal welding site; By comparing the first surface information and the second surface information, a comprehensive judgment value is obtained; If the comprehensive judgment value is greater than the preset threshold, then the welding of the dissimilar metal welding position based on the second working condition is performed.

3. The laser welding control method for metal parts according to claim 2, characterized in that, The acquisition of the first surface information and the second surface information on both sides of the joint at the dissimilar metal welding site includes: Surface image data and magnetic field strength data of the dissimilar metal welding site were collected respectively; Weld joints are identified based on the surface image data; The surface image data is separated into a first surface and a second surface on both sides of the weld joint to obtain corresponding first image information and second image information, respectively. The magnetic field strength data is separated into a first magnetic field and a second magnetic field on both sides of the welded joint, and the corresponding first magnetic field information and second magnetic field information are obtained respectively.

4. The laser welding control method for metal parts according to claim 3, characterized in that, The comparison of the first surface information and the second surface information to obtain the comprehensive judgment value includes: By comparing the first image information and the second image information, the reflectance difference value is obtained; By comparing the first magnetic field information and the second magnetic field information, the magnetic field difference value is obtained; Based on preset visual weight coefficients and magnetic weight coefficients, calculate the first product of the visual weight coefficient and the reflectivity difference value, and the second product of the magnetic weight coefficient and the magnetic field difference value; The sum of the first product and the second product is calculated to obtain the comprehensive judgment value.

5. The laser welding control method for metal parts according to claim 1, characterized in that, The step of obtaining the same-metal welding position, dissimilar-metal welding position, and welding thickness of the dissimilar-metal welding position based on the information of the workpiece to be welded includes: Based on the information of the workpiece to be welded, the first metal information and the second metal information of the dissimilar metal welding position are obtained; Based on the first metal information and the second metal information, the corresponding first thickness and second thickness are obtained respectively; Obtain the tolerance gap; The welding thickness is obtained based on the first thickness, the second thickness, and the tolerance gap.

6. The laser welding control method for metal parts according to claim 5, characterized in that, The step of controlling the laser to switch to the second working condition corresponding to the welding thickness includes: The laser power, spot diameter, and pulse data corresponding to the laser are obtained based on the welding thickness; the pulse data includes pulse frequency, pulse width, pulse energy, duty cycle, and peak power.

7. The laser welding control method for metal parts according to claim 1, characterized in that, After welding the dissimilar metal welding position based on the second working condition, the method further includes: Acquire weld seam image data; Weld uniformity is identified based on the weld image data; If the weld uniformity meets the preset uniformity, then welding qualification information is generated; If the weld uniformity does not meet the preset uniformity, the dissimilar metal welding position is re-welded based on the third working condition; the spot diameter corresponding to the third working condition is greater than the spot diameter corresponding to the second working condition.

8. The laser welding control method for metal parts according to claim 7, characterized in that, The step of identifying weld uniformity based on the weld image data includes: Based on the weld seam image data, the average width, maximum width, and minimum width of the weld seam are identified. Calculate the width difference between the maximum width and the minimum width; Calculate the ratio of the width difference to the measured average width; The condition that the weld uniformity does not conform to the preset uniformity includes: If the measured ratio is greater than the preset ratio, it is determined that the weld uniformity does not meet the preset uniformity.

9. The laser welding control method for metal parts according to claim 7, characterized in that, Also includes: Each time the second welding of the dissimilar metal welding position is performed based on the third working condition, the spot diameter corresponding to the third working condition is increased by a preset step size.

10. A laser welding control system for metal parts, characterized in that, include: The pre-configuration module is configured to: acquire information about the workpiece to be welded; fix the workpiece to be welded on the welding station; and obtain the same-metal welding position, dissimilar-metal welding position, and welding thickness of the dissimilar-metal welding position of the workpiece to be welded based on the information about the workpiece to be welded. A mobile control module is communicatively connected to the pre-configuration module. The mobile control module is configured to control the laser to move to the position of the workpiece to be welded corresponding to the current welding sequence. A welding control module is communicatively connected to the pre-configured module. The welding control module is configured to: control the laser to switch to a first working condition to weld the same type of metal at the welding position; control the laser to switch to a second working condition corresponding to the welding thickness; and weld the dissimilar metal at the welding position based on the second working condition.

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