Intelligent metal plate rack welding method, system and equipment based on numerical control machine tool

Abstract

The invention relates to the technical field of numerical control machine tool welding, and provides a metal plate rack intelligent welding method, system and equipment based on a numerical control machine tool. The method comprises the steps that the thermal deformation amount is obtained according to the thermal expansion coefficient, the welding temperature rise and the metal plate thickness; the structural rigidity of the bending area is obtained according to the Young modulus and the sectional inertia moment, and thermal inertia is obtained according to the thermal conductivity, the density and the specific heat capacity; according to the thermal deformation, the structural rigidity of the bending area and the thermal inertia, the local deformation of metal plate welding is obtained; and an initial welding path is planned, the initial welding path is optimized according to the local deformation amount, and a final welding path is obtained. According to the method, the structural characteristics that a sheet of the sheet metal rack is prone to deformation, multiple in bending and stress concentration in a bending area are considered, a CNC system can be guided to reduce the path compensation amount in the bending area and increase compensation in a plane area, welding stress concentration caused by excessive compensation is avoided, the welding precision and the welding quality are improved, and the production efficiency is improved. And the assembling precision of the sheet metal rack is further improved.

Description

Technical Field

[0001] This invention relates to the field of CNC machine tool welding technology, and more specifically, to an intelligent welding method, system and equipment for sheet metal frames based on CNC machine tools. Background Technology

[0002] Sheet metal frames, as the name suggests, are frames manufactured using sheet metal processing techniques. Sheet metal processing is a comprehensive technology for processing thin metal sheets, including shearing, stamping, bending, welding, and other methods. Through these processes, thin metal sheets can be processed into parts of various shapes and sizes, and then these parts are assembled into a complete frame structure. Computer Numerical Control Machines (CNC machine tools) are precision mechanical devices that achieve automated processing through computer program control. Their core is to replace traditional manual operations with "digital instructions," accurately completing processing tasks such as cutting, milling, drilling, and grinding of materials such as metals and plastics. Welding sheet metal frames using CNC machine tools can improve the efficiency and quality of sheet metal frame welding, overcoming the shortcomings of low precision and slow efficiency in traditional machining.

[0003] When performing automated laser welding on sheet metal frames using CNC machine tools, the welding path is typically planned first, and then the welding operation is completed according to the path. However, sheet metal frames are characterized by thin plates, numerous bends, complex welds, susceptibility to deformation, and stress concentration in the bending areas. Some existing automated laser welding technologies for sheet metal frames using CNC machine tools plan their paths based on the assumption that the sheet metal frame deforms uniformly. This ignores the structural characteristics of the sheet metal frame, reduces welding accuracy and quality, and can easily lead to a decrease in assembly precision, thus requiring optimization. Summary of the Invention

[0004] Based on this, in order to improve the welding accuracy and quality of sheet metal frames, the present invention provides an intelligent welding method, system and equipment for sheet metal frames based on CNC machine tools, the specific technical solution of which is as follows: A method for intelligent welding of sheet metal frames based on CNC machine tools includes the following steps: Obtain the coefficient of thermal expansion, welding temperature rise, and sheet metal thickness of the sheet metal material, and obtain the amount of thermal deformation based on the coefficient of thermal expansion, welding temperature rise, and sheet metal thickness; The Young's modulus, moment of inertia, thermal conductivity, density, and specific heat capacity of the sheet metal material are obtained. The structural stiffness of the bending zone is obtained based on the Young's modulus and moment of inertia, and the thermal inertia is obtained based on the thermal conductivity, density, and specific heat capacity. The local deformation of sheet metal welding is obtained based on the amount of thermal deformation, the structural stiffness of the bending area, and thermal inertia. Plan the initial welding path, optimize the initial welding path based on the local deformation, and obtain the final welding path.

[0005] The intelligent welding method for sheet metal frames based on CNC machine tools obtains thermal deformation, structural stiffness of the bending zone, and thermal inertia. Based on these parameters, it obtains the local deformation of the sheet metal during welding and optimizes the initial welding path accordingly. This method takes into account the structural characteristics of sheet metal frames, such as easy deformation of thin plates, multiple bends, and stress concentration in the bending zone. It guides the CNC system to reduce path compensation in the bending zone and increase compensation in the planar zone, avoiding stress concentration caused by over-compensation. This improves welding accuracy and quality, thereby enhancing the assembly precision of the sheet metal frame.

[0006] Preferably, the specific method for obtaining the local deformation amount of sheet metal welding includes: The stiffness-heat capacity term is obtained based on the structural stiffness and thermal inertia of the bending zone to characterize the dynamic competition between the structural stiffness and thermal inertia of the bending zone. By coupling the stiffness heat fusion term and the thermal deformation, the local deformation of sheet metal welding can be predicted.

[0007] Preferably, the specific method for optimizing the initial welding path based on the local deformation includes: Obtain the normal vector and process coefficient of the local region; The welding path offset of the local area is obtained based on the local deformation, normal vector, and process coefficient. The initial welding path is optimized based on the welding path offset.

[0008] Preferably, the intelligent welding method for sheet metal frames further includes: Obtain real-time relative humidity and real-time ambient temperature; Obtain humidity sensitivity and temperature sensitivity; Obtain the reference laser power, and correct the reference laser power based on the real-time relative humidity, real-time ambient temperature, humidity sensitivity coefficient, and temperature sensitivity coefficient to obtain the corrected laser power.

[0009] Preferably, the specific method for correcting the reference laser power includes: Obtain the popularity data of at least one keyword related to welding defects in sheet metal frames, and obtain a comprehensive popularity index based on the keyword popularity data; The reference laser power is corrected based on the comprehensive thermal index, real-time relative humidity, real-time ambient temperature, humidity sensitivity coefficient, and temperature sensitivity coefficient.

[0010] Preferably, the specific method for correcting the reference laser power based on the comprehensive thermal index, real-time ambient relative humidity, real-time ambient temperature, humidity sensitivity coefficient, and temperature sensitivity coefficient includes: The humidity sensitivity coefficient and temperature sensitivity coefficient are dynamically adjusted based on the comprehensive heat index. The reference laser power is corrected based on real-time relative humidity, real-time ambient temperature, dynamically adjusted humidity sensitivity coefficient, and temperature sensitivity coefficient.

[0011] A CNC machine tool-based intelligent welding system for sheet metal frames, used to implement the aforementioned intelligent welding method for sheet metal frames, includes: The material parameter acquisition module is used to acquire the thermal expansion coefficient, welding temperature rise, sheet metal thickness, Young's modulus, moment of inertia, thermal conductivity, density, and specific heat capacity of sheet metal materials. The thermal deformation acquisition module is used to obtain the amount of thermal deformation based on the coefficient of thermal expansion, welding temperature rise, and sheet metal thickness. The structural stiffness acquisition module is used to obtain the structural stiffness of the bending zone based on Young's modulus and the moment of inertia of the cross section. The thermal inertia acquisition module is used to obtain thermal inertia based on thermal conductivity, density, and specific heat capacity. The deformation acquisition module is used to acquire the local deformation of sheet metal welding based on thermal deformation, structural stiffness of the bending area, and thermal inertia. The welding path optimization module is used to plan the initial welding path, optimize the initial welding path based on the local deformation, and obtain the final welding path.

[0012] Preferably, the deformation acquisition module includes: The stiffness-heat capacity term acquisition unit is used to acquire the stiffness-heat capacity term, which characterizes the dynamic competition between the stiffness and thermal inertia of the bending zone structure, based on the structural stiffness and thermal inertia of the bending zone structure. The local deformation acquisition unit is used to couple the stiffness heat fusion term and the thermal deformation to predict the local deformation of sheet metal welding.

[0013] Preferably, according to the formula Obtaining local deformation ; in, These represent the coefficient of thermal expansion, welding temperature rise, and sheet metal thickness, respectively. These represent Young's modulus, moment of inertia, thermal conductivity, density, and specific heat capacity, respectively. Represents the coupling coefficient. This represents the natural exponential function. These represent the thermal deformation and stiffness heat transfer terms, respectively. These represent the structural stiffness and thermal inertia of the bending zone, respectively.

[0014] A smart welding device for sheet metal frames based on CNC machine tools, comprising: Controller; Memory, which stores executable instructions; The executable instructions can run on the controller to implement the intelligent welding method for sheet metal frames. Attached Figure Description

[0015] The invention will be further understood from the following description taken in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but rather the emphasis is on illustrating the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0016] Figure 1 This is a schematic diagram of the overall process of an intelligent welding method for sheet metal frames based on CNC machine tools in one embodiment of the present invention; Figure 2 This is a flowchart illustrating a specific method for obtaining the local deformation of sheet metal welding in one embodiment of the present invention. Figure 3 This is a flowchart illustrating a specific method for optimizing the initial welding path based on local deformation in one embodiment of the present invention. Figure 4 This is a schematic diagram of the overall process of an intelligent welding method for sheet metal frames based on CNC machine tools in another embodiment of the present invention; Figure 5 This is a flowchart illustrating a specific method for correcting a reference laser power in one embodiment of the present invention. Figure 6 This is a flowchart illustrating a specific method for correcting a reference laser power in one embodiment of the present invention. Figure 7 This is a schematic diagram of the overall functional structure of an intelligent welding system for sheet metal frames based on CNC machine tools, according to one embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to its embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the scope of protection of the invention.

[0018] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly attached to the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0020] In this invention, "first" and "second" do not represent a specific quantity or order, but are merely used to distinguish names.

[0021] The design and manufacture of sheet metal racks is a highly precise and complex process. Designers need to rationally plan the rack's structure, dimensions, and load-bearing capacity based on the equipment's requirements. During the design process, not only the rack's stability and durability must be considered, but also its lightweight design, heat dissipation, and electromagnetic shielding. For example, in communication equipment, the rack needs excellent electromagnetic shielding performance to prevent signal interference; while in industrial automation equipment, the rack needs to withstand significant weight and vibration to ensure stable operation.

[0022] The choice of materials for manufacturing sheet metal frames is equally crucial. Commonly used materials include cold-rolled steel sheets, stainless steel sheets, and aluminum alloy sheets. Different materials have different physical and chemical properties, making them suitable for different environments and needs. For example, cold-rolled steel sheets have high strength and hardness, making them suitable for frames that bear heavy loads; while stainless steel sheets have excellent corrosion resistance, making them suitable for humid or highly corrosive environments.

[0023] During manufacturing, sheet metal frames undergo multiple processing steps. First, thin metal sheets are cut into the required shapes and sizes using shearing or stamping processes. Next, the flat metal sheets are bent into three-dimensional shapes using bending processes. Then, the various components are connected together using processes such as welding or riveting to form a complete frame structure. Finally, surface treatments, such as spraying and electroplating, are required to improve the frame's corrosion resistance and aesthetics.

[0024] like Figure 1 As shown, an embodiment of the present invention provides an intelligent welding method for sheet metal frames based on CNC machine tools, comprising the following steps: S1: Obtain the coefficient of thermal expansion, welding temperature rise, and sheet metal thickness of the sheet metal material; obtain the amount of thermal deformation based on the coefficient of thermal expansion, welding temperature rise, and sheet metal thickness.

[0025] Specifically, the coefficient of thermal expansion (CTE) characterizes the degree of thermal expansion of a material. It can be understood as a quantitative indicator of the change in length or volume of a material when heated, measured in degrees Celsius (°C). The CTE of stainless steel is mainly affected by its crystal structure, alloy composition, and temperature. Austenitic stainless steels have a higher CTE due to their face-centered cubic structure, while ferritic and martensitic stainless steels have a lower CTE due to their body-centered cubic structure. The CTE of stainless steel varies depending on the specific type and temperature range. Common austenitic stainless steels (such as 304 and 316) have a linear expansion coefficient of approximately 17.3–19.0 ​​× 10⁻⁶ in the range of 20–100°C. -6 / ℃. The coefficient of thermal expansion of carbon steel (carbon content 0.008%-2.11%) varies depending on the type: low-carbon steel is 11.7×10. -6 / ℃ (increases slightly with increasing temperature), for medium carbon steel it is 12.1×110 -6 / ℃ (highly temperature sensitive), while for high carbon steel it is 10.8×10 -6 / ℃ (good stability).

[0026] Welding temperature rise refers to the increase in temperature of the weld area relative to the ambient temperature, typically between 800℃ and 1500℃, which can be obtained by real-time monitoring of the molten pool temperature using an infrared thermal imager. Sheet metal thickness refers to the actual thickness of the sheet metal, such as 0.5mm-3mm for thin plates and 3mm-6mm for medium-thick plates.

[0027] S2, obtain the Young's modulus, moment of inertia, thermal conductivity, density and specific heat capacity of the sheet metal material, obtain the structural stiffness of the bending zone based on the Young's modulus and moment of inertia, and obtain the thermal inertia based on the thermal conductivity, density and specific heat capacity.

[0028] Young's modulus, also known as the elastic modulus, is the ability of a solid material to resist tensile or compressive deformation; its value reflects the material's rigidity. The formula for calculating Young's modulus is: E = (F * I) 2 ΔL / (A*AL), where F is the applied force, I is the material length, A is the cross-sectional area of ​​the material, and ΔL is the length of the material's elongation or compression. The Young's modulus of 304 stainless steel is typically between 190 GPa and 210 GPa, and this value can be used as a measure of material stiffness. However, the specific Young's modulus is affected by factors such as material thickness and temperature.

[0029] The moment of inertia of a cross-section is a key parameter in the bending zone, reflecting the cross-section's resistance to bending deformation. It is the integral of the product of the area of ​​each infinitesimal element of the cross-section and the square of the distance from each infinitesimal element to a specified axis on the cross-section. Generally, the moment of inertia of a cross-section = bending width × cube of cross-section thickness / 12. Thermal conductivity is the material's ability to conduct heat. For example, aluminum has a thermal conductivity of 237 W / (m·K), while steel's thermal conductivity is typically between 15-50 W / (m·K), depending on the type and composition of the steel. For instance, carbon steel has a thermal conductivity of approximately 45 W / (m·K), while stainless steel is lower, around 15-20 W / (m·K). Density is the mass density of a material, while specific heat capacity is the amount of heat required to raise the temperature of a unit mass of material by 1°C. For example, the specific heat capacity of steel is approximately 502 J / (kg·K).

[0030] Due to the characteristics of sheet metal bending, the moment of inertia of the cross-section increases significantly at the bending angle, typically several times that of a flat plate. The structural stiffness of the bending zone is used to quantify the deformation resistance advantage of the bending zone.

[0031] S3, the local deformation of sheet metal welding is obtained based on thermal deformation, structural stiffness of the bending zone, and thermal inertia.

[0032] As a preferred technical solution, such as Figure 2 As shown, the specific methods for obtaining the local deformation amount of sheet metal welding include: S31, obtain the stiffness-heat capacity term based on the structural stiffness and thermal inertia of the bending zone to characterize the dynamic competition between the structural stiffness and thermal inertia of the bending zone.

[0033] S32 couples the stiffness heat fusion term and the thermal deformation amount to predict the local deformation amount of sheet metal welding.

[0034] For example, local deformation amount .in, These represent the coefficient of thermal expansion, welding temperature rise, and sheet metal thickness, respectively. These represent Young's modulus, moment of inertia, thermal conductivity, density, and specific heat capacity, respectively. Represents the coupling coefficient. This represents the natural exponential function. These represent the thermal deformation and stiffness heat transfer terms, respectively. These represent the structural stiffness and thermal inertia of the bending zone, respectively.

[0035] when When the value is greater than 5, the local deformation tends to zero, requiring prevention of residual stress exceeding the limit and issuing a critical warning; when At this time, plastic deformation may occur, which can reduce heat input and issue corresponding critical warnings.

[0036] The coupling coefficient can be set empirically or calibrated experimentally, generally between 0.65 and 0.85. Its correlation stiffness and the strength of the interaction between heat capacity are important parameters, such as those found in SUS304 stainless steel. 6061 aluminum alloy Specifically, welding experiments of specimens with different thicknesses can be obtained in more than three groups. Based on the measured values ​​of the corresponding local deformation and the corresponding thermal deformation and stiffness-thermal melting parameters, the average value of the coupling coefficient can be calculated, and the final coupling coefficient value can be calibrated.

[0037] Here, the stiffness-thermal melting term represents the dynamic competition between structural stiffness and thermal inertia in the bending region through an exponentially decaying function. In high-stiffness regions (such as bends), the structural stiffness increases, the stiffness-thermal melting term approaches 0, and the local deformation δ decreases to suppress deformation; in high-heat-capacity regions (such as large planes), thermal inertia increases, the stiffness-thermal melting term approaches 1, and the local deformation δ approaches the traditional predicted value, i.e., thermal deformation.

[0038] S4. Plan the initial welding path, optimize the initial welding path based on the local deformation, and obtain the final welding path.

[0039] Specifically, the method for optimizing the initial welding path based on local deformation includes: first, obtaining material parameters such as coefficient of thermal expansion, Young's modulus, density, specific heat capacity, and thermal conductivity; measuring the sheet metal thickness and bending geometry; calculating the moment of inertia of the cross section; then, monitoring and collecting the welding temperature rise in real time; calculating the local deformation; and determining whether the local deformation exceeds the deformation threshold. If so, the initial welding path is compensated and corrected based on the local deformation to obtain the final welding path; otherwise, welding continues.

[0040] Of course, a mapping relationship between local deformation and path compensation offset can also be constructed, and the initial welding path can be dynamically corrected based on the mapping relationship to obtain the final welding path.

[0041] Generally speaking, the larger the value of the local deformation, the more severe the deformation at that point, and the greater the compensation required. The planning of the initial welding path is a conventional technique in this field and will not be elaborated upon here.

[0042] Because the local deformation takes into account the structural characteristics of sheet metal frame thin plates that are easy to deform, have many bends, and have stress concentration in the bending area, and couples the stiffness heat melting term and thermal deformation amount, it deeply integrates the geometric features of the sheet metal structure (such as the moment of inertia of the section) with the thermophysical properties (such as thermal conductivity, density and specific heat capacity). It can guide the CNC system to reduce the path compensation amount in the bending area and increase the compensation in the planar area, so as to avoid welding stress concentration caused by excessive compensation.

[0043] In summary, the intelligent welding method for sheet metal frames based on CNC machine tools obtains thermal deformation, bending zone structural stiffness, and thermal inertia. Based on these parameters, it obtains the local deformation of sheet metal during welding and optimizes the initial welding path accordingly. This method takes into account the structural characteristics of sheet metal frames, such as easy deformation of thin plates, numerous bends, and stress concentration in bending zones. It guides the CNC system to reduce path compensation in bending zones and increase compensation in planar zones, avoiding stress concentration caused by over-compensation. This improves welding accuracy and quality, thereby enhancing the assembly precision of the sheet metal frame.

[0044] As a preferred technical solution, in step S4, such as Figure 3 As shown, specific methods for optimizing the initial welding path based on local deformation include: S41, Obtain the normal vector of the local region. and process coefficient .

[0045] The normal vector is the unit vector along the deformation direction of the workpiece surface, used to ensure compensation reacts along the deformation direction. The process coefficient is an empirical parameter, serving as a correction factor for the compensation effect, offsetting material nonlinearity effects and equipment mechanical errors; it is generally between 0.8 and 1.2, with a typical value of 1.05. The process coefficient can be calculated using the formula... Obtain.

[0046] S42, obtain the welding path offset of the local area based on the local deformation, normal vector and process coefficient.

[0047] S43, optimize the initial welding path based on the welding path offset.

[0048] For example, welding path offset .in, The coordinates are two-dimensional, reflecting the spatial non-uniformity of deformation. The welding path offset is represented by the product of local deformation, normal vector, and process coefficient, which can achieve a linear mapping between deformation and compensation.

[0049] If the normal vector of a point in the flat plate region is (0,0,1), the process coefficient is 1.05, and the local deformation is 0.82, then the path offset is... That is, the welding torch moves down 0.861mm in the Z-axis direction.

[0050] If the direction vector of the bending area point is (-0.25, 0.12, 0.96), the process coefficient is 1.05, and the local deformation is 0.82, then the path offset is... The welding torch is offset in three directions: X, Y, and Z. Specifically, it moves 0.215 mm in the positive direction of the X-axis, 0.103 mm in the negative direction of the Y-axis, and 0.827 mm in the negative direction of the Z-axis. The main compensation is towards the inside of the bend to avoid stress concentration caused by overcompensation.

[0051] In this embodiment, the welding path offset of the local area is first obtained based on the local deformation, normal vector, and process coefficient. Then, the initial welding path is optimized based on the welding path offset. This method integrates materials science model (δ) and geometric topology analysis. Obtaining executable control instructions from the process database (η) helps improve the accuracy of welding path deviation compensation.

[0052] The compensated welding path can be smoothed. When the offset difference between adjacent points is greater than 0.1mm, a transition point is inserted. Continuous CNC machine tool G-code is generated using cubic spline curves.

[0053] For welds longer than two meters, the process coefficient can be assigned in segments to eliminate cumulative system errors.

[0054] Thin plates are prone to high-frequency vibrations when heated, causing fluctuations in the distance between the welding torch and the workpiece, which increases local penetration depth. If the welding path offset of thin plates is not compensated for, the thin plates may overheat due to high-frequency vibrations.

[0055] To suppress overheating caused by vibration, micro-compensation can be performed on ultra-thin plates with a diameter of less than 0.5 mmd. The offset of the welding path after micro-compensation is [not specified]. Where d represents the actual thickness of the sheet metal frame workpiece, ranging from 0.1mm to 0.5mm, which can be measured using a laser thickness gauge. (Exponential decay term) Used to map thickness to standard dimensions, 0.2 mm is the critical thickness. When the thickness d = 0.2 mm, the exponent term = 1, and the attenuation is strongest.

[0056] The squaring operation enhances the attenuation sensitivity of the thin region, avoiding excessive attenuation when d > 0.3 mm, achieving maximum attenuation at d = 0.2 mm, and smoothly transitioning to both sides. The attenuation coefficient of 0.25 represents the maximum reduction ratio of the theoretical compensation, calibrated experimentally by vibration amplitude. Specifically, the vibration amplitude can be measured using a laser interferometer. With melting depth increment Calculate the attenuation coefficient. The final attenuation coefficient = maximum penetration increment / 0.6 times the theoretical compensation amount of the welding path. The theoretical compensation amount of the welding path is calculated according to the function... calculate.

[0057] More specifically, the penetration depth increment is the deviation between the actual penetration depth and the theoretical value. It is caused by abnormal energy concentration in the molten pool due to vibration. It can be calculated by metallographically inspecting the weld and measuring the penetration depth using a microscope, then subtracting it from the target penetration depth. For example, for 0.2mm thick 304 stainless steel, the penetration depth increment is approximately 0.15mm. The theoretical compensation for ultra-thin plates is generally between 0.05mm and 0.3mm. The parameter 0.6 can be understood as the vibration energy-penetration depth conversion efficiency factor, set empirically or calibrated experimentally.

[0058] The specific method for experimentally calibrating the parameter vibration energy-melt depth conversion efficiency factor includes: fixing the vibration amplitude and welding parameters, obtaining different theoretical compensation amounts and corresponding maximum melt depth increments through multiple sets of experiments, and obtaining the parameter vibration energy-melt depth conversion efficiency factor after fitting the attenuation coefficient function.

[0059] When the vibration amplitude increases, the laser-workpiece distance oscillates, causing fluctuations in energy density and leading to a greater increase in penetration depth, which in turn makes the area more susceptible to vibration hazards. This is based on the weld path offset after micro-compensation. In this way, the welding torch can be pre-deflected from the vibration-sensitive area, thereby reducing energy fluctuations, reducing the increase in penetration depth, and achieving the compensation and suppression function.

[0060] Vibration amplitude can be monitored using a laser interferometer, while weld penetration depth can be measured by dissecting the weld using a metallographic microscope. A correlation between vibration amplitude and penetration depth increment can be established, and the maximum value can be recorded. The larger the vibration amplitude, the greater the increase in penetration depth. The relationship between vibration amplitude and maximum penetration depth increment can be expressed by the formula... Expression. Among them, This represents the adjustment coefficient, typically 1. .

[0061] Micro-compensation is enabled when the plate thickness is less than 0.5 mm and the welding speed is greater than 20 mm / s. When the resonant frequency is detected... For frequencies above 300Hz, high-frequency compensation can be superimposed to execute a collaborative compensation strategy. The final welding path offset... The amplitude of 0.02 is used to suppress the resonance peak. The phase is indicated and can be obtained in real time from the accelerometer feedback.

[0062] In summary, the micro-compensation function for ultra-thin plate welding described above is designed to suppress the problem of high-frequency vibration and overheating in ultra-thin plate welding. By dynamically reducing the theoretical compensation amount Δr, it avoids the loss of control of the molten pool caused by plate vibration.

[0063] In one embodiment, such as Figure 4 As shown, the intelligent welding method for sheet metal frames also includes: S5, acquires real-time ambient relative humidity (RH) and real-time ambient temperature. .

[0064] S6, Obtain Humidity Sensitivity Coefficient and temperature sensitivity coefficient .

[0065] Both humidity sensitivity coefficient and temperature sensitivity coefficient can be used for experimental calibration or set empirically. The humidity sensitivity coefficient reflects the material's sensitivity to changes in humidity, with a typical range of 0.05-0.2. The temperature sensitivity coefficient reflects the material's sensitivity to environmental temperature differences, with a typical range of 0.1-0.5.

[0066] S7, Obtain the reference laser power The reference laser power is corrected based on real-time relative humidity, real-time ambient temperature, humidity sensitivity coefficient, and temperature sensitivity coefficient to obtain the corrected laser power. .

[0067] For example, the corrected laser power The reference laser power is the set power under standard operating conditions. This indicates the reference relative humidity, which is the standard operating humidity and is usually set to 50%. The ambient temperature difference is the difference between the real-time ambient temperature and the standard operating temperature (usually set to 25℃). exp() is the natural exponential function used to describe the nonlinear effect of the ambient temperature difference on power.

[0068] Specifically This is a linear compensation term. When... When humidity increases, the laser power should be appropriately increased to compensate for the scattering loss of the laser by water vapor; when When humidity decreases, the laser power can be appropriately reduced to avoid excessive energy leading to burn-through.

[0069] This is an exponentially decaying term. When... This means that as the environment heats up, the laser power can be reduced exponentially to compensate for the heat accumulation effect of materials under hot conditions; when When the environment cools down, the laser power can be increased exponentially to compensate for the energy loss caused by heat dissipation.

[0070] The linear compensation term and the exponential decay term are coupled to achieve a synergistic control effect. Humidity and temperature compensation act independently, but together affect the final power. In high-humidity and low-temperature environments, if both humidity and temperature increase simultaneously, the laser power will increase significantly; in low-humidity and high-temperature environments, if both humidity and temperature decrease simultaneously, the laser power will decrease significantly.

[0071] For the humidity sensitivity coefficient, under the condition of fixed ambient temperature as standard operating temperature, the humidity RH is changed, the laser power required to maintain the same melting depth is measured, and then the value is calculated back based on the known parameters. For the temperature sensitivity coefficient, under the condition of fixed humidity RH as standard operating humidity, the ambient temperature is changed, the laser power required to maintain the same melting depth is measured, and then the value is calculated back based on the known parameters.

[0072] For example, for 304 stainless steel, the humidity sensitivity coefficient and temperature sensitivity coefficient can be set to 0.08 and 0.25 respectively; for aluminum alloy 6061, the humidity sensitivity coefficient and temperature sensitivity coefficient can be set to 0.15 and 0.40 respectively; for galvanized steel sheet, the humidity sensitivity coefficient and temperature sensitivity coefficient can be set to 0.12 and 0.18 respectively.

[0073] In low-temperature environments where heat dissipation is too rapid and in humid environments where water vapor scatters laser light, the problems of laser scattering loss and thermal instability can be solved by using the laser power correction function mentioned above and through the synergistic design of linear humidity compensation and exponential temperature compensation.

[0074] As a preferred technical solution, such as Figure 5 As shown, the specific methods for correcting the reference laser power include: S71, obtain the popularity data of at least one keyword related to welding defects of sheet metal frame, and obtain the comprehensive popularity index based on the keyword popularity data.

[0075] S72 corrects the reference laser power based on the comprehensive thermal index, real-time ambient relative humidity, real-time ambient temperature, humidity sensitivity coefficient, and temperature sensitivity coefficient.

[0076] Specifically, the keywords related to welding defects include, but are not limited to, porosity, cracks, and deformation. If the welding defect is porosity, the corresponding keywords include, but are not limited to, sheet metal welding porosity and laser welding porosity; if the welding defect is cracks, the corresponding keywords include, but are not limited to, thin plate welding cracks and heat-affected zone cracks; if the welding defect is deformation, the corresponding keywords include, but are not limited to, frame welding deformation and bending zone warping.

[0077] Keyword popularity data includes, but is not limited to, search index and discussion popularity. Popularity data for keywords related to welding defects can be obtained from online platforms (such as Baidu Index, WeChat Index, industry forums, etc.). For example, select keywords: K1="sheet metal welding porosity", K2="sheet metal welding cracks", K3="welding deformation", etc. The popularity indices of multiple keywords are weighted and fused to obtain a comprehensive popularity index H(t), where t represents time. H(t) can be expressed as: H(t) = w1*H1(t) + w2*H2(t) + w3*H3(t), where w1, w2, and w3 are the weights of each keyword, determined according to the importance of the keywords, for example, through expert scoring or relevance analysis.

[0078] Generally, an increase in the popularity of a certain type of defect may indicate that the defect has occurred more frequently in recent production, possibly related to changes in environmental factors. Therefore, we can consider: When the heat of pores increases, the influence of humidity on welding may increase, requiring enhanced humidity compensation, i.e., increasing γ; when the heat of cracks increases, the influence of temperature changes may be more significant, requiring adjustment of the temperature compensation coefficient λ; when the heat of deformation increases, it may be necessary to adjust both γ and λ simultaneously.

[0079] As a preferred technical solution, such as Figure 6 As shown, the specific methods for correcting the reference laser power based on the comprehensive thermal index, real-time relative humidity, real-time ambient temperature, humidity sensitivity coefficient, and temperature sensitivity coefficient include: S721 dynamically adjusts the humidity sensitivity coefficient and temperature sensitivity coefficient based on the comprehensive heat index.

[0080] S721 corrects the reference laser power based on real-time ambient relative humidity, real-time ambient temperature, dynamically adjusted humidity sensitivity coefficient, and temperature sensitivity coefficient.

[0081] For example, the humidity sensitivity coefficient is dynamically adjusted based on the comprehensive heat index. Humidity sensitivity coefficient dynamically adjusted based on comprehensive heat index .in, The initial calibration value, The popularity of the keyword "stoma". The popularity of the keyword "crack" These are the reference heat values ​​for pore defects and crack defects, respectively. These are the adjustment gains for the sensitivity coefficients of pore heat and crack heat, respectively, which can be adjusted based on experience or different actual scenarios.

[0082] In practical applications, a time window (e.g., every 24 hours) can be set to update the heat index and recalculate the humidity sensitivity coefficient and temperature sensitivity coefficient, which can then be applied to the welding process in the next time period.

[0083] In step S721, the optimized dynamic power correction function can be used as a basis. The reference laser power is corrected. Here... The baseline value for the overall popularity index can be the historical average value for the same period. This represents the overall adjustment coefficient of power based on comprehensive heat, which can be obtained by fitting historical data. This indicates the maximum warning level, which can be set to the industry safety standard or a benchmark value of 2.5 times the comprehensive heat index.

[0084] The following method is given for calibrating the relevant parameters: First, determine them through welding experiments under standard laboratory conditions. Next, the moving average of the corresponding keyword popularity over the past 90 days is obtained, including the popularity of keywords related to porosity and cracking, as well as the baseline value of the comprehensive popularity index. Finally, regression analysis of historical data is used to determine the adjustment gain and overall adjustment coefficient of porosity and cracking popularity on the corresponding sensitivity coefficients. Specifically, welding quality data (such as porosity and crack count) under different popularity indices over a period of time can be collected to establish a regression model between popularity changes and changes in optimal compensation parameters, thereby determining the adjustment gain and overall adjustment coefficient.

[0085] The dynamic power correction function has the following advantages: 1. Dynamic learning of environmental compensation parameters: By monitoring the popularity of defect keywords on the network platform in real time, the system can perceive the changing trend of the main factors causing welding defects in the current environment (such as the general increase in humidity during the rainy season leading to an increase in porosity, which in turn increases the porosity heat, and at this time automatically increases the humidity compensation coefficient γ), thus realizing the adaptive adjustment of compensation parameters.

[0086] 2. Comprehensive popularity index as a global correction: multiplier factor Used for overall power adjustment. When the overall heat index is higher than the benchmark value, it indicates that the overall welding defects are on the rise, and the system automatically increases the welding power to enhance penetration; conversely, it appropriately reduces the power to prevent overheating.

[0087] 3. Independent feedback for multiple defect types: For the two main defect types, porosity and cracks, the humidity sensitivity coefficient and temperature sensitivity coefficient are adjusted independently to make the compensation more targeted.

[0088] 4. Data-driven optimization: By leveraging big data from the network, it breaks through the limitations of traditional methods that rely solely on physical sensors, and achieves the integration of macro-environmental trends and micro-process control.

[0089] In summary, the optimized dynamic power correction function, based on big data analysis of welding defect keywords across the entire network (data sources: Baidu Index, CNKI Academic Trends, and industrial forum crawlers), can construct a defect prediction-power compensation closed-loop system. By combining environmental parameters and real-time public opinion data, it achieves intelligent pre-control of welding quality.

[0090] like Figure 7 As shown, an embodiment of the present invention also provides an intelligent welding system for sheet metal frames based on CNC machine tools, used to implement the intelligent welding method for sheet metal frames, which includes a material parameter acquisition module, a thermal deformation acquisition module, a structural stiffness acquisition module, a thermal inertia acquisition module, a deformation acquisition module, and a welding path optimization module.

[0091] The material parameter acquisition module is used to acquire the thermal expansion coefficient, welding temperature rise, sheet metal thickness, Young's modulus, moment of inertia, thermal conductivity, density, and specific heat capacity of sheet metal materials; the thermal deformation acquisition module is used to acquire the amount of thermal deformation based on the thermal expansion coefficient, welding temperature rise, and sheet metal thickness; the structural stiffness acquisition module is used to acquire the structural stiffness of the bending zone based on Young's modulus and moment of inertia.

[0092] The thermal inertia acquisition module is used to obtain thermal inertia based on thermal conductivity, density, and specific heat capacity; the deformation acquisition module is used to obtain the local deformation of sheet metal welding based on thermal deformation, structural stiffness of the bending zone, and thermal inertia; the welding path optimization module is used to plan the initial welding path, optimize the initial welding path based on the local deformation, and obtain the final welding path.

[0093] The aforementioned intelligent welding system for sheet metal frames also includes a CNC machine tool, which includes laser welding equipment such as a six-axis laser welding machine for automatically welding sheet metal frames placed and fixed on a welding platform. Since the structure of the automated welding equipment for CNC machine tools is a conventional technique in this field, it will not be described in detail here.

[0094] As a preferred technical solution, the deformation acquisition module includes a stiffness heat capacity acquisition unit and a local deformation acquisition unit.

[0095] The stiffness-heat capacity term acquisition unit is used to acquire the stiffness-heat capacity term, which characterizes the dynamic competition between the stiffness and thermal inertia of the bending zone structure, based on the structural stiffness and thermal inertia of the bending zone. The local deformation amount acquisition unit is used to couple the stiffness-heat melting term and the thermal deformation amount to predict the local deformation amount of sheet metal welding.

[0096] For example, according to the formula Obtaining local deformation ;in, These represent the coefficient of thermal expansion, welding temperature rise, and sheet metal thickness, respectively. These represent Young's modulus, moment of inertia, thermal conductivity, density, and specific heat capacity, respectively. Represents the coupling coefficient. This represents the natural exponential function. These represent the thermal deformation and stiffness heat transfer terms, respectively. These represent the structural stiffness and thermal inertia of the bending zone, respectively.

[0097] For high stiffness areas (such as bending / reinforcing rib areas). Therefore, the stiffness-thermal melting term is approximately 0, the local deformation is approximately 0, and the local deformation is effectively suppressed by the stiffness; for low stiffness regions (such as flat / thin-walled regions). Therefore, the stiffness heat melting term is approximately 1, and the local deformation is approximately equal to the amount of thermal deformation, which allows for complete release of thermal deformation.

[0098] Generally speaking, for the flat area of ​​the sheet metal frame, the predicted local deformation is relatively large, and the initial welding path needs to be compensated inward, that is, the compensation amount is increased (the path shrinks inward) to suppress warping; for the bending area of ​​the sheet metal frame, the predicted local deformation is relatively small, and the initial welding path needs to be compensated outward, that is, the compensation amount is reduced (the path is finely adjusted outward) to avoid overcompensation.

[0099] Inward and outward compensation of the laser welding path refers to dynamically adjusting the laser beam's trajectory during the welding process based on the weld shape or material deformation to ensure welding quality. Specifically, "inward compensation" in laser welding refers to the adjustment method of shifting the welding path a certain distance inward towards the workpiece to ensure that the final weld size meets design requirements. This compensation is typically used for thin-plate welding or in scenarios with high precision requirements to counteract the effects of energy distribution and material deformation during laser beam focusing. When the laser beam is focused on the workpiece surface, the power density at the focal point is the highest, which may lead to localized melting or evaporation of the material. If welding is performed directly according to the design dimensions, the actual weld pool edge may exceed expectations, resulting in over-welding or deformation. By shifting the path inward, the weld pool can be contracted to the target size, maintaining the stability of the weld pool and the weld width, preventing insufficient weld width or uneven penetration due to material deformation.

[0100] Outward compensation refers to the system adjusting the laser beam to deflect outward when the weld edge expands outward (such as due to excessive welding speed or energy) to prevent the molten pool from overflowing or the weld height from exceeding the standard, thus ensuring the flatness of the joint (error ≤ 0.2mm) and the structural strength.

[0101] For example, for the flat plate area, assuming the local deformation δ = 0.82 mm, the compensation direction is inward, and the CNC compensation is -0.86 mm. For the bending area, assuming the local deformation δ = 0.26 mm, which is only 31.7% of that of the flat plate area, the compensation direction is outward, and the CNC compensation is +0.04 mm.

[0102] Here, the stiffness hot melt term quantifies the effect of the structural stiffness in the bending zone on the suppression of thermal deformation through an exponential decay function, so as to achieve differentiated welding path compensation.

[0103] The coupling coefficient can be calibrated experimentally. As a preferred technical solution, this invention proposes an adaptive adjustment algorithm. Exemplarily, the coupling coefficient is determined according to the formula... Calculate and adaptively adjust. Among them, These are all empirical constants; generally speaking, Corresponding to the minimum value of the coupling coefficient, It can be set based on the maximum and minimum values ​​of the coupling coefficient, that is, equal to the maximum coupling coefficient minus the minimum coupling coefficient. Assuming the coupling coefficient is between 0.65 and 0.85, then... Set them to 0.65 and 0.2 respectively.

[0104] Specifically, These represent the reference moment of inertia of the flat plate and the moment of inertia of the current section, respectively. The ratio of the moments of inertia to the cross-sections. This is a local stiffness enhancement factor, characterizing the degree of stiffness enhancement relative to the flat plate in the current region. It quantifies the effect of geometric features on stiffness enhancement. For the flat plate region, the ratio of the moment of inertia is approximately 1.0; for the 90° bend region, the ratio is approximately 3.6; and the moment of inertia in the stiffener intersection region is generally greater than 5.0. The tanh hyperbolic tangent function, as a smoothing saturation function, has a range of (-1, 1) and can represent the nonlinear mapping from stiffness ratio to β value. It provides smooth interpolation in the transition region, avoids abrupt changes in β value in the stiffness abrupt change region, and suppresses discontinuities in the compensation path. Parameters Used to control the transition slope, it can be adjusted based on actual experience or different scenarios, generally between 0.5 and 1.5.

[0105] Assuming parameters Let's assume a value of 1.0. For the flat plate region, since the ratio of the moments of inertia of the cross sections is approximately 1.0, the hyperbolic tangent function tanh is approximately 0, and the coupling coefficient is defined as 0.65. For the bending region, since the ratio of the moments of inertia of the cross sections is approximately 3.6, the hyperbolic tangent function tanh is approximately 1, and the coupling coefficient is approximately 0.85. For the transition region, assuming the ratio of the moments of inertia of the cross sections is 2.3, the hyperbolic tangent function tanh(1.3) is approximately 0.86, and the coupling coefficient is approximately 0.82.

[0106] For high-stiffness regions such as bending regions, the coupling coefficient is approximately 0.85, which can amplify the effect of stiffness on deformation suppression and reduce the predicted deformation. For low-stiffness regions such as flat plate regions, the coupling coefficient is approximately 0.65, which can weaken the influence of stiffness and more closely approximate free thermal deformation.

[0107] The globally constant coupling coefficient β often fails to reflect the stiffness gradient. In this embodiment, the coupling coefficient is adjusted by an adaptive adjustment algorithm. The sensitivity of the stiffness suppression effect can be improved by dynamically adjusting the value of β, thereby improving the accuracy of matching the stiffness effect.

[0108] Because the local deformation takes into account the structural characteristics of sheet metal frame thin plates that are easy to deform, have many bends, and have stress concentration in the bending area, and couples the stiffness heat melting term and thermal deformation amount, it deeply integrates the geometric features of the sheet metal structure (such as the moment of inertia of the section) with the thermophysical properties (such as thermal conductivity, density and specific heat capacity). It can guide the CNC system to reduce the path compensation amount in the bending area and increase the compensation in the planar area, so as to avoid welding stress concentration caused by excessive compensation.

[0109] In summary, the intelligent welding system for sheet metal frames based on CNC machine tools acquires thermal deformation, structural stiffness of the bending zone, and thermal inertia. Based on these parameters, it obtains the local deformation of sheet metal during welding and optimizes the initial welding path accordingly. This system takes into account the structural characteristics of sheet metal frames, such as easy deformation of thin plates, numerous bends, and stress concentration in the bending zone. It guides the CNC system to reduce path compensation in the bending zone and increase compensation in the planar zone, avoiding stress concentration caused by over-compensation. This improves welding accuracy and quality, thereby enhancing the assembly precision of the sheet metal frame.

[0110] An embodiment of the present invention also provides an intelligent welding device for sheet metal frames based on CNC machine tools, comprising: a controller; and a memory storing executable instructions; wherein the executable instructions can run on the controller to implement the intelligent welding method for sheet metal frames.

[0111] The technical features of the embodiments described can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0112] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A sheet metal frame intelligent welding method based on a numerical control machine tool, characterized in that, The sheet metal frame intelligent welding method comprises the following steps: The thermal expansion coefficient, the welding temperature rise and the sheet metal thickness of the sheet metal material are obtained, and the thermal deformation amount is obtained according to the thermal expansion coefficient, the welding temperature rise and the sheet metal thickness; The Young's modulus, the cross-sectional moment of inertia, the thermal conductivity, the density and the specific heat capacity of the sheet metal material are obtained, the bending zone structural stiffness is obtained according to the Young's modulus and the cross-sectional moment of inertia, and the thermal inertia is obtained according to the thermal conductivity, the density and the specific heat capacity; The local deformation amount of the sheet metal welding is obtained according to the thermal deformation amount, the bending zone structural stiffness and the thermal inertia; The initial welding path is planned, and the initial welding path is optimized according to the local deformation amount to obtain the final welding path.

2. The intelligent welding method of sheet metal frame based on CNC machine tool according to claim 1, characterized in that, The specific method for obtaining the local deformation amount of the sheet metal welding comprises: The stiffness thermal capacity term for representing the dynamic competition relationship between the bending zone structural stiffness and the thermal inertia is obtained according to the bending zone structural stiffness and the thermal inertia; The stiffness thermal capacity term and the thermal deformation amount are coupled to predict the local deformation amount of the sheet metal welding.

3. The sheet metal frame intelligent welding method based on a numerical control machine tool according to claim 2, characterized in that, The specific method for optimizing the initial welding path according to the local deformation amount comprises: The normal vector and the process coefficient of the local area are obtained; The welding path offset amount of the local area is obtained according to the local deformation amount, the normal vector and the process coefficient; The initial welding path is optimized according to the welding path offset amount.

4. The sheet metal frame intelligent welding method based on a numerical control machine tool according to claim 3, characterized in that, The sheet metal frame intelligent welding method further comprises: The real-time environmental relative humidity and the real-time environmental temperature are obtained; The humidity sensitivity coefficient and the temperature sensitivity coefficient are obtained; The reference laser power is obtained, and the reference laser power is corrected according to the real-time environmental relative humidity, the real-time environmental temperature, the humidity sensitivity coefficient and the temperature sensitivity coefficient to obtain the corrected laser power.

5. The sheet metal rack intelligent welding method based on a numerical control machine tool according to claim 4, characterized in that, The specific method for correcting the reference laser power comprises: At least one keyword heat data related to the sheet metal frame welding defect is obtained, and a comprehensive heat index is obtained according to the keyword heat data; The reference laser power is corrected according to the comprehensive heat index, the real-time environmental relative humidity, the real-time environmental temperature, the humidity sensitivity coefficient and the temperature sensitivity coefficient.

6. The sheet metal rack intelligent welding method based on a numerical control machine tool according to claim 5, wherein, The specific method for correcting the reference laser power according to the comprehensive heat index, the real-time environmental relative humidity, the real-time environmental temperature, the humidity sensitivity coefficient and the temperature sensitivity coefficient comprises: The humidity sensitivity coefficient and the temperature sensitivity coefficient are dynamically adjusted according to the comprehensive heat index; The reference laser power is corrected according to the real-time environmental relative humidity, the real-time environmental temperature, the dynamically adjusted humidity sensitivity coefficient and the temperature sensitivity coefficient.

7. A sheet metal frame intelligent welding system based on a numerical control machine tool, used to implement the sheet metal frame intelligent welding method according to any one of claims 1-6, characterized in that, The sheet metal frame intelligent welding system comprises: A material parameter acquisition module for obtaining the thermal expansion coefficient, the welding temperature rise, the sheet metal thickness, the Young's modulus, the cross-sectional moment of inertia, the thermal conductivity, the density and the specific heat capacity of the sheet metal material; A thermal deformation acquisition module for obtaining the thermal deformation amount according to the thermal expansion coefficient, the welding temperature rise and the sheet metal thickness; A structural stiffness acquisition module for obtaining the bending zone structural stiffness according to the Young's modulus and the cross-sectional moment of inertia; A thermal inertia acquisition module for obtaining the thermal inertia according to the thermal conductivity, the density and the specific heat capacity; A deformation amount acquisition module for obtaining the local deformation amount of the sheet metal welding according to the thermal deformation amount, the bending zone structural stiffness and the thermal inertia; The welding path optimization module is configured to plan an initial welding path, optimize the initial welding path according to a local deformation amount, and obtain a final welding path.

8. The sheet metal rack intelligent welding system based on a numerical control machine tool according to claim 7, characterized in that, The deformation amount acquisition module comprises: The stiffness thermal capacity item acquisition unit is configured to acquire a stiffness thermal capacity item for representing a dynamic competition relationship between the stiffness of the bending area structure and the thermal inertia according to the stiffness of the bending area structure and the thermal inertia. The local deformation amount acquisition unit is configured to couple the stiffness thermal capacity item and the thermal deformation amount, and predict a local deformation amount of the sheet metal welding.

9. The sheet metal rack intelligent welding system based on a numerical control machine tool according to claim 8, characterized in that, According to the formula Obtaining the local deformation ; wherein, respectively denote the coefficient of thermal expansion, the welding temperature rise and the sheet metal thickness, respectively denote the Young's modulus, the cross-sectional moment of inertia, the thermal conductivity, the density and the specific heat capacity, denotes the coupling coefficient, denotes the natural exponential function, respectively denote the thermal deformation and the stiffness thermal spring term, respectively denote the bending zone structural stiffness and the thermal inertia.

10. A sheet metal frame intelligent welding device based on a numerical control machine tool, characterized in that, The sheet metal rack intelligent welding device comprises: a controller; a memory storing executable instructions; wherein the executable instructions are executable on the controller and implement the sheet metal rack intelligent welding method according to any one of claims 1 to 6.

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