A method for generating an industrial robot component adaptation model

Abstract

The application relates to the technical field of robots, in particular to a method for generating an industrial robot accessory adaptation model. The method comprises the following steps: obtaining industrial robot structure data, and extracting mechanical arm structure features according to the industrial robot structure data, so as to obtain mechanical arm structure data; constructing a three-dimensional mechanical arm model according to the mechanical arm structure data, so as to obtain the three-dimensional mechanical arm model; extracting gripper features and welding device features according to the three-dimensional mechanical arm model, so as to obtain gripper data and welding device data; performing dynamic load analysis according to the gripper data and the welding device data, so as to obtain mechanical arm dynamic load data; and extracting sliding joint features and rotating joint features according to the three-dimensional mechanical arm model, so as to obtain sliding joint data and rotating joint data. The application improves the performance and maintenance efficiency of the mechanical arm based on the robot technology.

Description

Technical Field

[0001] This invention relates to the field of robotics, and in particular to a method for generating an adaptation model for industrial robot parts. Background Technology

[0002] Traditional methods typically employ simplified mathematical models and empirical formulas for component fitting calculations. These models often fail to adequately account for the interactions between various factors, especially for components with complex structures and high precision requirements. By neglecting factors such as the nonlinear characteristics of materials and variations in the actual working environment, traditional fitting methods frequently fail to provide sufficiently accurate fitting solutions, leading to inaccurate component fitting and impacting the performance and stability of the final product. Traditional component fitting methods often rely on manually collected parameter data, such as dimensions, shape, and weight. However, when faced with complex component data (such as multidimensional parameters and dynamic working states), traditional methods struggle to effectively integrate and apply this data. This makes it difficult for traditional methods to handle highly complex and dynamically changing component types, especially in modern intelligent manufacturing environments, where they cannot meet the requirements for real-time data processing and rapid fitting. Summary of the Invention

[0003] Therefore, the present invention needs to provide a method for generating an industrial robot accessory adaptation model to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for generating an industrial robot accessory adaptation model includes the following steps:

[0005] Step S1: Obtain the structural data of the industrial robot, and extract the structural features of the robotic arm based on the structural data of the industrial robot to obtain the structural data of the robotic arm; construct a three-dimensional robotic arm model based on the structural data of the robotic arm to obtain the three-dimensional robotic arm model.

[0006] Step S2: Extract features from the gripper and welder based on the 3D robotic arm model to obtain gripper data and welder data; perform dynamic load analysis based on the gripper data and welder data to obtain dynamic load data of the robotic arm.

[0007] Step S3: Extract sliding joint features and rotary joint features based on the 3D robotic arm model to obtain sliding joint and rotary joint data; perform vibration analysis based on the sliding joint and rotary joint data to obtain robotic arm vibration data.

[0008] Step S4: Perform correlation analysis on the robot arm vibration data based on the robot arm dynamic load data to obtain the robot arm dynamic load-vibration data; perform material wear assessment based on the robot arm dynamic load-vibration data to obtain the robot arm material wear data;

[0009] Step S5: Estimate the service life of the robotic arm based on the wear data of the robotic arm material to obtain the service life data of the robotic arm; generate the robotic arm industrial robot accessory adaptation model based on the service life data of the robotic arm to obtain the robotic arm industrial robot accessory adaptation model, and upload it to the industrial robot management platform to execute the robotic arm accessory generation task.

[0010] This invention acquires structural data of industrial robots and extracts structural features of the robotic arm, accurately reconstructing its structural characteristics. This provides a solid foundation for subsequent 3D robotic arm model construction, ensuring high precision and operability of the model. Based on this, combined with feature extraction of the gripper and welder and dynamic load analysis, the load on each component of the robotic arm can be evaluated in real time, further optimizing its working efficiency and safety. Furthermore, the extraction of sliding and rotary joint features and vibration analysis allow for precise capture of the robotic arm's vibration characteristics, helping to reduce mechanical failures caused by vibration and improving its stability and long-term operational reliability. Correlation analysis of dynamic load and vibration data allows for in-depth exploration of the interaction between load and vibration, providing more accurate wear assessment and enabling precise adaptation of robotic arm components. This process effectively predicts the service life of the robotic arm, allowing for the early generation of industrial robot component adaptation models. This provides decision support for subsequent maintenance and replacement, ensuring efficient operation and stability of the robotic arm and avoiding performance degradation or failures due to inaccurate adaptation. Meanwhile, this method enables rapid adaptation and intelligent updates of parts, significantly shortening the replacement cycle in practical applications, improving production efficiency, and providing strong data support for the long-term maintenance and management of industrial robots. Overall, this method not only overcomes the shortcomings of traditional methods in fully considering the interaction of multiple factors, but also provides a precise solution for adapting parts with complex structures and high precision requirements, possessing significant engineering application value.

[0011] Optionally, step S1 specifically includes:

[0012] Step S11: Obtain the structural data of the industrial robot, and extract the structural features of the robotic arm based on the structural data of the industrial robot to obtain the structural data of the robotic arm.

[0013] Step S12: Establish reference coordinates based on the robotic arm structure data to obtain the robotic arm structure reference coordinate data;

[0014] Step S13: Model the joint structure based on the reference coordinate data of the robotic arm structure to obtain the joint modeling data of the robotic arm;

[0015] Step S14: Model the link structure based on the reference coordinate data of the robotic arm structure to obtain the robotic arm link modeling data;

[0016] Step S15: Connect the robotic arm joint modeling data in three dimensions based on the robotic arm link modeling data to obtain a three-dimensional robotic arm model.

[0017] This invention lays a solid foundation for subsequent precise modeling through structural feature extraction and reference coordinate establishment, enabling each part of the robotic arm to possess higher accuracy and adaptability in actual working environments. In particular, the establishment of reference coordinates clearly defines the robotic arm's range of motion and workspace, avoiding the inaccurate adaptation problems caused by the lack of a unified reference system in traditional methods. Based on this, the modeling of joints and links provides detailed parameter data for the robotic arm's dynamic control and structural stability, ensuring coordination and seamless connection between components. The realization of three-dimensional connections ensures the rational layout of the various parts of the robotic arm in three-dimensional space, thereby improving the overall flexibility and stability of the robotic arm's movement. Through the collaborative work of these steps, a precise three-dimensional model of the robotic arm can be generated. This not only improves the adaptation accuracy of robotic arm components but also solves the problem of traditional methods' inability to quickly and effectively integrate complex, multi-dimensional data. Ultimately, this method effectively improves the accuracy of component adaptation and real-time response capabilities, providing a solid technical guarantee for the design of robotic arms with high precision and high reliability requirements.

[0018] Optionally, step S2 specifically includes:

[0019] Step S21: Extract features from the gripper and the welder based on the 3D robotic arm model to obtain gripper data and welder data;

[0020] Step S22: Perform mechanical load stability analysis on the gripper data to obtain gripper mechanical load stability data;

[0021] Step S23: Perform thermal load analysis on the welder data to obtain the welder thermal load data;

[0022] Step S24: Based on the gripper's mechanical load stability data and the welder's thermal load data, integrate the robot arm's dynamic load to obtain the robot arm's dynamic load data.

[0023] This invention ensures accurate modeling of key components of the robotic arm by acquiring a 3D model and extracting feature data from the gripper and welder, thus providing detailed and high-precision parameter input for subsequent load analysis. The gripper's mechanical load stability analysis and the welder's thermal load analysis help comprehensively evaluate the mechanical and thermal performance of each component under different working conditions, enabling a better understanding of the load conditions of each component in actual operation. These analyses not only provide stability data for the gripper and welder under specific working environments but also provide a basis for optimizing the overall performance of the robotic arm, avoiding the insufficient adaptation accuracy caused by neglecting the nonlinear characteristics of materials and dynamic load changes in traditional methods. By integrating the mechanical load data of the gripper with the thermal load data of the welder, a comprehensive evaluation of the overall dynamic load of the robotic arm can be achieved. This not only improves the adaptability to complex parts and working conditions but also meets the requirements of high precision, real-time response, and dynamic adjustment, thereby significantly improving the adaptability and stability of the robotic arm in various working environments and providing a more accurate and efficient solution for the adaptation of complex parts in intelligent manufacturing.

[0024] Optionally, step S22 specifically includes:

[0025] Step S221: Simulate gripping data using the gripper data to obtain gripper gripping simulation data;

[0026] Step S222: Calculate the clamping force based on the gripper grasping simulation data to obtain the clamping force data;

[0027] Step S223: Extract the features of the gripper contact points from the gripping force data to obtain the gripper contact point data;

[0028] Step S224: Perform force calculation based on the contact point data of the gripper to obtain the contact point force data;

[0029] Step S225: Perform slip analysis based on the force data at the contact point to obtain slip data;

[0030] Step S226: Perform load stability assessment on the gripper data based on the sliding data to obtain gripper mechanical load stability data.

[0031] This invention simulates gripper data to accurately predict gripper behavior in different working environments, providing a reliable basis for subsequent gripping force calculations. The calculation of gripping force data allows understanding of the contact force between the gripper and the target object during actual operation, thus helping to optimize gripper design and ensure sufficient gripping capability under different load conditions. Gripper contact point feature extraction further helps identify key contact areas between the gripper and the target object, which is crucial for further force analysis and optimization. Force calculations reveal the specific force conditions at the contact points, providing data support for subsequent slippage analysis and helping to understand whether unstable slippage will occur during gripping. Slippage analysis provides a stability assessment of the gripper under different force states, enabling the early identification of potential slippage problems and the implementation of corresponding design optimization measures. Ultimately, load stability assessment based on slip data not only improves the performance of the gripper, ensuring its stable operation in complex dynamic environments, but also avoids the low-precision adaptation problem in traditional methods, thereby achieving more accurate adaptation and improving the overall stability and adaptability of the product, making it particularly suitable for applications with complex structures and high precision requirements.

[0032] Optionally, step S225 specifically includes:

[0033] The contact point normal force features are extracted based on the contact point force data to obtain the contact point normal force data;

[0034] Obtain the coefficient of friction at the contact point;

[0035] Friction force is calculated based on the contact point friction coefficient and contact point normal force data to obtain friction force data;

[0036] Based on the force data at the contact point, the horizontal component force feature and the vertical component force feature at the contact point are extracted to obtain the horizontal component force data and the vertical component force data at the contact point.

[0037] Tangential force data is obtained by calculating the horizontal and vertical force components at the contact point.

[0038] Slippage is determined based on friction and tangential force data, thereby obtaining slippage data.

[0039] This invention extracts the normal force features at the contact point, accurately obtaining the force value in the vertical direction. This is crucial for subsequent friction calculations and slippage assessments. Obtaining the contact point friction coefficient is a key step in ensuring the accuracy of friction calculations, as it considers the influence of material properties and environmental changes, thus improving the realism of the calculation results. Combining the contact point normal force data with the friction coefficient for friction calculations allows for accurate prediction of the frictional resistance between the contact surfaces, providing important data support for stable gripping by the gripper. Extracting the horizontal and vertical force components at the contact point further refines the force situation at the contact point, providing fundamental data for tangential force calculations and helping to comprehensively assess the interaction forces between the gripper and the target object. Through tangential force calculations, the lateral slippage tendency of the gripper during operation can be understood more accurately, providing a scientific basis for slippage assessment. Ultimately, by combining friction and tangential forces for slippage detection, potential slippage problems can be effectively identified. This avoids the shortcomings of traditional fitting methods that ignore nonlinear material characteristics and dynamic working states, thereby improving the accuracy of accessory fitting and ensuring that the robotic arm works stably in complex working environments. It also avoids the problems of inaccurate fitting and inefficient response that exist in traditional methods.

[0040] Optionally, step S23 specifically includes:

[0041] Step S231: Extract the power feature and thermal conductivity feature of the welder data to obtain the power data and thermal conductivity of the welder.

[0042] Step S232: Perform heat conduction simulation based on the welder power data and the welder thermal conductivity to obtain the welder heat conduction data;

[0043] Step S233: Calculate the expansion length of the welder based on the heat conduction data of the welder, thereby obtaining the expansion length data of the welder;

[0044] Step S234: Obtain the initial length data of the welder;

[0045] Step S235: Calculate the thermal expansion load based on the initial length data and expansion length data of the welder to obtain the thermal load data of the welder.

[0046] This invention, through the extraction of welder power and thermal conductivity characteristics, accurately captures the energy transfer characteristics and heat conduction performance of the welder during operation, providing detailed foundational data for subsequent heat conduction simulations and enhancing simulation accuracy. Combining welder power data with thermal conductivity data for heat conduction simulation realistically reflects the heat distribution of the welder under different operating conditions, thus providing strong support for optimized design and load calculation. Calculating the expansion length from the welder's heat conduction data accurately assesses the impact of thermal effects on welder dimensional changes, laying the foundation for further thermal expansion load calculations. Obtaining the initial length data of the welder ensures that the actual physical state of the welder is considered during the calculation process, thereby improving calculation accuracy. By combining the initial length and expansion length data for thermal expansion load calculation, the deformation and stress of the welder under high-temperature operating environments can be comprehensively evaluated, ensuring its stability and reliability in complex working environments and avoiding the inaccurate adaptation problems caused by neglecting material nonlinearity and environmental factors in traditional methods.

[0047] Optionally, step S26 specifically includes:

[0048] Step S31: Extract sliding joint features and rotary joint features based on the 3D robotic arm model to obtain sliding joint and rotary joint data;

[0049] Step S32: Perform harmonic resonance analysis on the sliding joint data to obtain the sliding joint harmonic resonance data;

[0050] Step S33: Perform radial vibration analysis on the rotary joint data to obtain the radial vibration data of the rotary joint;

[0051] Step S34: Integrate the vibration characteristics of the robotic arm based on the harmonic resonance data of the sliding joint and the radial vibration data of the rotary joint to obtain the vibration data of the robotic arm.

[0052] This invention, through feature extraction from sliding and rotary joints, comprehensively captures the key dynamic characteristics of different joints in a robotic arm during movement, laying the foundation for subsequent vibration analysis and feature integration. Harmonic resonance analysis of the sliding joints helps identify their resonance behavior at different frequencies, providing valuable data for optimizing the robotic arm's stability under high load and high-speed conditions. Radial vibration analysis of the rotary joints reveals the radial vibration characteristics caused by imbalances, friction, and other factors during operation, helping to avoid mechanical failures due to unstable vibrations. Integrating the robotic arm's vibration features by combining sliding joint harmonic resonance data and rotary joint radial vibration data allows for a comprehensive evaluation of the overall vibration behavior of the robotic arm, helping to optimize motion control strategies during design and manufacturing, and reducing performance degradation or damage caused by vibration. By implementing these steps, the shortcomings of traditional adaptation methods in neglecting the interaction of multiple factors can be effectively overcome, improving the overall performance and reliability of the robotic arm.

[0053] Optionally, step S32 specifically includes:

[0054] Step S321: Extract the structural features of the sliding guide rail and the damping coefficient features of the guide rail from the sliding joint data to obtain the structural data of the sliding guide rail and the damping coefficient of the guide rail.

[0055] Step S322: Construct the sliding joint spring damping model based on the sliding guide rail structure data and guide rail damping coefficient, thereby obtaining the sliding joint spring damping model;

[0056] Step S323: Apply harmonic excitation to the sliding joint spring damping model to obtain harmonic excitation data;

[0057] Step S324: Plot the resonant amplitude-frequency response curve based on the harmonic excitation data to obtain the resonant amplitude-frequency response curve data;

[0058] Step S325: Perform amplitude statistics on the resonance amplitude-frequency response curve data to obtain high-amplitude resonance data;

[0059] Step S326: Perform peak value statistics on the resonance amplitude-frequency response curve data to obtain wide peak value resonance data;

[0060] Step S327: Integrate the abnormal harmonic resonance of the sliding joint based on the high amplitude resonance data and the wide peak value resonance data to obtain the harmonic resonance data of the sliding joint.

[0061] This invention, through the extraction of structural features and damping coefficient features of the sliding guide rail, can accurately capture the dynamic characteristics of the sliding joint, thus providing fundamental data for the optimized design of the sliding joint. This data helps analyze the structural strength and damping characteristics of the guide rail, thereby improving the overall system stability. By constructing a spring-damped model of the sliding joint, the dynamic behavior of the sliding joint under different operating conditions can be comprehensively described, providing a more accurate physical model for further vibration analysis. Testing the spring-damped model of the sliding joint by applying harmonic excitation helps to understand how the system responds to excitations of different frequencies during actual operation, thereby revealing potential resonance problems. By plotting the resonance amplitude-frequency response curve, the response of the system at specific frequencies can be visually displayed, helping to identify the frequency range that causes faults. Statistical analysis of the amplitude and peak values ​​of the resonance amplitude-frequency response curve not only clarifies the region of high-amplitude resonance, preventing damage to mechanical components under resonance conditions, but also provides key vibration parameters for optimizing system design by analyzing wide-peak-value resonance data. Ultimately, by integrating high-amplitude resonance data and wide-peak-value resonance data, abnormal harmonic resonance in sliding joints can be accurately identified. This provides a scientific basis for preventative maintenance and fault diagnosis of the system, avoiding performance degradation or structural damage caused by vibration issues. These steps provide precise data support for high-precision fitting of sliding joints, overcoming the shortcomings of traditional fitting methods and enabling rapid response and adjustment in complex dynamic environments.

[0062] Optionally, step S33 specifically includes:

[0063] Step S331: Extract the structural features of the rotating disk from the rotary joint data to obtain the structural data of the rotating disk;

[0064] Step S332: Calculate the angular acceleration of the rotary joint data to obtain the angular acceleration data;

[0065] Step S333: Construct a vibration model based on the rotating disk structure data and angular acceleration data to obtain the rotating disk vibration model;

[0066] Step S334: Calculate the radial vibration response based on the rotating disk vibration model to obtain radial vibration response data;

[0067] Step S335: Obtain standard radial vibration response data;

[0068] Step S336: Divide the radial vibration response data into abnormal radial vibrations of the rotary joint according to the standard radial vibration response data, thereby obtaining the radial vibration data of the rotary joint.

[0069] This invention, through the extraction of structural features from the rotating disk, provides a detailed understanding of the geometry and structural characteristics of the rotary joint, offering more accurate foundational data for vibration analysis. This data helps identify the stability and performance of the rotating disk in actual operation, providing direction for subsequent optimization. Calculation of angular acceleration reveals the dynamic response of the rotary joint under different loads, helping to evaluate its motion characteristics and thus improving the system's adaptability in complex environments. The vibration model constructed based on the rotating disk's structural and angular acceleration data provides an accurate physical model for the vibration analysis of the rotary joint, ensuring the accuracy of vibration prediction. Calculating the radial vibration response of the vibration model allows for a clearer understanding of the rotating disk's vibration behavior under different operating conditions, providing a basis for vibration control decisions. After obtaining standard radial vibration response data, it can be used as a benchmark for comparative analysis of actual measured radial vibration response data, promptly identifying anomalies. Finally, by classifying abnormal radial vibrations of the rotary joint, normal and abnormal vibration responses can be accurately identified and distinguished, allowing for early prediction and troubleshooting of potential problems, thus improving equipment reliability and extending its service life. These steps, combined with advanced data processing and model analysis technologies, can overcome the limitations of traditional methods, provide high-precision dynamic analysis and adaptation solutions, and meet the needs of modern intelligent manufacturing for real-time data processing and refined management.

[0070] Optionally, step S4 specifically includes:

[0071] Step S41: Perform dynamic load time statistics based on the dynamic load data of the robotic arm to obtain the dynamic load time data of the robotic arm;

[0072] Step S42: Perform vibration time statistics based on the robotic arm vibration data to obtain robotic arm vibration time data;

[0073] Step S43: Calculate the time overlap based on the dynamic load time data and vibration time data of the robotic arm to obtain the dynamic load-vibration data of the robotic arm;

[0074] Step S44: Construct a wear model from the dynamic load-vibration data of the robotic arm to obtain the wear model;

[0075] Step S45: Calculate the material wear of the robotic arm structure data based on the wear model to obtain the material wear data of the robotic arm.

[0076] This invention accurately captures the load changes of a robotic arm under different operating conditions by performing time-based statistical analysis on its dynamic load data. This helps analyze the temporal characteristics of its load behavior, providing support for subsequent optimization design and load allocation. Vibration time-based statistical analysis of the robotic arm's vibration data precisely records the frequency and duration of vibrations, helping to identify vibration problems during operation and optimize vibration control strategies. By calculating the time overlap between dynamic load time data and vibration time data, the correlation between load and vibration can be revealed, providing more multi-dimensional data support for analyzing the robotic arm's performance and stability. The wear model built based on this data can more accurately predict the wear behavior of the robotic arm during long-term use, identifying key factors affecting performance and providing a basis for equipment maintenance and lifespan management decisions. Finally, by calculating the wear of the robotic arm's materials according to the wear model, wear patterns under different operating conditions can be predicted, effectively extending the robotic arm's service life and reducing the failure rate. These steps, by comprehensively considering load, vibration, and wear factors, overcome the limitations of traditional methods, providing more accurate dynamic analysis and helping robotic arms adapt to the high-precision and real-time processing requirements of modern intelligent manufacturing. Attached Figure Description

[0077] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0078] Figure 1 This is a schematic diagram of the steps of the method of the present invention;

[0079] Figure 2 This is a detailed flowchart of step S1 in the present invention;

[0080] Figure 3 This is a detailed flowchart of step S2 in the present invention;

[0081] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0082] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0083] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0084] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0085] To achieve the above objectives, please refer to Figures 1 to 3 The present invention provides a method comprising the following steps:

[0086] Step S1: Obtain the structural data of the industrial robot, and extract the structural features of the robotic arm based on the structural data of the industrial robot to obtain the structural data of the robotic arm; construct a three-dimensional robotic arm model based on the structural data of the robotic arm to obtain the three-dimensional robotic arm model.

[0087] In this embodiment, the specific method for acquiring industrial robot structural data includes using standardized technical documents provided by the industrial robot manufacturer and directly using laser scanning equipment to collect three-dimensional data of the robot body. The scanning range should cover each joint, connection point, and end effector position of the robotic arm. After data acquisition, the point cloud data is processed using reverse engineering software to extract three-dimensional geometric features from key structural parts of the robotic arm, such as the main axis, secondary axis, and joint connection points, and to correct data points with errors greater than 0.05 mm. The robotic arm structural data is further optimized and integrated using computer-aided design (CAD) software based on finite element modeling to generate a three-dimensional robotic arm model in a standard format (such as STEP or IGES file format).

[0088] Step S2: Extract features from the gripper and welder based on the 3D robotic arm model to obtain gripper data and welder data; perform dynamic load analysis based on the gripper data and welder data to obtain dynamic load data of the robotic arm.

[0089] In this embodiment, based on the 3D robotic arm model, an automated boundary recognition algorithm based on point cloud data (e.g., a polygon fitting method based on convex hull algorithm) is used to extract the gripping area, size, and maximum gripping force features of the gripper. The gripper data specifically includes parameters such as the gripper opening range (mm) and clamping pressure (Newtons). Welder feature extraction includes the nozzle size of the welding head (mm), welding current (Amperes), welding voltage (Volts), and welding material supply path. Dynamic load analysis uses a dynamic simulation tool (e.g., the Simscape module in ANSYS or MATLAB), inputting the robotic arm mass distribution, the welder mass (in grams), and the motion trajectory of the gripper-welder combination during welding (in millimeters per second). By establishing a mass matrix and inertia tensor model, the dynamic load of the robotic arm under different welding conditions is calculated, with load data accurate to 0.1 Newtons.

[0090] Step S3: Extract sliding joint features and rotary joint features based on the 3D robotic arm model to obtain sliding joint and rotary joint data; perform vibration analysis based on the sliding joint and rotary joint data to obtain robotic arm vibration data.

[0091] In this embodiment, based on a 3D robotic arm model, a specific joint feature recognition algorithm (e.g., the Harris corner detection method based on gradient changes) is used to locate the sliding trajectory and range of motion (in millimeters) of the sliding joint, and the joint sliding friction coefficient (dimensionless) is extracted. The friction coefficient is obtained by collecting torque sensor data during the sliding process. Rotary joint feature extraction includes rotation angle range (in degrees), maximum rotational speed (in revolutions per second), and bearing type, among other technical parameters. During vibration analysis, combined with the robotic arm's dynamic load data and joint motion characteristics, vibration sensors (such as accelerometers) are used to collect the three-axis vibration amplitude of the robotic arm (in m / s²), and the collected data is subjected to spectral analysis based on Fast Fourier Transform (FFT) to identify the main vibration frequencies and amplitudes with an accuracy of 0.01 Hz.

[0092] Step S4: Perform correlation analysis on the robot arm vibration data based on the robot arm dynamic load data to obtain the robot arm dynamic load-vibration data; perform material wear assessment based on the robot arm dynamic load-vibration data to obtain the robot arm material wear data;

[0093] In this embodiment, the Pearson correlation coefficient method based on correlation calculation is used, with a threshold of 0.7, to screen out load-vibration data pairs with high correlation. During the material wear assessment, finite element simulation is used to calculate the stress distribution and wear trend of the main components of the robotic arm (such as joints, sliding parts, and welder connections), simulating the surface loss of materials under dynamic loads and vibrations. Specific simulation conditions include the dynamic load range (in Newtons), vibration frequency range (in Hertz), and material properties (such as hardness and fatigue limit). The material wear depth per unit time (in micrometers) is calculated using a cumulative wear model, and the results are output to the material wear data table.

[0094] Step S5: Estimate the service life of the robotic arm based on the wear data of the robotic arm material to obtain the service life data of the robotic arm; generate the robotic arm industrial robot accessory adaptation model based on the service life data of the robotic arm to obtain the robotic arm industrial robot accessory adaptation model, and upload it to the industrial robot management platform to execute the robotic arm accessory generation task.

[0095] In this embodiment, material wear data is used to calculate the remaining lifespan of key components of the robotic arm based on the linear cumulative damage theory (Palmgren-Miner's rule). Input parameters include the damage percentage per unit operating cycle (dimensionless) and the total damage limit (typically 1). Based on the lifespan prediction data, a component adaptation algorithm (e.g., an optimization method based on a genetic algorithm) is used, inputting the robotic arm's lifespan data, component geometric parameters, and standardized installation requirements, to generate a robotic arm industrial robot component adaptation model. The model includes the structural parameters, material requirements, and replacement cycle (in hours) of the adapted components. The generated model is uploaded to a cloud database via the industrial robot management platform's API interface for subsequent automated component production tasks.

[0096] Optionally, step S1 specifically includes:

[0097] Step S11: Obtain the structural data of the industrial robot, and extract the structural features of the robotic arm based on the structural data of the industrial robot to obtain the structural data of the robotic arm.

[0098] In this embodiment, acquiring the structural data of the industrial robot requires the use of industrial-grade high-precision 3D scanning equipment, such as a portable laser scanner, with a scanning accuracy of 0.01 mm. The data is then verified against the original technical drawings of the industrial robot design to ensure consistency. The scanning range covers all motion units of the robotic arm, including joints, links, base, and end effector. The acquired data must include 3D coordinate point cloud information and material properties. The acquired data undergoes noise filtering using point cloud processing software (such as Geomagic Design X), with a noise threshold set to 0.05 mm. A mesh reconstruction tool is then used to perform topology optimization on the point cloud data. The processed 3D point cloud is compared with the robot design drawings to extract key structural features of the robotic arm. Feature data includes joint center points, link lengths, connecting axis directions, and rotational limits. The data is stored in XML format.

[0099] Step S12: Establish reference coordinates based on the robotic arm structure data to obtain the robotic arm structure reference coordinate data;

[0100] In this embodiment, the reference coordinate system is established with the bottom plane of the robotic arm base as a reference. According to the industry standard ISO9283, the reference coordinate system must include an origin and three orthogonal coordinate axes to ensure that the coordinate axis directions are aligned with the main motion directions of the robotic arm. Specific operations include extracting the geometric parameters of the base plane from the robotic arm structural data, determining the Z-axis direction using the base plane normal vector, and simultaneously determining the Y-axis direction by using the long edge of the base parallel to the X-axis and the right-hand rule. Using 3D modeling software (such as SolidWorks or AutoCAD), the actual size parameters of the robotic arm base (in millimeters) are input to generate a standardized 3D reference coordinate model. The model file adopts the STEP format for easy subsequent analysis and use.

[0101] Step S13: Model the joint structure based on the reference coordinate data of the robotic arm structure to obtain the joint modeling data of the robotic arm;

[0102] In this embodiment, joint structure modeling requires combining reference coordinate data and joint feature information from the robotic arm structure, including joint type (e.g., rotary joint, sliding joint), range of motion (in angles or millimeters), and the position of the joint's center point (represented by three-dimensional coordinates in the reference coordinate system). The specific modeling operation employs a parametric modeling-based calculation method. In the 3D modeling software, the joint center point is set as the reference point for rotation or sliding, and the corresponding active area is set according to the joint's motion constraints. Taking a rotary joint as an example, the set motion parameters include the maximum rotation angle (e.g., ±120 degrees), rotation speed limit (e.g., 5 revolutions per second), and torque constraint (e.g., 50 N·m). After modeling is completed, the joint structure is saved as a modular, independent file for subsequent integration with other structural components.

[0103] Step S14: Model the link structure based on the reference coordinate data of the robotic arm structure to obtain the robotic arm link modeling data;

[0104] In this embodiment, the linkage structure modeling requires determining the three-dimensional spatial positions of the connecting joints at both ends of the linkage based on the reference coordinate data of the robotic arm structure, and generating the geometry of the linkage based on this. The length of the linkage is obtained by calculating the Euclidean distance between the center points of the two connecting joints, in millimeters. The cross-sectional shape of the linkage needs to be selected according to the actual structure; for example, the diameter of a circular cross-section linkage is provided by the design drawings, with the value accurate to 0.1 millimeters. During the modeling process, the material properties of the linkage (such as density and elastic modulus) are set using 3D modeling tools. The material parameters need to be looked up in a standard database; for example, the density of aluminum alloy is 2.7 g / cm³, and the elastic modulus is 69 GPa. After the modeling is completed, the linkage data is saved in a modular format compatible with the joint data to achieve unified integration.

[0105] Step S15: Connect the robotic arm joint modeling data in three dimensions based on the robotic arm link modeling data to obtain a three-dimensional robotic arm model.

[0106] In this embodiment, a 3D assembly tool (such as CATIA or Creo) is used to connect the various parts of the robotic arm in 3D. The connection operation must ensure that both ends of the connecting rod coincide with the center point of the joint, and connection constraints are set according to the joint's motion characteristics. For example, for the connection between a rotary joint and a connecting rod, the rotational degree of freedom is constrained to ±120 degrees, while other degrees of freedom are fixed. The connection strength between the connecting rod and the joint needs to be constrained according to design requirements, such as the allowable force range (e.g., 500N) and torque range (e.g., 50N·m) at the connection point. By progressively connecting the various parts of the robotic arm, the overall 3D robotic arm model is generated. The generated model needs to undergo force verification to ensure that there is no displacement or abnormal deformation when the maximum load is applied. After verification, the 3D model is saved in STEP format for subsequent development and application of welding methods.

[0107] Optionally, step S2 specifically includes:

[0108] Step S21: Extract features from the gripper and the welder based on the 3D robotic arm model to obtain gripper data and welder data;

[0109] In this embodiment, model analysis tools (such as ANSYS Workbench) are used to separate the geometric data of the gripper, including parameters such as the gripper's width, length, thickness, and maximum stroke during opening and closing. The motion characteristics of the gripper are annotated by extracting the range of motion (in millimeters) and torque limit (in Newton-meters) of the drive joint. Furthermore, the coefficient of friction between the gripper surface and the welding target needs to be confirmed through design drawings, typically a fixed value between 0.4 and 0.6. Welder feature extraction requires determining the geometry of the welding head (e.g., the apex angle and diameter of a conical welding head), welding energy parameters (in watts), and thermal conductivity (in W / m·K). Welder thermal parameters can be imported from a standard library according to welding process requirements; for example, the welding power used in CO2 welding is typically between 5 and 25 kilowatts, and the thermal conductivity of the welding head is generally 58 W / m·K (for copper). The features of the gripper and welder are 3D annotated using feature extraction tools, and their detailed feature data is stored in JSON format.

[0110] Step S22: Perform mechanical load stability analysis on the gripper data to obtain gripper mechanical load stability data;

[0111] In this embodiment, the three-dimensional model data of the gripper is loaded using finite element analysis software (such as ABAQUS), and the material properties of the gripper are defined (e.g., the elastic modulus of carbon steel is 210 GPa, and Poisson's ratio is 0.3). Based on the standard gripping force range for industrial robots, an external pressure (typically 50-200 N) is applied to the gripper. The boundary conditions for analysis require defining motion constraints at the fixed end of the gripper, restricting the degrees of freedom of the root nodes, including zero displacement in the X, Y, and Z directions, while allowing a certain degree of elastic deformation on the gripper surface. The stress distribution, displacement amplitude, and strain of the gripper under maximum gripping load are simulated and analyzed, and a stability threshold is set, for example, the maximum displacement must not exceed 0.1% of the total length of the gripper. After the analysis, the mechanical load stability data is extracted, including the maximum stress value (in MPa), deformation curvature, and elastic limit (in N / m²), and output in TXT format for subsequent processing.

[0112] Step S23: Perform thermal load analysis on the welder data to obtain the welder thermal load data;

[0113] In this embodiment, a three-dimensional geometric model of the welder is imported using a thermal analysis tool (such as COMSOL Multiphysics), and relevant parameters of the welding material, such as thermal conductivity, specific heat capacity, and density, are set. These parameters are typically obtained based on the properties of the welding material; for example, copper has high thermal conductivity and density. Heat flux boundary conditions are specified on the welder surface, with the heat flux calculated from specific data on welding power and welding contact area. The contact area is determined by the diameter of the welding head and, combined with a preset welding power value, is applied to the welding contact surface to define the heat flux boundary conditions. Subsequently, a numerical simulation of the temperature distribution of the welder is performed to analyze the temperature distribution during welding, paying particular attention to whether the highest temperature of the welding head approaches or exceeds the melting point of the material. Simultaneously, the heat generated during welding is evaluated to determine how it is conducted along the welder's structure to other components, identifying the thermal gradient range and main heat conduction paths. After the simulation, the heat load data of the welder is extracted, including the maximum temperature in the welding head region, the thermal gradient distribution, and the main heat conduction paths. This data is saved in a standard format, such as CSV, for subsequent dynamic load integration and analysis. Throughout the process, the analysis results must meet the design requirements of the welding materials and processes to ensure the safety and stability of the welding equipment during operation.

[0114] Step S24: Based on the gripper's mechanical load stability data and the welder's thermal load data, integrate the robot arm's dynamic load to obtain the robot arm's dynamic load data.

[0115] In this embodiment, multiphysics simulation tools (such as ANSYS Multiphysics) are used to load the mechanical load data of the gripper and the thermal load data of the welder into the corresponding modules of the overall robot arm model. The mechanical load of the gripper needs to be mapped to the end effector, the maximum force point of the gripper is set as the concentrated load application point, and the load direction is defined (e.g., a vertical force of 50-200N is applied in the Y-axis direction). The thermal load of the welder needs to be mapped to the welding head area, and heat flux conditions are applied to ensure that the loading parameters are consistent with the heat flux calculation results in step S23. During integration, the impact of the two load conditions on the overall dynamic performance of the robot arm needs to be verified, for example, by analyzing the vibration frequency and stress distribution of the robot arm through transient dynamic simulation with a time step of 0.01 seconds. The output data of the dynamic load integration includes the total load distribution diagram of the robot arm, motion stability evaluation index, and peak stress location, and is finally saved in EXCEL format for the next stage of welding path optimization analysis.

[0116] Optionally, step S22 specifically includes:

[0117] Step S221: Simulate gripping data using the gripper data to obtain gripper gripping simulation data;

[0118] In this embodiment, 3D gripper data is imported into a physical simulation tool (such as Abaqus or ANSYS) to define the geometric features, material properties, and motion parameters of the gripper structure. The mechanical properties of the gripper material need to be clearly defined, such as Young's modulus (e.g., 200 GPa for steel), Poisson's ratio (0.3), and density (7.85 g / cm³). Then, based on the preset geometry and material properties of the target object, the contact stiffness and coefficient of friction of the target object are set (e.g., the coefficient of friction is set to 0.3). By applying the gripper's opening and closing motion path and specifying the initial range of the gripping force (e.g., 50 N to 100 N), the gripping action under different mechanical conditions is simulated. During the simulation, the gripper's motion trajectory, contact point position, and changes in gripping force need to be recorded. After the simulation is completed, the gripping simulation data, including the gripper's motion state, contact stress distribution, and gripping stability information, is output and saved as a standard format file (such as CSV or JSON).

[0119] Step S222: Calculate the clamping force based on the gripper grasping simulation data to obtain the clamping force data;

[0120] In this embodiment, the finite element analysis tool is used to load and capture the simulation results, extracting stress data in the contact area between the gripper and the target object. Based on the contact area, the displacement range applied by the gripper, and the contact stiffness of the material, the clamping force of the gripper is calculated. The calculation of the clamping force requires a clear understanding of the force distribution, such as the total clamping force, the component forces at each contact point, and their directional components. Assuming symmetrical clamping, the uniformity of the clamping force distribution can be verified. Finally, the clamping force data is processed into a force-displacement curve or a contact point moment distribution diagram, and the data is stored as a digital file for subsequent analysis.

[0121] Step S223: Extract the features of the gripper contact points from the gripping force data to obtain the gripper contact point data;

[0122] In this embodiment, regions with significant force values ​​are selected as contact points based on the clamping force distribution, ensuring that the force at each contact point exceeds a set threshold (e.g., 10N). Then, the spatial location and normal vector of these contact points are recorded using geometric modeling tools, and combined with the force direction and magnitude data to form a contact point feature set. The contact point feature extraction process requires specifying the physical properties of each contact point, such as the contact point radius, contact depth, and normal stress. The output contact point data includes point coordinates, normal force, and shear force magnitudes, and all data is stored in a structured tabular file.

[0123] Step S224: Perform force calculation based on the contact point data of the gripper to obtain the contact point force data;

[0124] In this embodiment, contact point data is imported into a mechanical analysis tool to perform detailed calculations on the force conditions at each contact point. Based on the normal and shear forces at the contact points, the total magnitude and direction of the force at each contact point are assessed. The influence of friction is considered during the calculation; the friction coefficient is derived from the surface material parameters of the target object (e.g., 0.3 to 0.5). Vector decomposition is performed on the force results at each contact point to clarify the moment distribution of the normal and tangential forces and to verify the mechanical equilibrium state of each contact point. The calculated force data is compiled into a distribution chart, marking areas of concentrated force and potential slippage risk points, providing a reliable mechanical basis for subsequent analysis.

[0125] Step S225: Perform slip analysis based on the force data at the contact point to obtain slip data;

[0126] In this embodiment, the ratio of tangential force to normal force is calculated by analyzing the force data at the contact point and compared with the coefficient of friction. If the ratio exceeds the coefficient of friction, the contact point is considered to have a risk of slippage. Furthermore, dynamic simulation tools are used to simulate the slippage behavior of the gripper under different loads to clarify the critical conditions for slippage initiation and the slippage distance. The slippage analysis requires detailed recording of the slippage trajectory, time, and velocity, and indicates the impact of slippage on the gripper's grasping stability. After the analysis is completed, the slippage data is stored as a time-series file, providing detailed data support for load stability assessment.

[0127] Step S226: Perform load stability assessment on the gripper data based on the sliding data to obtain gripper mechanical load stability data.

[0128] In this embodiment, the overall load stability coefficient is calculated by statistically analyzing the force distribution and slippage state at the contact points. The load stability coefficient is defined as the proportion of slippage points to the total number of contact points, and the force direction and magnitude distribution at each contact point must be clearly defined in the evaluation. Simulation results are used to verify the maximum allowable slippage range of the gripper under the rated load (e.g., 100N), and the gripping strategy is optimized by combining the gripper's motion path. Finally, the gripper load stability data is output in the form of multi-dimensional charts, including contact point force distribution diagrams, stability time curves, and slippage risk zones, ensuring the accuracy of subsequent overall load integration of the robotic arm.

[0129] Optionally, step S225 specifically includes:

[0130] The contact point normal force features are extracted based on the contact point force data to obtain the contact point normal force data;

[0131] In this embodiment, contact point force data is imported into a finite element analysis tool (such as Abaqus or ANSYS) to clarify the force direction and magnitude at each contact point. The contact force vector is decomposed into normal and tangential components using a force decomposition formula. The normal force is calculated based on the direction of the normal vector of the plane containing the contact point, and its magnitude is determined through dot product operations. For example, for a perpendicular contact surface, the force value in the vertical direction is directly extracted as the normal force. During the extraction process, force value filtering conditions are set to ignore contact points with values ​​less than a threshold (e.g., 0.5N) to ensure data quality. The extracted contact point normal force data includes the normal force magnitude, contact point coordinates, and normal vector information, and is saved in tabular format for subsequent analysis.

[0132] Obtain the coefficient of friction at the contact point;

[0133] In this embodiment, the friction coefficient at the contact point is determined based on the physical properties of the contact material and experimental test data. The friction coefficient parameters are adjusted by consulting a standard table of friction coefficients for the contact material (e.g., 0.6 for steel and rubber) and considering the actual working environment (e.g., humidity or surface treatment). If standard data is unavailable, actual measurements can be performed using a friction coefficient tester to record the friction behavior at different contact points and calculate the actual friction coefficient. In the experiment, a specified loading force (e.g., 10 N) and sliding speed (e.g., 10 mm / s) are used, and the friction coefficient is determined by measuring the ratio of tangential resistance to normal force. The friction coefficient data needs to be correlated with the contact point coordinates to form a friction coefficient distribution table.

[0134] Friction force is calculated based on the contact point friction coefficient and contact point normal force data to obtain friction force data;

[0135] In this embodiment, the friction force is calculated based on the normal force data at the contact point and the corresponding friction coefficient. Using formula F... 摩 =μ·F 法 Where μ is the coefficient of friction, F 法 The normal force is calculated. Using computational tools (such as MATLAB or Python scripts), the normal force data and friction coefficient at the contact points are imported, and the magnitude of the friction force is calculated point by point. All calculation results are sorted by the contact point coordinates, generating a friction force distribution table containing the magnitude of the friction force and the corresponding contact point coordinates. Simultaneously, a friction force distribution map is plotted, marking high-friction areas as the basis for slip analysis.

[0136] Based on the force data at the contact point, the horizontal component force feature and the vertical component force feature at the contact point are extracted to obtain the horizontal component force data and the vertical component force data at the contact point.

[0137] In this embodiment, the horizontal and vertical components of the force are extracted using contact point force data through vector decomposition. First, the force direction at each contact point is determined, assuming the horizontal and vertical directions as reference coordinate axes. Using finite element analysis software or mathematical tools (such as Excel or Python scripts), the projection values ​​of the force vector in the horizontal and vertical directions are calculated as the horizontal and vertical components, respectively. For example, for a contact point with a force vector magnitude of 10N and an angle of 45°, its horizontal component is calculated as F. 水平 =10·cos(45°); the vertical component of the force is F 垂直 =10·sin(45°); Set a threshold (e.g., force data less than 0.5N is ignored) to ensure the validity of the extracted data. Finally, a distribution table containing the horizontal force component, vertical force component, and contact point coordinates is generated.

[0138] Tangential force data is obtained by calculating the horizontal and vertical force components at the contact point.

[0139] In this embodiment, the tangential force calculation is based on the horizontal and vertical component force data, using the formula... The magnitude of the tangential force is calculated point-by-point by writing a calculation script or importing data into analysis software. For each contact point, the squares of the horizontal and vertical components are summed, and the square root is calculated to obtain the tangential force value. The tangential force calculation result needs to be correlated with the contact point coordinates to ensure the consistency of the spatial distribution of the output data. Finally, the calculation results are stored in the form of tables and distribution maps for slip judgment analysis.

[0140] Slippage is determined based on friction and tangential force data, thereby obtaining slippage data.

[0141] In this embodiment, slippage is determined based on the relationship between the magnitudes of frictional force and tangential force. For each contact point, the magnitude of the tangential force is compared to see if it exceeds the corresponding frictional force. If F 切 >F 垂直 If a contact point is identified as having a risk of slippage, it is marked as such. A programming tool automatically iterates through the friction and tangential force data of all contact points, generating a slippage judgment table that includes the slippage state (slippage or no slippage), the coordinates of the slipping contact point, and the ratio of tangential force to friction. Slipping contact points are analyzed in detail, recording the slippage initiation conditions and critical parameters (e.g., the tangential force value and slippage time at the slipping contact point). Finally, the slippage data is output as a time-series slippage distribution map for load stability assessment.

[0142] Optionally, step S23 specifically includes:

[0143] Step S231: Extract the power feature and thermal conductivity feature of the welder data to obtain the power data and thermal conductivity of the welder.

[0144] In this embodiment, the extraction of the welder's power characteristics requires recording current, voltage, and time data during the welding process to calculate instantaneous and average power. Current and voltage values ​​are collected using a power measurement device (such as a power analyzer), recording data in the frequency range of 50Hz to 60Hz, and sampling at time intervals (e.g., every 1 second). The calculation formula is P = U·I, where U is the welding voltage and I is the welding current, and the data is stored in tabular format. Thermal conductivity feature extraction is based on the physical properties of the welding material. The thermal conductivity is determined by consulting a thermal property database of the welder material (e.g., the ASM material data sheet). If no readily available data is available, experimental testing is used, such as the steady-state flat plate heat flow test, to measure the heat flux density q and temperature gradient ΔT, and calculate the thermal conductivity k = q / ΔT. The experimental conditions are controlled to a constant heat source power (e.g., 100W) and a stable temperature gradient (e.g., 20°C). The thermal conductivity data is then stored in association with the welder parameters.

[0145] Step S232: Perform heat conduction simulation based on the welder power data and the welder thermal conductivity to obtain the welder heat conduction data;

[0146] In this embodiment, the heat conduction simulation is based on the welder's power data and thermal conductivity, and is implemented using finite element thermal analysis software (such as ANSYS or COMSOL Multiphysics). A three-dimensional geometric model of the welder is established, and the welding point power is set as the heat source input condition, for example, the power value is set to 1000W. Then, the heat conduction material properties are set according to the thermal conductivity k, material density ρ, and specific heat capacity c, for example, k = 50W / mK and ρ = 7800kg / m for steel. 3 The parameters are: c = 500 J / kgK. Boundary conditions are set as follows: ambient temperature 25℃, heat conduction time step 1 second, duration 60 seconds, and output temperature distribution data of the welding area. The simulation results are output as temperature field data, including information on the spatial distribution of temperature at each time point, for subsequent expansion length calculation.

[0147] Step S233: Calculate the expansion length of the welder based on the heat conduction data of the welder, thereby obtaining the expansion length data of the welder;

[0148] In this embodiment, the expansion length is calculated based on the welder's heat conduction data and the coefficient of thermal expansion. According to the formula for the thermal expansion of the welding material, ΔL = L0·α·ΔT, where L0 is the initial length, α is the linear expansion coefficient of the material, and ΔT is the temperature difference. The temperature change value at each welding point in the heat conduction simulation data is used as ΔT, combined with the welder length (e.g., 1m) and the expansion coefficient (e.g., for steel, α = 1.2 × 10⁻⁶). -5 K -1The calculation is performed using a loop program written in a scripting tool (such as Python) to calculate and store the expansion length for each weld point. The results are recorded in the form of an expansion length distribution table, which includes the welder coordinates, temperature change value, and expansion length.

[0149] Step S234: Obtain the initial length data of the welder;

[0150] In this embodiment, the initial length data is determined through direct measurement or design drawing parameters. For welders with a known design, the initial length value (e.g., 1.2m) is directly read from the CAD model. If no design parameters are available, a laser rangefinder is used to measure the end positions of the welder, with an accuracy controlled within ±0.1mm. The measurement environment must ensure temperature stability (e.g., 25℃) to reduce the impact of thermal expansion. The measurement data is recorded as scalar values. If the welder has a segmented structure, the initial length of each segment is recorded, and a complete length distribution table is generated.

[0151] Step S235: Calculate the thermal expansion load based on the initial length data and expansion length data of the welder to obtain the thermal load data of the welder.

[0152] In this embodiment, the thermal expansion load is calculated based on the expansion length and initial length of the welder, using mechanical analysis formulas. Calculate the load caused by thermal expansion. Where E is the elastic modulus of the welder material (e.g., E = 210 GPa for steel), and A is the cross-sectional area of ​​the welder (e.g., A = 0.0025 m²). 2 ΔL represents the expansion length, and L0 represents the initial length. A calculation program is written to import the expansion length and initial length data, and calculates the thermal expansion load value point by point. After calculation, the load data is plotted according to the spatial distribution of the welder, including the load magnitude, direction, and coordinates of the point of application, for welding quality assessment and template verification analysis.

[0153] Optionally, step S26 specifically includes:

[0154] Step S31: Extract sliding joint features and rotary joint features based on the 3D robotic arm model to obtain sliding joint and rotary joint data;

[0155] In this embodiment, the feature extraction of sliding and rotary joints is based on the CAD model of the 3D robotic arm and is performed using feature recognition tools (such as the SolidWorks API or OpenCASCADE library). The 3D model of the robotic arm is loaded, its structural hierarchy is analyzed, and the spatial positions and types of all joints are located. Sliding joint feature extraction is performed by analyzing the joint's motion constraints, extracting the sliding range (e.g., 0–100 mm along the X-axis), and simultaneously recording the guide rail length and sliding direction. The data is saved in tabular form. Rotary joint feature extraction is performed by calculating the rotation angle range (e.g., 0–180° around the Z-axis) and the coordinates of the rotation center through the joint's rotation axis direction. If the 3D model does not contain explicit constraint information, inverse kinematics algorithms are used to verify the joint type and motion parameters to ensure data accuracy. All extracted joint parameters include sliding distance, rotation angle range, and degrees of freedom, and the data is stored in JSON format.

[0156] Step S32: Perform harmonic resonance analysis on the sliding joint data to obtain the sliding joint harmonic resonance data;

[0157] In this embodiment, the harmonic resonance analysis of the sliding joint is based on extracted sliding joint data and material property parameters. The joint material is frequency-scanned using a resonance testing device (such as a Dynamic Mechanical Analyzer (DMA)) to obtain the material's natural frequency range. Combined with the sliding joint's range of motion, numerical calculation methods (such as finite element analysis) are used to analyze the structural resonance characteristics. Specifically, the geometric parameters of the sliding joint are imported into simulation software (such as ANSYS), material properties are set (e.g., elastic modulus 210 GPa, density 7800 kg / m³), boundary conditions are set (e.g., free boundary or fixed boundary), and the resonant frequency scan range (e.g., 10 Hz to 1000 Hz) and step size (e.g., 1 Hz) are input. The response amplitude of the joint at different frequencies is calculated, and the harmonic resonance frequency points (e.g., 100 Hz, 300 Hz) and their corresponding amplitude values ​​are recorded. After the analysis is completed, the harmonic resonance data is output as a frequency-amplitude relationship graph and a numerical data file.

[0158] Step S33: Perform radial vibration analysis on the rotary joint data to obtain the radial vibration data of the rotary joint;

[0159] In this embodiment, the radial vibration analysis of the rotary joint is performed through a combination of dynamic testing and numerical simulation. Based on the rotary joint data, a high-precision laser vibration meter (such as Polytec PSV-500) is used to conduct radial vibration tests. The rotary joint is set to run at different speeds (such as 100 rpm, 200 rpm, and 300 rpm), and radial amplitude and frequency data are recorded. Simultaneously, based on the geometric parameters and material properties of the rotary joint, a finite element model of the rotary joint is constructed, and the vibration characteristics are simulated and calculated. Specific steps include inputting the axial deviation and mass distribution of the rotary joint into the simulation tool, setting the loading conditions (such as a torque of 0.5 Nm) and the speed range (such as 0–1000 rpm). Radial vibration displacement data is obtained through time-domain and frequency-domain analysis, and the peak amplitude and resonant frequency are calculated. After comparing and verifying the test and simulation data, the radial vibration data of the rotary joint is output and stored in the form of time-displacement curves and frequency-amplitude graphs.

[0160] Step S34: Integrate the vibration characteristics of the robotic arm based on the harmonic resonance data of the sliding joint and the radial vibration data of the rotary joint to obtain the vibration data of the robotic arm.

[0161] In this embodiment, the vibration characteristics of the robotic arm are integrated based on harmonic resonance data of the sliding joints and radial vibration data of the rotary joints, using vibration data comprehensive analysis tools (such as MATLAB or the Python library SciPy). The harmonic resonance frequency and amplitude data of the sliding joints are aligned with the radial vibration displacement and frequency data of the rotary joints along the time axis. A data fusion algorithm (such as the weighted average method) is used to calculate the comprehensive vibration amplitude of each joint at the same time step. Vibration characteristic indicators of the robotic arm, such as total amplitude, are defined. Where A_i represents the vibration amplitude of the i-th joint, and n is the total number of joints. The calculation results are categorized and organized according to the structural hierarchy of the robotic arm, and a three-dimensional vibration characteristic chart is generated. The data includes the correlation between vibration amplitude, frequency distribution, and joint positions. The data results are exported in CSV format for the evaluation and optimization of the robotic arm's motion performance.

[0162] Optionally, step S32 specifically includes:

[0163] Step S321: Extract the structural features of the sliding guide rail and the damping coefficient features of the guide rail from the sliding joint data to obtain the structural data of the sliding guide rail and the damping coefficient of the guide rail.

[0164] In this embodiment, high-precision measuring equipment, such as a coordinate measuring machine, is used to accurately measure the geometric parameters of the guide rail, including its length, width, height, and groove shape. Through measurement, surface roughness data (such as Ra value) and physical properties of the guide rail material, such as elastic modulus and density, are obtained. To obtain the damping coefficient of the guide rail, an actual motion damping test is performed. This process involves setting the initial displacement of the slider and allowing it to decay freely, then using damping testing equipment to record the displacement change of the slider during vibration. By analyzing the displacement decay curve of the slider, the damping characteristics of the guide rail are extracted. Finally, these measurement results will form a complete set of sliding guide rail structural data and guide rail damping coefficient data, which will be organized and archived in tabular form.

[0165] Step S322: Construct the sliding joint spring damping model based on the sliding guide rail structure data and guide rail damping coefficient, thereby obtaining the sliding joint spring damping model;

[0166] In this embodiment, the stiffness and damping characteristics of the spring in the system are determined based on the geometry, material properties, and damping coefficient of the sliding guide rail. The spring stiffness is calculated by the displacement response of the slider under external force. Next, a model of the spring and damping system is built using mechanical modeling tools such as MATLAB Simulink. This model is based on the static state of the slider as the initial condition, a known external force (e.g., 1N) is applied, and the stability of the system is verified. Through this process, parameters such as spring stiffness and damping coefficient can be set and adjusted in the model to ensure that the model can accurately simulate the dynamic response of the actual sliding joint. Finally, after the model is completed, the parameters such as spring stiffness and damping coefficient are stored in a configuration file to prepare for subsequent harmonic excitation analysis.

[0167] Step S323: Apply harmonic excitation to the sliding joint spring damping model to obtain harmonic excitation data;

[0168] In this embodiment, harmonic excitation is applied based on a sliding joint spring-damped model, implemented using a dynamic loading tool (such as a shaking table or computational simulation tool). The amplitude and frequency range of the harmonic excitation are set, for example, an amplitude of 0.5 N, a frequency range from 10 Hz to 500 Hz, and a step size of 5 Hz. Periodic forces are applied to the spring-damped model using the shaking table control software, and the displacement and acceleration responses of the slider at different frequencies are recorded. In the simulation method, the periodic external force F(t) = F0sin(2πft) is set in the simulation tool, where F0 = 0.5 N, f is the excitation frequency, and t is time (in seconds). The response data is recorded as time-displacement and time-acceleration curves and output as a CSV format harmonic excitation data file for subsequent amplitude-frequency analysis.

[0169] Step S324: Plot the resonant amplitude-frequency response curve based on the harmonic excitation data to obtain the resonant amplitude-frequency response curve data;

[0170] In this embodiment, the resonant amplitude-frequency response curve is plotted through frequency domain analysis of the harmonic excitation data. The time-domain data is converted to frequency-domain data using a Fourier transform tool (such as the `fft` function in MATLAB), and the displacement amplitude corresponding to each frequency point is extracted. The amplitude-frequency response curve is plotted using a data visualization tool (such as MATLAB or OriginPro) with frequency as the horizontal axis and displacement amplitude as the vertical axis. The frequency points and corresponding amplitudes are clearly marked on the curve; for example, the amplitude is 0.8 mm at 50 Hz and 1.5 mm at 200 Hz. After plotting, the curve data is output as an Excel file, containing the specific data points for frequency and amplitude, for further analysis.

[0171] Step S325: Perform amplitude statistics on the resonance amplitude-frequency response curve data to obtain high-amplitude resonance data;

[0172] In this embodiment, high-amplitude resonance data statistics are based on peak value analysis of the resonance amplitude-frequency response curve. The response curve data is imported, and data processing tools (such as Python's Pandas library or MATLAB) are used to filter frequency points whose amplitude exceeds a specific threshold. A threshold for amplitude statistics is set (e.g., 1.0 mm), and all frequency points with amplitudes greater than the threshold and their corresponding amplitudes are filtered out. For example, at 150 Hz, the amplitude is 1.2 mm, and at 200 Hz, the amplitude is 1.5 mm. The filtered data is stored in a table format, including frequency, amplitude, and threshold exceedance indicators, and exported as a CSV file.

[0173] Step S326: Perform peak value statistics on the resonance amplitude-frequency response curve data to obtain wide peak value resonance data;

[0174] In this embodiment, the wide peak resonance data statistics are completed by analyzing the width of the peaks in the amplitude-frequency response curve. Data analysis tools (such as the `find_peaks` function in Python's SciPy library) are used to locate all local peaks in the curve, and the frequency position of each peak and its corresponding full width at half maximum (FWHM) are recorded. The frequency range of the FWHM is calculated by interpolation; for example, if the peak is located at 200Hz and the FWHM range is 180–220Hz, then the peak width is 40Hz. Frequency points with peak widths greater than a set threshold (e.g., 20Hz) and their corresponding peak information are selected, and this data is compiled into a table, including peak frequency, peak amplitude, and peak width data.

[0175] Step S327: Integrate the abnormal harmonic resonance of the sliding joint based on the high amplitude resonance data and the wide peak value resonance data to obtain the harmonic resonance data of the sliding joint.

[0176] In this embodiment, the integration of abnormal harmonic resonances in the sliding joint is achieved through comprehensive analysis of high-amplitude resonance data and wide-peak resonance data. Data processing tools (such as Python's NumPy library) are used to perform intersection operations on high-amplitude and wide-peak resonance points, filtering out frequency points that simultaneously meet the conditions of high amplitude and wide peak, and recording their frequency, amplitude, and peak width information. For example, an amplitude of 1.5 mm and a peak width of 40 Hz at a frequency of 200 Hz meet the abnormal resonance conditions. All filtered abnormal resonance data are organized into a table, and their corresponding abnormality categories (such as amplitude abnormality or wide-peak abnormality) are labeled. The final data is saved as a JSON file for the diagnosis and optimization design of the resonance characteristics of the sliding joint.

[0177] Optionally, step S33 specifically includes:

[0178] Step S331: Extract the structural features of the rotating disk from the rotary joint data to obtain the structural data of the rotating disk;

[0179] In this embodiment, the extraction of the structural features of the rotating disk begins with the measurement of the geometric parameters of the rotating joints. A coordinate measuring machine (CMM) is used to perform a high-precision scan of the rotating disk, measuring geometric data such as the diameter, thickness, groove shape, and hole positions, and recording the data in millimeters. For surface roughness, a surface roughness meter (such as MITUTOYOS J-210) is used to scan the surface of the rotating disk, ensuring that the Ra value is not greater than 0.8 μm. If the rotating disk is made of metal, a hardness tester is also used to perform a hardness test, recording the hardness value, such as HV350. Furthermore, X-ray non-destructive testing is used to scan the interior of the rotating disk to detect potential internal cracks or porosity. All collected data needs to be stored in a structured numerical format for subsequent analysis.

[0180] Step S332: Calculate the angular acceleration of the rotary joint data to obtain the angular acceleration data;

[0181] In this embodiment, a three-axis gyroscope sensor is installed on the rotary joint to acquire real-time angular velocity data. The acquisition frequency is set to 100Hz, i.e., 100 data acquisitions per second. Angular acceleration data is obtained by performing a first-order difference on the angular velocity data. Specifically, the change in angular velocity values ​​between adjacent time points is calculated, and this change is divided by the time interval (e.g., 0.01 seconds). This method is used to calculate the angular acceleration at each moment, and the data is smoothed to reduce the influence of noise. Finally, the obtained angular acceleration data should have high accuracy, with units of rad / s². The data is stored in CSV format for easy subsequent analysis and comparison.

[0182] Step S333: Construct a vibration model based on the rotating disk structure data and angular acceleration data to obtain the rotating disk vibration model;

[0183] In this embodiment, the vibration model is constructed by combining the structural feature data and angular acceleration data of the rotating disk, using the finite element analysis (FEA) method. The geometric data (e.g., diameter, thickness) and material properties (e.g., elastic modulus, density) of the rotating disk are input into finite element analysis software (e.g., ANSYS). Then, the angular acceleration data is applied as an external load to the model. The boundary conditions of the rotating disk are set as fixed end and free end. By simulating the dynamic response of the rotating disk at different rotational speeds, the vibration modes of the rotating disk are calculated. This model obtains the vibration response of the rotating disk at different frequencies by solving the vibration equation. Frequency analysis is performed using 20 frequency points to ensure coverage of the operating frequency range of the rotating disk (e.g., 0Hz to 1000Hz). Through this process, the vibration model of the rotating disk is obtained for subsequent vibration response analysis.

[0184] Step S334: Calculate the radial vibration response based on the rotating disk vibration model to obtain radial vibration response data;

[0185] In this embodiment, the rotational speed (e.g., 3000 rpm) and stress conditions (e.g., 10 N axial load) of the rotating disk during actual operation are input into the vibration model of the rotating disk. Dynamic simulation is performed in finite element analysis software to simulate the radial vibration response of the rotating disk. The simulation process uses a time-domain analysis method to simulate the vibration of the rotating disk under different rotational speeds and load conditions, and extracts its radial displacement data. To improve calculation accuracy, the simulation is set to 1000 times per second, accurately recording the vibration amplitude at each moment. Finally, the radial vibration response data of the rotating disk under these conditions is obtained, including the amplitude, frequency, and phase information of the vibration. The data results should be in millimeters and stored in matrix format for subsequent processing.

[0186] Step S335: Obtain standard radial vibration response data;

[0187] In this embodiment, a vibration table is used to simulate the standard operating conditions of the rotating disk, such as a rotation speed of 3000 rpm and a load of 10 N. An accelerometer is installed on the vibration table, ensuring that the sensor's measurement range is ±500 m / s², its resolution is 0.01 m / s², and its sampling frequency is set to 1000 Hz. According to standard testing specifications, the rotating disk needs to undergo at least three vibration tests to ensure the accuracy and consistency of the data. In each test, the radial vibration response of the rotating disk driven by the vibration table is recorded, including parameters such as vibration amplitude, frequency, and acceleration. All data should be recorded in real time through a data acquisition system, and the results should be compared with standard vibration response data to ensure that the experimental error is less than ±5%. Finally, the standard data should be saved in CSV format as a benchmark for subsequent anomaly detection.

[0188] Step S336: Divide the radial vibration response data into abnormal radial vibrations of the rotary joint according to the standard radial vibration response data, thereby obtaining the radial vibration data of the rotary joint.

[0189] In this embodiment, the permissible error range of the standard data is determined, with ±10% of the vibration amplitude as the boundary; vibration data exceeding this range is considered abnormal. Next, the radial vibration response data obtained in step S334 is compared and analyzed with the standard radial vibration response data. Specifically, the difference between the two sets of data is calculated point by point to obtain the deviation value. If the deviation value at a certain point exceeds ±10%, it is marked as abnormal vibration data. A threshold is set when classifying abnormal data; for example, a deviation exceeding 10% of the standard value is considered abnormal. This threshold was experimentally verified. After classification, all normal and abnormal vibration data are saved separately for subsequent fault diagnosis and maintenance analysis.

[0190] Optionally, step S4 specifically includes:

[0191] Step S41: Perform dynamic load time statistics based on the dynamic load data of the robotic arm to obtain the dynamic load time data of the robotic arm;

[0192] In this embodiment, load sensors are installed on each joint of the robotic arm, and strain gauge technology is used for data acquisition. This ensures that the load sensor's range covers the robotic arm's maximum load range (e.g., 0-1000N), and that the sensor's accuracy is 0.1N. The data collected by the sensors is uploaded in real-time through a data acquisition system, with a sampling frequency set to 100Hz to ensure data detail. During data acquisition, a load threshold is set; for example, a load exceeding 50N is considered a dynamic load state. By performing time interval analysis on the load data, the time period during which the load exceeds 50N is calculated, i.e., the dynamic load time of the robotic arm. The duration of each load state exceeding this threshold is accumulated to obtain the duration of the dynamic load in each operation. Finally, the dynamic load time data for all operations is stored in CSV format for easy subsequent analysis.

[0193] Step S42: Perform vibration time statistics based on the robotic arm vibration data to obtain robotic arm vibration time data;

[0194] In this embodiment, the statistical analysis of the robotic arm's vibration time relies on vibration sensor data. Accelerometers are installed at each joint or key location to measure the robotic arm's vibration state during operation. The accelerometer range is set to ±50 m / s², and the sampling frequency is 1000 Hz to ensure accurate vibration signal acquisition. The vibration intensity is determined by analyzing the collected vibration data. If the vibration acceleration exceeds a set threshold, such as 1 m / s², the robotic arm is considered to have vibrated. For vibration time statistics, the vibration data is first filtered to remove low-frequency noise and unnecessary interference. Then, the time periods when the vibration acceleration exceeds 1 m / s² are recorded, and these time periods are accumulated to obtain the vibration time of the robotic arm throughout its entire working cycle. All vibration time data is recorded in seconds and stored in a data file for further data analysis and processing.

[0195] Step S43: Calculate the time overlap based on the dynamic load time data and vibration time data of the robotic arm to obtain the dynamic load-vibration data of the robotic arm;

[0196] In this embodiment, the dynamic load time period and vibration time period of the robotic arm are compared within a time window. For each load time period, it is checked whether it overlaps with the vibration time period. If the two time periods overlap, it is considered that the robotic arm has both dynamic load and vibration during this period. When calculating the overlap, the length of the overlapping portion is compared with the total length of the load time period and the vibration time period to obtain the percentage of time overlap. The specific calculation method is: overlap time / maximum time period length × 100%, where the overlap time is the length of the time period during which load and vibration coexist, and the maximum time period length is the longer of the load time period or the vibration time period. In this way, the dynamic load-vibration time overlap data of the robotic arm during operation is obtained, and the data results are recorded in percentage form and stored as a structured file.

[0197] Step S44: Construct a wear model from the dynamic load-vibration data of the robotic arm to obtain the wear model;

[0198] In this embodiment, the wear model is constructed based on the dynamic load-vibration data of the robotic arm, combining mechanical analysis methods with experimental data. The main influencing factors of wear are determined, such as dynamic load, vibration frequency, vibration amplitude, and the material properties of the robotic arm components. Based on existing wear theories, it is assumed that the coupling effect of load and vibration has a direct impact on the wear of mechanical components. By introducing a wear coefficient (e.g., wear coefficient k = 0.01), and combining dynamic load and vibration data, a formula is used to calculate wear. The formula is:

[0199] W = k·F·Δx·N;

[0200] Where W represents the wear amount, k is the wear coefficient, F is the applied dynamic load, Δx is the displacement, and N is the number of vibration cycles. Based on experimental data (e.g., wear conditions under different loads), an appropriate value for the wear coefficient k is determined, and the dynamic load and vibration data are substituted into the formula to calculate the wear amount. Finally, through model fitting, a wear model of the robotic arm under different operating conditions is obtained. This model can describe the degree and variation of wear of mechanical components under given load and vibration conditions.

[0201] Step S45: Calculate the material wear of the robotic arm structure data based on the wear model to obtain the material wear data of the robotic arm.

[0202] In this embodiment, geometric data of each joint and moving component is extracted from the robotic arm structure, including dimensions, surface treatment, and material type. For example, a joint has a diameter of 50mm, a surface hardness of HRB60, and is made of steel. Then, combined with the wear model constructed in step S44, relevant dynamic load and vibration data are input to calculate the wear amount of each structural component. For each component, its wear condition over a certain operating time is calculated using a formula, and the remaining material thickness and durability are output. For more accurate calculations, historical working data of the robotic arm, such as load frequency, vibration amplitude, and working duration, are used to perform multiple calculations to obtain material wear data for each component under different working conditions. This data includes the wear depth, wear rate, and estimated service life of each component. Finally, all calculation results are compiled into a table to facilitate subsequent maintenance decisions and material selection.

[0203] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0204] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for generating an industrial robot accessory adaptation model, characterized in that, Includes the following steps: Step S1: Obtain the structural data of the industrial robot, and extract the structural features of the robotic arm based on the structural data of the industrial robot to obtain the structural data of the robotic arm. A three-dimensional robotic arm model is constructed based on the robotic arm's structural data, thereby obtaining the three-dimensional robotic arm model. Step S2: Extract features from the gripper and welder based on the 3D robotic arm model to obtain gripper data and welder data; perform dynamic load analysis based on the gripper data and welder data to obtain dynamic load data of the robotic arm. Step S3: Extract sliding joint features and rotary joint features from the 3D robotic arm model to obtain sliding joint and rotary joint data; perform vibration analysis based on the sliding joint and rotary joint data to obtain robotic arm vibration data. Step S3 specifically involves: Step S31: Extract sliding joint features and rotary joint features based on the 3D robotic arm model to obtain sliding joint and rotary joint data; Step S32: Perform harmonic resonance analysis on the sliding joint data to obtain the sliding joint harmonic resonance data; Step S33: Perform radial vibration analysis on the rotary joint data to obtain the radial vibration data of the rotary joint. Step S33 specifically involves: Step S331: Extract the structural features of the rotating disk from the rotary joint data to obtain the structural data of the rotating disk; Step S332: Calculate the angular acceleration of the rotary joint data to obtain the angular acceleration data; Step S333: Construct a vibration model based on the rotating disk structure data and angular acceleration data to obtain the rotating disk vibration model; Step S334: Calculate the radial vibration response based on the rotating disk vibration model to obtain radial vibration response data; Step S335: Obtain standard radial vibration response data; Step S336: Divide the radial vibration response data into abnormal radial vibrations of the rotary joint based on the standard radial vibration response data to obtain the radial vibration data of the rotary joint; Step S34: Integrate the vibration characteristics of the robotic arm based on the harmonic resonance data of the sliding joint and the radial vibration data of the rotary joint to obtain the vibration data of the robotic arm. Step S4: Perform correlation analysis on the robotic arm vibration data based on the robotic arm dynamic load data to obtain the robotic arm dynamic load-vibration data; perform material wear assessment based on the robotic arm dynamic load-vibration data to obtain the robotic arm material wear data. Step S4 specifically involves: Step S41: Perform dynamic load time statistics based on the dynamic load data of the robotic arm to obtain the dynamic load time data of the robotic arm; Step S42: Perform vibration time statistics based on the robotic arm vibration data to obtain robotic arm vibration time data; Step S43: Calculate the time overlap based on the dynamic load time data and vibration time data of the robotic arm to obtain the dynamic load-vibration data of the robotic arm; Step S44: Construct a wear model from the dynamic load-vibration data of the robotic arm to obtain the wear model; Step S45: Calculate the material wear of the robotic arm structure based on the wear model to obtain the material wear data of the robotic arm; Step S5: Estimate the service life of the robotic arm based on the wear data of the robotic arm material to obtain the service life data of the robotic arm; generate the robotic arm industrial robot accessory adaptation model based on the service life data of the robotic arm to obtain the robotic arm industrial robot accessory adaptation model, and upload it to the industrial robot management platform to execute the robotic arm accessory generation task.

2. The method for generating an industrial robot accessory adaptation model according to claim 1, characterized in that, Step S1 is as follows: Step S11: Obtain the structural data of the industrial robot, and extract the structural features of the robotic arm based on the structural data of the industrial robot to obtain the structural data of the robotic arm. Step S12: Establish reference coordinates based on the robotic arm structure data to obtain the robotic arm structure reference coordinate data; Step S13: Model the joint structure based on the reference coordinate data of the robotic arm structure to obtain the joint modeling data of the robotic arm; Step S14: Model the link structure based on the reference coordinate data of the robotic arm structure to obtain the robotic arm link modeling data; Step S15: Connect the robotic arm joint modeling data in three dimensions based on the robotic arm link modeling data to obtain a three-dimensional robotic arm model.

3. The method for generating an industrial robot accessory adaptation model according to claim 1, characterized in that, Step S2 is as follows: Step S21: Extract features from the gripper and the welder based on the 3D robotic arm model to obtain gripper data and welder data; Step S22: Perform mechanical load stability analysis on the gripper data to obtain gripper mechanical load stability data; Step S23: Perform thermal load analysis on the welder data to obtain the welder thermal load data; Step S24: Based on the gripper's mechanical load stability data and the welder's thermal load data, integrate the robot arm's dynamic load to obtain the robot arm's dynamic load data.

4. The method for generating an industrial robot accessory adaptation model according to claim 3, characterized in that, Step S22 is as follows: Step S221: Simulate gripping data using the gripper data to obtain gripper gripping simulation data; Step S222: Calculate the clamping force based on the gripper grasping simulation data to obtain the clamping force data; Step S223: Extract the features of the gripper contact points from the gripping force data to obtain the gripper contact point data; Step S224: Perform force calculation based on the contact point data of the gripper to obtain the contact point force data; Step S225: Perform slip analysis based on the force data at the contact point to obtain slip data; Step S226: Perform load stability assessment on the gripper data based on the sliding data to obtain gripper mechanical load stability data.

5. The method for generating an industrial robot accessory adaptation model according to claim 4, characterized in that, Step S225 specifically includes: The contact point normal force features are extracted based on the contact point force data to obtain the contact point normal force data; Obtain the coefficient of friction at the contact point; Friction force is calculated based on the contact point friction coefficient and contact point normal force data to obtain friction force data; Based on the force data at the contact point, the horizontal component force feature and the vertical component force feature at the contact point are extracted to obtain the horizontal component force data and the vertical component force data at the contact point. Tangential force data is obtained by calculating the horizontal and vertical force components at the contact point. Slippage is determined based on friction and tangential force data, thereby obtaining slippage data.

6. The method for generating an industrial robot accessory adaptation model according to claim 3, characterized in that, Step S23 is as follows: Step S231: Extract the power feature and thermal conductivity feature of the welder data to obtain the power data and thermal conductivity of the welder. Step S232: Perform heat conduction simulation based on the welder power data and the welder thermal conductivity to obtain the welder heat conduction data; Step S233: Calculate the expansion length of the welder based on the heat conduction data of the welder, thereby obtaining the expansion length data of the welder; Step S234: Obtain the initial length data of the welder; Step S235: Calculate the thermal expansion load based on the initial length data and expansion length data of the welder to obtain the thermal load data of the welder.

7. The method for generating an industrial robot accessory adaptation model according to claim 1, characterized in that, Step S32 is as follows: Step S321: Extract the structural features of the sliding guide rail and the damping coefficient features of the guide rail from the sliding joint data to obtain the structural data of the sliding guide rail and the damping coefficient of the guide rail. Step S322: Construct the sliding joint spring damping model based on the sliding guide rail structure data and guide rail damping coefficient, thereby obtaining the sliding joint spring damping model; Step S323: Apply harmonic excitation to the sliding joint spring damping model to obtain harmonic excitation data; Step S324: Plot the resonant amplitude-frequency response curve based on the harmonic excitation data to obtain the resonant amplitude-frequency response curve data; Step S325: Perform amplitude statistics on the resonance amplitude-frequency response curve data to obtain high-amplitude resonance data; Step S326: Perform peak value statistics on the resonance amplitude-frequency response curve data to obtain wide peak value resonance data; Step S327: Integrate the abnormal harmonic resonance of the sliding joint based on the high amplitude resonance data and the wide peak value resonance data to obtain the harmonic resonance data of the sliding joint.

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