Mechanical arm joint kinetic model correction method

Abstract

The invention relates to the technical field of industrial robots, in particular to a mechanical arm joint kinetic model correction method. The method comprises the following steps: acquiring joint data of the mechanical arm, and performing geometric structure feature extraction according to the joint data of the mechanical arm so as to obtain geometric structure data; mechanical arm joint topological structure analysis is carried out according to the geometric structure data, so that a mechanical arm joint topological structure model is obtained; knuckle bearing feature extraction and connection joint feature extraction are conducted according to the mechanical arm joint data, and accordingly knuckle bearing data and connection joint data are obtained; and abnormal operation vibration analysis is carried out according to the joint bearing data and the connection joint data, so that abnormal operation vibration data of the mechanical arm joint are obtained. Dynamic characteristics of the mechanical arm joint are accurately described based on the industrial robot technology, and the positioning precision and the working stability of the mechanical arm can be remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial robots, and in particular to a method for correcting a mechanical arm joint dynamics model. Background Art

[0002] Robotic arms have been widely used in many fields such as modern industry, medical treatment, and logistics. One of its core components is the dynamic model of the robot arm joint, which directly determines the performance of the robot arm in terms of accuracy, stability, speed, etc. The traditional dynamic model of the robot arm joint is established through a large amount of experimental data and formula derivation, usually using rigid body dynamics or elastic body dynamics models. However, the robot arm joints are affected by multiple external factors such as high temperature, vibration, fatigue, and load fluctuations in different usage environments, resulting in deviations between their dynamic characteristics and the standard model, making it difficult to effectively meet the needs of high-precision application scenarios. Especially in high-load and high-frequency operation scenarios, traditional models often cannot accurately describe the actual state of the robot arm joints, thereby affecting the positioning accuracy, motion consistency, and service life of the robot arm. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method for correcting a robot arm joint dynamics model to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for correcting a mechanical arm joint dynamics model comprises the following steps:

[0005] S1: Acquire the robot arm joint data, and extract the geometric structure features according to the robot arm joint data, so as to obtain the geometric structure data; perform the topological structure analysis of the robot arm joint according to the geometric structure data, so as to obtain the robot arm joint topological structure model;

[0006] S2: Extract joint bearing features and connection joint features according to the robot arm joint data, so as to obtain joint bearing data and connection joint data; perform abnormal operation vibration analysis according to the joint bearing data and connection joint data, so as to obtain abnormal operation vibration data of the robot arm joint; perform abnormal vibration structure division on the robot arm joint topology structure model according to the abnormal operation vibration data of the robot arm joint, so as to obtain the abnormal vibration topology structure model of the robot arm joint;

[0007] S3: Performing high temperature simulation according to the abnormal vibration topological structure model of the robot arm joint to obtain high temperature simulation data; performing material expansion analysis according to the high temperature simulation data to obtain material thermal expansion data; performing expansion deformation calculation on the abnormal vibration topological structure model of the robot arm joint according to the material thermal expansion data to obtain expansion deformation data;

[0008] S4: Acquire robot arm joint sensor data; perform robot arm joint deformation analysis on the abnormal vibration topological structure model of the robot arm joint according to the robot arm joint sensor data, so as to obtain the robot arm joint deformation data;

[0009] S5: Dynamically correct the deformation data of the robot arm joints according to the expansion deformation data, so as to obtain the robot arm joint deformation correction data, and upload it to the robot arm joint flexible body dynamics model management system to perform the robot arm joint structure deformation correction task.

[0010] The present invention can construct an accurate joint geometry model by acquiring the data of the mechanical arm joint and extracting the geometric structure features, laying the foundation for subsequent analysis. The geometric structure data provides the required coordinates, connections and spatial information for the topological structure analysis. By modeling the topological structure of the geometric data, it is helpful to fully understand the internal structure and mutual connection relationship of the mechanical arm joint, which is convenient for subsequent abnormal operation analysis. The extraction of joint bearing and connection joint features can deeply analyze the working state of key components, ensure that the data accurately reflects the actual working conditions of the bearing and connection components, and the abnormal vibration analysis can not only quickly identify the abnormal vibration signal of the joint, but also locate the vibration area through the topological structure division, forming an "abnormal vibration topological structure model" based on the actual state. Through the early detection and division of such abnormal vibration, it is helpful to timely predict potential fault risks and extend the life of the equipment. Simulating the operation state of the mechanical arm joint in a high temperature environment can identify the influence of high temperature on the stress distribution and thermal expansion coefficient of the material. The thermal expansion data of the material is obtained through thermal expansion analysis. These data reveal the deformation of the material under temperature changes. Further, through the expansion deformation calculation, the high temperature expansion deformation of different joints can be obtained, which helps to compensate for the components sensitive to temperature, and ensure that the mechanical arm still has high precision and stability under different temperature environments. By collecting sensor data of the robot arm joints, the working state of the robot arm joints can be monitored in real time, and their actual deformation can be identified. By combining with the topological structure model for deformation analysis, the actual deformation under specific working conditions can be obtained, which directly reflects the dynamic response of the robot arm joints in a complex stress environment. It has guiding value and provides accurate actual deformation feedback for the correction model. By combining the expansion deformation data with the sensor deformation data, the joint deformation can be dynamically corrected, and the obtained joint deformation correction data can be uploaded to the robot arm joint flexible body dynamics model management system to perform the structural deformation correction task. This dynamic correction process allows the robot arm to maintain a high-precision working state under changing temperature and load conditions, improve the accuracy and adaptability of the overall model, provide important data for subsequent flexible body dynamics model management, and extend the service life of the equipment.

[0011] Optionally, step S1 specifically includes:

[0012] Step S11: acquiring the robot arm joint data, and performing geometric structure feature extraction based on the robot arm joint data, thereby obtaining geometric structure data;

[0013] Step S12: extracting the three-dimensional vertex coordinates of the robot arm joints according to the geometric structure data, thereby obtaining the three-dimensional vertex coordinate data of the robot arm joints;

[0014] Step S13: constructing a three-dimensional surface according to the three-dimensional vertex coordinate data of the robot arm joints, thereby obtaining three-dimensional surface data;

[0015] Step S14: performing spatial mapping of the robot arm joint topology structure model according to the three-dimensional surface data, thereby obtaining the robot arm joint topology structure model.

[0016] The present invention can obtain key geometric information, including the shape, size and connection relationship of the joint, etc., by acquiring the mechanical arm joint data and extracting the geometric structure features, laying a solid foundation for subsequent three-dimensional modeling, ensuring the accuracy and precision of the subsequent model, and making it more operable when performing structural analysis and vibration evaluation under different working environments. By extracting the three-dimensional vertex coordinates of the mechanical arm joint, the precise position of each point in the three-dimensional space can be obtained, providing detailed data support for building a real geometric model. This coordinate data directly determines the effect of subsequent three-dimensional facet construction, helps to improve the accuracy of the model, and makes it closer to the real structure of the joint during simulation and analysis. Facet construction is carried out using three-dimensional vertex coordinates to form the surface structure of the joint. By converting the vertex coordinate data into triangular facets, a complete three-dimensional geometric model can be formed, so that the appearance and morphology of the joint are clearly presented. The constructed three-dimensional facet data is very important in high-precision simulation and structural optimization analysis, and lays a solid foundation for the spatial mapping of topological structures. After obtaining the three-dimensional patch data, spatial mapping of the topological structure model can fully present the geometric features of the joint in the model, and then generate a topological structure model of the joint. This model can reflect the connection relationship and spatial layout of the joint, and provide a global structural view for subsequent mechanical analysis and dynamic simulation, so that the dynamic characteristics of the joint under different working conditions can be more accurate. Spatial mapping provides data support for the prediction and dynamic adjustment of joint behavior under abnormal conditions, which helps to enhance the accuracy and applicability of the model.

[0017] Optionally, step S13 is specifically:

[0018] Step S131: triangulate the three-dimensional vertex coordinate data of the robot arm joints to obtain triangular facet data;

[0019] Step S132: determining the adjacent relationship according to the three-dimensional vertex coordinate data of the robot arm joint, thereby obtaining vertex adjacent relationship data;

[0020] Step S133: Calculating the normal vector according to the three-dimensional vertex coordinate data of the robot arm joint, thereby obtaining the normal vector data;

[0021] Step S134: constructing a three-dimensional patch on the triangular patch data according to the normal vector data and the vertex adjacent relationship data, thereby obtaining three-dimensional patch data.

[0022] The present invention connects vertex coordinate data into triangular facets by using triangulation, which is the basis for building a complete three-dimensional structure. The triangular facet data obtained by triangulation can accurately reflect the surface shape and geometric features of the joint, improve the surface accuracy of the model, ensure the accuracy of the model, and provide a basis for subsequent structural mechanics analysis and surface deformation analysis, so that the accuracy and reliability of the simulation are greatly improved. The adjacent relationship of the vertices is determined to generate the associated data between the vertices and the facets in the three-dimensional model. The adjacent relationship is the key to the topological structure, which can ensure the integrity and continuity of the triangular facets. In the subsequent dynamic simulation or deformation calculation, the adjacent relationship can help the model achieve accurate local response and overall coordination, and provide an accurate description for the actual structural characteristics of the joint, especially for local stress or deformation analysis. Calculating the normal vector for each triangular facet to make the facet direction consistent is a key step in achieving correct rendering and analysis of the three-dimensional model. The normal vector data can help determine the direction of illumination and shadow during the rendering process, and can also be used to calculate the direction of the force in mechanical analysis. Through accurate normal vector data, the dynamic characteristics of the mechanical arm joint can be simulated more intuitively and accurately, which is convenient for detecting and controlling abnormal vibration or stress concentration. The three-dimensional facets are constructed based on the normal vectors and adjacent relationship data to ensure that the connection relationship and directionality of each facet are consistent, thereby generating a complete three-dimensional model. This model can not only accurately reflect the geometric structure of the joint, but also support complex dynamic simulation and stress analysis. Through the construction of three-dimensional faces, the model has more complete spatial information, which provides support for simulating the response of joints in high temperature, high stress and other environments, while improving the accuracy and reliability of subsequent topological structure corrections.

[0023] Optionally, step S2 specifically includes:

[0024] Step S21: extracting joint bearing features and connecting joint features according to the robot arm joint data, thereby obtaining joint bearing data and connecting joint data;

[0025] Step S22: performing radial vibration analysis according to the spherical plain bearing data, thereby obtaining radial vibration data of the spherical plain bearing;

[0026] Step S23: performing impact vibration analysis according to the connection joint data, thereby obtaining the connection joint impact vibration data;

[0027] Step S24: integrating the abnormal vibration data of the joint operation of the robot arm according to the radial vibration data of the joint bearing and the impact vibration data of the connection joint, so as to obtain the abnormal vibration data of the joint operation of the robot arm;

[0028] Step S25: dividing the abnormal vibration structure of the robot arm joint topology structure model according to the abnormal vibration data of the robot arm joint operation, so as to obtain the abnormal vibration topology structure model of the robot arm joint.

[0029] The present invention generates accurate joint bearing and connection joint data by extracting the core features of joint bearings and connection joints, providing a basis for subsequent vibration analysis. By capturing the characteristics of bearings and connection parts, it can identify the key factors affecting the stability of joint motion, help locate potential mechanical failure points and structural weak areas, and improve the prediction and response capabilities of the model under dynamic loads. Radial vibration analysis can detect the radial dynamic characteristics of joint bearings and identify abnormal vibration trends. The radial vibration of joint bearings is a key indicator of the smooth operation of mechanical arm joints. Analysis of radial vibration data can detect bearing abnormalities, wear or failure in advance. Radial vibration data can also support subsequent high-precision topological structure correction and dynamic characteristic regulation, thereby enhancing the overall accuracy and reliability of the mechanical arm. Through impact vibration analysis, the transient load influence on the connection joint of the mechanical arm during operation can be identified. The impact vibration data of the connection joint provides an important basis for determining the transient response of the joint, which helps to identify stress concentration caused by high-frequency operation and transient load changes. Impact vibration data helps to improve joint design to adapt to high-frequency operation and improve the durability and adaptability of the joint. Integrate radial and impact vibration data to generate overall abnormal vibration characteristic data, making the operation status of the robot arm joint more comprehensive. By integrating vibration data, the abnormal vibration source can be more accurately identified and quantified, which is convenient for further precise fault location and diagnosis. The integrated data provides an important reference for the dynamic characteristic correction of the model, making the joint more stable in complex operating environments. Dividing the topological structure based on abnormal vibration data and isolating the abnormal vibration area will help further optimize the model structure and improve the joint design. By dividing the abnormal vibration structure model, specific target areas can be provided for subsequent model corrections, making the corrections more targeted and effectively improving the accuracy and dynamic adaptability of the model. This topological structure division helps to improve the fault tolerance and fault recovery of the model and extend the service life and maintenance cycle of the robot arm joints.

[0030] Optionally, step S22 is specifically:

[0031] Step S221: extracting radial vibration sensing features of the spherical joint bearing according to the spherical joint bearing data, thereby obtaining radial vibration sensing data of the spherical joint bearing;

[0032] Step S222: performing fast Fourier transform according to the spherical plain bearing radial vibration sensor data, so as to obtain the spherical plain bearing radial vibration frequency domain data;

[0033] Step S223: performing harmonic frequency statistics according to the spherical plain bearing radial vibration frequency domain data, thereby obtaining high-order harmonic vibration frequency data;

[0034] Step S224: performing frequency domain regional statistics based on the spherical plain bearing radial vibration sensing frequency domain data, thereby obtaining vibration sensing high frequency region data;

[0035] Step S225: performing uniform statistics on the peak interval of the vibration sensing high-frequency area data, thereby obtaining irregular peak data in the vibration frequency domain;

[0036] Step S226: Integrate the radial vibration characteristics of the spherical plain bearing according to the high-order harmonic vibration frequency data and the irregular peak data in the vibration frequency domain, so as to obtain the radial vibration data of the spherical plain bearing.

[0037] The present invention provides a preliminary reflection of the vibration condition by extracting the radial vibration sensor characteristic data of the spherical bearing, laying the foundation for subsequent frequency domain analysis. These sensor data can quickly detect the abnormality in the radial vibration of the bearing, form real-time feedback of the joint operation status, help to achieve early fault diagnosis, reduce the risk of joint wear and fatigue caused by vibration, and extend the maintenance cycle of the equipment. Through fast Fourier transform, the radial vibration sensor data is converted from the time domain to the frequency domain, thereby revealing the distribution of vibration components at different frequencies. The frequency domain data can not only analyze the frequency characteristics of the vibration, but also help identify potential resonance and high-frequency anomalies, and timely judge the stability problems of the joints in different frequency bands, so as to facilitate the formulation of corresponding vibration suppression strategies. Harmonic frequency statistics can identify high-order harmonic frequencies, which is the key to determining whether the bearing vibration is normal. The appearance of high-order harmonics usually indicates a nonlinear vibration source caused by loose components, wear, etc. This data provides a sensitive indicator for subsequent bearing health monitoring, which can more accurately analyze and evaluate potential problems in bearing operation, and help to discover fault hazards early. Frequency domain regional statistics divide the high-frequency area in the vibration sensor data and focus on the high-frequency vibration characteristics. The vibration in the high-frequency area is usually closely related to bearing failure, impact and friction instability. It can effectively lock the key frequency band data and optimize the spectrum analysis process. Through the data statistics in the high-frequency area, the accuracy and pertinence of the diagnostic analysis are further improved. The peak interval in the high-frequency area is counted to identify the irregularity of the vibration. The uniformity or irregularity of the peak interval can reflect the movement characteristics and abnormal trends of the bearing in a specific frequency band, especially the diagnosis of impact vibration characteristics. The obtained irregular peak data provides a reference for the fine diagnosis of the joint operation state and improves the dynamic adaptability and operation stability of the model. By integrating high-order harmonics and irregular peak data, a comprehensive data set of radial vibration characteristics of joint bearings is formed. The integrated data provides detailed characteristics of bearings in different frequency bands and different vibration modes, accurately describes the radial dynamic response of joints, and the final radial vibration data provides a comprehensive basis for modeling and abnormal vibration prediction, making the dynamic model more accurate and practical, and laying a solid foundation for vibration control in high-load and high-frequency scenarios.

[0038] Optionally, step S23 is specifically:

[0039] Step S231: extracting vibration sensing features according to the connection joint data, thereby obtaining connection joint vibration sensing data;

[0040] Step S232: performing Hilbert transform according to the connection joint vibration sensing data, thereby obtaining connection joint vibration analysis signal data;

[0041] Step S233: Calculate the instantaneous amplitude according to the connection joint vibration analysis signal data, so as to obtain instantaneous amplitude data;

[0042] Step S234: constructing an envelope signal according to the instantaneous amplitude data, thereby obtaining envelope signal data;

[0043] Step S235: performing signal abnormal fluctuation statistics according to the envelope signal data, thereby obtaining signal abnormal fluctuation data;

[0044] Step S236: performing frequency statistics according to the envelope signal data, thereby obtaining high-frequency envelope signal data;

[0045] Step S237: Integrate the impact vibration characteristics according to the abnormal signal fluctuation data and the high-frequency envelope signal data, so as to obtain the impact vibration data of the connection joint.

[0046] The present invention reflects the vibration of the connection joint through the extracted vibration sensing feature data, provides basic information for subsequent parsing and analysis steps, can help identify the vibration anomalies of the connection joint during operation, and conduct early detection and evaluation of the health status of the mechanical arm joint, which helps to prevent premature failure or positioning problems. The analytical signal of the vibration is generated by Hilbert transform, which helps to analyze the instantaneous characteristics of the vibration signal. The analytical signal provides the instantaneous amplitude and phase information of the vibration signal, which helps to identify the time-varying characteristics and fluctuation frequency of the joint vibration, and provides reliable frequency domain information for instantaneous dynamic analysis and fault feature extraction. The instantaneous amplitude calculation reveals the intensity and change trend of the vibration signal, provides the vibration state information of the joint at different time points, can help identify short-term abnormal vibration and impact phenomena, and provide accurate data support for subsequent signal envelope analysis and dynamic performance evaluation. Envelope signal construction can extract the outer contour of the vibration signal, effectively strip off the high-frequency components and noise interference of the signal, so as to obtain pure vibration characteristics. Envelope analysis is often used to detect impact and periodic vibration, which helps to accurately identify the signs of mechanical failure and optimize the recognition effect and diagnostic accuracy of the signal. Abnormal fluctuation statistics can identify and quantify abnormal fluctuations in vibration signals, providing a basis for abnormal analysis. Abnormal fluctuations usually correspond to potential fault locations or abnormal wear of components. By quantifying abnormal amplitudes and frequencies, early warning of mechanical arm joint failures can be achieved, helping to achieve efficient fault location. Frequency statistics are used to analyze high-frequency components in envelope signals to help identify vibration characteristics of connected joints, especially the frequency characteristics of impact vibrations. Statistics of high-frequency envelope signals can display the resonant frequency of joints during operation, identify abnormal states corresponding to high-frequency vibrations, and help evaluate and improve the dynamic performance of joints. By integrating abnormal fluctuations and high-frequency envelope signal data, a more comprehensive impact vibration feature is formed, providing a complete vibration characteristic perspective for dynamic analysis and fault diagnosis of connected joints. Impact vibration data can reveal the stress distribution and impact characteristics of joints during operation, facilitating further optimization of dynamic models and improving the adaptability and reliability of mechanical arms in high-frequency and load environments.

[0047] Optionally, step S234 is specifically:

[0048] Performing smoothing processing on the instantaneous amplitude data to obtain instantaneous amplitude smoothing data;

[0049] Identify the local maximum points of instantaneous amplitude smoothing data to obtain local maximum point data;

[0050] Perform time sequence statistics based on local maximum point data to obtain time sequence data;

[0051] Envelope connection is performed on the local maximum point data according to the time sequence data, so as to obtain envelope data;

[0052] Envelope signal features are extracted according to the envelope curve data, thereby obtaining envelope signal data.

[0053] The present invention can reduce the noise and outliers in the instantaneous amplitude data through smoothing processing, and improve the stability and reliability of the data. By eliminating spikes or mutations, the smoothing processing makes the signal smoother, which is helpful for subsequent feature identification and analysis, improves the accuracy of vibration feature extraction, and ensures more effective fault diagnosis. Local maximum point identification can help detect important feature points in the signal. These maxima usually correspond to specific states or fault characteristics of joint vibration. The identified local maximum points provide key information for subsequent dynamic analysis, which helps to analyze the potential abnormalities of the mechanical arm joints during operation and provide data support for diagnosis. Time sequence statistics can help analyze the distribution characteristics of local maximum points in time, which is crucial for understanding the evolution law and dynamic behavior of vibration signals. Through time sequence statistics, the operation mode and law of the mechanical arm joints can be effectively revealed, which helps to evaluate the stability and reliability of the operation state. Envelope connection helps to obtain the overall change trend of the signal by depicting the outline of the local maximum points in the time series. The construction of the envelope makes the vibration characteristics clearer, helps to identify the periodicity and sudden events of the signal, and provides an intuitive visual representation for signal feature extraction and analysis. By extracting the features of the envelope line, key information related to the vibration state of the robot arm joint, such as vibration frequency, amplitude change and operation stability, can be obtained. The extraction of envelope signal features can help effectively monitor the working state of the robot arm, and by comparing the characteristic parameters with normal values, abnormal conditions and potential faults can be discovered in time, thereby ensuring the efficient and safe operation of the robot arm.

[0054] Optionally, step S3 specifically includes:

[0055] Step S31: performing high temperature simulation according to the abnormal vibration topological structure model of the robot arm joint, thereby obtaining high temperature simulation data;

[0056] Step S32: analyzing the thermal expansion coefficient of the material according to the high temperature simulation data, thereby obtaining the thermal expansion coefficient data of the material;

[0057] Step S33: performing material thermal stress analysis according to the high temperature simulation data, thereby obtaining material thermal stress data;

[0058] Step S34: integrating the material thermal expansion characteristics according to the material thermal expansion coefficient data and the material thermal stress data, thereby obtaining the material thermal expansion data;

[0059] Step S35: performing expansion deformation calculation on the abnormal vibration topological structure model of the robot arm joint according to the material thermal expansion data, so as to obtain expansion deformation data.

[0060] The present invention can reproduce the actual working conditions of the robot arm in a high temperature environment through high temperature simulation, which provides a basis for understanding the behavior of materials and structures under extreme conditions. Through high temperature simulation, the dynamic characteristics of the robot arm joint at high temperature and its impact on performance can be evaluated, thereby providing important initial data for subsequent analysis. The thermal expansion coefficient of the material is a key parameter that describes the change in volume or length of the material when the temperature changes. By analyzing the high temperature simulation data, the thermal expansion characteristics of the robot arm joint material can be accurately obtained, which provides a basis for thermal stress analysis and structural performance evaluation, and helps to consider the thermal characteristics of the material in the design stage, thereby reducing structural problems caused by thermal expansion. Thermal stress analysis helps to understand the internal stress distribution of the material due to the temperature gradient in a high temperature environment. By obtaining the thermal stress data of the material, the stress concentration area faced by the robot arm joint during operation can be evaluated, thereby providing data support for optimizing the design, material selection and structural improvement, and helping to improve the durability and stability of the overall structure. Integrating the thermal expansion coefficient and thermal stress data can provide a comprehensive overview of the thermal properties of the material, allowing engineers to more accurately predict the behavior of the robot arm joints under high-temperature operation. This integration provides a basis for evaluating the applicability and reliability of the material, which helps to optimize the design of the robot arm and ensure that it can still maintain good performance in a changing working environment. Expansion deformation calculation can reveal the deformation characteristics and effects of the robot arm joints at high temperatures, and help understand the impact of material expansion on the kinematics and dynamics of the robot arm joints, thereby providing data support for subsequent dynamic model corrections. By understanding the deformation of the robot arm in real time, it can effectively improve the control accuracy of the system, extend the service life of the robot arm, and enhance its reliability in practical applications.

[0061] Optionally, step S32 is specifically:

[0062] Step S321: extracting material strain features according to the high temperature simulation data, thereby obtaining material strain data;

[0063] Step S322: performing temperature statistics according to the material strain data, thereby obtaining material temperature-strain data;

[0064] Step S323: dividing the material temperature-strain data into temperature intervals, thereby obtaining temperature interval-strain data;

[0065] Step S324: Calculate the mean value of thermal expansion coefficient of each partition according to the temperature range-strain data, so as to obtain the mean value data of thermal expansion coefficient of each partition;

[0066] Step S325: performing curve fitting according to the mean value of the thermal expansion coefficient of each partition, thereby obtaining thermal expansion coefficient-temperature curve data;

[0067] Step S326: extracting the thermal expansion coefficient of the material in the high temperature range according to the thermal expansion coefficient-temperature curve data, thereby obtaining the thermal expansion coefficient data of the material.

[0068] The present invention can reveal the deformation behavior of the joint of the mechanical arm under high temperature environment through material strain feature extraction, help engineers understand the response of materials under different load and temperature conditions, which is crucial for optimizing material selection and structural design, and helps to ensure the stability and performance of the mechanical arm. Temperature statistics help to establish the relationship between strain and temperature. By obtaining the temperature-strain data of the material, engineers can better understand the deformation characteristics of the material under different temperature conditions, thereby providing a data basis for the thermal treatment and performance prediction of the material. Temperature interval division can classify the behavior of materials at different temperatures, so as to better understand and analyze the influence of different temperatures on material properties, which helps to make more targeted optimization in the design stage and ensure that the performance of the material within a specific temperature range meets the requirements. By calculating the mean value of the thermal expansion coefficient in each temperature interval, the expansion characteristics of the material within a specific temperature range can be accurately described, which provides a reliable basis for subsequent thermal expansion analysis and helps engineers to fully consider the influence of temperature changes of materials on performance when designing. Curve fitting can generate a relationship model between the thermal expansion coefficient and temperature, providing a mathematical description for understanding the behavior of materials under high temperature conditions. This model not only helps to predict the performance of materials in practical applications, but also can be used for comparative analysis of other materials. Extracting the thermal expansion coefficient data in the high temperature range from the thermal expansion coefficient-temperature curve can provide accurate material parameters for practical engineering applications, which is crucial for the design and application of robotic arms in high temperature environments, and helps to improve the reliability and stability of robotic arms under harsh conditions.

[0069] Optionally, step S33 is specifically:

[0070] Step S331: extracting the characteristics of the robot arm joint driver according to the high temperature simulation data, thereby obtaining the robot arm joint driver data;

[0071] Step S332: performing heat distribution statistics according to the robot arm joint driver data, thereby obtaining heat distribution data;

[0072] Step S333: identifying the high temperature area according to the heat distribution data, thereby obtaining the high temperature area data of the robot arm joint driver;

[0073] Step S334: Acquire the thermal expansion coefficient data of the robot arm joint driver;

[0074] Step S335: Calculate material stress based on the thermal expansion coefficient data of the robot arm joint driver and the high temperature area data of the robot arm joint driver to obtain material thermal stress data.

[0075] The present invention can provide basic information for subsequent performance analysis and design optimization by extracting characteristic data of the robot arm joint driver. By understanding the behavior of the driver in a high temperature environment, the design can be improved to make it more stable and efficient under high temperature conditions. Heat distribution statistics can reveal the temperature distribution inside the driver, which helps to identify potential thermal problem areas. By understanding the heat distribution in different areas, engineers can take corresponding measures during design to optimize heat dissipation, thereby improving the overall performance and life of the robot arm. High-temperature area identification helps to locate the parts of the driver that are most susceptible to thermal damage, and can provide a specific basis for improving material selection, designing heat dissipation solutions, and monitoring thermal conditions, thereby reducing the risk of failure and extending the service life of the robot arm. Understanding the coefficient of thermal expansion can help engineers foresee changes in materials at high temperatures during the design stage, which is crucial to improving the reliability of the robot arm in extreme environments and ensuring that the various components of the robot arm can work normally under high temperature conditions without excessive deformation or failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0077] Figure 1 A schematic diagram of the steps of the method for correcting the mechanical arm joint dynamics model of the present invention;

[0078] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0079] Figure 3 Detailed step flow diagram of step S13 in the present invention;

[0080] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0081] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0082] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0083] 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 only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0084] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for correcting a mechanical arm joint dynamics model, the method comprising the following steps:

[0085] S1: Acquire the robot arm joint data, and extract the geometric structure features according to the robot arm joint data, so as to obtain the geometric structure data; perform the topological structure analysis of the robot arm joint according to the geometric structure data, so as to obtain the robot arm joint topological structure model;

[0086] In this embodiment, in the stage of acquiring data of the joints of the manipulator, high-precision sensors are used to collect the geometric dimensions, angles, positions and relative motion data of the joints. CAD (computer-aided design) software is used to extract geometric structure features, and the basic geometric parameters of the joints, such as joint radius, length, angle, etc., are analyzed to form detailed geometric structure data. On this basis, the extracted geometric structure data are processed using a topological analysis tool to construct a topological structure model of the joints of the manipulator. This process includes an analysis of the joint connection mode, sequence and mutual relationship to generate a complete topological structure of the joints of the manipulator, providing basic data for further analysis. Ensure that in the topological structure analysis, appropriate parameter thresholds are used to define the type and relationship of the connection to avoid misjudgment.

[0087] S2: Extract joint bearing features and connection joint features according to the robot arm joint data, so as to obtain joint bearing data and connection joint data; perform abnormal operation vibration analysis according to the joint bearing data and connection joint data, so as to obtain abnormal operation vibration data of the robot arm joint; perform abnormal vibration structure division on the robot arm joint topology structure model according to the abnormal operation vibration data of the robot arm joint, so as to obtain the abnormal vibration topology structure model of the robot arm joint;

[0088] In this embodiment, based on the acquired robot arm joint data, the characteristics of the joint bearings and connecting joints are extracted through a specific signal processing algorithm. Specifically, the dynamic response data of the joints are collected using a vibration analysis device, and key parameters such as frequency, amplitude, and phase are extracted to form joint bearing data and connecting joint data. Next, the collected data is analyzed using an anomaly detection algorithm to identify abnormal vibration signals, thereby obtaining abnormal vibration data of the robot arm joint operation. At this time, reasonable thresholds and standards need to be set to ensure that only vibration signals that exceed the normal operating range are identified as abnormal. By comparing the abnormal vibration data with the previous topological structure model, the abnormal vibration structure is divided, and a topological structure model of abnormal vibration of the robot arm joint is formed, which provides an important basis for subsequent high-temperature simulation and expansion deformation calculation.

[0089] S3: Performing high temperature simulation according to the abnormal vibration topological structure model of the robot arm joint to obtain high temperature simulation data; performing material expansion analysis according to the high temperature simulation data to obtain material thermal expansion data; performing expansion deformation calculation on the abnormal vibration topological structure model of the robot arm joint according to the material thermal expansion data to obtain expansion deformation data;

[0090] In this embodiment, during the high temperature simulation stage, it is necessary to use finite element analysis software to perform simulation under high temperature conditions based on the abnormal vibration topological structure model of the robotic arm joints. During the simulation process, the model is exposed to a set high temperature environment, the thermal response of each joint is observed, and high temperature simulation data is obtained. At this time, it is necessary to clarify the specific parameters of the high temperature conditions, such as the temperature range (for example: 100°C to 500°C) and the heating rate, in order to accurately evaluate the behavior of the material. Next, the thermal expansion coefficient of the material is obtained through a material thermal expansion experiment, and this data is applied to the topological structure model to perform expansion deformation calculations and obtain expansion deformation data. Ensure that in this analysis, the thermal expansion coefficient of the material is obtained by standard experimental methods (such as thermomechanical analysis) and is associated with the specific material of each joint in the model.

[0091] S4: Acquire robot arm joint sensor data; perform robot arm joint deformation analysis on the abnormal vibration topological structure model of the robot arm joint according to the robot arm joint sensor data, so as to obtain the robot arm joint deformation data;

[0092] In this embodiment, the sensor data of the mechanical arm joint is obtained, and a combination of multiple sensors (such as strain gauges, accelerometers and temperature sensors) is used to monitor the working state of the joint in real time. The collected data include the strain, acceleration change and temperature change of the joint. These data will be used to perform deformation analysis on the abnormal vibration topological structure model of the mechanical arm joint. In the specific operation, the data processing algorithm is used to calculate the deformation of the joint under actual working conditions, and the mechanical arm joint deformation data is generated. In this process, a specific algorithm (such as Kalman filtering) is used to eliminate noise interference, ensure the accuracy of the data, and establish deformation analysis standards under different working conditions to facilitate subsequent dynamic correction.

[0093] S5: Dynamically correct the deformation data of the robot arm joints according to the expansion deformation data, so as to obtain the robot arm joint deformation correction data, and upload it to the robot arm joint flexible body dynamics model management system to perform the robot arm joint structure deformation correction task.

[0094] In this embodiment, in the deformation correction stage, the expansion deformation data and the mechanical arm joint deformation data are comprehensively analyzed. The linear regression analysis method is used to dynamically correct the two sets of data to ensure the accuracy and reliability of the final deformation correction data. This process requires setting a correction threshold to ensure that the joint deformation after correction does not exceed a reasonable range. The corrected data will be uploaded to the mechanical arm joint flexible body dynamics model management system, which can automatically update the joint structure deformation amount to ensure that the system reflects the actual working state of the mechanical arm in real time. In this way, effective management of the mechanical arm joint deformation amount is achieved, and the adaptability and work efficiency of the mechanical arm are improved.

[0095] Optionally, step S1 specifically includes:

[0096] Step S11: acquiring the robot arm joint data, and performing geometric structure feature extraction based on the robot arm joint data, thereby obtaining geometric structure data;

[0097] In this embodiment, the process of acquiring the joint data of the robotic arm involves the use of a high-precision sensor array, such as a laser rangefinder and an optical camera, to record the geometric features and positions of the joints in real time. Sensors are arranged at the joints of the robotic arm to obtain comprehensive three-dimensional data. The data obtained includes the size, shape, position and relative motion state of the joints. Data processing uses computer vision algorithms, especially feature point extraction techniques such as SIFT (Scale Invariant Feature Transform) or SURF (Speeded Robust Features), to extract key geometric features. The extracted geometric features will form a geometric structure data set that contains detailed geometric parameters of each joint, such as length, width, angle, etc., as the basis for subsequent steps.

[0098] Step S12: extracting the three-dimensional vertex coordinates of the robot arm joints according to the geometric structure data, thereby obtaining the three-dimensional vertex coordinate data of the robot arm joints;

[0099] In this embodiment, the three-dimensional vertex coordinates of the robotic arm joints are extracted based on the acquired geometric structure data. The previously obtained joint geometric feature data is converted into vertex coordinates in a three-dimensional coordinate system using a coordinate conversion method. This process involves establishing a coordinate system and calculating the three-dimensional coordinate position of each joint based on the geometric parameters of the joint (such as angle, radius, etc.). Specifically, for each joint, trigonometric functions (such as sine and cosine) are used for calculation to ensure the accuracy of the vertex coordinates. After extraction, the generated three-dimensional vertex coordinate data includes the x, y, and z coordinates of the joints, forming a set of points in a three-dimensional space, representing the precise position of each joint of the robotic arm.

[0100] Step S13: constructing a three-dimensional surface according to the three-dimensional vertex coordinate data of the robot arm joints, thereby obtaining three-dimensional surface data;

[0101] In this embodiment, three-dimensional facets are constructed based on the three-dimensional vertex coordinate data of the robot arm joint. Using computational geometry methods, the Delaunay triangulation algorithm is used to generate corresponding facet data based on the extracted three-dimensional vertex coordinates. The algorithm can divide the point set in three-dimensional space into multiple triangular facets to form the external surface of the robot arm joint. During the facet construction process, it is necessary to ensure that the normal vector of each facet is calculated accurately to ensure subsequent lighting and visual effects. The constructed facet data will include the vertex index and normal vector information of each triangle to form a complete three-dimensional model, providing basic data for the mapping of subsequent topological structure models.

[0102] Step S14: performing spatial mapping of the robot arm joint topology structure model according to the three-dimensional surface data, thereby obtaining the robot arm joint topology structure model.

[0103] In this embodiment, spatial mapping of the robot arm joint topological structure model is performed based on the generated three-dimensional patch data. A spatial mapping algorithm is used to convert the constructed three-dimensional patch data into a topological structure model. In this process, it is necessary to clarify the connection relationship between each patch, and use the adjacency matrix to represent the mutual connection between the patches to form a complete topological structure. In the specific implementation, it is necessary to classify the vertices of each patch, establish the relationship between the vertices, edges and faces, and make it an undirected graph. The final generated robot arm joint topological structure model will clearly describe the connection method, movement sequence and mutual relationship of the joints, and provide key data support for subsequent analysis and control.

[0104] Optionally, step S13 is specifically:

[0105] Step S131: triangulate the three-dimensional vertex coordinate data of the robot arm joints to obtain triangular facet data;

[0106] In this embodiment, triangulation is performed based on the three-dimensional vertex coordinate data of the robot arm joint, and the vertex coordinate set is processed using the Delaunay triangulation algorithm. The algorithm can effectively convert a three-dimensional point set into a triangular face. All three-dimensional vertex coordinate data are obtained and input into the Delaunay triangulation program. This program calculates all triangles based on the coordinates of each vertex to ensure that the generated triangles meet the conditions of no overlap and no gaps. The generated triangular face data includes three vertex indices for each triangle, forming a face mesh, and the connection relationship of these facets will be used for subsequent adjacent relationship determination and normal vector calculation.

[0107] Step S132: determining the adjacent relationship according to the three-dimensional vertex coordinate data of the robot arm joint, thereby obtaining vertex adjacent relationship data;

[0108] In this embodiment, the adjacent relationship is determined based on the three-dimensional vertex coordinate data of the robot arm joint to establish the connection information between the facets. Based on the triangular facet data, an adjacency matrix is ​​created to store the relationship between each vertex and its adjacent facets. In the specific implementation, each triangular facet is traversed, the three vertices of the facet are identified, and the corresponding connection information is updated in the adjacency matrix. If two triangular facets share one or more vertices, their adjacent relationship is marked in the matrix. The vertex adjacent relationship data finally formed not only contains the number of each vertex, but also includes a list of facets adjacent to it, which is convenient for subsequent normal vector calculation and three-dimensional facet construction.

[0109] Step S133: Calculating the normal vector according to the three-dimensional vertex coordinate data of the robot arm joint, thereby obtaining the normal vector data;

[0110] In this embodiment, the normal vector is calculated based on the three-dimensional vertex coordinate data of the robot arm joint, and the normal vector estimation method is used to generate the normal vector for each triangle. In specific implementation, two edge vectors are calculated from the three vertex coordinates of each triangle using vector operations, and then the normal vector is obtained by vector cross multiplication. The calculation formula of the normal vector is:

[0111] N = (P2-P1) × (P3-P1)

[0112] P1, P2, and P3 are the three vertices of the triangle. The generated normal vector data includes the normal vector direction and size information of each triangle face, and these normal vectors will be used for subsequent 3D face construction.

[0113] Step S134: constructing a three-dimensional patch on the triangular patch data according to the normal vector data and the vertex adjacent relationship data, thereby obtaining three-dimensional patch data.

[0114] In the present embodiment, the three-dimensional patch is constructed for the triangular patch data according to the normal vector data and the vertex adjacent relationship data, and the normal vector and the adjacency relationship are used to ensure the rationality and continuity of the patch. The normal vector data is combined with the vertex of each triangular patch to determine the direction of each patch. Then, the information of the adjacency matrix is ​​used to ensure that adjacent patches share the same edge during the construction process and that the normal vectors are in the same direction. After the construction is completed, the generated three-dimensional patch data contains the vertex index, normal vector and adjacent information of all patches. These patch data form a complete three-dimensional model, which can be used for subsequent robot arm motion simulation and welding path planning, and provide basic support for the implementation of the industrial robot welding method.

[0115] Optionally, step S2 specifically includes:

[0116] Step S21: extracting joint bearing features and connecting joint features according to the robot arm joint data, thereby obtaining joint bearing data and connecting joint data;

[0117] In this embodiment, joint bearing feature extraction and connection joint feature extraction are performed based on the robot arm joint data to obtain joint bearing data and connection joint data. The geometric parameters of the robot arm joints are extracted, including the size, shape and material properties of the joints, which can be collected by laser scanning or three-dimensional modeling software. Joint bearing feature extraction includes analyzing the type, structure and material properties of the bearing, detecting the working state of the bearing through an acoustic sensor, and collecting corresponding frequency response data. Connection joint feature extraction includes checking the connection method, preload and material properties of the connecting parts, and obtaining the strength and stability data of the connection through static loading tests. The joint bearing data finally generated includes the working conditions and status information of the joints, while the connection joint data includes the connection strength and material properties, forming the basis for subsequent vibration analysis.

[0118] Step S22: performing radial vibration analysis according to the spherical plain bearing data, thereby obtaining radial vibration data of the spherical plain bearing;

[0119] In this embodiment, radial vibration analysis is performed based on the spherical bearing data, and an acceleration sensor is used to monitor the vibration signal of the bearing during operation. Sensors are set at key positions of the spherical bearing to capture radial vibration data. The vibration signal is recorded in real time through the signal acquisition system, and the sampling frequency should be set above 1000Hz to ensure the accurate capture of high-frequency vibrations. The collected vibration signal is processed by digital filtering to remove noise and interference, and the FFT (Fast Fourier Transform) algorithm is used to convert the time domain signal into a frequency domain signal to extract the amplitude and frequency characteristics of the vibration. The radial vibration data of the spherical bearing generated by the analysis results contains the vibration amplitude, frequency distribution and its changing trend, which provides the necessary parameters for the subsequent impact vibration analysis.

[0120] Step S23: performing impact vibration analysis according to the connection joint data, thereby obtaining the connection joint impact vibration data;

[0121] In this embodiment, impact vibration analysis is performed based on the connection joint data, and the connection joint is excited using an impact tester to detect its dynamic response when subjected to an instantaneous load. High-sensitivity acceleration sensors are installed at key positions of the connection joint to ensure that the instantaneous vibration during impact can be recorded. An impact hammer or exciter is used to apply an impact force, and the impact force and frequency are set to produce a typical impact response. The vibration signal is recorded and stored in real time, and the instantaneous displacement, velocity, and acceleration data are extracted using time domain analysis. By analyzing these data, the impact vibration characteristics of the connection joint are obtained, including the vibration amplitude and attenuation characteristics, and the generated connection joint impact vibration data provides support for subsequent abnormal vibration integration.

[0122] Step S24: integrating the abnormal vibration data of the joint operation of the robot arm according to the radial vibration data of the joint bearing and the impact vibration data of the connection joint, so as to obtain the abnormal vibration data of the joint operation of the robot arm;

[0123] In this embodiment, the abnormal vibration data of the robot arm joint operation is integrated based on the radial vibration data of the joint bearing and the impact vibration data of the connecting joint. First, the two sets of data are input into the data fusion system, and the weighted average method or Kalman filtering method is used to integrate the vibration data from these two different sources to improve the accuracy and reliability of the overall data. During the integration process, a threshold is set for the vibration data at each time point to filter out abnormal vibration signals. The threshold is usually selected for statistical analysis based on previous normal working data. After integration, the generated abnormal vibration data of the robot arm joint operation contains the comprehensive characteristics of radial and impact vibrations, forming a comprehensive description of the state of the robot arm joint, providing basic data support for abnormal analysis.

[0124] Step S25: dividing the abnormal vibration structure of the robot arm joint topology structure model according to the abnormal vibration data of the robot arm joint operation, so as to obtain the abnormal vibration topology structure model of the robot arm joint.

[0125] In this embodiment, the abnormal vibration structure of the robot arm joint topological structure model is divided according to the abnormal vibration data of the robot arm joint operation. The integrated abnormal vibration data is input into the structure division algorithm, which identifies the abnormal area based on the change point of the vibration characteristic. By setting the standard deviation threshold, the abnormal vibration data is clustered and analyzed to determine the parts where the vibration characteristics deviate significantly from the normal range. In the division process, the interaction and connection relationship of each joint are considered to ensure the accuracy and rationality of the division. The final abnormal vibration topological structure model of the robot arm joint shows the abnormal areas of each joint and its connection relationship, which provides an important basis for subsequent fault diagnosis and maintenance decisions.

[0126] Optionally, step S22 is specifically:

[0127] Step S221: extracting radial vibration sensing features of the spherical joint bearing according to the spherical joint bearing data, thereby obtaining radial vibration sensing data of the spherical joint bearing;

[0128] In this embodiment, the radial vibration sensing feature extraction of the spherical bearing is performed based on the spherical bearing data to obtain the radial vibration sensing data of the spherical bearing. An accelerometer installed on the spherical bearing is used to record the vibration signal of the bearing under different working conditions. The selected sensor type should be a highly sensitive piezoelectric or MEMS accelerometer to ensure the accuracy of data acquisition. The data acquisition system is set at a sampling frequency of 1000Hz to ensure the complete capture of high-frequency vibration signals. The collected time domain vibration signal is preprocessed, including denoising and detrending processing, and a low-pass filter is used to remove high-frequency noise. The obtained signal is the radial vibration sensing data of the spherical bearing, which forms the basis for subsequent frequency domain analysis.

[0129] Step S222: performing fast Fourier transform according to the spherical plain bearing radial vibration sensor data, so as to obtain the spherical plain bearing radial vibration frequency domain data;

[0130] In this embodiment, a fast Fourier transform is performed based on the spherical bearing radial vibration sensor data to obtain the spherical bearing radial vibration frequency domain data. The extracted time domain signal is input into the fast Fourier transform (FFT) algorithm to perform frequency domain conversion of the signal. The selected FFT algorithm should be based on the form of a window function to reduce the spectrum leakage phenomenon. Usually, a Hamming window or a Henning window is used for window processing, and the window length is set to 256 points to ensure the frequency domain resolution. The frequency domain signal obtained after the FFT calculation is formed with the frequency as the horizontal axis and the amplitude as the vertical axis to form a spectrum diagram. The spherical bearing radial vibration frequency domain data generated by the analysis result provides a basis for subsequent harmonic frequency statistics.

[0131] Step S223: performing harmonic frequency statistics according to the spherical plain bearing radial vibration frequency domain data, thereby obtaining high-order harmonic vibration frequency data;

[0132] In this embodiment, harmonic frequency statistics are performed based on the frequency domain data of the radial vibration of the spherical bearing to obtain high-order harmonic vibration frequency data. The fundamental frequency and its higher harmonics are identified by analyzing the amplitude of each frequency component in the spectrum diagram. Usually, when selecting the fundamental frequency, its threshold is set to the frequency with an amplitude greater than 10% of the peak value. Subsequently, the integer multiple frequencies of the higher harmonics are calculated based on the fundamental frequency, and their corresponding amplitudes are recorded. The statistical process of the higher harmonic frequencies needs to pay special attention to the obvious peaks in the spectrum, and record them according to the significance of the amplitude and the regularity of the frequency. The high-order harmonic vibration frequency data finally generated provides important parameters for subsequent vibration analysis, indicating potential equipment failure risks.

[0133] Step S224: performing frequency domain regional statistics based on the spherical plain bearing radial vibration sensing frequency domain data, thereby obtaining vibration sensing high frequency region data;

[0134] In this embodiment, frequency domain regional statistics are performed based on the frequency domain data of the radial vibration of the spherical bearing to obtain vibration sensing high-frequency area data. The standard for defining the high-frequency area is usually set to a frequency component greater than twice the fundamental frequency. The frequency domain data is divided into regions, and frequency data in the frequency range from fundamental frequency to high frequency is extracted. In the statistical process, the relative amplitude is used as the screening basis, and the threshold is set to an amplitude greater than the standard deviation of the background noise to ensure the validity of the statistical data. The vibration data in the high-frequency area should be sorted and marked for further analysis in subsequent steps. The vibration sensing high-frequency area data finally obtained helps to identify potential resonance phenomena and other failure modes.

[0135] Step S225: performing uniform statistics on the peak interval of the vibration sensing high-frequency area data, thereby obtaining irregular peak data in the vibration frequency domain;

[0136] In this embodiment, the peak interval statistics of the vibration sensing high-frequency area data are uniformly performed to obtain irregular peak data in the vibration frequency domain. By analyzing the high-frequency area data, each significant peak is identified, and its frequency and amplitude are recorded. To ensure the uniformity of the data, the peak interval threshold is set to 0.1Hz, and the frequency components with intervals less than this value are screened out and marked as irregular peaks. The interval statistics of each peak are further calculated, and the mean and standard deviation are compared to identify abnormal vibration characteristics. The final irregular peak data in the vibration frequency domain provides a deep insight into the operating status of the equipment and facilitates the subsequent comprehensive feature integration.

[0137] Step S226: Integrate the radial vibration characteristics of the spherical plain bearing according to the high-order harmonic vibration frequency data and the irregular peak data in the vibration frequency domain, so as to obtain the radial vibration data of the spherical plain bearing.

[0138] In this embodiment, the radial vibration characteristics of the joint bearing are integrated according to the high-order harmonic vibration frequency data and the irregular peak data in the vibration frequency domain, so as to obtain the radial vibration data of the joint bearing. The high-order harmonic frequency data and the irregular peak data are cross-analyzed, and matching conditions are set to ensure the correlation between the two sets of data. For each high-order harmonic frequency, its corresponding vibration amplitude and the irregular peak amplitude in the frequency domain are checked, and integrated using a weighted synthesis method. The weight setting is based on the amplitude size, and the data with a larger amplitude has a higher weight. The radial vibration data of the joint bearing finally integrated covers high-frequency vibration characteristics and harmonic characteristics, providing a reliable basis for fault diagnosis and health monitoring of the robot arm joints.

[0139] Optionally, step S23 is specifically:

[0140] Step S231: extracting vibration sensing features according to the connection joint data, thereby obtaining connection joint vibration sensing data;

[0141] In this embodiment, vibration sensing feature extraction is performed based on the connection joint data to obtain the connection joint vibration sensing data. Vibration signal collection is performed by installing a high-sensitivity acceleration sensor at the connection joint. The acceleration sensor uses a piezoelectric sensor, which has a high frequency response range and can accurately capture the tiny vibrations of the connection joint during operation. The data acquisition system is configured with a sampling frequency of 1000Hz to ensure complete capture of rapidly changing signals. After the data recording is completed, the collected time domain signal is preprocessed, including removing the DC component and low-frequency noise, and the obtained signal is the vibration sensing data of the connection joint.

[0142] Step S232: performing Hilbert transform according to the connection joint vibration sensing data, thereby obtaining connection joint vibration analysis signal data;

[0143] In this embodiment, Hilbert transform is performed on the connection joint vibration sensor data to obtain the connection joint vibration analytical signal data. The pre-processed vibration sensor data is processed using the Hilbert transform algorithm to obtain the instantaneous frequency and instantaneous amplitude of the signal. The selected Hilbert transform algorithm needs to ensure that the edge effect in the signal spectrum is minimized, so a zero-phase filter can be used for smoothing. After the calculation is completed, the generated analytical signal data contains the amplitude and phase information of the original signal, which is convenient for the subsequent instantaneous amplitude calculation and ensures that the complete characteristics of the vibration signal are retained.

[0144] Step S233: Calculate the instantaneous amplitude according to the connection joint vibration analysis signal data, so as to obtain instantaneous amplitude data;

[0145] In this embodiment, the instantaneous amplitude is calculated based on the analytical signal data of the joint vibration, so as to obtain the instantaneous amplitude data. The instantaneous amplitude is extracted using the complex amplitude calculation formula using the analytical signal obtained from the Hilbert transform. The calculation formula is:

[0146]

[0147] Where x(t) is the original signal and y(t) is the Hilbert transform result. To improve the calculation accuracy, the instantaneous amplitude at each moment is selected for smoothing, and the moving average method is used. The window size is set to 5 sampling points to eliminate short-term noise interference. The final instantaneous amplitude data will be used for subsequent envelope signal construction.

[0148] Step S234: constructing an envelope signal according to the instantaneous amplitude data, thereby obtaining envelope signal data;

[0149] In this embodiment, an envelope signal is constructed based on the instantaneous amplitude data to obtain envelope signal data. This step extracts the envelope signal based on the instantaneous amplitude data and selects a calculation method for the envelope signal, commonly used including an envelope method based on the instantaneous amplitude. The instantaneous amplitude data is smoothed, a low-pass filter is used, and the cutoff frequency is set to 200Hz to remove high-frequency components and extract the envelope. After processing, the generated envelope signal data reflects the overall trend of the connection joint vibration and characterizes the health status of the connection joint.

[0150] Step S235: performing signal abnormal fluctuation statistics according to the envelope signal data, thereby obtaining signal abnormal fluctuation data;

[0151] In this embodiment, abnormal signal fluctuation statistics are performed based on the envelope signal data to obtain abnormal signal fluctuation data. A threshold for abnormal fluctuation is set, usually set to the mean of the envelope signal plus twice the standard deviation, as a detection standard for abnormal fluctuations. The envelope signal is monitored in real time to identify fluctuation areas that exceed the set threshold. A statistical method is used to record the frequency and duration of the identified abnormal fluctuations, and corresponding abnormal fluctuation data is generated to characterize the abnormal working state of the connection joint. This data provides an important basis for subsequent fault diagnosis.

[0152] Step S236: performing frequency statistics according to the envelope signal data, thereby obtaining high-frequency envelope signal data;

[0153] In this embodiment, frequency statistics are performed based on the envelope signal data to obtain high-frequency envelope signal data. The envelope signal is analyzed in the frequency domain, and the time domain signal is converted into a frequency domain signal using the fast Fourier transform (FFT). The sampling frequency of the FFT is set to 1000 Hz, and a 2048-point FFT calculation is used to ensure that the frequency domain resolution is high enough. In the spectrum diagram, components with a frequency greater than 300 Hz are extracted. Usually, this part of the frequency signal is associated with potential faults. The statistics of the high-frequency envelope signal data provide important frequency domain information for the subsequent integration of impact vibration characteristics.

[0154] Step S237: Integrate the impact vibration characteristics according to the abnormal signal fluctuation data and the high-frequency envelope signal data, so as to obtain the impact vibration data of the connection joint.

[0155] In this embodiment, the impact vibration characteristics are integrated according to the abnormal signal fluctuation data and the high-frequency envelope signal data, so as to obtain the impact vibration data of the connection joint. The abnormal signal fluctuation data is combined with the high-frequency envelope signal data, and the relationship between the two is identified through the set association rules to ensure that the abnormal components in the high-frequency envelope signal are included in the calculation of the impact vibration characteristics. The abnormal fluctuation data and the high-frequency envelope data are integrated by weighted synthesis, and the weights are adjusted according to the amplitude and frequency characteristics of each signal. The resulting connection joint impact vibration data reflects the impact characteristics of the connection joint in actual work, providing a basis for fault prediction and maintenance decisions.

[0156] Optionally, step S234 is specifically:

[0157] Performing smoothing processing on the instantaneous amplitude data to obtain instantaneous amplitude smoothing data;

[0158] In this embodiment, smoothing is performed according to the instantaneous amplitude data to obtain instantaneous amplitude smoothed data. A suitable smoothing method is selected to reduce high-frequency noise in the instantaneous amplitude data. A moving average method or a median filter method can be used, wherein the window size of the moving average method needs to be set to 5 to 10 data points according to the changing characteristics of the instantaneous amplitude data to ensure accurate capture of the signal trend. By calculating the average value of the data in the selected window, the generated smoothed data can effectively reduce the influence of noise, making subsequent analysis more reliable. The smoothed instantaneous amplitude data is used as the basis for identifying local maximum points in subsequent steps.

[0159] Identify the local maximum points of instantaneous amplitude smoothing data to obtain local maximum point data;

[0160] In this embodiment, local maximum points are identified for instantaneous amplitude smoothed data to obtain local maximum point data. This step first requires setting the identification criteria for local maxima, including the relative threshold of the maximum value, to ensure that the identified local maximum value is significant. In the specific implementation, a sliding window algorithm is used to traverse the smoothed data and compare each data point. If its value is greater than the previous and next data points and exceeds the set relative threshold, the data point is identified as a local maximum. The local maximum point data obtained in this way will provide important information in the subsequent time series statistics.

[0161] Perform time sequence statistics based on local maximum point data to obtain time sequence data;

[0162] In this embodiment, time sequence statistics are performed based on the local maximum point data to obtain time sequence data. This step first records the timestamp of the identified local maximum points to ensure that each maximum point corresponds to its corresponding time information. Standard time series analysis methods can be used to organize the time information of the local maximum points into a time series data set. By counting the appearance time of the local maximum points, the generated time sequence data will reflect the fluctuation law in the signal and its distribution characteristics on the time axis, providing the necessary time reference for the construction of the envelope.

[0163] Envelope connection is performed on the local maximum point data according to the time sequence data, so as to obtain envelope data;

[0164] In this embodiment, the local maximum point data are envelope-connected according to the time sequence data to obtain the envelope data. This step requires combining the time sequence data with the local maximum point data to construct an envelope connecting the local maximum points. Linear interpolation can be used to connect each maximum point one by one based on the data value of the local maximum point and the corresponding timestamp to form an envelope. To ensure the smoothness and continuity of the envelope, a suitable interpolation accuracy needs to be set during the interpolation process, usually within 100ms, to maintain the accuracy of the envelope. The envelope data finally generated reflects the overall trend of the signal in time and can be used for further feature extraction.

[0165] Envelope signal features are extracted according to the envelope curve data, thereby obtaining envelope signal data.

[0166] In this embodiment, envelope signal features are extracted based on envelope data to obtain envelope signal data. This step first requires determining the standard for extracting features. Common features include the maximum value, minimum value, mean value, and variance of the envelope signal. By analyzing the generated envelope data, feature statistics are performed using a calculation formula. For example, the calculation formula for the mean value is

[0167]

[0168] where x i is the value of each point of the envelope, and N is the total number of data points. The extracted feature data will be used to evaluate the state of the connection joint, determine its stability and failure risk during the welding process, and provide a basis for subsequent decision support.

[0169] Optionally, step S3 specifically includes:

[0170] Step S31: performing high temperature simulation according to the abnormal vibration topological structure model of the robot arm joint, thereby obtaining high temperature simulation data;

[0171] In this embodiment, high temperature simulation is performed based on the abnormal vibration topological structure model of the robotic arm joint to obtain high temperature simulation data. Finite element analysis (FEA) software is required for high temperature simulation, and the input parameters include the initial conditions, geometric structure, boundary conditions and applied temperature range of the robotic arm joint material. Typically, the temperature range is set from room temperature (about 20°C) to the expected operating temperature (up to 600°C or higher). Under high temperature conditions, the physical properties of the material (such as elastic modulus and yield strength) need to be set based on the material manual. The high temperature simulation process will calculate the deformation and stress distribution of the material at different temperatures, and finally generate high temperature simulation data, which will provide a basis for subsequent material analysis.

[0172] Step S32: analyzing the thermal expansion coefficient of the material according to the high temperature simulation data, thereby obtaining the thermal expansion coefficient data of the material;

[0173] In this embodiment, the thermal expansion coefficient of the material is analyzed based on the high temperature simulation data to obtain the thermal expansion coefficient data of the material. It is necessary to analyze and calculate the thermal expansion coefficient of the material based on the temperature-displacement relationship obtained by the high temperature simulation using a linear regression method. The calculation formula for the thermal expansion coefficient is:

[0174]

[0175] Among them, ΔL is the length change caused by temperature change, L0 is the original length, and ΔT is the temperature change. The required parameters can be obtained from high-temperature simulation data. The analysis process needs to set the calculation accuracy threshold, usually 0.01, to ensure the accuracy of the thermal expansion coefficient. The final thermal expansion coefficient data will reflect the deformation ability of the material in a high-temperature environment.

[0176] Step S33: performing material thermal stress analysis according to the high temperature simulation data, thereby obtaining material thermal stress data;

[0177] In this embodiment, material thermal stress analysis is performed based on high temperature simulation data to obtain material thermal stress data. The temperature distribution data generated by high temperature simulation is used to calculate using the thermal stress formula. The basic calculation formula for thermal stress is:

[0178] σ=E·α·ΔT

[0179] Among them, σ is thermal stress, E is the elastic modulus of the material, α is the thermal expansion coefficient, and ΔT is the temperature change. Here, the elastic modulus can be set based on the experimental data of the material. By combining the high temperature simulation data with the above formula, the thermal stress values ​​at different temperatures are calculated. During the analysis process, the limit value of the stress calculation needs to be set, which is generally the yield strength of the material, to evaluate whether the material will fail at high temperature. The final thermal stress data provides the necessary information for subsequent feature integration.

[0180] Step S34: integrating the material thermal expansion characteristics according to the material thermal expansion coefficient data and the material thermal stress data, thereby obtaining the material thermal expansion data;

[0181] In this embodiment, the thermal expansion characteristics of the material are integrated according to the material thermal expansion coefficient data and the material thermal stress data to obtain the material thermal expansion data. This step requires a comprehensive analysis of the thermal expansion coefficient and thermal stress data obtained in the first two steps. By constructing a relationship diagram between thermal stress and thermal expansion coefficient, the correlation between the two is observed. Using data processing tools and applying multivariate regression analysis methods, a comprehensive characteristic model including thermal expansion and thermal stress is obtained. The model needs to set a significance level, usually 0.05, to ensure the reliability of the analysis results. The thermal expansion data of the material obtained after the final integration provides detailed information for subsequent expansion deformation calculations.

[0182] Step S35: performing expansion deformation calculation on the abnormal vibration topological structure model of the robot arm joint according to the material thermal expansion data, so as to obtain expansion deformation data.

[0183] In this embodiment, the expansion deformation calculation of the abnormal vibration topological structure model of the mechanical arm joint is performed according to the thermal expansion data of the material, so as to obtain the expansion deformation data. The thermal expansion data is applied to the topological structure model of the mechanical arm joint, and the deformation calculation is performed by the finite element analysis tool. The input parameters include the expansion coefficient, the initial geometric model, the boundary conditions and the external load. To ensure the accuracy of the calculation, the corresponding analytical accuracy and mesh division need to be set in the model, and the subdivided grid is usually used to improve the calculation accuracy. After the calculation is completed, the expansion deformation data is output, including the displacement and stress distribution at high temperature. These data will provide an important basis for the performance evaluation and design optimization of the mechanical arm under high temperature conditions.

[0184] Optionally, step S32 is specifically:

[0185] Step S321: extracting material strain features according to the high temperature simulation data, thereby obtaining material strain data;

[0186] In this embodiment, material strain feature extraction is performed based on high temperature simulation data to obtain material strain data. This step first requires extracting material displacement and deformation information from high temperature simulation analysis. Using finite element analysis tools, the node displacement data obtained from high temperature simulation is used as input, and the material strain is calculated using a formula. The strain calculation formula is:

[0187]

[0188] Among them, ΔL is the deformation of the material under high temperature conditions, and L0 is the original length. Substitute the displacement data at different temperatures into the formula to obtain the corresponding strain value. To ensure the accuracy of the calculation, the tolerance of the strain calculation needs to be set, which is generally set to 0.01%. The material strain data finally generated will provide basic information for subsequent temperature statistics.

[0189] Step S322: performing temperature statistics according to the material strain data, thereby obtaining material temperature-strain data;

[0190] In this embodiment, temperature statistics are performed based on the material strain data to obtain material temperature-strain data. The material strain data needs to be integrated with the corresponding temperature data to form a temperature-strain relationship. To achieve this goal, all strain data are classified by temperature, and a temperature-strain scatter plot is generated through a data processing tool. When processing data, a temperature range needs to be set, usually from 20°C to 600°C, to cover all temperature ranges of the material under working conditions. The material temperature-strain data finally generated will help analyze the relationship between strain and temperature.

[0191] Step S323: dividing the material temperature-strain data into temperature intervals, thereby obtaining temperature interval-strain data;

[0192] In this embodiment, the material temperature-strain data is divided into temperature intervals to obtain temperature interval-strain data. This step requires setting multiple temperature intervals according to the material properties and application scenarios. For example, it can be set to 0-100°C, 100-200°C, 200-300°C, etc., with one interval every 100°C. The temperature-strain data is classified according to the preset intervals, and the strain data in each interval is counted. Use data analysis tools to calculate the strain mean and standard deviation in each temperature interval to ensure a full understanding of the behavior of the material in each temperature interval. The final temperature interval-strain data will provide the necessary data support for the calculation of the mean thermal expansion coefficient of the partition.

[0193] Step S324: Calculate the mean value of thermal expansion coefficient of each partition according to the temperature range-strain data, so as to obtain the mean value data of thermal expansion coefficient of each partition;

[0194] In this embodiment, the mean thermal expansion coefficient of each partition is calculated based on the temperature interval-strain data to obtain the mean thermal expansion coefficient data of each partition. This step requires analyzing the strain value in each temperature interval and calculating the mean thermal expansion coefficient using the following formula:

[0195]

[0196] Among them, ΔT is the change in the temperature interval. The strain mean and temperature range in each temperature interval are used as input to obtain the mean thermal expansion coefficient of each interval. To ensure the effectiveness of the calculation, a sample quantity threshold needs to be set, usually at least 30 samples to ensure the reliability of the mean. The final generated partition thermal expansion coefficient mean data will provide a basis for subsequent curve fitting.

[0197] Step S325: performing curve fitting according to the mean value of the thermal expansion coefficient of each partition, thereby obtaining thermal expansion coefficient-temperature curve data;

[0198] In this embodiment, curve fitting is performed based on the mean thermal expansion coefficient of the partition to obtain the thermal expansion coefficient-temperature curve data. The mean thermal expansion coefficient of the partition and the corresponding temperature are fitted using a data fitting tool through polynomial regression or spline curve interpolation method. During the fitting process, the polynomial order of the fitting needs to be set, and the third or fourth order is usually selected to ensure the fitting accuracy and avoid overfitting. After the fitting is completed, the goodness of fit is evaluated through residual analysis, usually using R 2 The value is measured and R 2 The value is greater than 0.95. The final thermal expansion coefficient-temperature curve data will provide an intuitive expression of the thermal expansion characteristics of the material at different temperatures.

[0199] Step S326: extracting the thermal expansion coefficient of the material in the high temperature range according to the thermal expansion coefficient-temperature curve data, thereby obtaining the thermal expansion coefficient data of the material.

[0200] In this embodiment, the thermal expansion coefficient of the material in the high temperature range is extracted based on the thermal expansion coefficient-temperature curve data, so as to obtain the thermal expansion coefficient data of the material. A specific high temperature range is selected, such as 400°C to 600°C, and the fitted curve is sampled and analyzed. The thermal expansion coefficient data within the temperature range is extracted, and the specific operation includes calculating the thermal expansion coefficient value at the specified temperature by interpolation. At the same time, a threshold value needs to be set to determine whether the extracted data is within a reasonable range, which is generally set to a tolerance of ±5%. The final material thermal expansion coefficient data will be used to evaluate the performance of the material under high temperature conditions, directly supporting the material selection and process design in the robotic arm welding process.

[0201] Optionally, step S33 is specifically:

[0202] Step S331: extracting the characteristics of the robot arm joint driver according to the high temperature simulation data, thereby obtaining the robot arm joint driver data;

[0203] In this embodiment, the robot arm joint driver feature extraction is performed based on the high temperature simulation data to obtain the robot arm joint driver data. By analyzing the temperature, displacement and strain information of the joint driver in the high temperature simulation results, the features related to the driver performance are extracted. First, the temperature field and strain field data of the joint driver are obtained from the high temperature simulation data, and the key parameters such as temperature gradient and strain rate are calculated using a feature extraction algorithm. Use tools such as MATLAB or Python for data processing, and set a temperature threshold (for example, set to 100°C) to screen out features that have a significant impact on the driver performance. The final generated robot arm joint driver data will provide a basis for subsequent heat distribution statistics.

[0204] Step S332: performing heat distribution statistics according to the robot arm joint driver data, thereby obtaining heat distribution data;

[0205] In this embodiment, heat distribution statistics are performed based on the data of the robot arm joint driver to obtain heat distribution data. In this step, a heat distribution model is formed by calculating the temperature data of different positions of the joint driver. Finite element analysis software, such as ANSYS, is used to calculate the heat flux density of the joint driver. A three-dimensional model of the joint driver is established, boundary conditions and initial conditions are set, and the acquired temperature data is applied to the model. Then, the heat transfer of each node is analyzed, and the heat distribution of each part is statistically analyzed, with particular attention to the heat density of the high-temperature area. Heat distribution data is generated based on the statistical results to facilitate subsequent identification of high-temperature areas.

[0206] Step S333: identifying the high temperature area according to the heat distribution data, thereby obtaining the high temperature area data of the robot arm joint driver;

[0207] In this embodiment, high temperature area identification is performed based on heat distribution data to obtain high temperature area data of the robot joint driver. The heat distribution data obtained by statistics is used to set a heat threshold (for example, set to 500W / m 2 ), analyze the data and identify areas where the heat is higher than the threshold. Apply image processing technology to convert the heat distribution data into a heat distribution map. Use pixel classification algorithms to mark high-temperature areas, and use local maximum detection methods to identify the specific location and size of high-temperature areas. The resulting high-temperature area data of the robot arm joint driver will provide necessary information for subsequent material stress calculations.

[0208] Step S334: Acquire the thermal expansion coefficient data of the robot arm joint driver;

[0209] In this embodiment, the thermal expansion coefficient data of the robot arm joint driver is obtained. The thermal expansion characteristics of the driver material are analyzed, and the commonly used thermal expansion coefficient values ​​of the materials are derived from the standard material manual or experimental data. In order to ensure the accuracy of the thermal expansion coefficient data, it is necessary to experimentally test the expansion characteristics of the material at different temperatures, and use a linear expansion meter to measure the expansion of the material. In the experiment, multiple groups of temperature points are set, usually room temperature, 100°C, 200°C, etc. The calculated average value of the expansion coefficient is used as the thermal expansion coefficient data of the robot arm joint driver for subsequent stress calculations.

[0210] Step S335: Calculate material stress based on the thermal expansion coefficient data of the robot arm joint driver and the high temperature area data of the robot arm joint driver to obtain material thermal stress data.

[0211] In this embodiment, material stress calculation is performed based on the thermal expansion coefficient data of the robot joint driver and the high temperature area data of the robot joint driver, thereby obtaining material thermal stress data. In this step, the thermal stress calculation formula is used:

[0212] σ=E·α·ΔT

[0213] Among them, σ is thermal stress, E is the Young's modulus of the material, α is the thermal expansion coefficient, and ΔT is the temperature change. Using the temperature data of the high-temperature area and the acquired thermal expansion coefficient, the thermal stress of the material in the high-temperature area is calculated one by one. During the stress calculation process, the thermal stress values ​​of different areas need to be calculated separately according to the location of the high-temperature area and the specific properties of the material. The material thermal stress data finally obtained will be used to evaluate the performance and safety of the robot arm under high-temperature working conditions, providing a basis for subsequent optimization and adjustment.

[0214] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0215] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for correcting a mechanical arm joint dynamics model, characterized in that: The following steps are involved: S1: Acquire the robot arm joint data, and extract the geometric structure features according to the robot arm joint data, so as to obtain the geometric structure data; Performing a topological structure analysis of the robot arm joints according to the geometric structure data, thereby obtaining a topological structure model of the robot arm joints; S2: Extract joint bearing features and connection joint features according to the robot arm joint data, so as to obtain joint bearing data and connection joint data; An abnormal vibration analysis is performed based on the joint bearing data and the connection joint data, so as to obtain the abnormal vibration data of the robot arm joint operation; an abnormal vibration structure is divided into the robot arm joint topology structure model based on the abnormal vibration data of the robot arm joint operation, so as to obtain the abnormal vibration topology structure model of the robot arm joint; S3: Performing high temperature simulation according to the abnormal vibration topological structure model of the robot arm joint to obtain high temperature simulation data; performing material expansion analysis according to the high temperature simulation data to obtain material thermal expansion data; performing expansion deformation calculation on the abnormal vibration topological structure model of the robot arm joint according to the material thermal expansion data to obtain expansion deformation data; S4: Acquire the sensor data of the robot arm joints; Performing a deformation analysis on the abnormal vibration topological structure model of the robot arm joints according to the robot arm joint sensor data, thereby obtaining the robot arm joint deformation data; S5: Dynamically correct the deformation data of the robot arm joints according to the expansion deformation data, so as to obtain the robot arm joint deformation correction data, and upload it to the robot arm joint flexible body dynamics model management system to perform the robot arm joint structure deformation correction task.

2. The method for correcting the mechanical arm joint dynamics model according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: acquiring the robot arm joint data, and performing geometric structure feature extraction based on the robot arm joint data, thereby obtaining geometric structure data; Step S12: extracting the three-dimensional vertex coordinates of the robot arm joints according to the geometric structure data, thereby obtaining the three-dimensional vertex coordinate data of the robot arm joints; Step S13: constructing a three-dimensional surface according to the three-dimensional vertex coordinate data of the robot arm joints, thereby obtaining three-dimensional surface data; Step S14: performing spatial mapping of the robot arm joint topology structure model according to the three-dimensional surface data, thereby obtaining the robot arm joint topology structure model.

3. The method for correcting the mechanical arm joint dynamics model according to claim 2, characterized in that: Step S13 is specifically as follows: Step S131: triangulate the three-dimensional vertex coordinate data of the robot arm joints to obtain triangular facet data; Step S132: determining the adjacent relationship according to the three-dimensional vertex coordinate data of the robot arm joint, thereby obtaining vertex adjacent relationship data; Step S133: Calculating the normal vector according to the three-dimensional vertex coordinate data of the robot arm joint, thereby obtaining the normal vector data; Step S134: constructing a three-dimensional patch on the triangular patch data according to the normal vector data and the vertex adjacent relationship data, thereby obtaining three-dimensional patch data.

4. The method for correcting the mechanical arm joint dynamics model according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: extracting joint bearing features and connecting joint features according to the robot arm joint data, thereby obtaining joint bearing data and connecting joint data; Step S22: performing radial vibration analysis according to the spherical plain bearing data, thereby obtaining radial vibration data of the spherical plain bearing; Step S23: performing impact vibration analysis according to the connection joint data, thereby obtaining the connection joint impact vibration data; Step S24: integrating the abnormal vibration data of the joint operation of the robot arm according to the radial vibration data of the joint bearing and the impact vibration data of the connection joint, so as to obtain the abnormal vibration data of the joint operation of the robot arm; Step S25: dividing the abnormal vibration structure of the robot arm joint topology structure model according to the abnormal vibration data of the robot arm joint operation, so as to obtain the abnormal vibration topology structure model of the robot arm joint.

5. The method for correcting the mechanical arm joint dynamics model according to claim 4, characterized in that: Step S22 is specifically as follows: Step S221: extracting radial vibration sensing features of the spherical joint bearing according to the spherical joint bearing data, thereby obtaining radial vibration sensing data of the spherical joint bearing; Step S222: performing fast Fourier transform according to the spherical plain bearing radial vibration sensor data, so as to obtain the spherical plain bearing radial vibration frequency domain data; Step S223: performing harmonic frequency statistics according to the spherical plain bearing radial vibration frequency domain data, thereby obtaining high-order harmonic vibration frequency data; Step S224: performing frequency domain regional statistics based on the spherical plain bearing radial vibration sensing frequency domain data, thereby obtaining vibration sensing high frequency region data; Step S225: performing uniform statistics on the peak interval of the vibration sensing high-frequency area data, thereby obtaining irregular peak data in the vibration frequency domain; Step S226: Integrate the radial vibration characteristics of the spherical plain bearing according to the high-order harmonic vibration frequency data and the irregular peak data in the vibration frequency domain, so as to obtain the radial vibration data of the spherical plain bearing.

6. The method for correcting the mechanical arm joint dynamics model according to claim 4, characterized in that: Step S23 is specifically as follows: Step S231: extracting vibration sensing features according to the connection joint data, thereby obtaining connection joint vibration sensing data; Step S232: performing Hilbert transform according to the connection joint vibration sensing data, thereby obtaining connection joint vibration analysis signal data; Step S233: Calculate the instantaneous amplitude according to the connection joint vibration analysis signal data, so as to obtain instantaneous amplitude data; Step S234: constructing an envelope signal according to the instantaneous amplitude data, thereby obtaining envelope signal data; Step S235: performing signal abnormal fluctuation statistics according to the envelope signal data, thereby obtaining signal abnormal fluctuation data; Step S236: performing frequency statistics according to the envelope signal data, thereby obtaining high-frequency envelope signal data; Step S237: Integrate the impact vibration characteristics according to the abnormal signal fluctuation data and the high-frequency envelope signal data, so as to obtain the impact vibration data of the connection joint.

7. The method for correcting the mechanical arm joint dynamics model according to claim 6, characterized in that: Step S234 is specifically as follows: Performing smoothing processing on the instantaneous amplitude data to obtain instantaneous amplitude smoothing data; Identify the local maximum points of instantaneous amplitude smoothing data to obtain local maximum point data; Perform time sequence statistics based on local maximum point data to obtain time sequence data; Envelope connection is performed on the local maximum point data according to the time sequence data, so as to obtain envelope data; Envelope signal features are extracted according to the envelope curve data, thereby obtaining envelope signal data.

8. The method for correcting the mechanical arm joint dynamics model according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: performing high temperature simulation according to the abnormal vibration topological structure model of the robot arm joint, thereby obtaining high temperature simulation data; Step S32: analyzing the thermal expansion coefficient of the material according to the high temperature simulation data, thereby obtaining the thermal expansion coefficient data of the material; Step S33: performing material thermal stress analysis according to the high temperature simulation data, thereby obtaining material thermal stress data; Step S34: integrating the material thermal expansion characteristics according to the material thermal expansion coefficient data and the material thermal stress data, thereby obtaining the material thermal expansion data; Step S35: performing expansion deformation calculation on the abnormal vibration topological structure model of the robot arm joint according to the material thermal expansion data, so as to obtain expansion deformation data.

9. The method for correcting the mechanical arm joint dynamics model according to claim 8, characterized in that: Step S32 is specifically as follows: Step S321: extracting material strain features according to the high temperature simulation data, thereby obtaining material strain data; Step S322: performing temperature statistics according to the material strain data, thereby obtaining material temperature-strain data; Step S323: dividing the material temperature-strain data into temperature intervals, thereby obtaining temperature interval-strain data; Step S324: Calculate the mean value of thermal expansion coefficient of each partition according to the temperature range-strain data, so as to obtain the mean value data of thermal expansion coefficient of each partition; Step S325: performing curve fitting according to the mean value of the thermal expansion coefficient of each partition, thereby obtaining thermal expansion coefficient-temperature curve data; Step S326: extracting the thermal expansion coefficient of the material in the high temperature range according to the thermal expansion coefficient-temperature curve data, thereby obtaining the thermal expansion coefficient data of the material.

10. The method for correcting the mechanical arm joint dynamics model according to claim 8, characterized in that: Step S33 is specifically as follows: Step S331: extracting the characteristics of the robot arm joint driver according to the high temperature simulation data, thereby obtaining the robot arm joint driver data; Step S332: performing heat distribution statistics according to the robot arm joint driver data, thereby obtaining heat distribution data; Step S333: identifying the high temperature area according to the heat distribution data, thereby obtaining the high temperature area data of the robot arm joint driver; Step S334: Acquire the thermal expansion coefficient data of the robot arm joint driver; Step S335: Calculate material stress based on the thermal expansion coefficient data of the robot arm joint driver and the high temperature area data of the robot arm joint driver to obtain material thermal stress data.

Images