Swing guide rod electric cylinder intelligent driving method and system

Abstract

This invention relates to the field of electric actuator control technology, and particularly to an intelligent drive method and system for a swing guide rod electric cylinder. The method includes the following steps: acquiring electric cylinder design data; performing structural initialization based on the electric cylinder design data to obtain an initial electric cylinder structure; simulating guide rod swing based on the initial electric cylinder structure to obtain guide rod swing data; performing guide rod swing jamming analysis based on the guide rod swing data to obtain guide rod swing jamming data; detecting the wear degree of the sliding bearing based on the guide rod swing jamming data; performing magnetic particle crack detection based on the sliding bearing wear degree to generate crack data; predicting the metal fatigue life of the guide rod based on the crack data; and evaluating the guide rod deflection degree based on the guide rod swing data. This invention, based on electric actuator control technology, improves the reliability, accuracy, and service life of the electric cylinder, and reduces the failure rate.

Description

Technical Field

[0001] This invention relates to the field of electric actuator control technology, and in particular to an intelligent drive method and system for a swing guide rod electric cylinder. Background Technology

[0002] Traditional electric cylinder drives for swing guide rods lack dynamic monitoring and intelligent recognition capabilities for the actual operating status of the guide rod. They cannot detect jamming or wear on sliding bearings during the swing process in real time, leading to a failure to provide timely warnings for minor structural faults, potentially causing guide rod jamming and guide rail damage. For ball screw structures, clearance assessment and maintenance typically rely on manual periodic inspections, lacking automatic detection and compensation mechanisms for abnormal screw clearance. This can easily lead to positioning deviations, efficiency reduction, and mechanical fatigue over long-term operation. In terms of trajectory planning, existing technologies mostly rely on task paths or servo strategies, failing to fully consider the metal fatigue life variation of the guide rod material. This results in excessively rigid paths, increased fatigue risk, shortened service life, and even compromised operational safety. More critically, traditional systems suffer from weak coordination between analysis modules, lacking an integrated linkage mechanism from structural design, status monitoring, life assessment to drive optimization. This makes it difficult to form a complete closed-loop intelligent drive solution, limiting the system's intelligence, adaptability, and high reliability. Summary of the Invention

[0003] Therefore, it is necessary for the present invention to provide an intelligent driving method and system for a swing guide rod electric cylinder to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a smart drive method for a swing guide rod electric cylinder includes the following steps: Step S1: Obtain the electric cylinder design data; perform structural initialization based on the electric cylinder design data to obtain the electric cylinder initialization structure; perform guide rod swing simulation based on the electric cylinder initialization structure to obtain guide rod swing data; Step S2: Perform guide rod swing and jamming analysis based on guide rod swing data to obtain guide rod swing and jamming data; detect the wear degree of sliding bearing based on guide rod swing and jamming data; perform magnetic particle crack detection based on sliding bearing wear degree to generate crack data; predict the fatigue life of guide rod metal based on crack data. Step S3: Evaluate the degree of guide rod runout based on the guide rod swing data; perform ball screw backlash anomaly analysis based on the degree of guide rod runout to obtain ball screw backlash anomaly data; design an automatic backlash compensation structure based on the ball screw backlash anomaly data. Step S4: Based on the fatigue life of the guide rod metal, perform intelligent trajectory planning for the guide rod to generate the intelligent trajectory of the guide rod; optimize the initialization structure of the electric cylinder based on the automatic clearance compensation structure to obtain the optimized structure of the electric cylinder; perform intelligent drive simulation of the guide rod based on the optimized structure of the electric cylinder and the intelligent trajectory of the guide rod to generate intelligent drive data of the guide rod.

[0005] This invention, through structural initialization and guide rod oscillation simulation, enables the system to fully grasp the structural characteristics of the electric cylinder and the motion features of the guide rod in the early design stage, laying the foundation for subsequent state perception and optimization. In-depth analysis of the guide rod oscillation data can identify jamming behavior that occurs during actual operation. Furthermore, by combining sliding bearing wear status and magnetic particle crack detection technology, early diagnosis of internal structural anomalies and prediction of life trends can be achieved, improving the safety and predictability of system operation. Simultaneously, by integrating guide rod runout assessment and ball screw backlash anomaly analysis, dynamic monitoring of the screw-nut fit accuracy can be achieved, effectively avoiding positioning deviations and motion lag problems caused by backlash loss. Furthermore, the design and integration of an automatic backlash compensation structure makes the compensation behavior adaptive and effective, significantly improving transmission accuracy and the overall reliability of the device. In addition, intelligent planning of the guide rod trajectory, based on metal fatigue life prediction results, can proactively adjust the running trajectory according to fatigue-sensitive areas, avoiding repeated loading of high-fatigue paths, effectively extending the service life of the guide rod and reducing the failure rate. Ultimately, fatigue information, clearance compensation, and optimized structure are linked and applied to drive simulation to achieve synchronous matching between drive path and structural performance, forming an intelligent closed-loop system covering design, monitoring, diagnosis, optimization, and control. This breaks through the limitations of traditional isolated analysis and manual maintenance, significantly improving the intelligence level, adaptability, and high reliability of the electric cylinder system.

[0006] Preferably, step S1 specifically includes: Step S11: Obtain the electric lever design data and extract the servo motor structure data and ball screw structure data; Step S12: Identify the lead screw drive structure based on the servo motor structure data; Step S13: Construct a guide rail support structure model based on the screw drive structure; Step S14: Analyze the screw drive parameters based on the ball screw structure data; Step S15: Input the screw drive parameters into the guide rail support structure model and perform coordination matching of the electric cylinder assembly to obtain the coordination matching data of the electric cylinder assembly; Step S16: Perform virtual assembly based on the electric cylinder assembly coordination and matching data to obtain the electric cylinder initialization structure; Step S17: Simulate the guide rod swing based on the electric cylinder initialization structure to obtain guide rod swing data.

[0007] This invention acquires the design data of the electric cylinder and extracts the structural data of the servo motor and ball screw, providing a foundation for subsequent structural analysis and optimization, ensuring the comprehensiveness and accuracy of the electric cylinder design. By identifying the screw drive structure, the interrelationship and transmission efficiency between the screw and other components can be effectively determined, ensuring the high efficiency and stability of the drive system. Based on the establishment of the screw drive structure, a guide rail support structure model is constructed, which accurately reflects the motion support frame of the electric cylinder, ensuring the stability and coordination of the system during operation. After analyzing the screw drive parameters, combined with the guide rail support structure model, the entire electric cylinder assembly can achieve precise coordination and matching during the design process, thereby improving the system's matching degree and adaptability, laying the foundation for subsequent virtual assembly, and reducing errors and adjustment costs in physical assembly. The electric cylinder initialization structure obtained after virtual assembly ensures accurate simulation and functional verification before actual operation, effectively eliminating potential problems in the design stage and detecting anomalies such as guide rod sway in advance. Finally, through guide rod sway simulation, not only can the motion data of the guide rod be obtained, but also the jamming phenomenon that may occur can be predicted in the simulation, providing data support for subsequent maintenance and improvement, and further improving the reliability and intelligence level of the system. By comprehensively applying this series of steps, the problems of lack of dynamic monitoring of structural status, inability to assess mechanical fatigue in real time, and insufficient automatic compensation in traditional methods can be effectively compensated, thereby improving the overall intelligence and efficient operation of electric cylinders and enhancing the system's early warning and adaptability to faults.

[0008] Preferably, step S16 specifically includes: Step S161: Identify the component interface based on the electric cylinder assembly coordination and matching data; Step S162: Perform geometric alignment processing of the electric cylinder based on the component interface to obtain the assembly reference data of the electric cylinder; Step S163: Set assembly constraint relationships based on the electric cylinder assembly reference data; Step S164: Perform virtual assembly based on assembly constraints to obtain the initial structure of the electric cylinder.

[0009] This invention, by identifying the interfaces of electric cylinder components, can accurately analyze the connection relationships between various components, providing clear interface data for the subsequent assembly process. This step makes the docking during assembly more precise, avoiding assembly errors and instability caused by unclear assembly interfaces in traditional methods. Next, by aligning the geometric structure of the electric cylinder based on the component interfaces, not only is the geometric accuracy of each component ensured during assembly, but also the coordination and cooperation between components, making the overall system operation more efficient and stable. The obtained electric cylinder assembly reference data provides strong support for setting assembly constraints, effectively reducing the error accumulation caused by the lack of constraints in traditional assembly. By setting these assembly constraints, the relative position and movement accuracy of each component during assembly can be ensured, thereby identifying potential problems in advance during virtual assembly and ensuring that the electric cylinder does not experience jamming or failure due to inaccurate component docking in actual applications. Finally, the electric cylinder initialization structure obtained through virtual assembly provides sufficient data support for subsequent performance testing and optimization. This method effectively avoids the problem of insufficient dynamic monitoring of guide rods and other components in traditional technologies, improving the intelligent recognition and adaptability of the entire system. By optimizing this process, the risk of structural failures caused by improper assembly can be greatly reduced, the operational reliability and safety of the electric cylinder system can be improved, and a solid foundation can be laid for subsequent condition monitoring and intelligent adjustment.

[0010] Preferably, step S2 specifically includes: Step S21: Calculate the guide rod swing speed based on the guide rod swing data; Step S22: Calculate the instantaneous swing deceleration time period of the base guide rod swing speed; Step S23: Detect sudden changes in swing amplitude stagnation based on the instantaneous swing deceleration time period to obtain swing amplitude stagnation data; Step S24: Determine the guide rod swing jamming based on the swing amplitude stagnation data, and obtain the guide rod swing jamming data; Step S25: Detect the wear degree of the sliding bearing based on the guide rod swing and jamming data; Step S26: Perform magnetic particle crack detection based on the wear degree of the sliding bearing to generate crack data; Step S27: Predict the fatigue life of the guide rod metal based on crack data.

[0011] This invention accurately identifies the working state of the guide rod by statistically analyzing its swing speed, providing accurate foundational data for subsequent analysis. By further calculating the instantaneous swing deceleration time using the swing speed, the deceleration characteristics of the guide rod during swing are effectively captured, providing a reliable time window for detecting jamming behavior. Based on this time period, the ability to detect sudden changes in swing amplitude stagnation can provide early warning of potential jamming problems, reducing the neglect of such minor faults in traditional methods and preventing them from evolving into serious failures in later stages. Further analysis of swing amplitude stagnation data accurately determines whether the guide rod has jammed, thereby identifying jamming risks, which provides important early warning information for subsequent maintenance. Based on jamming data, combined with wear detection of sliding bearings, the degree of bearing wear can be detected in a timely manner, avoiding operational anomalies caused by bearing damage in traditional methods. Next, magnetic particle crack detection can further analyze the crack state of sliding bearings or other key components, generating crack data, which provides accurate data support for timely replacement or repair of worn components. Finally, the prediction of the guide rod's metal fatigue life based on crack data enables the system to identify metal fatigue risks early, avoiding structural failures caused by fatigue damage, extending the electric cylinder's service life, and improving operational safety. Through this series of steps, the entire electric cylinder system achieves comprehensive monitoring and intelligent optimization of its dynamic state, greatly improving the system's reliability, adaptability, and self-adjustment capabilities. This overcomes the shortcomings of insufficient intelligence and fault warning in traditional technologies, enhancing overall performance and safety.

[0012] Preferably, step S25 specifically includes: Step S251: Extract the swing position offset information based on the guide rod swing jamming data; Step S252: Extract the position of the sliding bearing based on the swing position offset information; Step S253: Perform sliding simulation based on the position of the sliding bearing to obtain bearing sliding data; Step S254: Perform surface oxidation analysis on the material based on the bearing sliding data to obtain surface oxidation data of the material; Step S255: Detect metal particles based on the surface oxidation data of the material to obtain metal particle data; Step S256: Determine the degree of wear of the sliding bearing based on the metal particle data.

[0013] This invention, by extracting the swing position offset information, enables the system to accurately identify positional changes during guide rod swing, laying the foundation for subsequent sliding bearing position identification and wear analysis. Based on the extracted sliding bearing position, the system can perform sliding simulations to obtain bearing sliding data, providing precise data support for further analysis of the bearing's working state. Analysis of the sliding data can identify abnormalities during the sliding process and provide important clues about material surface oxidation. Oxidation analysis reveals the degree of oxidation on the material surface, allowing for timely detection of surface degradation caused by wear or environmental factors, thereby preventing serious failures due to material aging. Furthermore, based on the material surface oxidation data, the system further performs metal particle detection, generating metal particle data. This process helps detect wear and contamination at the microscopic level, thus more accurately assessing the wear condition of the sliding bearing. Finally, through the metal particle data, the system can determine the degree of wear of the sliding bearing, providing timely information to maintenance personnel, preventing jamming or other mechanical failures caused by bearing damage, and ensuring the efficient and stable operation of the electric cylinder system. Overall, this series of steps not only provides more comprehensive real-time monitoring and fault warning, but also enables precise analysis of the sliding bearings in the guide rod electric cylinder, greatly enhancing the system's intelligence level and fault diagnosis capabilities, thereby improving the reliability and service life of the electric cylinder.

[0014] Preferably, step S26 specifically includes: Step S261: Identify high-wear areas based on the degree of wear of the sliding bearing; Step S262: Set longitudinal magnetization for the high wear area, and set the magnetization current range to 300A-2000A and the magnetization duration to 0.5s-3s to obtain longitudinal magnetization wear area data; Step S263: Apply magnetic powder for a duration of 1s-5s based on the longitudinal magnetization wear area data to obtain magnetic powder application area data; Step S264: Calculate the magnetic powder aggregation degree based on the data of the area where the magnetic powder is applied; Step S265: Identify magnetic powder aggregation regions based on the magnetic powder application area data according to the magnetic powder aggregation degree, and obtain magnetic powder aggregation region data; Step S266: Based on the preset crack magnetic particle data, crack determination is performed on the magnetic particle aggregation area data to generate crack data.

[0015] This invention identifies high-wear areas, enabling the system to accurately pinpoint potential hazards and providing strong support for subsequent maintenance and repair. Next, by applying longitudinal magnetization to the high-wear areas with appropriate magnetization current and duration, the system helps to further reveal microstructural changes in the wear area. This process enhances the magnetic response of the wear area, providing more data support for subsequent magnetic particle application and crack detection. After applying magnetic particles, their distribution in the wear area reflects the degree of surface cracks or localized damage. By calculating the aggregation degree of the magnetic particles, potential damage areas can be effectively assessed; higher aggregation degree indicates more severe damage. Based on the aggregation degree data, the system can identify areas of magnetic particle aggregation, further confirming the location of cracks. Finally, by comparing with preset crack magnetic particle data, the system can accurately determine the presence of cracks, generate crack data, and provide a basis for subsequent maintenance decisions. Overall, these steps enable the system to efficiently and in real-time detect minor damage to sliding bearings and other critical components, improve the accuracy of fault prediction, and reduce the risk of major equipment failures due to insufficient early warning, thereby greatly improving the safety, reliability, and service life of electric cylinders.

[0016] Preferably, step S3 specifically includes: Step S31: Evaluate the degree of guide rod deflection based on the guide rod swing data; Step S32: Acquire images of the ball screw based on the degree of guide rod deflection; Step S33: Calculate the ball screw clearance based on the ball screw image; Step S34: Calculate the vibration frequency based on the guide rod's sway and extract the low-frequency vibration data of the guide rod; Step S35: Simulate the transmission of ball screw clearance based on the low-frequency vibration data of the guide rod, and detect the wear degree of the screw nut; Step S36: Perform anomaly analysis on the ball screw clearance based on the wear degree of the lead screw nut to obtain abnormal ball screw clearance data; Step S37: Design an automatic backlash compensation structure based on abnormal ball screw backlash data.

[0017] This invention provides crucial information about the guide rod's operating status by assessing its runout, enabling real-time monitoring of its performance and preventing system errors caused by excessive runout. By acquiring images of the ball screw and calculating the screw clearance, the system accurately determines the wear state and degree of the screw, thus identifying potential structural problems early and preventing positioning deviations and decreased system accuracy due to excessive screw clearance. Statistical analysis of vibration frequencies and extraction of low-frequency vibration data further aids in detecting abnormalities in the guide rod's operation. Low-frequency vibration characteristics allow for earlier identification of minute deviations and faults, enabling proactive corrective measures. Next, by simulating the ball screw clearance and detecting the wear degree of the screw nut, the system not only identifies the specific location of wear but also evaluates the overall system performance and lifespan through simulation and analysis. The anomaly analysis process helps identify potential problems with abnormal screw clearance, providing detailed data support and effectively preventing efficiency losses and mechanical failures caused by excessive clearance or wear. Finally, an automatic clearance compensation structure is designed to ensure real-time automatic adjustment during operation, avoiding the lag of human intervention and maintaining efficient and precise system operation. Overall, these steps, while ensuring system accuracy and reliability, improve its intelligence, dynamic response, and adaptive capabilities, ensuring the long-term stable operation of the electric cylinder system and effectively extending the service life of the equipment.

[0018] Preferably, step S37 specifically includes: Step S371: Design a clearance compensation strategy based on abnormal ball screw clearance data; Step S372: Configure an active gap adjustment structure based on the gap compensation strategy; Step S373: Configure a passive gap adjustment structure based on the gap compensation strategy; Step S374: Integrate the active clearance adjustment structure and the passive clearance adjustment structure to obtain the automatic clearance compensation structure.

[0019] This invention designs a targeted clearance compensation strategy based on clearance anomaly data, enabling the system to precisely adjust according to actual working conditions, avoiding positioning errors and reduced work efficiency caused by lead screw clearance issues. By configuring an active clearance adjustment structure, the system can respond to clearance changes in real time and actively adjust when deviations are detected, ensuring accuracy and consistency in the lead screw transmission process. Furthermore, configuring a passive clearance adjustment structure allows for automatic adjustment under changes in system load or environmental conditions, providing auxiliary compensation without the need for additional control signals and enhancing the system's adaptability. Finally, integrating the active and passive clearance adjustment structures into an automatic clearance compensation system allows the entire drive system to respond more flexibly to different working conditions, achieving a balance between precision control and efficient operation. This integration effectively reduces the need for human intervention, improves system response speed and stability, reduces wear and malfunctions caused by clearance anomalies, and thus significantly improves the overall reliability, service life, and operating efficiency of the electric cylinder system.

[0020] Preferably, step S4 specifically includes: Step S41: Identify fatigue-sensitive areas based on the fatigue life of the guide rod metal; Step S42: Detect trajectory influencing factors based on fatigue-sensitive areas; Step S43: Design the intelligent trajectory of the guide rod based on the trajectory influence factor and generate the intelligent trajectory of the guide rod; Step S44: Optimize the electric cylinder initialization structure based on the automatic clearance compensation structure to obtain the optimized electric cylinder structure; Step S45: Based on the optimized structure of the electric cylinder and the intelligent trajectory of the guide rod, perform intelligent drive simulation of the guide rod to generate intelligent drive data for the guide rod.

[0021] This invention identifies fatigue-sensitive areas of the guide rod, enabling the system to precisely pinpoint which parts are prone to fatigue damage. This allows for early warning of potential problems and the implementation of corresponding optimization measures. Based on these fatigue-sensitive areas, the detection of trajectory influencing factors allows the system to assess the impact of different working paths on the guide rod's lifespan, thereby optimizing the trajectory design. The designed intelligent guide rod trajectory can adaptively adjust the trajectory path, reducing fatigue risk, extending the guide rod's service life, and improving system operating efficiency. Through integration with an automatic clearance compensation structure, the optimized electric cylinder initialization structure adapts more accurately to different working conditions, ensuring the electric cylinder's stability and long-term reliable operation. Furthermore, the intelligent guide rod drive simulation can simulate various states in actual operation by adjusting parameters and optimizing path planning, ensuring stable operation of the guide rod under various load conditions. This avoids excessive wear or uneven load distribution caused by unreasonable path planning, further improving the system's reliability, accuracy, and adaptability. Ultimately, this comprehensive optimization scheme forms a closed-loop feedback system, enabling the electric cylinder to automatically adapt to different working environments during use and ensuring efficient and stable operation of the equipment through intelligent control, reducing failures and downtime caused by fatigue, wear, and other problems.

[0022] Preferably, this specification also provides an intelligent drive system for a swing guide rod electric cylinder, used to execute the intelligent drive method for a swing guide rod electric cylinder as described above. The intelligent drive system for the swing guide rod electric cylinder includes: The guide rod swing simulation module is used to acquire electric cylinder design data; perform structural initialization based on electric cylinder design data to obtain the electric cylinder initialization structure; and perform guide rod swing simulation based on the electric cylinder initialization structure to obtain guide rod swing data. The guide rod metal fatigue life prediction module is used to perform guide rod swing and jamming analysis based on guide rod swing data to obtain guide rod swing and jamming data; detect the wear degree of sliding bearing based on guide rod swing and jamming data; perform magnetic particle crack detection based on sliding bearing wear degree to generate crack data; and predict the fatigue life of guide rod metal based on crack data. An automatic clearance compensation structure design module is used to evaluate the degree of guide rod runout based on guide rod swing data; perform ball screw clearance anomaly analysis based on the degree of guide rod runout to obtain ball screw clearance anomaly data; and design an automatic clearance compensation structure based on the ball screw clearance anomaly data. The intelligent drive simulation module for the guide rod is used to plan the intelligent trajectory of the guide rod based on the fatigue life of the guide rod metal, and generate the intelligent trajectory of the guide rod; optimize the initialization structure of the electric cylinder based on the automatic clearance compensation structure, and obtain the optimized structure of the electric cylinder; and simulate the intelligent drive of the guide rod based on the optimized structure of the electric cylinder and the intelligent trajectory of the guide rod, and generate the intelligent drive data of the guide rod.

[0023] The present invention relates to an intelligent drive system for a swing guide rod electric cylinder. This system can implement any one of the intelligent drive methods for a swing guide rod electric cylinder of the present invention. It serves as a medium for coordinating the operation and signal transmission between various modules to complete the intelligent drive method for the swing guide rod electric cylinder. The internal modules of the system cooperate with each other, which improves the reliability, accuracy and service life of the electric cylinder and reduces the failure rate. Attached Figure Description

[0024] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating the steps of an intelligent driving method for a swing guide rod electric cylinder according to the present invention. Figure 2 This is a detailed flowchart of step S1 in the present invention; Figure 3 This is a detailed flowchart of step S16 in the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0025] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0026] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0027] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0028] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides an intelligent drive method for a swing guide rod electric cylinder, the method comprising the following steps: Step S1: Obtain the electric cylinder design data; perform structural initialization based on the electric cylinder design data to obtain the electric cylinder initialization structure; perform guide rod swing simulation based on the electric cylinder initialization structure to obtain guide rod swing data; In this embodiment, design data for the electric cylinder is acquired using measuring tools or CAD software. This data includes the cylinder's geometric parameters, dynamic parameters, and the mechanical properties of the materials used (such as elastic modulus and tensile strength). This data includes the cylinder's length, diameter, drive motor power, and load. After acquiring the design data, a three-dimensional geometric model of the electric cylinder is created using dedicated 3D modeling software (such as SolidWorks). During structural initialization based on the design data, structural simulation is performed on the preliminary geometric model of the electric cylinder using mechanical analysis software (such as ANSYS or ABAQUS) to simulate the cylinder's deformation, stress, and motion characteristics under different operating conditions. The preliminary simulation results yield the initial structure of the electric cylinder, including the relative positions and dimensions of each component. A dynamic simulation tool is used to simulate the guide rod's swing. During the simulation, the load, drive voltage, and speed of the electric cylinder are input as operating conditions. Specific simulation parameters include the electric cylinder's swing angle, speed, and acceleration, with reasonable swing range and resistance parameters set. Using this data, the swing behavior of the electric cylinder during operation is simulated, and the guide rod's swing data, such as the swing angle and speed at different time points, is obtained. These oscillation data provide a foundation for subsequent jamming analysis, wear detection, and other processes.

[0029] Step S2: Perform guide rod swing and jamming analysis based on guide rod swing data to obtain guide rod swing and jamming data; detect the wear degree of sliding bearing based on guide rod swing and jamming data; perform magnetic particle crack detection based on sliding bearing wear degree to generate crack data; predict the fatigue life of guide rod metal based on crack data. In this embodiment, jamming analysis involves analyzing changes in the swing angle and velocity data, and using signal processing methods (such as Fast Fourier Transform and time-domain analysis) to identify jamming phenomena occurring during the swing of the guide rod. Specifically, a threshold is set to detect abnormal stagnation during the swing of the guide rod; for example, jamming is considered to have occurred when the swing speed is below 0.1 rad / s for a certain period. The jamming data includes information such as the time, duration, and location of the jamming, serving as the basis for subsequent sliding bearing wear detection. Based on the jamming data, the degree of wear of the sliding bearing is detected. By comparing the sliding bearing motion data before and after the jamming phenomenon, it is analyzed whether the bearing has experienced excessive friction or wear. Using materials science knowledge, the degree of wear is calculated using parameters such as the friction coefficient and contact surface hardness of the sliding bearing. A sliding friction threshold (e.g., a friction coefficient greater than 0.3 indicates excessive wear) is set to determine the degree of wear. Based on this, magnetic particle crack detection is performed. A magnetic particle detection system is set up, using magnetization technology to apply a magnetic particle coating to the surface of the sliding bearing, and a magnetic field is applied using standardized testing equipment to detect the location of cracks. The parameters used in this process include magnetization current and magnetization duration. Specifically, the magnetization current ranges from 300A to 2000A, and the duration is set from 0.5s to 3s. The area where magnetic powder is applied is determined based on the wear level of the sliding bearing. Crack data is obtained from the detection results, recording information such as the location and size of the cracks. Based on the crack data, the fatigue life of the guide rod metal is predicted using a material fatigue analysis model. The parameters used for fatigue life prediction include crack depth, length, and crack propagation velocity. Combined with the material's fatigue resistance data (such as the SN curve), the fatigue life of the metal can be estimated using fatigue life prediction models (such as Miner's rule) to obtain the remaining service life of the guide rod.

[0030] Step S3: Evaluate the degree of guide rod runout based on the guide rod swing data; perform ball screw backlash anomaly analysis based on the degree of guide rod runout to obtain ball screw backlash anomaly data; design an automatic backlash compensation structure based on the ball screw backlash anomaly data. In this embodiment, the degree of guide rod sway is evaluated by analyzing the guide rod's swing data. The degree of sway is assessed by measuring the guide rod's offset at multiple points and calculating the deviation from the ideal trajectory. In practice, sensors (such as displacement sensors or photoelectric sensors) are used to monitor the guide rod's sway behavior in real time. A data acquisition system (such as an NI data acquisition card) records the offset at different time points, and then calculates data such as the sway angle and vibration frequency. A sway threshold is set (e.g., a sway angle greater than 0.5° is considered excessive sway) and used as the basis for subsequent analysis. Based on the sway data, ball screw backlash anomaly analysis is performed. The working state of the ball screw, including the nut's movement speed and backlash changes, is measured to determine if a backlash anomaly exists. In practice, a high-precision displacement sensor or optical measurement device is used to monitor the screw's movement process in real time. By calculating the rate of backlash change, if the rate of backlash change exceeds a set threshold (e.g., 0.02 mm / s), it is determined to be a backlash anomaly. The backlash anomaly data will include the time, location, and degree of the anomaly. Based on gap anomaly data, an automatic gap compensation structure is designed. The design of the compensation structure must consider factors such as gap size and motion accuracy, and based on the gap variation pattern, an automatically adjustable compensation mechanism is designed. The automatic gap compensation structure can include active adjustment mechanisms (such as servo motor-controlled adjustment devices) and passive compensation mechanisms (such as springs, buffer devices, etc.). During the design process, dynamic simulation data needs to be incorporated to ensure that the compensation structure can achieve effective gap compensation under different operating conditions.

[0031] Step S4: Based on the fatigue life of the guide rod metal, perform intelligent trajectory planning for the guide rod to generate the intelligent trajectory of the guide rod; optimize the initialization structure of the electric cylinder based on the automatic clearance compensation structure to obtain the optimized structure of the electric cylinder; perform intelligent drive simulation of the guide rod based on the optimized structure of the electric cylinder and the intelligent trajectory of the guide rod to generate intelligent drive data of the guide rod.

[0032] In this embodiment, the key to trajectory planning lies in considering factors such as the fatigue life, load characteristics, and working environment of the guide rod to design a suitable working trajectory. During implementation, trajectory optimization algorithms based on fatigue life data (such as shortest path algorithms and genetic algorithms) are used to adjust the motion path of the guide rod. By inputting fatigue life constraints, it is ensured that the trajectory of the guide rod is not too rigid throughout the entire working process, thereby reducing the risk of metal fatigue. The generated intelligent trajectory data of the guide rod includes information such as the position, velocity, and acceleration of trajectory points at various times. Based on the data from the automatic gap compensation structure, the electric cylinder's structural design is optimized. The optimized structural design, by adjusting the position and shape of the internal components of the electric cylinder and combining it with the gap compensation mechanism, ensures that the electric cylinder can maintain high precision and high reliability under different working environments. The optimized electric cylinder structure not only has higher working efficiency but also reduces the negative impact caused by abnormal gaps. Combining the optimized electric cylinder structure and intelligent trajectory data, intelligent drive simulation of the guide rod is performed. By inputting the optimized electric cylinder structure and intelligent trajectory data into the simulation system, dynamic simulation is performed to obtain the drive response data of the electric cylinder in actual operation. The driving simulation process utilizes dedicated dynamic simulation software (such as MATLAB / Simulink) to simulate various working states and load changes, ultimately generating intelligent drive data for the guide rod, including performance indicators such as driving force, torque, and speed.

[0033] Preferably, step S1 specifically includes: Step S11: Obtain the electric lever design data and extract the servo motor structure data and ball screw structure data; In this embodiment, complete electric cylinder system design data is obtained through technical drawings, design databases, or digital design documents. The obtained electric cylinder design data should include basic parameters such as the maximum stroke of the guide rod, guide rod cross-sectional dimensions, electric cylinder working load, electric cylinder stroke speed, and expected operating frequency. For the servo motor, structural data is extracted, including rated power (kW), rated speed (rpm), rated torque (Nm), rotor inertia (kg·m²), motor mounting method (e.g., flange mounting dimensions), and shaft diameter (mm). For the ball screw structure, extracted structural data should include screw lead (mm), pitch (mm), nut diameter (mm), effective stroke length (mm), preload grade (e.g., P1-P5), thread angle (°), and axial stiffness (N / μm). All data is acquired and verified using standard technical measuring equipment (e.g., laser length measuring instrument, coordinate measuring machine), and its structural compliance and integrity are verified according to national standards (e.g., GB / T 17587.3-1998).

[0034] Step S12: Identify the lead screw drive structure based on the servo motor structure data; In this embodiment, based on the servo motor parameters extracted in S11, a lead screw drive structure matching its output characteristics is determined. The identification process is based on the rated speed of the servo motor and the required linear movement speed of the guide rod, using the following formula for conversion: Guide rod speed V (mm / s) = Lead P (mm) × Motor speed n (rpm) / 60. Based on this formula, the required lead screw lead is calculated by reverse calculation. If the required guide rod speed is 300mm / s and the motor speed is 3000rpm, then the required lead is 6mm. On this basis, lead screw models that meet the lead, diameter, and pitch requirements are selected from the standard ball screw series. Combining the rated load requirements and the servo motor output torque, it is ensured that the rated static load of the lead screw is higher than the maximum working load of the electric cylinder (for example, if the maximum load of the electric cylinder is 5kN, then the rated static load of the selected lead screw must be ≥5.5kN). Finally, an integrated motor-lead screw transmission structure is formed, and the structure identification is completed.

[0035] Step S13: Construct a guide rail support structure model based on the screw drive structure; In this embodiment, the construction of the guide rail support structure is based on the lead screw drive structure, and mechanical modeling is performed according to the installation position of the lead screw, the support method, and the load requirements of the slider. During the modeling process, the effective length of the guide rail is first determined based on the lead screw length and the nut stroke (for example, if the nut stroke is 400mm, a guide rail length of ≥450mm is required to meet the boundary allowance). The guide rail selection follows the rolling linear guide rail standard (such as HG / T 21556-95), selecting the number of sliders, the guide rail width, and the preload level. If the maximum offset of the guide rod must not exceed 0.05mm, a high-rigidity double-slider structure must be selected, with a single slider rated load of not less than 2.5kN. The support structure should include components such as linear guide rails, support bases, limit end caps, oil return channels, and dustproof sealing strips. A support structure model is established using a 3D modeling platform, indicating the assembly relationships, installation tolerances, and geometric tolerances of all structural components to ensure high stability and vibration resistance when the guide rod is supported.

[0036] Step S14: Analyze the screw drive parameters based on the ball screw structure data; In this embodiment, analyzing the helical transmission parameters requires combining the structural data and dynamic calculation principles of the ball screw. Specifically, this includes lead (P), screw diameter (d), thread angle (α), screw efficiency (η), lead angle (λ), and load distribution coefficient (z). The lead P has been extracted from S11. The thread angle is calculated using the formula tan(α) = P / (π·d). For example, if P = 6mm and d = 20mm, then α ≈ 5.45°. The transmission efficiency η depends on the thread angle and the friction coefficient μ (ranging from 0.01 to 0.03), and is calculated using η = tan(λ) / [tan(λ) + μ]. The lead angle λ can be obtained from λ = arctan(P / (π·d)). Under rated load, the load distribution in the ball joint is analyzed, and the load distribution coefficient z (generally taken as 1.2-1.5) is set for subsequent load simulation and torque analysis. All helical parameters will be used as input to accurately describe the motion characteristics and power transmission efficiency of the ball screw.

[0037] Step S15: Input the screw drive parameters into the guide rail support structure model and perform coordination matching of the electric cylinder assembly to obtain the coordination matching data of the electric cylinder assembly; In this embodiment, this step imports all the screw drive parameters calculated in S14 into the guide rail support structure model and performs dimensional coordination and component docking analysis of the mechanical structure. During the matching process, the relative layout and connection interface between components are adjusted according to the ball screw drive path, guide rail support position, and motor shaft centerline position. The coordination and matching process must consider key indicators such as lead error (≤0.02mm / 300mm), radial runout (≤0.01mm), and axial concentricity (≤0.03mm) to ensure that the screw rotation axis and the guide rail slider sliding trajectory remain coaxial. Using tolerance analysis methods, the relative offset and assembly interference between the servo motor end cover, screw support seat, and nut connecting plate are evaluated. According to the standard limit deviation values ​​recommended by the National Mechanical Design Manual (such as H7 / k6 fit), dimensional adjustments and selections are performed. All matching data (including fit clearance, connecting bolt specifications, coupling type, number of sliders, etc.) are uniformly output as electric cylinder component coordination and matching data, serving as the basic information for subsequent virtual assembly.

[0038] Step S16: Perform virtual assembly based on the electric cylinder assembly coordination and matching data to obtain the electric cylinder initialization structure; In this embodiment, the coordinated matching data obtained in S15 is used to complete the full virtual assembly operation of the electric cylinder system. The virtual assembly includes components such as lead screw, nut, guide rail, slider, servo motor, coupling, end cover, bearing, and support. Each component is inserted into the system model in sequence according to the assembly order in the mechanical drawings, and reasonable mating relationships are set. For example, a sliding fit is used between the slider and the guide rail, and a helical pair is used between the lead screw and the nut. During assembly, the axis overlap is set to not exceed 0.01mm, and constraints are used to prevent component interference, such as ensuring that the nut does not collide with the support frame when sliding. Assembly tolerance values ​​are set, and axial and radial preloads are applied to the connections between components to ensure that the system has no interference deformation or assembly interference in the initial state. The final completed virtual assembly model is a complete initial structure of the electric cylinder, including the precise geometry, structural position, and connection method of all core components.

[0039] Step S17: Simulate the guide rod swing based on the electric cylinder initialization structure to obtain guide rod swing data.

[0040] In this embodiment, a multibody dynamics simulation method is used to simulate the actual working environment of the guide rod. Input parameters such as the electric cylinder's operating frequency (e.g., 5Hz), guide rod stroke (e.g., 200mm), swing angle range (e.g., ±15°), and load mass (e.g., 50kg) are set to construct the actual working environment of the guide rod's motion. A motor input torque (e.g., 2Nm) is applied, and the velocity, acceleration, and torque of the guide rod at different time points are calculated by combining the lead screw lead and transmission efficiency. A flexible constraint model of the guide rod's connecting components is established to simulate the minute offsets or swing amplitude changes that occur due to the flexible connection during actual operation. During the simulation, the sampling frequency is set to 1000Hz, and the instantaneous swing angle, center offset value, velocity, and acceleration data of the guide rod are recorded every 0.1 seconds throughout its full stroke range. Finally, a complete guide rod swing data file is output as the basic input for subsequent steps such as jamming analysis and life prediction.

[0041] Preferably, step S16 specifically includes: Step S161: Identify the component interface based on the electric cylinder assembly coordination and matching data; In this embodiment, the identification includes the shaft-hole interface between the servo motor output shaft and the coupling input hole, the threaded or keyed connection interface between the coupling output hole and the lead screw shaft end, the flange or screw connection interface between the outer wall of the nut and the slider mounting hole, and the matching relationship between the reference surface and hole position between the guide rail base and the support structure. Interface identification employs a method based on three-dimensional CAD geometric boundary feature recognition. This involves extracting geometric data such as the assembly surface normal vector, hole center coordinates, and assembly reference axis direction of each component, and matching the hole-shaft fit type, thread standard, tolerance grade, and assembly interference according to national mechanical assembly standards (such as GB / T 1182-2008). For example, if the servo motor shaft end has a diameter of φ14 mm and a tolerance grade of h6, the corresponding shaft hole of the coupling must be φ14H7. When identifying such interfaces, the assembly surface number, geometric center coordinates (e.g., X=35mm, Y=0mm, Z=82mm), and fit type (e.g., tight fit H7 / h6) should be accurately recorded. All interface information must be uniformly numbered and output in tabular form as the geometric basis for subsequent alignment and assembly constraint definitions.

[0042] Step S162: Perform geometric alignment processing of the electric cylinder based on the component interface to obtain the assembly reference data of the electric cylinder; In this embodiment, geometric registration in the global coordinate system needs to be performed based on the assembly interfaces identified in step S161. This operation should follow the "master-slave alignment principle," that is, taking the electric cylinder base or guide rail support structure as a static reference body, other components (such as servo motors, ball screw assemblies, sliders and nuts) are rigidly transformed and aligned according to the identified interfaces. The alignment uses a combination of rotation matrix and translation vector to achieve six degrees of freedom adjustment. Specifically, the operation includes: first, aligning the two components along the Z-axis using the identified reference plane normal vector; then, calculating the translation vectors Δx, Δy, and Δz using the center coordinates of the holes on the coaxial line for position adjustment. Taking the alignment of the screw and guide rail slider as an example, if the normal of the guide rail reference plane is (0,0,1) and the normal of the screw and nut mounting surface is (0.01,0,0.9998), then a rotation matrix needs to be calculated to finely adjust the screw coordinate system around the X-axis to align with the guide rail direction, and then precise alignment is performed based on the mounting hole center deviation Δz = 0.15mm. All alignment parameters (rotation angle, translation amount, datum plane number, etc.) are output in the form of a three-dimensional coordinate matrix. After being summarized, they constitute the electric cylinder assembly datum data, which is used for assembly constraint definition.

[0043] Step S163: Set assembly constraint relationships based on the electric cylinder assembly reference data; In this embodiment, when setting assembly constraints, the assembly connection definition should be strictly based on the geometric alignment results and component functional logic. Assembly constraints are divided into three categories: face-to-face coincidence constraints, shaft-to-hole coaxial constraints, and planar distance constraints. The specific operation is as follows: Set the servo motor output shaft end face and the coupling input end face as face-to-face coincidence constraints, setting the tolerance to ±0.01mm; set the coupling output shaft and the ball screw shaft end as shaft-to-hole coaxial constraints, with a fit grade of H7 / h6; set the screw nut flange face and the slider mounting surface as planar distance constraints, maintaining a 0mm net distance, thus achieving tight contact installation. All constraint relationships should be assigned unique identifiers, and detailed parameters such as constraint direction vector, starting coordinates, ending coordinates, and tolerance limits should be recorded. Redundancy or excessive constraints are not allowed during constraint setting; therefore, assembly motion degree of freedom analysis is required to ensure that each sub-component has only the controlled expected degree of freedom in virtual space (e.g., the guide rod moves linearly along the Z-axis). Once all constraints are defined, a standard-format constraint table is output, including parameters such as component number, constraint type, constraint direction, fit tolerance, and error correction value, which constitutes the assembly executable instruction set.

[0044] Step S164: Perform virtual assembly based on assembly constraints to obtain the initial structure of the electric cylinder.

[0045] In this embodiment, the assembly process should be performed on a CAD / CAE platform. In a 3D environment, structural components such as the servo motor, coupling, ball screw, guide rail, slider, and support base are inserted sequentially according to component numbers and constraint order. The assembly system will automatically align the components and apply mating positioning relationships according to constraint instructions. If mating interference occurs (e.g., the outer diameter of the screw is larger than the inner diameter of the bearing), the system will stop the assembly and output an interference alarm message. During the assembly verification process, the coaxiality of the critical transmission path (allowable error ≤ 0.01 mm), the slider's degree of freedom (only Z-axis movement is allowed), and the consistency between the motor output and the screw rotation direction (angle error < 0.5°) should be carefully checked. Furthermore, the initial position of the guide rod should be calibrated, setting the guide rod zero point at the midpoint of the screw stroke for subsequent motion control simulation. The final output electric cylinder initialization structure should include the 3D spatial position, assembly direction, and mating tolerance information of all components. The assembly results should be saved as a 3D assembly drawing (e.g., STEP or IGES format) and an assembly list for subsequent guide rod swing simulation and analysis.

[0046] Preferably, step S2 specifically includes: Step S21: Calculate the guide rod swing speed based on the guide rod swing data; In this embodiment, angular velocity data output from a triaxial accelerometer and angle sensor (such as ADIS16470) mounted on the guide rod is acquired via a high-speed data acquisition card, with a sampling frequency set to 1000 Hz. The acquired angular velocity data is filtered using a fifth-order Butterworth low-pass filter with a cutoff frequency set to 20 Hz to eliminate high-frequency interference signals. Subsequently, the angular velocity is converted into angular displacement using a numerical integration method (such as the composite trapezoidal rule), and the velocity is calculated in conjunction with time series data. The specific calculation formula is: v(t) = (θ(t) - θ(t-Δt)) / Δt, where Δt is 0.001s.

[0047] Step S22: Calculate the instantaneous swing deceleration time period of the base guide rod swing speed; In this embodiment, the velocity sequence is subjected to first-order difference to obtain the acceleration change trend. A threshold method is used to select time points where the absolute value of acceleration is greater than 200° / s² and the sign changes from positive to negative as the deceleration start point; when the acceleration drops below -100° / s², it is regarded as the deceleration end point, defined as the instantaneous deceleration time period, and its start and end times are recorded and marked in the oscillation sequence.

[0048] Step S23: Detect sudden changes in swing amplitude stagnation based on the instantaneous swing deceleration time period to obtain swing amplitude stagnation data; In this embodiment, the last 200 ms data segment of each deceleration time period is extracted, and the slope change rate of the angle change is calculated. If the slope change is less than 1° / s within 100 ms and the duration exceeds 150 ms, the segment is defined as "swing stagnation". It is used as stagnation data and the angle value, corresponding timestamp, acceleration trend before and after are extracted to form a stagnation data packet.

[0049] Step S24: Determine the guide rod swing jamming based on the swing amplitude stagnation data, and obtain the guide rod swing jamming data; In this embodiment, the guide rod jamming behavior is analyzed by combining stagnation data. If the same guide rod continuously exhibits stagnation data within 3 cycles, and its angle fluctuation is less than 2°, it is considered that structural jamming has occurred. Information such as the maximum swing speed, angle change, and acceleration change within the corresponding time period is extracted to form guide rod swing jamming data, and the jamming location area and time window are marked for subsequent structural wear analysis.

[0050] Step S25: Detect the wear degree of the sliding bearing based on the guide rod swing and jamming data; In this embodiment, the energy dissipation trend is determined by measuring the rate of decrease in acceleration before and after the jamming period. If the energy decrease rate exceeds 30% within the oscillation cycle, and the jamming location is concentrated in the bearing support section, it is determined that the sliding bearing is worn. A laser displacement sensor (resolution of 1 µm) is used to measure the clearance between the bearing hole and the guide rod. If the clearance is greater than 0.2 mm (manufacturing standard is 0.05 mm), it is defined as severe wear. The coordinates of the worn part, the clearance value, and the wear level are recorded.

[0051] Step S26: Perform magnetic particle crack detection based on the wear degree of the sliding bearing to generate crack data; In this embodiment, a magnetic particle flaw detector (model Y-6 portable magnetic particle detector) was used to perform wet fluorescent magnetic particle testing on the suspicious area of ​​the guide rod. The concentration of the fluorescent magnetic particle solution was controlled at 0.15 g / mL, and an AC electric field of 0.6 A was applied to observe the crack distribution. After capturing the images, image enhancement processing was performed (using an edge detection operator such as Canny) to extract the crack length and density. If a crack length exceeding 3 mm or the number of cracks exceeds 5 is detected, fatigue cracks are identified.

[0052] Step S27: Predict the fatigue life of the guide rod metal based on crack data.

[0053] In this embodiment, the Miner linear cumulative damage method is used for life assessment. The crack growth rate da / dN is calculated using the Paris formula da / dN = C·(ΔK)^m, where C = 2.5 × 10^-12, m = 3, and ΔK is the stress intensity factor. The stress condition of the guide rod is extracted using the finite element method, and the remaining life is estimated by combining the crack length a and the fracture toughness of the guide rod material K_IC = 30 MPa·m^0.5. The fatigue life cycle number N_f is calculated and converted into time by combining the actual working load frequency to complete the life prediction.

[0054] Preferably, step S25 specifically includes: Step S251: Extract the swing position offset information based on the guide rod swing jamming data; In this embodiment, the guide rod swing lag data includes time-series swing angle, angular velocity, and angular acceleration parameters. First, a differential processing method is used to perform first-order differentiation on the swing angle change curve to extract the instantaneous swing velocity. Using a set stable operating baseline (linear swing within ±0.2°) as a reference, the swing offset trend is identified during the lag period. The center position of the guide rod during normal operation is used as the reference position (with the guide rail center point coordinates as zero). If a swing position offset exceeding ±0.8mm is detected, it is recorded as an offset event. The offset information is obtained jointly from the combined measurement results of a triaxial accelerometer and a laser displacement sensor. The triaxial accelerometer has a sampling frequency of 5kHz and an accuracy error not exceeding ±0.5%, while the displacement sensor uses an optical-grade 0.01mm accuracy class. During the lag period, swing positions with sustained offsets exceeding 50ms are extracted and the offset direction and coordinates are recorded for subsequent analysis of the relative position of the sliding bearing.

[0055] Step S252: Extract the position of the sliding bearing based on the swing position offset information; In this embodiment, based on the obtained swing position offset information, combined with the guide rod structure layout diagram and sliding bearing layout parameters (including guide rod installation length, bearing spacing, and bearing center position), the relative position of the sliding bearing is determined using the spatial back projection method. The electric cylinder structure assembly drawing is read using CAD import, and the offset point coordinates are intersected with the guide rod reference line in the 3D geometric coordinate system to calculate the rotation axis of the guide rod in the offset segment. Based on the 0.5mm gap between the guide rod outer diameter (set to φ32mm) and the sliding bearing inner diameter (φ33mm), a sliding space envelope model is established. Boolean operations are used to identify the annular region intersecting with the rotation axis, and this region is defined as the actual bearing point of the sliding bearing. The bearing position coordinates are output and compared with the initial design coordinates to obtain bearing offset data for further simulation analysis.

[0056] Step S253: Perform sliding simulation based on the position of the sliding bearing to obtain bearing sliding data; In this embodiment, a sliding contact interface model is constructed using a multibody dynamics simulation platform (such as RecurDyn or ADAMS) based on the identified actual position of the sliding bearing. The actual running trajectory of the guide rod and the bearing offset position are imported, and material property parameters are set: the sliding bearing is made of bronze alloy (elastic modulus E = 1.1 × 10¹¹ Pa, Poisson's ratio 0.33), and the guide rod is made of 45 steel (elastic modulus E = 2.0 × 10¹¹ Pa). The sliding friction factor μ is set to 0.12, and the lubrication state is set to quasi-dry friction mode. The guide rod motion speed is input based on oscillation data statistics and set to a variable speed cycle between 0.3 and 1.2 m / s. The simulation time is set to 10 seconds, recording the bearing contact pressure, relative sliding speed, and contact area every 0.001 seconds. The output bearing sliding data includes total contact energy consumption, sliding path length per unit time, bearing inner surface force frequency, and contact surface temperature rise data. If the temperature rise exceeds 85°C, it is determined to be a region of mild lubrication failure, providing input basis for material surface analysis.

[0057] Step S254: Perform surface oxidation analysis on the material based on the bearing sliding data to obtain surface oxidation data of the material; In this embodiment, oxidation analysis of the material surface in the sliding region is performed based on the temperature rise data and contact area change information output from the bearing sliding data. First, continuous friction time periods are extracted from areas where the temperature rise exceeds the critical temperature of 85°C, and the total friction energy density (cumulative friction energy per unit area, in J / mm²) is calculated. An oxidation initiation energy threshold of 15 J / mm² is set; areas exceeding this threshold are included in the oxidation analysis process. Infrared thermal imaging combined with energy dispersive spectroscopy (EDS) is used to detect the failure area. The thermal imager has a resolution of 0.05°C, identifying changes in the area of ​​local hot spots, and the EDS analyzes the proportions of Fe₂O₃ and CuO oxides. The degree of oxidation on the material surface is determined by the oxide layer thickness: less than 1 μm is considered light oxidation, 1–3 μm is moderate oxidation, and greater than 3 μm is considered heavily oxidized. The area, distribution location, and oxide layer thickness of the oxidized region are recorded, and the output is the material surface oxidation data, used for subsequent metal particle identification.

[0058] Step S255: Detect metal particles based on the surface oxidation data of the material to obtain metal particle data; In this embodiment, after extracting the surface oxidation data of the material, areas with moderate or severe oxidation were identified as target areas for metal particle detection. These areas were located using thermal imaging and oxidation thickness data, and then precisely sampled using a focused ion beam (FIB) instrument. The sample extraction depth was controlled within 50 μm below the oxide layer to avoid surface contamination interfering with the actual distribution of wear particles. Samples underwent pretreatment in a clean environment, including 60 seconds of ultrasonic cleaning with ethanol and vacuum drying, ensuring surface cleanliness for electron microscopy analysis. Subsequently, a scanning electron microscope (SEM) was used for microstructural observation, with the electron beam acceleration voltage set to 15 kV and a resolution of 3 nm. A single sample was divided into 10 equal-area detection units using a field-of-view partitioning method, with each unit having a fixed area of ​​500 μm × 500 μm. Images were acquired within each area, and metal particle data were extracted. Image processing algorithms (such as binary segmentation based on the Otsu threshold) were used to separate the particles from the background, extract edges, and calculate the actual area of ​​each particle, converting it to an equivalent particle size. All extracted particles were categorized into four sizes based on their particle size distribution: <10 μm, 10–30 μm, 30–50 μm, and >50 μm. The number density (particles / mm²) of each size was calculated. An area-weighted average was used to reduce outlier interference from large particles. After particle number density calculation, samples from the same detection area were subjected to X-ray diffraction (XRD) for phase composition analysis. XRD used a Cu-Kα radiation source, with a scanning angle range of 10°–90°, a step size of 0.02°, and a scanning rate of 2° / min. Peak fitting and database matching were performed on the diffraction patterns to identify the presence of Cu, Pb, Sn, and other sliding bearing alloy matrix derivative phases, as well as inclusions representing severe wear, such as Fe3C and Fe-Cu composite particles. The content of each phase was estimated by peak area integration, with a proportional error controlled within ±5%. When the total particle density exceeds 1500 particles / mm², and the proportion of particles with a diameter greater than 30 μm accounts for 15% or more of the total particles, the area is identified as a "high-intensity wear characteristic zone" based on the set metal debris distribution intensity standard. The final output metal particle data includes the number density statistics for each particle size range, the maximum particle size, a particle density distribution map, a list of main phase compositions and proportions, and the matching results with the wear material type. All data are used as reference input for the next step of wear degree assessment and fatigue life prediction.

[0059] Step S256: Determine the degree of wear of the sliding bearing based on the metal particle data.

[0060] In this embodiment, the Wear Composite Index (WCI) is used to assess the wear degree of the sliding bearing based on the metal particle data. The WCI calculation formula is: WCI = (ρ × D × F) / A, where ρ is the particle density (particles / mm²), D is the average particle size (μm), F is the wear material proportion factor (1.0 for copper-based materials, 1.2 for iron-containing particles), and A is the detection area (mm²). The WCI results are compared with the experimental standard library: WCI < 100 indicates mild wear, 100 ≤ WCI < 300 indicates moderate wear, and WCI ≥ 300 indicates severe wear. The consistency of the metal particle type, distribution area, and sliding direction in the detection is considered to confirm the wear distribution trend and severity. Finally, the sliding bearing wear level data and location coordinate information are output for setting input parameters for metal fatigue assessment.

[0061] Preferably, step S26 specifically includes: Step S261: Identify high-wear areas based on the degree of wear of the sliding bearing; In this embodiment, based on the sliding bearing wear data determined in the previous steps, the metal particle density distribution map and particle size distribution information are read. Regions with more than 1500 particles / mm² per unit area and a particle size greater than 30μm accounting for more than 15% are identified as high-wear areas. During implementation, a two-dimensional distribution mapping technique is used to map the wear data to the bearing's physical coordinates, defining the specific location coordinates of the high-wear areas. Here, the arc position (in °) and width length (in mm) of the high-wear area on the bearing ring surface need to be recorded to form target area boundary data for subsequent magnetization. During region delineation, a microscopic image recognition tool is used to extract the contours, and a closed boundary region is constructed based on grayscale contrast and particle cluster density, outputting the high-wear area identification result.

[0062] Step S262: Set longitudinal magnetization for the high wear area, and set the magnetization current range to 300A-2000A and the magnetization duration to 0.5s-3s to obtain longitudinal magnetization wear area data; In this embodiment, for the identified high-wear areas, the magnetization direction of the longitudinal magnetization device is set parallel to the axis of the sliding bearing. An adjustable constant current magnetization power supply is used to set the magnetization current, with an initial setting of 300A, gradually adjusted to 2000A in 100A increments to ensure coverage of the saturation magnetization range of materials with different permeability. The magnetization duration is set by the magnetization controller from 0.5 seconds to 3 seconds. After each magnetization, a Hall voltage sensor is used to detect the stability of the magnetic flux distribution, ensuring that the magnetic flux density is between 1.5T and 2.2T, which conforms to the response range of microcracks on the bearing surface. After each magnetization, the magnetization current, duration, and magnetic flux density are recorded, and "longitudinal magnetization wear area data" is established for synchronous control of magnetic powder application.

[0063] Step S263: Apply magnetic powder for a duration of 1s-5s based on the longitudinal magnetization wear area data to obtain magnetic powder application area data; In this embodiment, the magnetic powder application process uses standard type A black magnetic powder with a particle size range controlled between 40 μm and 80 μm. The magnetic powder application device uniformly sprays the magnetic powder onto the aforementioned magnetized area through a pneumatic nozzle. The spraying time is set to 1 to 5 seconds by a timer controller, and the air pressure is controlled at 0.2 MPa to 0.3 MPa to ensure that the magnetic powder forms a complete coverage on the surface. A high-resolution camera is used to record the initial distribution characteristics of the magnetic powder under the action of the magnetic field during the spraying process, forming "magnetic powder application area data". This data uses the grayscale layer of an RGB image as a reference and is subsequently used for calculating the magnetic powder aggregation degree.

[0064] Step S264: Calculate the magnetic powder aggregation degree based on the data of the area where the magnetic powder is applied; In this embodiment, image data of the magnetic powder application area is read, and pixel regions with gray levels higher than 200 are extracted using the image gray-level distribution histogram method. The cumulative sum of gray values ​​per unit area (mm²) is calculated and defined as the magnetic powder aggregation degree. Aggregation degree analysis employs the threshold and contour algorithms from the OpenCV image processing library, setting the gray-level threshold for aggregation regions to 220 and the area threshold to 0.3 mm². The image is scanned pixel by pixel, and the total number and distribution locations of pixels meeting the aggregation conditions are counted to establish a magnetic powder aggregation degree coordinate distribution map, which is used to identify powder enrichment phenomena caused by cracks.

[0065] Step S265: Identify magnetic powder aggregation regions based on the magnetic powder application area data according to the magnetic powder aggregation degree, and obtain magnetic powder aggregation region data; In this embodiment, based on the magnetic powder aggregation degree coordinate distribution map, regions with abrupt changes in magnetic powder concentration are identified, namely, regions where the aggregation density exceeds 1.8 times the average of the surrounding area and the area is continuously greater than 2 mm². A region growing method is used to extract the boundaries of these high-aggregation regions, setting a minimum continuous boundary growth length of 1.5 mm, and outputting boundary morphology features (including opening width, length, and corners). These regions are defined as magnetic powder aggregation areas, and their location, size, and aggregation density are recorded to form "magnetic powder aggregation area data."

[0066] Step S266: Based on the preset crack magnetic particle data, crack determination is performed on the magnetic particle aggregation area data to generate crack data.

[0067] In this embodiment, based on the data of the magnetic powder aggregation region, a preset crack magnetic powder data standard is invoked to determine the cracks. This standard includes three types of magnetic powder distribution morphology characteristics: typical fatigue cracks, spalling cracks, and surface pyrolysis, such as an opening angle less than 30°, a length greater than 3mm, and an aggregation density greater than 3000 pixels / mm². Crack determination uses a neural network image recognition model to match crack morphology, outputting crack type labels and location coordinates. Simultaneously, the crack level (level 1-5) is quantified by combining magnetic powder aggregation intensity and geometric continuity. Finally, crack data is generated, including information such as crack quantity, type, location, size, and level, for subsequent extrapolation of the guide rod's fatigue life.

[0068] Preferably, step S3 specifically includes: Step S31: Evaluate the degree of guide rod deflection based on the guide rod swing data; In this embodiment, a joint evaluation method combining time-domain and frequency-domain analysis is used for the acquired guide rod oscillation data. First, the maximum deviation angle of the guide rod oscillation angle within each driving cycle is extracted and denoted as θ_max, in degrees. Then, the standard cycle reference angle θ_ref is calculated, obtained through simulation of the guide rod's motion trajectory under ideal working conditions; generally, ±0.3° is taken as the normal oscillation range. The oscillation deviation value is calculated using θ_dev = |θ_max - θ_ref|, and oscillation severity grading thresholds are set: θ_dev < 0.5° is slight oscillation, 0.5° ≤ θ_dev < 1.0° is moderate oscillation, and θ_dev ≥ 1.0° is severe oscillation. Finally, the guide rod oscillation severity data is output, including the oscillation amplitude (angle value), direction (clockwise / counterclockwise), and oscillation level label.

[0069] Step S32: Acquire images of the ball screw based on the degree of guide rod deflection; In this embodiment, the image acquisition frequency and resolution parameters are adjusted according to the runout level determined in step S31. The frame rate for the slight runout area is set to 10fps, and increased to 30fps for the moderate and severe runout areas; the image resolution is set to at least 2048×1536 pixels. An industrial camera (such as a Basler acA2500-60gc) is used to perform full-stroke tracking and imaging of the ball screw during the guide rod's operating cycle, combined with multi-angle lighting (side lighting + top lighting) to improve the contrast of the screw and nut contours. The acquired data includes images of the screw helical tooth groove edge, the nut edge contour, and cross-sectional structural images at 20mm positions along the axial direction.

[0070] Step S33: Calculate the ball screw clearance based on the ball screw image; In this embodiment, the image acquired in step S32 is processed using a subpixel edge detection algorithm, and the boundary line of the contact area between the ball screw helical teeth and the nut is extracted using the Canny operator. Subsequently, the image coordinates are converted into actual spatial coordinates using image calibration parameters (lens focal length, imaging size, distortion correction matrix). The radial clearance δ_r is calculated using the minimum distance between the helical tooth tip and the nut edge, and the axial clearance δ_a is calculated using the tooth deviation at two adjacent axial positions of the ball screw. With δ_r > 0.06 mm or δ_a > 0.08 mm as the clearance anomaly prediction threshold, the ball screw clearance data is output, including the maximum value, average value, and offset direction.

[0071] Step S34: Calculate the vibration frequency based on the guide rod's sway and extract the low-frequency vibration data of the guide rod; In this embodiment, a Fast Fourier Transform (FFT) is performed on the guide rod oscillation time series data, with the frequency range set from 0.1Hz to 100Hz. The portion of the spectrum with frequencies less than 10Hz is extracted and defined as the low-frequency band, commonly seen in cases of loose clearance or unstable fit. The maximum peak frequency f_max_lf (Hz) and the corresponding amplitude A_max_lf (rad or ° / s) are calculated. The low-frequency vibration energy integral is used as the low-frequency vibration energy index E_lf (unit: rad²·Hz), and combined with the guide rod sway level as a vibration trend index. This index provides basic vibration evidence for subsequent judgment of abnormalities in the ball screw drive.

[0072] Step S35: Simulate the transmission of ball screw clearance based on the low-frequency vibration data of the guide rod, and detect the wear degree of the screw nut; In this embodiment, δ_r and δ_a obtained in step S33 and E_lf obtained in step S34 are input into a two-dimensional dynamic contact simulation model to establish a dynamic fit structure between the ball screw and the nut. The simulation load is set to a maximum driving torque of 50 Nm, a working frequency of 5 Hz, and a stroke of 200 mm. The contact stress distribution and relative slip region of the screw in one working cycle are obtained through finite element contact simulation (explicit integration method can be used), and the working conditions in which the contact area between the nut and the screw decreases by more than 25% and the stress concentration region extends by more than 3 mm are identified. At the same time, the maximum shear stress value τ_max per unit area (unit: MPa) is statistically analyzed. If τ_max exceeds 60% of the material yield strength, it is preliminarily determined that the nut wear trend is obvious, and the nut wear index value is output.

[0073] Step S36: Perform anomaly analysis on the ball screw clearance based on the wear degree of the lead screw nut to obtain abnormal ball screw clearance data; In this embodiment, the wear index output in step S35 is compared with the clearance data in step S33. If both δ_r and δ_a exceed the threshold, and the nut wear index (contact area reduction rate, τ_max value) is at a high wear level, then the clearance at that location is determined to be abnormal. A ball screw clearance abnormality dataset is generated using the axial position (z-coordinate) where the clearance abnormality occurs as an index. The data format includes: abnormal position z_i (mm), abnormality type (radial / axial), δ value, wear level, and crack risk coefficient R_cr (calculated based on the ratio of the nut material fatigue limit to the contact stress). All abnormal sections are marked on the ball screw spatial location diagram for subsequent structural correction.

[0074] Step S37: Design an automatic backlash compensation structure based on abnormal ball screw backlash data.

[0075] In this embodiment, based on the gap anomaly section marked in step S36, an automatic gap compensation mechanism with axial elastic compensation and radial adjustment functions is designed. The specific design includes: a double-nut structure, where the first nut is fixedly connected to the lead screw, and the second nut is connected to the guide rail housing via a disc spring assembly. A preload of 0.1mm-0.3mm is applied in the radial direction to achieve automatic following compensation; in the axial direction, the adjustment stroke range is set to ±0.2mm, and the adjustment force is applied through a magnetostrictive actuator with a response time controlled within 20ms. The compensation mechanism is made of titanium alloy with an elastic modulus E>100GPa and a wear resistance grade WSD<0.05, or high-strength engineering plastic. A dynamic response strategy is set based on the guide rod swing frequency data, and force-displacement matching is performed according to frequency bands. Finally, structural drawings and compensation force verification data are output for electric cylinder structure optimization.

[0076] Preferably, step S37 specifically includes: Step S371: Design a clearance compensation strategy based on abnormal ball screw clearance data; In this embodiment, after detecting abnormal ball screw backlash data, the range of backlash variation, fluctuation period, and corresponding load frequency are extracted. The backlash variation amplitude is precisely quantified in micrometers, and the maximum and minimum backlash values ​​and their mapping relationship with the number of working cycles are recorded. If the backlash variation exceeds 120% of the initial design backlash value, it is determined that compensation is required. When designing the compensation strategy, a minimum compensation force model is established based on the load-backlash relationship graph, and a dynamic hysteresis control curve is adopted, limiting the response delay time to no more than 50ms. The compensation strategy includes three control modes: elastic loading preload control, slider tension adjustment, and axial offset correction. Specific parameters such as the ball screw pitch, lead, and ball diameter (e.g., a pitch of 5mm and a ball diameter of 3mm) are used as constraints, and the displacement compensation value is set to be no less than 30μm to prevent low-speed crawling and increased positioning errors.

[0077] Step S372: Configure an active gap adjustment structure based on the gap compensation strategy; In this embodiment, based on the aforementioned gap compensation strategy, the active gap adjustment structure is composed of a piezoelectric drive unit and a miniature roller slide. The structure is mounted on the ball screw nut seat, with installation tolerances controlled within ±0.02mm. The piezoelectric unit has a drive response accuracy of ±1μm, a voltage drive range of 0V to 150V, and a corresponding maximum stroke of 100μm. It is powered by a stepper power supply module in PWM (Pulse Width Modulation) mode, with a control frequency of not less than 1kHz. During configuration, the adjustment structure must be equidistantly arranged along the screw axis, with an adjustment point installed every 90°. Dynamic control is achieved through feedback from a closed-loop displacement sensor, with a sensor accuracy of ±0.5μm. The system response time is required to be controlled within 30ms, and the adjustment frequency operates in real-time within the 20Hz range to meet frequent adjustment conditions.

[0078] Step S373: Configure a passive gap adjustment structure based on the gap compensation strategy; In this embodiment, the passive clearance adjustment structure includes a spring-loaded radial preload ring and a thermally expandable adaptive shim. The radial preload ring employs a double-layer wave spring structure, made of 65Mn high-elasticity steel, with the single-turn spring stiffness controlled within the range of 10N / mm to 30N / mm, and a preload design value of 50N. The shim uses a material with an expansion coefficient of 18×10⁻⁶. -6 Made of PTFE composite material with a temperature range of -10°C to 60°C, the linear expansion compensation gap can reach 25μm when the operating temperature varies from -10°C to 60°C. During installation, the preload ring is fitted into the groove on the outer wall of the nut, and the washer is placed on the sliding surface between the nut and the lead screw. A limiting bayonet structure is used for fixation to prevent axial displacement. The passive structure does not undergo dynamic response adjustment; its configuration process depends on the initial gap range and the operating temperature environment to ensure that the compensation structure remains within its effective operating range throughout the entire temperature range.

[0079] Step S374: Integrate the active clearance adjustment structure and the passive clearance adjustment structure to obtain the automatic clearance compensation structure.

[0080] In this embodiment, the piezoelectric active adjustment device and the radial preload ring are coaxially mounted on the ball screw nut housing, ensuring that the output force direction of the adjustment device is consistent with the action direction of the passive ring. A pre-set mechanical limit stop block is incorporated into the integrated structure, with its maximum adjustment displacement not exceeding 150% of the nut's designed clearance. Finite element analysis is used to verify the stability of the integrated structure under the coupled effects of dynamic excitation and thermal load, controlling the maximum stress below 250 MPa. The adjustment signal is input into the controller, setting a master-slave control logic, where the active structure prioritizes adjustment, and if the adjustment displacement is less than 10 μm, the passive structure is triggered to enter auxiliary mode. The integrated structure must be tested on a standard DIN 69051 ball screw structure platform, ultimately outputting a complete data list of the automatic clearance compensation structure, including component specifications, clearance range, adjustment frequency, response time, load capacity, etc., forming a complete electric cylinder transmission compensation subsystem.

[0081] Preferably, step S4 specifically includes: Step S41: Identify fatigue-sensitive areas based on the fatigue life of the guide rod metal; In this embodiment, after the fatigue life prediction of the guide rod metal is completed, a life zoning distribution map is used to perform pixel-level analysis of the remaining life of different parts. The analysis uses a fatigue life lower than 20% of the initial life as the fatigue critical criterion threshold; that is, when the life of a certain area is lower than this threshold, it is classified as a fatigue-sensitive area. Fatigue life data is obtained by combining the aforementioned crack detection with SN curve (stress-life curve) statistics. The SN curve is obtained by loading the same material sample with a symmetrical bidirectional bending load at ±45° at room temperature. The actual residual life of each measuring point is mapped onto the guide rod CAD model, and life-equivalent zones are marked using spatial interpolation. An initial sensitive core area is defined by extending 20mm outward from the minimum life point as the center. This is combined with continuous areas where the life gradient between adjacent areas is less than 5% / mm as extended fatigue-sensitive zones, forming fatigue-sensitive area identifiers.

[0082] Step S42: Detect trajectory influencing factors based on fatigue-sensitive areas; In this embodiment, after identifying the fatigue-sensitive area, trajectory influence factors are extracted. These factors include: instantaneous oscillating load, impact frequency per unit time, rate of change of inertial impact direction, pressure gradient in the sliding zone, and boundary changes in the reciprocating stroke. Load data is acquired using a high-frequency strain gauge built into the electric cylinder. The frequency is obtained using raw data from a triaxial accelerometer with a sampling rate of 5kHz, followed by FFT spectral analysis. Pressure changes are collected using an internal pressure membrane sensor; the maximum-minimum pressure difference divided by the cycle time is used as the gradient index for each cycle. When the impact frequency at a fatigue-sensitive point exceeds 15 times / min, or its pressure change rate exceeds 2.5 MPa / s, and its inertial direction change angle exceeds 10° / 0.1s, that point is classified as a high trajectory influence zone. After normalizing all influence factors, an influence factor vector diagram is created for subsequent trajectory optimization.

[0083] Step S43: Design the intelligent trajectory of the guide rod based on the trajectory influence factor and generate the intelligent trajectory of the guide rod; In this embodiment, based on the trajectory influence factor vector diagram obtained in step S42, a piecewise cubic spline function is used for guide rod trajectory optimization design. The trajectory planning space is set as a three-dimensional coordinate system (X is the horizontal travel direction, Y is the vertical yaw direction, and Z is the guide rod axial direction). Trajectory control nodes are set in each fatigue-sensitive region, and the number of control points is no less than three times the number of edge points of the influence region. The control point constraints include: a uniform velocity segment with a derivative of 0 is used in the non-high-frequency impact region; a segment with a negative derivative is used in the pressure gradient region to avoid accelerated impact; and a curvature limit of no more than 0.02 mm is used in the direction change region. -1 The arc segment. After the trajectory design is completed, more than 1000 cycles of virtual motion simulation are performed. By calculating the stress-time integral at the maximum stress point of the control point, the fatigue index (cumulative damage value) is verified to be no more than 0.85, and the final guide rod intelligent trajectory dataset is generated.

[0084] Step S44: Optimize the electric cylinder initialization structure based on the automatic clearance compensation structure to obtain the optimized electric cylinder structure; In this embodiment, after the automatic gap compensation structure configuration is completed, the original electric cylinder initialization structure undergoes spatial topology optimization. The "Topology Study" module in the SolidWorks Simulation plugin is used, setting the target mass reduction to 5%, the stress point to the end of the guide rod, the constraint point to the fixed end of the cylinder body, and ensuring the gap compensation structure connection section remains non-deformable. Material properties are set as 45# steel, with an elastic modulus of 210 GPa and a Poisson's ratio of 0.28. Optimization conditions are set so that the maximum stress does not exceed 80% of the material's yield strength, and the objective function is to maximize structural stiffness. Unsupported solid blocks are removed from the topology optimization results, and the assembly gaps of each connection structure are refined according to tolerance level m in ISO 2768. This topology optimization result is then updated to the original CAD model, resulting in an optimized electric cylinder structure that includes the automatic gap compensation structure constraint interface, cable hole wiring channels, and adjusted heat dissipation groove shape.

[0085] Step S45: Based on the optimized structure of the electric cylinder and the intelligent trajectory of the guide rod, perform intelligent drive simulation of the guide rod to generate intelligent drive data for the guide rod.

[0086] In this embodiment, a drive simulation environment is constructed based on the optimized electric cylinder structure in step S44 and the intelligent trajectory data of the guide rod in step S43. An electric cylinder-guide rod dynamics simulation system is established using the ANSYS Motion module, with a simulation time of 30 minutes and a time step of 1ms. The HHT integral algorithm is used for solving the simulation. The simulation input is the guide rod drive function, which is a combined trajectory velocity-acceleration function, including an initial acceleration segment, a middle uniform swing segment, and a final slow-motion segment. The drive input torque is input by the servo motor drive model using current control, and the required torque is calculated in real time based on the set trajectory. Monitoring parameters include the actual displacement error of the guide rod, the ball screw backlash compensation response delay time, and the peak and average power of the drive torque in each segment. Finally, all operating data are sampled at a frequency of 1kHz and exported as intelligent drive data archives for subsequent system response optimization and lifespan prediction.

[0087] Preferably, this specification also provides an intelligent drive system for a swing guide rod electric cylinder, used to execute the intelligent drive method for a swing guide rod electric cylinder as described above. The intelligent drive system for the swing guide rod electric cylinder includes: The guide rod swing simulation module is used to acquire electric cylinder design data; perform structural initialization based on electric cylinder design data to obtain the electric cylinder initialization structure; and perform guide rod swing simulation based on the electric cylinder initialization structure to obtain guide rod swing data. The guide rod metal fatigue life prediction module is used to perform guide rod swing and jamming analysis based on guide rod swing data to obtain guide rod swing and jamming data; detect the wear degree of sliding bearing based on guide rod swing and jamming data; perform magnetic particle crack detection based on sliding bearing wear degree to generate crack data; and predict the fatigue life of guide rod metal based on crack data. An automatic clearance compensation structure design module is used to evaluate the degree of guide rod runout based on guide rod swing data; perform ball screw clearance anomaly analysis based on the degree of guide rod runout to obtain ball screw clearance anomaly data; and design an automatic clearance compensation structure based on the ball screw clearance anomaly data. The intelligent drive simulation module for the guide rod is used to plan the intelligent trajectory of the guide rod based on the fatigue life of the guide rod metal, and generate the intelligent trajectory of the guide rod; optimize the initialization structure of the electric cylinder based on the automatic clearance compensation structure, and obtain the optimized structure of the electric cylinder; and simulate the intelligent drive of the guide rod based on the optimized structure of the electric cylinder and the intelligent trajectory of the guide rod, and generate the intelligent drive data of the guide rod.

[0088] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0089] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A smart drive method for a swing guide rod electric cylinder, characterized in that, Includes the following steps: Step S1: Obtain the electric cylinder design data; perform structural initialization based on the electric cylinder design data to obtain the electric cylinder initialization structure; Based on the initialization structure of the electric cylinder, the guide rod swing is simulated to obtain the guide rod swing data; Step S2: Perform guide rod swing and jamming analysis based on guide rod swing data to obtain guide rod swing and jamming data; detect the wear degree of sliding bearing based on guide rod swing and jamming data; perform magnetic particle crack detection based on sliding bearing wear degree to generate crack data; predict the metal fatigue life of guide rod based on crack data. Step S2 specifically includes: Step S21: Calculate the guide rod swing speed based on the guide rod swing data; Step S22: Calculate the instantaneous swing deceleration time period of the base guide rod swing speed; Step S23: Detect sudden changes in swing amplitude stagnation based on the instantaneous swing deceleration time period to obtain swing amplitude stagnation data; Step S24: Determine the guide rod swing jamming based on the swing amplitude stagnation data, and obtain the guide rod swing jamming data; Step S25: Detect the wear degree of the sliding bearing based on the guide rod swing and jamming data; Step S26: Perform magnetic particle crack detection based on the wear degree of the sliding bearing to generate crack data; Step S27: Predict the fatigue life of the guide rod metal based on crack data, including: using Miner's linear cumulative damage method for life assessment and calculating the crack growth rate. Using the Paris formula ,in , , The stress intensity factor was used to extract the stress condition of the guide rod using the finite element method, combined with the crack length. fracture toughness of guide rod material Perform remaining life estimation; calculate the number of fatigue life cycles. And combine the actual workload frequency to convert it into time to complete the lifetime prediction; Step S3: Evaluate the guide rod runout degree based on the guide rod oscillation data; perform ball screw backlash anomaly analysis based on the guide rod runout degree to obtain ball screw backlash anomaly data; design an automatic backlash compensation structure based on the ball screw backlash anomaly data. Step S3 specifically includes: Step S31: Evaluate the guide rod deflection based on the guide rod oscillation data, including: using a combined time-domain and frequency-domain analysis method to evaluate the acquired guide rod oscillation data; extracting the maximum deviation angle value of the guide rod oscillation angle in each driving cycle, denoted as... The unit is degrees; the reference angle for calculating the standard period. The ideal working condition guide rod motion trajectory is obtained through simulation, and is generally taken as... The range within which is considered the normal oscillation range; Calculate the yaw deviation value and set the threshold for yaw severity classification: It is a slight oscillation. Moderate yaw. For severe runout, the final output data on the guide rod runout includes the runout amplitude, direction, and runout level label. Step S32: Acquire images of the ball screw based on the degree of guide rod deflection; Step S33: Calculate the ball screw clearance based on the ball screw image; Step S34: Calculate the vibration frequency based on the guide rod's sway and extract the low-frequency vibration data of the guide rod; Step S35: Simulate the transmission of ball screw clearance based on the low-frequency vibration data of the guide rod, and detect the wear degree of the screw nut; Step S36: Perform anomaly analysis on the ball screw clearance based on the wear degree of the lead screw nut to obtain abnormal ball screw clearance data; Step S37: Design an automatic backlash compensation structure based on abnormal ball screw backlash data; Step S4: Based on the fatigue life of the guide rod metal, perform intelligent trajectory planning for the guide rod to generate the intelligent trajectory of the guide rod; optimize the initialization structure of the electric cylinder based on the automatic clearance compensation structure to obtain the optimized structure of the electric cylinder; perform intelligent drive simulation of the guide rod based on the optimized structure of the electric cylinder and the intelligent trajectory of the guide rod to generate intelligent drive data of the guide rod.

2. The intelligent drive method for the swing guide rod electric cylinder according to claim 1, characterized in that, Step S1 is as follows: Step S11: Obtain the electric lever design data and extract the servo motor structure data and ball screw structure data; Step S12: Identify the lead screw drive structure based on the servo motor structure data; Step S13: Construct a guide rail support structure model based on the screw drive structure; Step S14: Analyze the screw drive parameters based on the ball screw structure data; Step S15: Input the screw drive parameters into the guide rail support structure model and perform coordination matching of the electric cylinder assembly to obtain the coordination matching data of the electric cylinder assembly; Step S16: Perform virtual assembly based on the electric cylinder assembly coordination and matching data to obtain the electric cylinder initialization structure; Step S17: Simulate the guide rod swing based on the electric cylinder initialization structure to obtain guide rod swing data.

3. The intelligent drive method for the swing guide rod electric cylinder according to claim 2, characterized in that, Step S16 is as follows: Step S161: Identify the component interface based on the electric cylinder assembly coordination and matching data; Step S162: Perform geometric alignment processing of the electric cylinder based on the component interface to obtain the assembly reference data of the electric cylinder; Step S163: Set assembly constraint relationships based on the electric cylinder assembly reference data; Step S164: Perform virtual assembly based on assembly constraints to obtain the initial structure of the electric cylinder.

4. The intelligent drive method for the swing guide rod electric cylinder according to claim 1, characterized in that, Step S25 is as follows: Step S251: Extract the swing position offset information based on the guide rod swing jamming data; Step S252: Extract the position of the sliding bearing based on the swing position offset information; Step S253: Perform sliding simulation based on the position of the sliding bearing to obtain bearing sliding data; Step S254: Perform surface oxidation analysis on the material based on the bearing sliding data to obtain surface oxidation data of the material; Step S255: Detect metal particles based on the surface oxidation data of the material to obtain metal particle data; Step S256: Determine the degree of wear of the sliding bearing based on the metal particle data.

5. The intelligent drive method for the swing guide rod electric cylinder according to claim 1, characterized in that, Step S26 is as follows: Step S261: Identify high-wear areas based on the degree of wear of the sliding bearing; Step S262: Set longitudinal magnetization for the high wear area, and set the magnetization current range to 300A-2000A and the magnetization duration to 0.5s-3s to obtain longitudinal magnetization wear area data; Step S263: Apply magnetic powder for a duration of 1s-5s based on the longitudinal magnetization wear area data to obtain magnetic powder application area data; Step S264: Calculate the magnetic powder aggregation degree based on the data of the area where the magnetic powder is applied; Step S265: Identify magnetic powder aggregation regions based on the magnetic powder application area data according to the magnetic powder aggregation degree, and obtain magnetic powder aggregation region data; Step S266: Based on the preset crack magnetic particle data, crack determination is performed on the magnetic particle aggregation area data to generate crack data.

6. The intelligent drive method for the swing guide rod electric cylinder according to claim 1, characterized in that, Step S37 is as follows: Step S371: Design a clearance compensation strategy based on abnormal ball screw clearance data; Step S372: Configure an active gap adjustment structure based on the gap compensation strategy; Step S373: Configure a passive gap adjustment structure based on the gap compensation strategy; Step S374: Integrate the active clearance adjustment structure and the passive clearance adjustment structure to obtain the automatic clearance compensation structure.

7. The intelligent drive method for the swing guide rod electric cylinder according to claim 1, characterized in that, Step S4 is as follows: Step S41: Identify fatigue-sensitive areas based on the fatigue life of the guide rod metal; Step S42: Detect trajectory influencing factors based on fatigue-sensitive areas; Step S43: Design the intelligent trajectory of the guide rod based on the trajectory influence factor and generate the intelligent trajectory of the guide rod; Step S44: Optimize the electric cylinder initialization structure based on the automatic clearance compensation structure to obtain the optimized electric cylinder structure; Step S45: Based on the optimized structure of the electric cylinder and the intelligent trajectory of the guide rod, perform intelligent drive simulation of the guide rod to generate intelligent drive data for the guide rod.

8. A smart drive system for a swing guide rod electric cylinder, characterized in that, For performing the intelligent drive method of the swing guide rod electric cylinder as described in claim 1, the intelligent drive system of the swing guide rod electric cylinder includes: The guide rod swing simulation module is used to acquire electric cylinder design data; perform structural initialization based on electric cylinder design data to obtain the electric cylinder initialization structure; and perform guide rod swing simulation based on the electric cylinder initialization structure to obtain guide rod swing data. The guide rod metal fatigue life prediction module is used to perform guide rod swing and jamming analysis based on guide rod swing data to obtain guide rod swing and jamming data; detect the wear degree of sliding bearing based on guide rod swing and jamming data; perform magnetic particle crack detection based on sliding bearing wear degree to generate crack data; and predict the fatigue life of guide rod metal based on crack data. An automatic clearance compensation structure design module is used to evaluate the degree of guide rod runout based on guide rod swing data; perform ball screw clearance anomaly analysis based on the degree of guide rod runout to obtain ball screw clearance anomaly data; and design an automatic clearance compensation structure based on the ball screw clearance anomaly data. The intelligent drive simulation module for the guide rod is used to plan the intelligent trajectory of the guide rod based on the fatigue life of the guide rod metal, and generate the intelligent trajectory of the guide rod; optimize the initialization structure of the electric cylinder based on the automatic clearance compensation structure, and obtain the optimized structure of the electric cylinder; and simulate the intelligent drive of the guide rod based on the optimized structure of the electric cylinder and the intelligent trajectory of the guide rod, and generate the intelligent drive data of the guide rod.

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