Dynamic evaluation and intelligent compensation system and method for contact rigidity of joint part of machine tool

Abstract

The invention discloses a dynamic evaluation and intelligent compensation system and method for contact rigidity of a machine tool joint part. The system comprises a digital twin and multi-source data interface module, a contact interface multi-scale morphology acquisition and characterization module, a joint part contact mechanical state online simulation module, a contact rigidity dynamic calculation model library, a performance evaluation and intelligent compensation decision-making unit and a compensation strategy execution and feedback module. According to the method, dynamic and high-precision contact stiffness analysis is achieved, the problem that a traditional static model is insufficient in precision is solved by deeply fusing microstructure measured data, a macrostructure model and a dynamic working condition load, and the time-varying characteristic of the joint part under the complex working condition can be reflected more truly. A perception-evaluation-compensation active control closed loop is constructed, and the digitization level of machine tool design and operation is improved. According to the method, the contact state of the joint part is actively optimized, so that the dynamic and static performance and machining quality of the machine tool are improved.

Description

A system and method for dynamic evaluation and intelligent compensation of contact stiffness at machine tool joints Technical Field

[0001] This invention relates to the field of CNC machine tool design and manufacturing technology, specifically to the modeling, analysis and performance optimization technology of mechanical properties of machine tool structural component joints, and in particular to a dynamic evaluation method and active intelligent compensation system for the contact stiffness of joints. Background Technology

[0002] The overall rigidity, dynamic performance, and machining accuracy of a machine tool are not determined by a single component, but are significantly influenced by the contact characteristics of the joints between components. Microscopically, these joints consist of two rough surfaces in contact, and their contact stiffness is one of the key parameters determining the overall static and dynamic performance of the machine tool. Insufficient contact stiffness leads to significant elastic deformation at the joint under load, resulting in a decrease in the overall rigidity of the machine tool, deterioration of its dynamic characteristics, and consequently, machining chatter and reduced surface finish.

[0003] Currently, research and practice on the contact stiffness of machine tool joints mainly suffer from the following limitations: (1) Although traditional theoretical models (such as contact models based on statistics or fractal theory) can describe contact behavior from a mechanistic perspective, their parameters often depend on offline, experimental calibration under specific conditions, making them difficult to directly apply to complex and variable actual machine tool assemblies and operating conditions; (2) Most existing methods treat the contact stiffness of joints as a fixed value. However, in actual machining processes, due to thermal deformation, wear, preload relaxation, and dynamic changes in cutting loads, the contact state and contact stiffness of the joints are time-varying. Static models cannot accurately reflect this dynamic characteristic, leading to deviations in the prediction of machine tool performance, especially dynamic stability; (3) Currently, improving the contact stiffness of joints mainly relies on early optimization design (such as optimizing surface morphology and preload) and the use of high-damping materials. These are all passive and one-off measures, lacking an active control mechanism for dynamic adjustment and compensation based on real-time performance evaluation results during machine tool operation.

[0004] Therefore, there is an urgent need for a method to dynamically evaluate the contact stiffness during machine tool operation, perform intelligent compensation based on the current state of the machine tool, and realize the digital design of machine tool performance. Summary of the Invention

[0005] This invention aims to overcome the shortcomings of existing technologies and provide a dynamic evaluation and intelligent compensation system and method for the contact stiffness of machine tool joints. Its core objective is to achieve dynamic and high-precision evaluation of the contact stiffness of key machine tool joints under real or simulated working conditions; and based on the evaluation results, to automatically generate and execute compensation strategies to proactively optimize the contact state of the joints, thereby improving the dynamic and static performance and machining quality of the machine tool.

[0006] A dynamic evaluation and intelligent compensation system for the contact stiffness of machine tool joints is characterized by comprising the following core modules: a digital twin and multi-source data interface module: connected to the machine tool's CAD design system, CAM programming system, and workshop IoT system to acquire the three-dimensional geometric model, assembly relationship, nominal preload, material properties, and real-time / simulated working condition load information (such as cutting force and inertial force) of the target joint.

[0007] Multi-scale morphology acquisition and characterization module for contact interfaces: This module integrates or calls upon non-contact optical measurement equipment (such as white light interferometers and laser confocal microscopes) to acquire actual microscopic morphology data of the mating surfaces at the interface. A micro-macroscale coupled characterization method is employed to perform complementary fractal and Euclidean geometry analysis on the surface morphology, extracting key morphology parameters for contact mechanics calculations.

[0008] Online simulation module for contact mechanics of joints: This module has a built-in fast solver based on the finite element method. It receives data from Module 1 and Module 2, automatically builds a refined finite element model of the joint that includes detailed surface morphology features and macroscopic structure, applies the current working condition load, and calculates the contact pressure distribution, actual contact area, and micro-slip state at each point on the joint surface.

[0009] The Contact Stiffness Dynamic Calculation Model Library integrates various validated contact stiffness theoretical models. Core models include: a nonlinear stiffness model based on improved fractal theory, applicable to fixed joints such as bolted connections, which couples the fractal dimension and characteristic scale of the microstructure with the macroscopic contact pressure distribution to calculate normal and tangential contact stiffness; and a dynamic stiffness mapping model based on machining state indices, applicable to joints where relative motion may occur, such as spindle-tool holder and workpiece-fixture connections. This model uses machine learning to establish a nonlinear mapping relationship between "machining state indices" (such as spindle speed, feed direction, and vibration spectrum characteristics) and dynamic contact stiffness.

[0010] Performance Evaluation and Intelligent Compensation Decision Unit: This unit inputs the calculated dynamic contact stiffness value into the overall machine dynamics model to predict key performance indicators such as the machine tool's natural frequency, mode shape, and tool tip frequency response function. By comparing it with performance target thresholds (such as minimum allowable damping ratio and chatter stability domain boundary), it determines whether the current contact state is optimal. If the target is not met, the compensation decision algorithm is activated to generate compensation instructions. Compensation strategies include, but are not limited to: adaptive preload adjustment: dynamically adjusting the bolt preload to the optimal range using intelligent bolts or hydraulic servo mechanisms; active interface damping injection: injecting / discharging controllable viscosity intelligent fluids (such as magnetorheological fluids or electrorheological fluids) as needed into preset joint gaps or cavities to change the damping characteristics of the joint; process parameter and surface treatment recommendations: recommending optimized cutting parameters to avoid chatter, or suggesting specific surface texture processing or coating treatments on the joint surface to improve long-term contact performance.

[0011] Compensation Strategy Execution and Feedback Module: This module drives the corresponding actuators (such as intelligent fasteners and fluid pumping systems) to implement compensation decisions. Simultaneously, the system monitors changes in the dynamic response of the machine tool after compensation using vibration, force, or acoustic emission sensors placed near the joints, forming a closed-loop control circuit of "evaluation-decision-execution-feedback".

[0012] A method for dynamic evaluation and intelligent compensation of contact stiffness using the above system is characterized by the following steps: Step S1: System initialization and data loading. Select the target machine tool and the key joint to be evaluated (such as the column-bed bolted joint surface), and load its three-dimensional model, assembly constraints, and basic material parameters from the digital twin platform.

[0013] Step S2: Surface Topography Digitization and Loading. Using integrated measurement equipment, the microstructure of the paired surfaces is acquired and digitized. Simultaneously, the load spectrum under the current or anticipated processing task is obtained from processing simulations or sensor networks.

[0014] Step S3: High-fidelity simulation of contact mechanics. In the online simulation module for contact mechanics, a simulation model integrating macroscopic geometry and microscopic morphology is established to calculate the contact pressure distribution cloud map and microscopic contact state at the interface under the load in step S2.

[0015] Step S4: Dynamic Contact Stiffness Calculation. Based on the joint type (fixed / moving), select a matching model from the dynamic contact stiffness calculation model library. Using the contact pressure distribution and actual contact area obtained in Step S3 as inputs, calculate the normal contact stiffness Kn, tangential contact stiffness Kt, and equivalent contact damping C of the joint under the current specific working condition.

[0016] Step S5: Overall Machine Performance Prediction and Evaluation. The dynamic contact stiffness parameters calculated in Step S4 are used as boundary conditions and substituted into the finite element dynamics model of the machine tool or a simplified model based on Yoshimura Yoshitaka's unit area method to calculate and analyze the dynamic characteristics of the machine tool. Evaluation indicators focus on the tool tip frequency response function, chatter stability domain Lobes plot, and key modal damping ratio under the target machining pose.

[0017] Step S6: Intelligent Compensation Decision Generation and Execution. The evaluation results from Step S5 are compared with the preset performance margin. If the predicted stability domain cannot cover the predetermined process parameters, or the damping ratio is too low, a compensation decision is triggered. For example, the decision system may calculate to increase the preload of a certain row of bolts by 15% to optimize the uniformity of the contact pressure distribution, thereby improving the overall contact stiffness. Subsequently, the instruction is sent to the intelligent bolt actuator.

[0018] Step S7: Closed-loop verification and optimization. After implementing compensation measures, the system can quickly execute steps S3 to S5 again to predict the performance improvement effect after compensation. Simultaneously, actual vibration data is acquired through physical sensors and compared with the predicted results to continuously calibrate model parameters, achieving self-learning and continuous optimization.

[0019] Compared with the prior art, the present invention has the following significant advantages: (1) It realizes the dynamic and high-precision analysis of contact stiffness: By deeply integrating the measured data of micro-morphology, macro-structure model and dynamic working condition load, it overcomes the problem of insufficient accuracy of traditional static model and can more realistically reflect the time-varying characteristics of joint under complex working conditions.

[0020] (2) A proactive control closed loop of "perception-evaluation-compensation" is constructed: This invention not only stops at evaluation and analysis, but more importantly, it establishes an intelligent compensation mechanism based on the evaluation results. The system can proactively intervene in the contact state of the joint, transforming performance maintenance from "post-event correction" to "pre-event prevention" and "in-event control", representing the intelligent development direction of machine tool performance assurance technology.

[0021] (3) Improved the digitalization level of machine tool design and operation: The system can serve as the core functional module of the machine tool digital twin. It can be used to predict and optimize the parameters of the joints during the design phase and to monitor and ensure performance during the operation phase, providing a powerful tool for realizing model-based engineering and predictive maintenance.

[0022] (4) It has good versatility and scalability: The core framework of the system and method is applicable to the joints of various machine tools, including bolted joints of heavy-duty CNC machine tools, spindle-tool holder joints of high-speed machining centers, hydrostatic guide rail joints of precision machine tools, and tool-workpiece contact interfaces in robot machining systems. The model library and compensation strategies can be expanded and customized according to specific application scenarios. Attached Figure Description

[0023] Figure 1 is a schematic diagram of the overall process of the system described in this invention; Figure 2 is a schematic diagram of the machine tool model; Figure 3 is a cloud map of the stress distribution of bolts at the crossbeam-column bolted joint. Detailed Implementation

[0024] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment takes the crossbeam-column bolted joint of a heavy-duty gantry milling machine as the application object to illustrate the implementation process of this system. The machine tool model is shown in Figure 2.

[0025] Data preparation (corresponding to steps S1 and S2): Obtain a complete 3D model of the machine tool from the machine tool manufacturer, including the location, size, and nominal preload (60kN) of the bolt holes. Use a portable white light interferometer to scan the machined mating surfaces in-situ to obtain morphological parameters such as surface roughness Ra, profile waviness, and fractal dimension D. Read the cutting force simulation data of a typical heavy-duty milling program from the CNC system as the working condition load.

[0026] Simulation and Calculation (corresponding to steps S3 and S4): A refined model is established in the "Online Simulation Module of Contact Mechanical State". The simulation shows that under heavy load conditions, the contact pressure in some areas at the edge of the joint surface is close to zero, while the pressure around the bolt holes is concentrated, and the contact pressure distribution is severely uneven, as shown in Figure 3. Subsequently, the "Dynamic Calculation Model Library" calls the "Nonlinear Stiffness Model Based on Improved Fractal Theory", and combined with the measured morphological parameters and the uneven pressure distribution data, calculates the overall equivalent normal contact stiffness of the joint under the current state to be 8.5 × 10^8 N / m.

[0027] Assessment and Decision (corresponding to steps S5 and S6): The calculated stiffness parameters are substituted into the overall machine dynamics model. Analysis shows that the machine tool has relatively low low-order modal damping in a specific direction, posing a risk of chatter. After analysis, the intelligent compensation decision unit believes that the reduction in effective bearing area due to uneven contact pressure distribution is the main cause. The decision algorithm calculates that increasing the preload of the bolts on both sides of the joint to 90kN can significantly improve the uniformity of pressure distribution. The command is issued to a set of electric servo tightening systems installed at the joint.

[0028] Execution and Verification (corresponding to step S7): The servo tightening system precisely executes the preload adjustment. After adjustment, the system can perform rapid simulation again, predicting that the contact stiffness will increase to approximately 1.1 × 10^9 N / m, and the critical modal damping ratio is expected to increase by 20%. Vibration signals during machining were measured using accelerometers mounted on the crossbeam, verifying a significant reduction in vibration amplitude and improved surface quality. All process data, model parameters, and compensation records are stored in a database for tracking the long-term performance evolution of the machine tool's joints and for model self-learning.

[0029] The above embodiments fully illustrate the effectiveness of the system and method of the present invention. For other types of joints, such as robotic grinding units that require constant contact force control, the "interface damping active injection" compensation method can be enabled. By adjusting the viscosity of the intelligent fluid, it adapts to changes in the curved surface, maintaining stable contact stiffness and machining force. For high-speed spindle systems, the "dynamic stiffness mapping model based on machining state index" can be applied to achieve online monitoring of joint stiffness and adaptive adjustment of process parameters at different speeds.

[0030] This invention is not limited to the specific embodiments described above. Those skilled in the art can make various changes and modifications within the scope of the concept and spirit of this invention, and all such changes and modifications should fall within the protection scope of this invention.

Claims

1. A dynamic evaluation and intelligent compensation system for the contact stiffness of machine tool joints, characterized in that, include: Digital twin and multi-source data interface module: Connects with the machine tool's CAD design system, CAM programming system and workshop IoT system to obtain the three-dimensional geometric model of the target joint, assembly relationship, nominal preload, material properties and real-time / simulated working load information; The multi-scale morphology acquisition and characterization module for the contact interface integrates or calls non-contact optical measurement equipment to acquire actual micro-morphology data of the mating surfaces of the joint; the online simulation module for the contact mechanical state of the joint has a built-in fast solver based on the finite element method; it receives data from the digital twin and multi-source data interface module and the multi-scale morphology acquisition and characterization module for the contact interface, automatically establishes a refined finite element model of the joint containing detailed surface morphology features and macrostructure, applies the current working condition load, and calculates the contact pressure distribution, the actual contact area, and the micro-slip state at each point on the joint surface; Contact Stiffness Dynamic Calculation Model Library: This model library integrates various validated contact stiffness theoretical models, including: a nonlinear stiffness model based on improved fractal theory: applicable to bolted fixed joints, coupling the fractal dimension and characteristic scale of the micro-morphology with the macro-contact pressure distribution to calculate normal and tangential contact stiffness; a dynamic stiffness mapping model based on the machining state index: applicable to joints where relative motion may occur between the spindle and tool holder, or between the workpiece and fixture; and a performance evaluation and intelligent compensation decision unit: this unit inputs the calculated dynamic contact stiffness values ​​into the overall machine dynamics model to predict key performance indicators such as the machine tool's natural frequencies, mode shapes, and tool tip frequency response function. By comparing with the performance target threshold, it is determined whether the current contact state is optimal; if it does not meet the target, the compensation decision algorithm is activated to generate compensation instructions; the compensation strategy execution and feedback module: this module drives the corresponding actuator to implement the compensation decision; at the same time, the system monitors the changes in the dynamic response of the machine tool after compensation by vibration, force or acoustic emission sensors arranged near the joint, forming a closed-loop control loop of evaluation-decision-execution-feedback.

2. The machine tool joint contact stiffness dynamic evaluation and intelligent compensation system according to claim 1, characterized in that, The multi-scale morphology acquisition and characterization module of the contact interface adopts a micro-macro scale coupled characterization method to perform complementary analysis of fractal geometry and Euclidean geometry on the surface morphology and extract morphology parameters for contact mechanics calculation.

3. The machine tool joint contact stiffness dynamic evaluation and intelligent compensation system according to claim 1, characterized in that, The compensation strategies of the performance evaluation and intelligent compensation decision unit include, but are not limited to: adaptive adjustment of preload: dynamically adjusting the bolt preload to the optimal range through intelligent bolts or hydraulic servo mechanisms; active injection of interface damping: injecting / discharging intelligent fluid with controllable viscosity as needed into the preset joint gap or cavity to change the damping characteristics of the joint. Recommended process parameters and surface treatments: Optimized cutting parameters are recommended to avoid chatter, or specific surface texturing or coating treatments are suggested for the mating surfaces to improve long-term contact performance.

4. The machine tool joint contact stiffness dynamic evaluation and intelligent compensation system according to claim 1, characterized in that, The contact stiffness dynamic calculation model library establishes a nonlinear mapping relationship between the processing state index and the contact dynamic stiffness through machine learning.

5. A method for applying the dynamic evaluation and intelligent compensation system for the contact stiffness of machine tool joints as described in any one of claims 1-4, characterized in that, Includes the following steps: Step S1: System initialization and data loading; Select the target machine tool and the key joint to be evaluated, and load its 3D model, assembly constraints, and basic material parameters from the digital twin platform; Step S2: Surface morphology digitization and working condition loading; Use integrated measurement equipment to collect and digitize the micro-morphology of the mating surfaces; Simultaneously, obtain the load spectrum under the current or expected machining task from machining simulation or sensor network; Step S3: High-fidelity simulation of contact mechanics; In the online simulation module of contact mechanics, establish a simulation model that integrates macro-geometry and micro-morphology, and calculate the contact pressure distribution cloud map and micro-contact state of the mating surface under the load in Step S2; Step S4: Dynamic contact stiffness calculation; Select a matching model from the contact stiffness dynamic calculation model library according to the joint type; Using the contact pressure distribution and actual contact area obtained in step S3 as input, calculate the normal contact stiffness Kn, tangential contact stiffness Kt and equivalent contact damping C of the joint under the current specific working conditions; Step S5: Overall performance prediction and evaluation. The dynamic contact stiffness parameters calculated in step S4 are substituted as boundary conditions into the finite element dynamics model of the machine tool or the simplified model based on Yoshimura Yoshitaka's unit area method to calculate and analyze the dynamic characteristics of the machine tool. The evaluation metrics focus on the tool tip frequency response function, chatter stability domain Lobes plot, and key mode damping ratio under the target machining pose; Step S6: Intelligent compensation decision generation and execution; The evaluation results of step S5 are compared with the preset performance margin; If the predicted stability domain cannot cover the predetermined process parameters, or the damping ratio is too low, a compensation decision is triggered; Step S7: Closed-loop verification and optimization; After implementing the compensation measures, steps S3 to S5 are executed again to predict the performance improvement effect after compensation; Actual vibration data is obtained through physical sensors and compared with the prediction results to continuously calibrate the model parameters, achieving self-learning and continuous optimization.

Images