A machining path intelligent planning method and system for a numerical control machine tool mechanical arm

Abstract

The application discloses a kind of machining path intelligent planning method and system of numerical control machine tool mechanical arm, it is related to mechanical arm machining path planning technical field, the method includes: with transmission mechanism the rotary motion of motor is converted into linear motion mode or rotary motion mode of mechanical arm joint;Speed control is carried out using the motor and speed reducer cooperation;According to the mechanical arm connecting rod between the mechanical constraint relationship, position control is carried out using the motor and speed reducer cooperation;Online correction is carried out to preset machining path, and the optimization machining path is determined.The application solves the technical problems of insufficient stability and precision in the prior art when planning the machining path of numerical control machine tool mechanical arm, achieves the intelligent optimization of the machining path of numerical control machine tool mechanical arm, and improves the technical effects of stability and precision of machining path.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of machining path planning of mechanical arm, and particularly relates to a machining path intelligent planning method and system of numerical control machine tool mechanical arm. BACKGROUND

[0002] In the machining process of the numerical control machine tool mechanical arm, the planning of the machining path is crucial to the machining quality and efficiency. In the prior art, the planning of the machining path of the numerical control machine tool mechanical arm often fails to fully consider the motion coupling relationship between the joints of the mechanical arm, the mechanical constraint relationship between the connecting rods and the complex environmental interference factors, resulting in insufficient stability and precision of the machining path, which is difficult to meet the high-precision machining requirements and cannot realize the intelligent optimization of the machining path.

[0003] The technical problem of insufficient stability and precision in the planning of the machining path of the numerical control machine tool mechanical arm in the prior art. SUMMARY

[0004] The present application provides a machining path intelligent planning method and system of numerical control machine tool mechanical arm, which is used to solve the technical problem of insufficient stability and precision in the planning of the machining path of the numerical control machine tool mechanical arm in the prior art.

[0005] In view of the above problems, the present application provides a machining path intelligent planning method and system of numerical control machine tool mechanical arm.

[0006] In a first aspect of the present application, a machining path intelligent planning method of numerical control machine tool mechanical arm is provided, which comprises:

[0007] The numerical control machine tool mechanical arm comprises mechanical arm joints and mechanical arm connecting rods, and a transmission mechanism is used to convert the rotary motion of a motor into a linear motion mode or a rotary motion mode of the mechanical arm joints. According to the motion coupling relationship between the mechanical arm joints, the motor and the reducer are used for speed control to determine a first set of cooperative motion control parameters suitable for the linear motion mode and a second set of cooperative motion control parameters suitable for the rotary motion mode. According to the mechanical constraint relationship between the mechanical arm connecting rods, the motor and the reducer are used for position control to determine a third set of cooperative motion control parameters suitable for the linear motion mode and a fourth set of cooperative motion control parameters suitable for the rotary motion mode. Based on the path performance index, the first set of cooperative motion control parameters, the second set of cooperative motion control parameters, the third set of cooperative motion control parameters and the fourth set of cooperative motion control parameters are combined to correct the preset machining path online, and an optimized machining path is determined.

[0008] In a second aspect of the present application, a machining path intelligent planning system of numerical control machine tool mechanical arm is provided, which comprises:

[0009] The rotation motion conversion module is used for a numerical control machine tool mechanical arm including mechanical arm joints and mechanical arm links to convert the rotation motion of the motor into a linear motion mode or a rotation motion mode of the mechanical arm joints through a transmission mechanism; the speed control module is used for performing speed control using the motor and the speed reducer in cooperation according to the motion coupling relationship between the mechanical arm joints to determine a first cooperative motion control parameter set adapted to the linear motion mode and a second cooperative motion control parameter set adapted to the rotation motion mode; the position control module is used for performing position control using the motor and the speed reducer in cooperation according to the mechanical constraint relationship between the mechanical arm links to determine a third cooperative motion control parameter set adapted to the linear motion mode and a fourth cooperative motion control parameter set adapted to the rotation motion mode; and the optimized machining path determination module is used for performing online correction on a preset machining path based on a path performance index and in combination with the first, second, third and fourth cooperative motion control parameter sets to determine an optimized machining path.

[0010] The one or more technical solutions provided in the application have at least the following technical effects or advantages:

[0011] The numerical control machine tool mechanical arm includes mechanical arm joints and mechanical arm links to convert the rotation motion of the motor into a linear motion mode or a rotation motion mode of the mechanical arm joints through a transmission mechanism; speed control is performed using the motor and the speed reducer in cooperation to determine a first cooperative motion control parameter set adapted to the linear motion mode and a second cooperative motion control parameter set adapted to the rotation motion mode; position control is performed using the motor and the speed reducer in cooperation to determine a third cooperative motion control parameter set adapted to the linear motion mode and a fourth cooperative motion control parameter set adapted to the rotation motion mode; and online correction is performed on a preset machining path to determine an optimized machining path. The technical effect of realizing intelligent optimization of the machining path of the numerical control machine tool mechanical arm is achieved, and the stability and precision of the machining path are improved. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort.

[0013] Figure 1 A machining path intelligent planning method flowchart of a numerical control machine tool mechanical arm provided by the embodiment of the present application;

[0014] Figure 2 A machining path intelligent planning system structure schematic diagram of a numerical control machine tool mechanical arm provided by the embodiment of the present application.

[0015] Explanation of reference signs: rotating motion conversion module 10, speed control module 20, position control module 30, optimized machining path determination module 40. DETAILED DESCRIPTION

[0016] The present application provides a machining path intelligent planning method and system for a numerical control machine tool mechanical arm, which is used to solve the technical problems of insufficient stability and precision in machining path planning of a numerical control machine tool mechanical arm in the prior art.

[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0018] Embodiment one, as shown in the present application provides a machining path intelligent planning method for a numerical control machine tool mechanical arm, the method comprising: Figure 1

[0019] Step S100: The numerical control machine tool mechanical arm includes a mechanical arm joint, a mechanical arm connecting rod, and a transmission mechanism for converting the rotating motion of a motor into a linear motion mode or a rotating motion mode of the mechanical arm joint.

[0020] Specifically, the numerical control machine tool mechanical arm converts the rotating motion of a servo motor into a linear motion mode (such as converting the motor rotation into linear displacement of the joint by a screw nut) or a rotating motion mode (such as transmitting the motor rotation to the joint to realize angular rotation by a gear transmission) of the mechanical arm joint through the structural design of the mechanical arm joint and the mechanical arm connecting rod, and the gear transmission, screw nut transmission and other transmission mechanisms, to provide an executable hardware motion conversion basis for subsequent determination of a speed control parameter set based on the joint motion coupling relationship, determination of a position control parameter set based on the connecting rod mechanical constraint relationship, and online correction of the machining path in combination with the path performance index.

[0021] Step S200: According to the motion coupling relationship between the mechanical arm joints, using the motor and the speed reducer to perform speed control, determining a first cooperative motion control parameter set adapted to the linear motion mode and a second cooperative motion control parameter set adapted to the rotating motion mode.

[0022] ​Specifically, for the motion coupling relationship between the joints of the robot arm, by collecting motion parameters such as joint angular velocity and angular acceleration, the speed transmission and mutual influence of each joint motion are analyzed, the speed closed-loop control algorithm (such as PID control) is used by cooperating the servo motor and the reducer, in the linear motion mode, the matching parameters of the motor speed and the joint linear motion speed are determined according to the speed conversion relationship of the screw transmission, the first collaborative motion control parameter set is formed; in the rotary motion mode, the corresponding relationship between the motor speed and the joint rotation angle is calculated based on the gear transmission ratio, and the second collaborative motion control parameter set is constructed, so as to realize the collaborative control of the joint speed in different motion modes, and provide the speed control basis for the stability of the subsequent machining path.

[0023] Step S300: According to the mechanical constraint relationship between the links of the robot arm, the position control is performed by cooperating the motor and the reducer to determine the third collaborative motion control parameter set adapted to the linear motion mode and the fourth collaborative motion control parameter set adapted to the rotary motion mode.

[0024] Specifically, according to the mechanical constraint relationship between the links of the robot arm, the collaborative motion control parameter set is determined by the following specific implementation means: first, the force data of the link under different loads is collected by using the sensor, and the link mechanical model is constructed by combining the finite element analysis to quantitatively analyze the influence of load torque, inertia force and external vibration and other factors on the position accuracy of the link; then, by cooperating the servo motor and the reducer, the position closed-loop control strategy is adopted, in the linear motion mode, the mapping parameters of the motor position pulse and the joint linear displacement are calculated through the displacement-torque relationship of the screw transmission, and the parameters are compensated in combination with the environmental interference factors such as thermal deformation caused by temperature change and joint clearance, to form the third collaborative motion control parameter set; in the rotary motion mode, based on the angle-torque transmission characteristics of the gear transmission, the influence of the flexible deformation of the link on the joint rotation angle is analyzed, the correction coefficient of the motor rotation angle and the actual rotation angle of the joint is determined through the interpolation algorithm and feedback compensation, the fourth collaborative motion control parameter set is constructed, so as to realize the accurate control of the link position in different motion modes.

[0025] Step S400: Based on the path performance index, the first collaborative motion control parameter set, the second collaborative motion control parameter set, the third collaborative motion control parameter set and the fourth collaborative motion control parameter set are combined to correct the preset machining path online, and the optimized machining path is determined.

[0026] Specifically, based on path performance indicators (including machining path stability, machining path accuracy, etc.), the preset machining path is corrected online through the following specific implementation means: first, four sets of coordinated motion control parameters are taken as decision variables, the cutting tool-workpiece cutting force parameters (such as workpiece material characteristic parameters, cutting speed, etc.) are embedded into the dynamic simulation model, the dynamic load in the machining process is simulated, the speed / acceleration data and position / attitude error of the preset machining path are recorded, and the path performance indicators are quantitatively evaluated; then the influence weight of each parameter set on the performance indicators is analyzed, a multi-objective optimization index with the goal of minimizing the parameter adjustment amount and maximizing the performance improvement degree is configured, the decision variables are updated through an iterative algorithm to determine the optimized motion control parameter combination; at the same time, joint current data are collected in real time to determine the load torque, and after comparison with the preset threshold, the path performance change trend is predicted, if the online correction mechanism is triggered, the potential fault probability is evaluated in combination with the Bayesian network fault prediction model, and finally the preset path is dynamically interpolated and corrected based on multi-source data fusion to determine the optimized machining path.

[0027] In a possible implementation manner, the step S100 further includes:

[0028] Step S110: introducing machining requirement information, and configuring the preset machining path.

[0029] Step S120: constructing a dynamic simulation model of the machining process of the numerical control machine tool mechanical arm, dynamically simulating and verifying the preset machining path, and evaluating path performance indicators, the path performance indicators including machining path stability and machining path accuracy.

[0030] Specifically, when the machining requirement information is introduced to configure the preset machining path, the following specific implementation means are used: a three-dimensional scanner is used to collect the three-dimensional structure of the workpiece, the workpiece material attributes (such as the hardness parameters of carbon steel and the ductility parameters of aluminum alloy) in the machining requirement information, the machining surface roughness requirements (such as Ra3.2), and the tool parameters (such as the diameter of the end mill and the helix angle) are read through a data interface; based on UG / NX and other CAD / CAM software, according to the curvature characteristics of the three-dimensional structure of the workpiece and the material cutting performance, an initial path node is generated using the equal residual height method, so that the node distribution adapts to the surface undulation; the initial path node is sorted and optimized through a genetic algorithm, the process principles of rough machining first and then fine machining, and outer contour first and then inner cavity are followed, and finally a B-spline curve interpolation algorithm is used to connect the nodes to generate the preset machining path including process parameters such as feed speed and cutting depth.

[0031] When constructing the dynamic simulation model of the machining process of the mechanical arm of the numerical control machine tool and performing dynamic simulation verification, the following specific implementation means are used: in the simulation software such as ANSYS, first, environmental interference factors such as thermal deformation caused by temperature change and external vibration interference are set; based on the influence analysis of joint clearance and flexible deformation of connecting rods, a dynamic model including joints, connecting rods and transmission mechanisms of the mechanical arm is established by using the finite element method; then, the tool-workpiece cutting force parameters (including workpiece material property parameters, tool geometric parameters, cutting speed, feed amount and cutting depth) are configured, the tool-workpiece contact area is meshed to analyze the size and direction of the cutting force, the multi-point constraint method is used to apply the cutting force as a dynamic load to the center point of the tool, and a cutting force parameter loading strategy is formed; then, the preset machining path is imported into the model, the dynamic load in the machining process is simulated, the speed / acceleration data and position / attitude error of the path are recorded in real time, the stability of the machining path is quantitatively evaluated by the speed fluctuation amplitude and the acceleration change rate, and the accuracy of the machining path is quantitatively evaluated according to the position error value and the attitude deviation angle, thereby providing data support for subsequent path correction.

[0032] In a possible implementation manner, the step S110 further includes:

[0033] Step S111: collecting a three-dimensional structure of a workpiece, and analyzing workpiece material properties, machining surface roughness requirements and tool parameters in the machining requirement information.

[0034] Step S112: generating initial path nodes conforming to the three-dimensional structure of the workpiece according to the workpiece material properties, machining surface roughness requirements and tool parameters in the machining requirement information.

[0035] Step S113: sorting and connecting the initial path nodes to generate the preset machining path.

[0036] Specifically, the three-dimensional structure data of the workpiece is collected by means of a three-dimensional scanner to form a three-dimensional model containing information such as the geometric shape and size of the workpiece, and the machining requirement information is analyzed to extract workpiece material properties (such as material type, hardness and toughness), machining surface roughness requirements (such as specific roughness numerical indicators) and tool parameters (such as tool type, diameter and edge angle) therein.

[0037] The path generation module of CAD / CAM software (such as UG, MasterCAM) is used to determine the cutting depth and feed threshold based on the material properties of the workpiece (such as the hardness parameters of 45 steel and the cutting coefficients of aluminum alloy), and the tool path step is calculated in combination with the tool parameters (such as the diameter of the end mill and the helix angle). According to the roughness requirement of the machined surface (such as Ra1.6), the residual height constraint is set, and the discrete points are generated on the surface of the three-dimensional model of the workpiece by using the equal residual height method, so that the residual height of the tool path between adjacent points does not exceed the roughness threshold. For the curved surface area, the node density is adaptively adjusted through curvature analysis, and the number of nodes is increased in the high curvature area to ensure the machining accuracy, and finally the initial path node set that meets the material cutting characteristics, tool process parameters and surface roughness requirements is formed.

[0038] When the initial path nodes are sorted and connected to generate the preset machining path, the initial path nodes are sorted and optimized by using a genetic algorithm according to the machining process rules (such as rough machining first and then fine machining, and outer contour first and then inner cavity) and the three-dimensional structural features of the workpiece (such as the distribution of bosses and grooves), so as to reduce the tool idle stroke and the number of reversals. Then, the feed speed and cutting depth parameters are set according to the tool parameters (such as diameter and number of cutting edges) and the material properties of the workpiece (such as hardness and thermal conductivity), and the sorted nodes are connected by using a B-spline curve algorithm to form a smooth and continuous tool path. At the same time, the kinetic constraint condition is embedded to avoid the occurrence of sudden acceleration and deceleration in the path, and finally the preset machining path containing the process parameters, the path trajectory and the kinetic constraint is generated, which provides a basic path model for the subsequent online correction based on the four sets of cooperative motion control parameter sets.

[0039] In one possible implementation manner, step S120 further includes:

[0040] Step S121: setting environmental disturbance factors including thermal deformation caused by temperature change and external vibration disturbance.

[0041] Step S122: based on the environmental disturbance factors, a dynamic simulation model is established by analyzing the influence of joint clearance and flexible deformation of connecting rods.

[0042] Specifically, when setting the environmental disturbance factors, the thermal deformation caused by temperature change is simulated by defining the temperature field distribution parameters (such as the local temperature rise gradient caused by motor heating during machine tool operation and the temperature fluctuation range of the workshop environment) in the simulation software, and the external vibration disturbance is simulated by inputting the frequency spectrum (such as the 10-50Hz periodic vibration generated by the operation of surrounding equipment) and amplitude parameters (such as 0.01-0.1mm) of the external vibration source, so as to construct the environmental disturbance conditions close to the actual machining scene.

[0043] Based on the environmental interference factors such as temperature change, thermal deformation, and external vibration, the joint clearance (such as the radial clearance caused by bearing wear) and the flexible deformation of the connecting rod (such as the bending amount of the aluminum alloy connecting rod under the action of cutting force) are quantitatively analyzed by using the finite element analysis software. The change amount of the joint clearance is converted into the equivalent stiffness matrix of the kinematic pair, and the modal parameters of the flexible deformation of the connecting rod are introduced into the multi-body dynamics model. Combined with the mass, inertia, and stiffness parameters of the transmission mechanism of each component of the mechanical arm, a dynamics simulation model including thermal-structure-vibration multi-physical field coupling is established. The model can reflect the influence of joint motion error and connecting rod deformation on the end trajectory of the mechanical arm in real time under environmental interference.

[0044] In one possible implementation, step S120 further includes:

[0045] Step S123: embedding the tool-workpiece cutting force parameters into the dynamics simulation model, simulating the dynamic load in the machining process of the numerical control machine tool mechanical arm, and recording the speed data and acceleration data, position error and attitude error of the preset machining path.

[0046] Step S124: quantitatively evaluating the machining path stability in the path performance index through the speed data and acceleration data of the preset machining path.

[0047] Step S125: quantitatively evaluating the machining path precision in the path performance index through the position error and attitude error of the preset machining path.

[0048] Specifically, when embedding the tool-workpiece cutting force parameters into the dynamics simulation model, the workpiece material characteristic parameters (such as the hardness of 45 steel and the cutting resistance of aluminum alloy), the tool geometric parameters (such as the rake angle and helix angle of the end mill), the cutting speed, the feed rate, the cutting depth, and other parameters are configured first. The tool-workpiece contact area is meshed by using the finite element method, and the cutting force size and direction are analyzed. Then, the cutting force size and direction are taken as the dynamic load, which is applied to the center point of the tool in the dynamics simulation model by using the multi-point constraint method, to simulate the dynamic load caused by the cutting force in the machining process. At the same time, the speed data and acceleration data, position error and attitude error of the preset machining path under the action of the dynamic load are recorded in real time, providing data support for subsequent evaluation of machining path stability and precision.

[0049] When the stability of the preset machining path is quantitatively evaluated by using the speed data and the acceleration data, the real-time speed sequence and the acceleration sequence recorded in the dynamic simulation are extracted first, and the standard deviation of the speed data is calculated to represent the fluctuation amplitude of the speed. At the same time, the mutation frequency and the absolute value of the peak of the acceleration curve are analyzed. Then, the fluctuation amplitude of the speed is compared with the industry standard threshold (such as ± 5% rated speed), and the stability evaluation matrix is constructed in combination with the acceleration change rate (the acceleration change amount per unit time). The smaller the fluctuation amplitude of the speed and the more gentle the acceleration change are, the higher the stability index is. Finally, the speed and the acceleration characteristic parameters are converted into the stability score of 0-100 through dimensionless processing, so as to realize the quantitative evaluation of the stability of the machining path.

[0050] When the accuracy of the preset machining path is quantitatively evaluated by using the position error and the attitude error, the deviation value of the actual position of the mechanical arm end from the preset position and the deviation angle of the actual attitude (such as the pitch angle, the yaw angle and the roll angle) from the ideal attitude are extracted first. Then, the position error is decomposed into the components in the X, Y and Z coordinate axis directions, and is compared with the tolerance band of the machining accuracy requirement. At the same time, whether the attitude error exceeds the allowable range is analyzed. Finally, the position error and the attitude error in each direction are converted into the comprehensive accuracy index by using the weighted average method, for example, the position error accounts for 70% and the attitude error accounts for 30%. The accuracy score of 0-100 is calculated, so as to realize the quantitative evaluation of the accuracy of the machining path.

[0051] In a possible implementation manner, the step S123 further includes:

[0052] The step S1231: The tool-workpiece cutting force parameters are configured, and the tool-workpiece cutting force parameters include the workpiece material characteristic parameters, the tool geometric parameters, the cutting speed, the feed amount and the cutting depth.

[0053] The step S1232: The tool-workpiece contact area is meshed, and the cutting force size and the cutting force direction are analyzed.

[0054] The step S1233: The cutting force size and the cutting force direction are used as the dynamic load, and are applied to the tool center point of the dynamic simulation model by multi-point constraint, so as to determine the cutting force parameter loading strategy of the tool-workpiece cutting force parameters.

[0055] Specifically, when configuring the tool-workpiece cutting force parameters, the material properties of the workpiece, such as hardness, tensile strength, and elastic modulus, are first obtained to determine the cutting performance of the material. At the same time, the tool geometry parameters, including the tool rake angle, relief angle, main offset angle, secondary offset angle, tool diameter, and blade radius, are obtained, which directly affect the distribution and size of the cutting force. In addition, the machining parameters such as cutting speed, feed rate, and cutting depth need to be determined, where the cutting speed affects the generation of cutting heat and cutting force, the feed rate determines the material removal rate per unit time, and the cutting depth relates to the cutting load. By comprehensively configuring these parameters, accurate parameter basis is provided for subsequent analysis of cutting force and simulation of dynamic load in the machining process.

[0056] When the tool-workpiece contact area is meshed and the cutting force is analyzed, ANSYS finite element software is used to set the meshing rules based on the tool geometry parameters (such as rake angle, relief angle, and cutting edge radius) and the material properties of the workpiece (such as hardness and plastic deformation coefficient). The adaptive mesh refinement technique is used in the initial contact area between the cutting edge and the workpiece to discretize the contact area into quadrilateral or triangular elements with a minimum size of 0.01 mm. Then, based on the empirical formula of cutting force (such as the Merchant cutting force model) or the finite element solver, the shear force, friction force, and plowing force components on each mesh element are calculated. The cutting force of a single mesh is obtained by vector synthesis, and the total cutting force is obtained by integrating along the tool main cutting edge direction. At the same time, according to the stress direction distribution of the mesh elements, the main direction of the cutting force relative to the tool coordinate system (such as tangential, radial, and axial components) is determined to provide accurate force field distribution data for subsequent dynamic load loading.

[0057] When the cutting force size and direction are used as dynamic load and applied to the tool center point of the dynamics simulation model using multi-point constraints, the cutting force is first decomposed into tangential, radial, and axial components. A load loading script is written using MATLAB or Python to calculate the time-domain variation curve of each component based on the cutting speed and feed rate. In the ANSYS dynamics simulation model, the tool center point is coupled with multiple key points on the cutting edge through the multi-point constraint (MPC) algorithm to form a load transmission path, so that the dynamic load is applied to the tool model according to the force field distribution obtained by meshing. Gradual loading rules are set for different machining stages (such as cutting in, cutting, and cutting out), for example, the cutting-in stage increases linearly from zero to 80% of the rated load following a sine curve, the cutting stage maintains the rated load and superimposes a ±5% fluctuation due to uneven workpiece material, and the cutting-out stage decays exponentially to zero. At the same time, the influence of the joint clearance and link flexibility of the mechanical arm on the load transmission is considered, and the dynamic response of the load in the mechanical arm system is calculated by the finite element method. Finally, a cutting force parameter loading strategy is formed, which includes load size, direction, loading sequence, and transmission path, to ensure that the simulation model accurately reflects the stress state in actual machining.

[0058] In one possible implementation, step S400 further includes:

[0059] Step S410: Taking the first, second, third, and fourth collaborative motion control parameter sets as decision variables, analyze the influence weight on the path performance index.

[0060] Step S420: Through the influence weight, configure a multi-objective optimization index, with the goal of minimizing parameter adjustment amount and maximizing path performance improvement degree, constantly iteratively update the decision variables, and determine the optimized motion control parameter combination corresponding to the preset machining path.

[0061] Specifically, when analyzing the influence weight by taking the first to fourth collaborative motion control parameter sets as decision variables, a parameter-performance mapping model is built in the MATLAB / Simulink environment, the value range of the first, second, third, and fourth collaborative motion control parameter sets (linear motion speed control parameters, rotary motion speed control parameters, linear motion position control parameters, and rotary motion position control parameters) is defined, then the Taguchi method is used to design an orthogonal experiment matrix, each parameter set is taken as a factor, and the path stability and precision index are taken as response values for dynamic simulation test; the contribution rate of each parameter set to the performance index is calculated through range analysis and variance analysis, for example, it is found that the influence weight of the linear motion speed control parameter on the stability index is 35%, and the influence weight of the rotary motion position control parameter on the precision index is 40%, and the simulation data is trained by using a neural network to build a nonlinear mapping relationship between the parameter set and the performance index, and finally the influence weight coefficient matrix of each parameter set is determined.

[0062] When configuring the multi-objective optimization index through the influence weight, first, multiply the influence weight of each collaborative motion control parameter set by the change rate of the performance index, build a multi-objective optimization function containing stability improvement coefficient and precision improvement coefficient, wherein the optimization goal is to minimize the parameter adjustment amount (the sum of squares of the change amplitude of each parameter set) and maximize the path performance improvement degree (the weighted sum of the stability and precision index); then, a non-dominated sorting genetic algorithm (NSGA-II) is used for iterative optimization, in each generation evolution, the decision variables (the first to fourth collaborative motion control parameter sets) are crossed and mutated according to the optimization index, and the Pareto optimal solution is selected through the elite reservation strategy; finally, by calculating the ratio of the performance improvement degree to the parameter adjustment amount (optimization efficiency index), the parameter combination that maximizes the ratio is determined as the optimized motion control parameter combination, achieving the goal of obtaining the maximum path performance improvement with the minimum parameter adjustment cost.

[0063] In one possible implementation, step S400 further includes:

[0064] Step S430: Real-time acquisition of joint current data, determination of the load torque of each joint of the robot arm.

[0065] Step S440: Comparing the load torque of each joint of the robot arm with a preset load torque threshold range, if the load torque of any joint of the robot arm exceeds the preset load torque threshold range, predicting the path performance change trend under the current motion control parameter combination configuration.

[0066] Step S450: Determine whether to trigger the online correction mechanism through the path performance change trend.

[0067] Specifically, high-precision current sensors are installed at each joint of the robot arm to collect real-time joint current data at a sampling frequency of 1000 Hz. The current signal is converted into the load torque of each joint of the robot arm through the motor torque constant. For example, for a servo motor with a rated torque of 1.5 N·m, the real-time load torque can be calculated by multiplying the current value by the torque constant (e.g., 0.5 N·m / A).

[0068] When comparing the load torque of each joint of the robot arm with the preset load torque threshold range, the real-time load torque signal is first compared with the preset threshold (e.g., a ±20% fluctuation range of the rated torque) in real time through a data acquisition card. If the load torque of a certain joint exceeds the threshold, the prediction model based on the LSTM neural network is immediately started. The model takes the current load torque deviation value, historical load-performance data, and motion control parameter combination as input, and outputs the change curve of the path stability index (e.g., speed fluctuation amplitude) and precision index (e.g., position error) within the next 50 ms. For example, when the elbow joint load torque exceeds the upper limit of 25%, it is predicted that the machining path stability will decrease at a rate of 0.8% / ms. The reliability of the prediction result is verified by combining the dynamics simulation model, so as to determine the path performance change trend under the current parameter combination.

[0069] When determining whether to trigger the online correction mechanism according to the path performance change trend, a preset performance degradation trigger threshold is first set, such as a machining path stability index decreasing by more than 10% or a position error exceeding 0.05 mm. When the performance index change predicted by the LSTM neural network in the future machining cycle exceeds the threshold, the online correction mechanism is automatically triggered. In specific implementation, the change rates of the predicted stability index (such as the speed fluctuation amplitude) and the accuracy index (such as the attitude error angle) are compared with the threshold in real time. If the stability index is predicted to decrease by 12% and the accuracy error is 0.06 mm, the optimization machining path determination module is immediately activated, the multi-objective optimization algorithm is called to regenerate the motion control parameter combination, and the improved effect of the new parameters on the performance index is quickly verified through the dynamic simulation model to ensure that the corrected parameter combination can control the performance fluctuation within the threshold range. If the performance change trend is within the threshold, the current parameter combination is maintained, and the prediction model is continuously updated at a frequency of 500 Hz to ensure real-time monitoring and dynamic adjustment of the path performance.

[0070] In one possible implementation, step S450 further includes:

[0071] Step S451: A fault prediction model based on a Bayesian network is constructed, the load torque exceeding the torque threshold, the current motion control parameter combination, and the historical fault data are taken as input variables, and the occurrence probabilities of different potential fault types are evaluated.

[0072] Step S452: According to the occurrence probabilities of different potential fault types, a risk assessment reminder is generated in combination with the path performance change trend.

[0073] Specifically, when constructing the fault prediction model based on the Bayesian network, first, the load torque data exceeding the torque threshold, the corresponding motion control parameter combination, and the occurred fault types (such as joint bearing wear, transmission gear slip, etc.) are extracted from the historical machining database to form a training data set containing input variables (load torque deviation value, parameter combination feature vector) and output variables (fault type); then the node structure of the Bayesian network is defined, including parent nodes (load torque abnormality degree, parameter combination stability) and child nodes (potential fault types), the conditional probability table between nodes is calculated by the maximum a posteriori probability estimation method, and the network topology structure is constructed; when the current load torque data exceeding the torque threshold, the motion control parameter combination, and the historical fault data are input, the posterior probability of each potential fault type is calculated by using the Bayesian inference algorithm (such as joint tree algorithm), for example, the occurrence probability of the motor overheating fault is 40%, and the gear meshing fault probability is 25%, so as to evaluate the possibility of different faults.

[0074] When generating a risk assessment reminder according to the occurrence probability of different potential fault types and in combination with the path performance change trend, the occurrence probability of each potential fault type (such as bearing wear 40%, gear slip 25%) output by the Bayesian network is first associated with the predicted path performance change trend (such as stability decrease 18%, accuracy error increase 0.04 mm) for analysis, and a risk matrix is constructed according to the fault severity and occurrence probability; then the risk level is divided into three levels: high (red), medium (yellow) and low (green), for example, when the occurrence probability of a certain fault exceeds 30% and may cause machining precision out-of-tolerance, it is marked as a red warning; finally, a visual reminder including the fault type, occurrence probability, performance impact degree and response suggestion is generated, such as red warning: bearing wear occurrence probability 40%, expected to cause machining path precision to decrease by 20%, suggest stopping immediately to check joint lubrication state and adjust position control parameters, to provide intuitive risk warning and decision support for the operator.

[0075] In the second embodiment, based on the same inventive concept as the machining path intelligent planning method of the mechanical arm of the numerical control machine tool in the foregoing embodiment, as shown in the following table, the present application provides a machining path intelligent planning system of a mechanical arm of a numerical control machine tool, and the system and method embodiments in the present application are based on the same inventive concept. Wherein, the system comprises: Figure 2

[0076] A rotary motion conversion module 10 is used for a numerical control machine tool mechanical arm including a mechanical arm joint, a mechanical arm link, to convert the rotary motion of a motor into a linear motion mode or a rotary motion mode of the mechanical arm joint through a transmission mechanism.

[0077] A speed control module 20 is used for performing speed control using the motor and the speed reducer in cooperation according to the motion coupling relationship between the mechanical arm joints, to determine a first set of coordinated motion control parameters adapted to the linear motion mode and a second set of coordinated motion control parameters adapted to the rotary motion mode.

[0078] A position control module 30 is used for performing position control using the motor and the speed reducer in cooperation according to the mechanical constraint relationship between the mechanical arm links, to determine a third set of coordinated motion control parameters adapted to the linear motion mode and a fourth set of coordinated motion control parameters adapted to the rotary motion mode.

[0079] An optimized machining path determination module 40 is used for performing online correction on a preset machining path based on a path performance index, in combination with the first, second, third and fourth sets of coordinated motion control parameters, to determine an optimized machining path.

[0080] Further, the system is also used to implement the following functions:

[0081] ​Introduce the processing requirement information, configure the preset machining path; build a dynamic simulation model of the machining process of the mechanical arm of the numerical control machine tool, dynamically simulate and verify the preset machining path, and evaluate the path performance index, the path performance index including machining path stability and machining path precision.

[0082] Further, the system is also used to implement the following functions:

[0083] Collect the three-dimensional structure of the workpiece, analyze the workpiece material properties, machining surface roughness requirements, and tool parameters in the processing requirement information; generate initial path nodes conforming to the three-dimensional structure of the workpiece through the workpiece material properties, machining surface roughness requirements, and tool parameters in the processing requirement information; sort and connect the initial path nodes to generate the preset machining path.

[0084] Further, the system is also used to implement the following functions:

[0085] Set environmental disturbance factors including thermal deformation caused by temperature change and external vibration interference; based on the environmental disturbance factors, establish a dynamic simulation model by analyzing the influence of joint clearance and flexible deformation of connecting rods.

[0086] Further, the system is also used to implement the following functions:

[0087] Embed the tool-workpiece cutting force parameters into the dynamic simulation model, simulate the dynamic load in the machining process of the mechanical arm of the numerical control machine tool, and record the speed data and acceleration data, position error and attitude error of the preset machining path; quantitatively evaluate the machining path stability in the path performance index through the speed data and acceleration data of the preset machining path; quantitatively evaluate the machining path precision in the path performance index through the position error and attitude error of the preset machining path.

[0088] Further, the system is also used to implement the following functions:

[0089] Configure the tool-workpiece cutting force parameters, including workpiece material characteristic parameters, tool geometric parameters, cutting speed, feed rate, and cutting depth; perform grid division on the tool-workpiece contact area, analyze the cutting force size and cutting force direction; take the cutting force size and cutting force direction as dynamic load, and apply multi-point constraints to the tool center point of the dynamic simulation model to determine the cutting force parameter loading strategy of the tool-workpiece cutting force parameters.

[0090] Further, the system is also used to implement the following functions:

[0091] The first cooperative motion control parameter set, the second cooperative motion control parameter set, the third cooperative motion control parameter set, and the fourth cooperative motion control parameter set are taken as decision variables to analyze the influence weight on the path performance index; through the influence weight, a multi-objective optimization index is configured, and the decision variables are iteratively updated to minimize the parameter adjustment amount and maximize the path performance improvement degree as the target, so as to determine the optimized motion control parameter combination corresponding to the preset machining path.

[0092] Further, the system is also used to realize the following functions:

[0093] Real-time joint current data is collected to determine the load torque of each joint of the robot arm; the load torque of each joint of the robot arm is compared with a preset load torque threshold range, and if the load torque of any joint of the robot arm exceeds the preset load torque threshold range, a path performance change trend under the current motion control parameter combination configuration is predicted; through the path performance change trend, it is judged whether the online correction mechanism is triggered.

[0094] Further, the system is also used to realize the following functions:

[0095] A fault prediction model based on a Bayesian network is constructed, the load torque exceeding the torque threshold, the current motion control parameter combination, and historical fault data are taken as input variables, and the occurrence probability of different potential fault types is evaluated; according to the occurrence probability of different potential fault types, a risk assessment reminder is generated in combination with the path performance change trend.

[0096] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the above describes a specific embodiment of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0097] The above only describes the preferred embodiments of the present application and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0098] The present application is only an exemplary description of the present application, and is considered to cover any and all modifications, changes, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art can make various modifications and changes to the present application without departing from the scope of the present application. Thus, if these modifications and changes of the present application belong to the scope of the present application and its equivalents, the present application intends to include these modifications and changes.

Claims

1. A method for intelligent planning of machining paths for a mechanical arm of a numerically controlled machine tool, characterized in that, The method comprises: The numerical control machine tool mechanical arm includes mechanical arm joints, mechanical arm links, and a transmission mechanism for converting the rotary motion of the motor into a linear motion mode or a rotary motion mode of the mechanical arm joints; According to the motion coupling relationship between the mechanical arm joints, the first set of cooperative motion control parameters suitable for the linear motion mode and the second set of cooperative motion control parameters suitable for the rotary motion mode are determined by using the motor and the speed reducer to cooperate in speed control; According to the mechanical constraint relationship between the mechanical arm links, the third set of cooperative motion control parameters suitable for the linear motion mode and the fourth set of cooperative motion control parameters suitable for the rotary motion mode are determined by using the motor and the speed reducer to cooperate in position control; Based on the path performance index, the first set of cooperative motion control parameters, the second set of cooperative motion control parameters, the third set of cooperative motion control parameters, and the fourth set of cooperative motion control parameters are combined to correct the preset machining path online, and an optimized machining path is determined; Wherein, the machining demand information is introduced to configure the preset machining path; A dynamic simulation model of the machining process of the numerical control machine tool mechanical arm is constructed, dynamic simulation verification is performed on the preset machining path, and the path performance index is evaluated, including machining path stability and machining path precision; Wherein, the dynamic simulation model of the machining process of the numerical control machine tool mechanical arm further comprises: Setting environmental disturbance factors including thermal deformation caused by temperature change and external vibration disturbance; Based on the environmental disturbance factors, the dynamic simulation model is established by analyzing the influence of joint clearance and flexible deformation of the connecting rod; Wherein, the dynamic simulation verification is performed on the preset machining path, and the path performance index is evaluated, including: Embedding the tool-workpiece cutting force parameters into the dynamic simulation model, simulating the dynamic load in the machining process of the numerical control machine tool mechanical arm, and recording the speed data and acceleration data, position error and attitude error of the preset machining path; The machining path stability in the path performance index is quantitatively evaluated through the speed data and acceleration data of the preset machining path; The machining path precision in the path performance index is quantitatively evaluated through the position error and attitude error of the preset machining path; Wherein, embedding the tool-workpiece cutting force parameters into the dynamic simulation model further comprises: Configuring the tool-workpiece cutting force parameters, including workpiece material characteristic parameters, tool geometric parameters, cutting speed, feed rate, and cutting depth; Grid division is performed on the tool-workpiece contact area, and the cutting force size and cutting force direction are analyzed; The cutting force parameter loading strategy of the tool-workpiece cutting force parameters is determined by taking the cutting force size and cutting force direction as the dynamic load and applying it to the tool center point of the dynamic simulation model through multi-point constraint; Wherein, based on the path performance index, the first set of cooperative motion control parameters, the second set of cooperative motion control parameters, the third set of cooperative motion control parameters, and the fourth set of cooperative motion control parameters are combined to correct the preset machining path online, including: The first cooperative motion control parameter set, the second cooperative motion control parameter set, the third cooperative motion control parameter set, and the fourth cooperative motion control parameter set are taken as decision variables to analyze the influence weight on the path performance index; By the influence weight, a multi-objective optimization index is configured to minimize the parameter adjustment amount and maximize the path performance improvement degree, and the decision variables are iteratively updated to determine the optimized motion control parameter combination corresponding to the preset machining path.

2. The machining path intelligent planning method of a CNC machine tool mechanical arm according to claim 1, characterized in that, The machining requirement information is introduced to configure the preset machining path, and the method comprises: Collecting a workpiece three-dimensional structure, and analyzing workpiece material properties, machining surface roughness requirements, and tool parameters in the machining requirement information; Generating initial path nodes conforming to the workpiece three-dimensional structure according to the workpiece material properties, machining surface roughness requirements, and tool parameters in the machining requirement information; The initial path nodes are sorted and connected to generate the preset machining path.

3. The method of claim 1, wherein, After the optimized motion control parameter combination corresponding to the preset machining path is determined, the method comprises: Real-time collection of joint current data to determine the load torque of each robot joint; The load torque of each robot joint is compared with a preset load torque threshold range, and if the load torque of any robot joint exceeds the preset load torque threshold range, the path performance change trend under the current motion control parameter combination configuration is predicted; The online correction mechanism is triggered according to the path performance change trend.

4. The machining path intelligent planning method of a CNC machine tool mechanical arm according to claim 3, characterized in that, The method further comprises: A fault prediction model based on a Bayesian network is constructed, the load torque exceeding the torque threshold, the current motion control parameter combination, and historical fault data are taken as input variables to evaluate the occurrence probability of different potential fault types; According to the occurrence probability of different potential fault types, a risk assessment reminder is generated in combination with the path performance change trend.

5. A system for intelligent planning of machining paths for a mechanical arm of a numerically controlled machine tool, characterized in that, The system is used to implement the machining path intelligent planning method of the numerical control machine tool robot arm according to any one of claims 1-4, and the system comprises: A rotary motion conversion module is used for a numerical control machine tool robot arm comprising robot joints and robot links, and a transmission mechanism is used to convert the rotary motion of a motor into a linear motion mode or a rotary motion mode of the robot joints; A speed control module is used to control the speed of the motor and the reducer according to the motion coupling relationship between the robot joints to determine a first cooperative motion control parameter set for the linear motion mode and a second cooperative motion control parameter set for the rotary motion mode; A position control module is used to control the position of the motor and the reducer according to the mechanical constraint relationship between the robot links to determine a third cooperative motion control parameter set for the linear motion mode and a fourth cooperative motion control parameter set for the rotary motion mode; An optimized machining path determination module is used to correct the preset machining path online based on the path performance index and the first cooperative motion control parameter set, the second cooperative motion control parameter set, the third cooperative motion control parameter set, and the fourth cooperative motion control parameter set to determine the optimized machining path. The machining demand information is introduced, and the preset machining path is configured; A dynamic simulation model of the machining process of the mechanical arm of the numerical control machine tool is constructed, dynamic simulation verification is performed on the preset machining path, and path performance indexes are evaluated. The path performance indexes include machining path stability and machining path precision. The dynamic simulation model of the machining process of the mechanical arm of the numerical control machine tool is constructed, and the model further includes: Environmental disturbance factors including thermal deformation caused by temperature change and external vibration interference are set; Based on the environmental disturbance factors, a dynamic simulation model is established by analyzing the influence of joint clearances and flexible deformation of connecting rods; The dynamic simulation verification is performed on the preset machining path, and the path performance indexes are evaluated, including: The tool-workpiece cutting force parameters are embedded in the dynamic simulation model to simulate the dynamic load in the machining process of the mechanical arm of the numerical control machine tool, and the speed data and acceleration data of the preset machining path, the position error and the attitude error are recorded; The machining path stability in the path performance indexes is quantitatively evaluated by the speed data and acceleration data of the preset machining path; The machining path precision in the path performance indexes is quantitatively evaluated by the position error and the attitude error of the preset machining path; The tool-workpiece cutting force parameters are embedded in the dynamic simulation model, and the model further includes: The tool-workpiece cutting force parameters are configured, including workpiece material characteristic parameters, tool geometric parameters, cutting speed, feed rate, and cutting depth; The tool-workpiece contact area is meshed, and the cutting force size and the cutting force direction are analyzed; The cutting force size and the cutting force direction are used as dynamic loads, and are applied to the tool center point of the dynamic simulation model by multi-point constraint to determine the cutting force parameter loading strategy of the tool-workpiece cutting force parameters; Based on the path performance indexes, the first, second, third, and fourth collaborative motion control parameter sets are combined to correct the preset machining path online, including: The first, second, third, and fourth collaborative motion control parameter sets are used as decision variables, and the influence weight of the path performance indexes is analyzed; By the influence weight, a multi-objective optimization index is configured, and the decision variables are iteratively updated to determine the optimized motion control parameter combination corresponding to the preset machining path, with the minimum parameter adjustment amount and the maximum path performance improvement degree as the target.

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