Multi-parameter collaborative optimization method for deterministic grinding and polishing of limited space robot

Abstract

The invention discloses a multi-parameter collaborative optimization method for deterministic grinding and polishing of a limited space robot, and the method comprises the steps: bringing a material removal amount prediction model, a grinding and polishing track, a robot posture and dynamic error compensation into a unified optimization process through a multi-step collaborative optimization framework; the nonlinear coupling influence of robot attitude adjustment and feeding speed fluctuation on removal amount distribution is reduced, the cooperative problems of removal amount out-of-tolerance, coverage uniformity and interference avoidance caused by multi-parameter coupling in a narrow flow channel are solved, finally, the high-dimensional nonlinear optimization problem is decomposed through an alternating direction multiplier method, the convergence efficiency is remarkably improved, and the convergence efficiency is improved. And it is ensured that the machined surface meets the preset tolerance requirement, and high-precision deterministic grinding and polishing under the limited space are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of complex integral component grinding and polishing, and particularly to a multi-parameter collaborative optimization method for deterministic grinding and polishing of a robot in a confined space. Background Art

[0002] Limitations of traditional grinding and polishing processes: The grinding and polishing of complex integral components (such as aero-engine impellers and nuclear reactor coolant pump disks) have long relied on manual operations, suffering from problems such as low efficiency, poor consistency, and uncontrollable surface quality, and it is difficult to meet the requirements of high-precision and batch production.

[0003] Industrial robots, with high flexibility and agility, have gradually been applied to the grinding and polishing of complex curved surfaces. However, restricted by working conditions such as narrow channels and large curvature changes, they still face the following problems: (1) The material removal model is inaccurate: The traditional Hertz contact theory fails under the assumption of nonlinear side-edge contact conditions, resulting in a large prediction error of the removal depth. (2) Insufficient trajectory planning ability: Existing trajectory planning methods (such as line cutting and cycloid) do not coordinate tool trajectories, robot configurations, and process parameters, leading to fluctuations in dwell time and over-tolerance of the removal amount.

[0004] Deterministic grinding and polishing uses a high-precision material removal amount prediction model to control the material removal amount by comprehensively optimizing parameters such as grinding and polishing trajectories and dwell time. However, in complex integral components such as integral impellers and disks, the motion space of robot grinding and polishing is severely restricted, making it difficult to accurately regulate multiple parameters. Summary of the Invention

[0005] In view of the above problems, the present invention proposes a multi-parameter collaborative optimization method for deterministic grinding and polishing of a robot in a confined space, and proposes a method for comprehensively optimizing the grinding and polishing trajectory, posture, and dwell time of the robot driven by material removal amount simulation, which is expected to improve the grinding and polishing removal accuracy and achieve higher-quality grinding and polishing processing.

[0006] To solve the above technical problems, the technical solution of the present invention is as follows:

[0007] A multi-parameter collaborative optimization method for deterministic grinding and polishing of a robot in a confined space, comprising the following steps:

[0008] Step 1, for the target narrow channel, establish a material removal amount prediction model based on the modified Preston equation and the side-edge contact mechanics model;

[0009] Step 2, plan the target workpiece model using a line cutting or circumferential cutting trajectory to generate a grinding and polishing trajectory of the center point of the side-edge tool;

[0010] Step 3: Discretize the grinding and polishing trajectory to obtain discrete machining points. Then, with collision-free machining as the constraint condition, establish the interference-free configuration space of the robot corresponding to each discrete machining point. Conduct smooth collision-free attitude optimization along the grinding and polishing trajectory within the interference-free configuration space of the robot to obtain the robot attitude change sequence.

[0011] Step 4: Calculate the material removal amount based on the grinding and polishing trajectory, the robot attitude change sequence, and in combination with the material removal amount prediction model.

[0012] Step 5: Combine the material removal amount with the material removal amount requirement of the task requirements, optimize and adjust the dwell time of each discrete machining point, and calculate the grinding and polishing speed of the robot according to the dwell time.

[0013] Step 6: Input the optimized robot attitude change sequence, the dwell time, and the grinding and polishing speed into the material removal amount prediction model to predict the material removal amount of the target workpiece model, generate the topography simulation result, and based on the topography simulation result, repeat Steps 2 - 5 using the alternating direction multiplier method until the preset material removal amount tolerance is met.

[0014] In some embodiments, in Step 2, it further includes introducing the computational conformal geometry method and optimizing different grinding and polishing trajectories using an adaptive algorithm.

[0015] In some embodiments, in Step 2, the planning strategy of the grinding and polishing trajectory includes:

[0016] The main path of the grinding and polishing trajectory uses the row cutting mode to suppress periodic ripples, and locally superimposes the cycloid trajectory to compensate for random removal fluctuations. Then, based on Monte Carlo simulation, evaluate the cumulative effect of the removal amount, construct a joint function with the uniformity of the residual height and the smoothness of the path as the optimization objectives, and dynamically correct the grinding and polishing trajectory and the overlap rate parameter according to the joint function.

[0017] In some embodiments, in Step 2, the parameters of the grinding and polishing trajectory at least include the trajectory spacing and the tool inclination angle.

[0018] In some embodiments, in Step 3, during the collision-free attitude optimization of the robot attitude change sequence, the set constraint conditions at least include the geometric smoothness of the grinding and polishing attitude of the robot, the joint angular velocity, and the angular acceleration.

[0019] In some embodiments, in Step 3, the method for obtaining the robot attitude change sequence includes:

[0020] Convert the interference - free configuration space of the robot into a weighted directed graph, establish the mapping relationship between the weighted directed graph and the mapping model, calculate the shortest distance of the weighted directed graph, and generate the robot posture change sequence through the swarm intelligence optimization algorithm for the shortest distance.

[0021] In some embodiments, in step 5, the optimization method of the dwell time includes: inputting the material removal amount into the inversion equation to solve the dwell time of the tool, and the inversion equation includes:

[0022]

[0023] In the formula, F -1 (·) is the inversion function, h target is the material removal amount, Δt comp is the compensated dwell time, which is used to correct the deviation caused by process parameters and other factors.

[0024] In some embodiments, in step 6, the topography simulation result is the set of the material removal amounts corresponding to all the discrete machining points.

[0025] The beneficial effects of the present invention are as follows: Through the multi - step collaborative optimization framework, the material removal amount prediction model, the grinding and polishing trajectory, the robot posture, and the dynamic error compensation are incorporated into a unified optimization process, reducing the non - linear coupling effect of the robot posture adjustment and the feed speed fluctuation on the removal amount distribution, solving the collaborative problems of the removal amount out - of - tolerance, the coverage uniformity, and the interference avoidance caused by the multi - parameter coupling in the narrow flow channel. Finally, the high - dimensional non - linear optimization problem is decomposed by the alternating direction multiplier method, significantly improving the convergence efficiency, ensuring that the machined surface meets the preset tolerance requirements, and realizing high - precision deterministic grinding and polishing in a limited space. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flowchart of the multi - parameter collaborative optimization method for deterministic grinding and polishing of a robot in a limited space disclosed in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] To make the objectives, technical solutions and advantages of the present invention clearer and more definite, the content of the present invention will be further described in detail below with reference to the drawings and specific embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. Additionally, it should be noted that for the sake of description, only parts related to the present invention are shown in the drawings, rather than all the content.

[0028] This embodiment proposes a multi-parameter collaborative optimization method for deterministic grinding and polishing of a confined space robot. Through a multi-step collaborative optimization framework (model establishment → trajectory planning → attitude optimization → closed-loop feedback → global iteration), the material removal amount prediction model, grinding and polishing trajectory, robot attitude, and dynamic error compensation are incorporated into a unified optimization process to reduce the non-linear coupling effect of robot attitude adjustment and feed speed fluctuation on the removal amount distribution, solve the collaborative problems of removal amount over-tolerance, coverage uniformity, and interference avoidance caused by multi-parameter coupling in narrow channels. Finally, the alternating direction multiplier method is used to decompose the high-dimensional non-linear optimization problem, significantly improving the convergence efficiency, ensuring that the machined surface meets the preset tolerance requirements, and achieving high-precision deterministic grinding and polishing in a confined space. As Figure 1 shown, it includes the following steps:

[0029] A multi-parameter collaborative optimization method for deterministic grinding and polishing of a confined space robot includes the following steps:

[0030] Step 1, for the target narrow channel, establish a material removal amount prediction model based on the modified Preston equation and the side-edge contact mechanics model. In this step, by establishing a material removal amount prediction model based on the modified Preston equation and the side-edge contact mechanics model, the limitations of the traditional Hertz contact theory under non-linear side-edge working conditions are broken through, significantly improving the prediction accuracy of the material removal depth in narrow channels, and providing a theoretical basis for subsequent trajectory planning and parameter optimization.

[0031] Step 2, the target workpiece model is planned using a row cutting or circumferential cutting trajectory to generate the grinding and polishing trajectory of the side-edge tool center point, improving the path coverage uniformity and smoothness of the grinding and polishing trajectory, and solving the contradiction between tool accessibility and removal amount stability in narrow channels.

[0032] Optionally, it further includes introducing the computational conformal geometry method and using an adaptive algorithm to optimize different grinding and polishing trajectories. In this optional solution, by introducing the computational conformal geometry method and the adaptive algorithm to optimize the grinding and polishing trajectory, the local geometric features of complex surfaces are dynamically matched, the residual height fluctuation caused by sudden changes in the tool axis is reduced, and the collaborative accuracy of trajectory planning and material removal model is enhanced, thereby improving the uniformity of allowance removal in narrow channels.

[0033] In an example, the planning strategy of the above grinding and polishing trajectory includes:

[0034] The main path of the grinding and polishing trajectory adopts a row cutting mode to suppress periodic ripples, locally superimposes a cycloid trajectory to compensate for randomness and remove fluctuations, then evaluates the cumulative effect of the removal amount based on Monte Carlo simulation, constructs a joint function with the uniformity of the residual height and the smoothness of the path as the optimization objectives, and dynamically corrects the grinding and polishing trajectory and the overlap rate parameter according to the joint function. In this example, a row cutting main path is used to suppress periodic ripples, a local cycloid trajectory is superimposed to compensate for randomness and remove fluctuations, and dynamic parameter optimization is combined with Monte Carlo simulation to effectively balance the coverage uniformity and the path smoothness, avoid the defects of fixed wave peaks and valleys or random overshoot in a single trajectory mode, and improve the overall grinding and polishing quality of complex surfaces.

[0035] In this solution, the parameters of the grinding and polishing trajectory at least include the trajectory spacing and the tool inclination angle. By coordinately adjusting the trajectory spacing (controlling the coverage density) and the tool inclination angle (optimizing the contact pressure distribution), the prediction error problem of the traditional model under the non-linear working condition of side edge contact is solved, the controllability of the material removal behavior in the narrow flow channel is enhanced, and high-consistency surface machining is achieved.

[0036] Step 3: Discretize the grinding and polishing trajectory to obtain discrete machining points, and then, with collision-free machining as the constraint condition, establish the robot interference-free configuration space corresponding to each discrete machining point. Smooth collision-free attitude optimization is carried out along the grinding and polishing trajectory within the robot interference-free configuration space to obtain the robot attitude change sequence. In this step, through the collision-free attitude optimization of discrete machining points, a robot smooth configuration sequence is generated in a strong constraint feasible region (collision-free, high stiffness, singularity-free), avoiding the cumulative dynamic error caused by sudden changes in the robot configuration in the traditional method and ensuring the motion stability of the grinding and polishing process in a narrow space.

[0037] During the process of obtaining the robot attitude change sequence through collision-free attitude optimization, the set constraint conditions at least include the geometric smoothness of the grinding and polishing attitude of the robot, the joint angular velocity, and the angular acceleration, so as to suppress sudden changes in the configuration and the transmission of dynamic errors, ensure the stability of the robot motion in a narrow space, and reduce the risk of surface quality deterioration caused by vibration or interference.

[0038] In an example, the method for obtaining the robot attitude change sequence includes:

[0039] Convert the robot interference-free configuration space into a weighted directed graph, establish the mapping relationship between the weighted directed graph and the mapping model, calculate the shortest distance of the weighted directed graph, and the shortest distance generates the robot attitude change sequence through a swarm intelligence optimization algorithm. In this example, by mapping the interference-free configuration space into a weighted directed graph and combining the swarm intelligence algorithm to quickly screen the global optimal attitude sequence, the high computational complexity of the traditional enumeration method is significantly reduced, and the efficient planning of the collision-free high-performance pose of the robot in the narrow flow channel is realized.

[0040] Step 4: Calculate the material removal amount based on the grinding and polishing trajectory, the robot pose change sequence, and in combination with the material removal amount prediction model. In this step, based on the material removal amount prediction model in Step 1 and the robot pose change sequence calculated in Step 3, the material removal amount distribution is calculated in real time, realizing the dynamic coupling analysis of trajectory-pose-process parameters, providing a data basis for the dwell time optimization, and solving the problem of the disconnection between the removal amount prediction and the actual processing in traditional open-loop planning.

[0041] Step 5: Combine the material removal amount with the material removal amount requirement of the task requirement, optimize and adjust the dwell time of each discrete processing point, and calculate the grinding and polishing speed of the robot according to the dwell time.

[0042] In Step 5, the optimization method of the dwell time includes: inputting the material removal amount into the inversion equation to solve the dwell time of the tool. The inversion equation includes:

[0043]

[0044] In the formula, F -1 (·) is the inversion function, h target is the material removal amount, Δt comp is the compensated dwell time, which is used to correct the deviation caused by process parameters and other factors. In Step 5, the theoretical dwell time is solved through the inversion equation, and a compensation term is introduced to correct dynamic errors such as speed fluctuation and tool wear, generating a grinding and polishing speed curve that meets the robot kinematic constraints, realizing the coordinated adaptation of process parameters and the dynamic performance of the robot, and improving the real-time performance and robustness of the removal amount control.

[0045] Step 6: Input the optimized robot pose change sequence, dwell time, and grinding and polishing speed into the material removal amount prediction model, predict the material removal amount of the target workpiece model, generate the topography simulation result, and based on the topography simulation result, repeat Steps 2-5 using the alternating direction method of multipliers until the preset material removal amount tolerance is met. In this step, the alternating direction method of multipliers (ADMM) is used to globally iteratively optimize the trajectory, configuration, and dwell time, decomposing the high-dimensional non-linear coupling problem into sub-modules that can be solved in parallel, significantly improving the convergence efficiency, ensuring that the material removal amount error finally converges within the preset tolerance range, and achieving the deterministic grinding and polishing goal under limited space.

[0046] Among them, the topography simulation result is the set of material removal amounts corresponding to all discrete processing points, aiming to illustrate that the iterative optimization process in Step 6 needs to be specific to the material removal amount corresponding to each discrete processing point.

[0047] The above embodiments are only for illustrating the technical concept and features of the present invention, and the purpose is to enable those of ordinary skill in the art to understand the content of the present invention and implement it accordingly, rather than to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the essence of the content of the present invention should be covered within the protection scope of the present invention.

Claims

1. A multi-parameter collaborative optimization method for deterministic grinding and polishing of robots in confined spaces, characterized in that, It includes the following steps: Step 1: For the target narrow flow channel, establish a material removal amount prediction model based on the modified Preston equation and the side-edge contact mechanics model; Step 2: Plan the target workpiece model using a row cutting or circumferential cutting trajectory to generate the grinding and polishing trajectory of the center point of the side-edge tool; Step 3: Discretize the grinding and polishing trajectory to obtain discrete machining points. Then, with collision-free machining as the constraint condition, establish the robot non-interference configuration space corresponding to each discrete machining point. Perform smooth collision-free posture optimization along the grinding and polishing trajectory in the robot non-interference configuration space to obtain the robot posture change sequence; Step 4: Calculate the material removal amount according to the grinding and polishing trajectory, the robot posture change sequence, and in combination with the material removal amount prediction model; Step 5: Combine the material removal amount with the material removal amount requirement of the task requirement, optimize and adjust the dwell time of each discrete machining point, and calculate the grinding and polishing speed of the robot according to the dwell time; Step 6: Input the optimized robot posture change sequence, the dwell time, and the grinding and polishing speed into the material removal amount prediction model to predict the material removal amount of the target workpiece model, generate the topography simulation result, and based on the topography simulation result, repeat Steps 2-5 using the alternating direction multiplier method until the preset material removal amount tolerance is met.

2. The multi-parameter collaborative optimization method for deterministic grinding and polishing of a confined space robot according to claim 1, wherein In Step 2, it also includes introducing the computational conformal geometry method and optimizing different grinding and polishing trajectories using an adaptive algorithm.

3. The multi-parameter collaborative optimization method for deterministic grinding and polishing of a confined space robot according to claim 1, characterized in that In Step 2, the planning strategy of the grinding and polishing trajectory includes: The main path of the grinding and polishing trajectory uses a row cutting mode to suppress periodic ripples, locally superimposes a cycloid trajectory to compensate for random removal fluctuations, then evaluates the cumulative effect of the removal amount based on Monte Carlo simulation, constructs a joint function with the uniformity of the residual height and the smoothness of the path as the optimization objectives, and dynamically corrects the grinding and polishing trajectory and the overlap rate parameter according to the joint function.

4. The multi-parameter collaborative optimization method for deterministic grinding and polishing of a confined space robot according to claim 1, characterized in that, In Step 2, the parameters of the grinding and polishing trajectory at least include the trajectory spacing and the tool inclination angle.

5. The multi-parameter collaborative optimization method for deterministic grinding and polishing of a confined space robot according to claim 1, characterized in that, In Step 3, during the collision-free posture optimization of the robot posture change sequence, the set constraint conditions at least include the geometric smoothness of the grinding and polishing posture of the robot, the joint angular velocity, and the angular acceleration.

6. The multi-parameter collaborative optimization method for deterministic grinding and polishing of a confined space robot according to claim 1, characterized in that In Step 3, the acquisition method of the robot posture change sequence includes: Convert the robot non-interference configuration space into a weighted directed graph, establish the mapping relationship between the weighted directed graph and the mapping model, calculate the shortest distance of the weighted directed graph, and generate the robot posture change sequence through the shortest distance by a swarm intelligence optimization algorithm.

7. The multi-parameter collaborative optimization method for deterministic grinding and polishing of a confined space robot according to claim 1, characterized in that, In Step 5, the optimization method of the dwell time includes: inputting the material removal amount into the inversion equation to solve the dwell time of the tool, and the inversion equation includes: In the formula, F -1 (·) is the inversion function, h target is the material removal amount, Δt comp is the compensation dwell time, which is used to correct the deviation caused by factors such as process parameters.

8. The multi-parameter collaborative optimization method for deterministic grinding and polishing of a confined space robot according to claim 1, characterized in that, In Step 6, the topography simulation result is the set of the material removal amounts corresponding to all discrete machining points.

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