Research On Error Compensation And Force Control For Robotic Milling

Large complex-shaped parts,such as aerospace structural components,wind turbine blades and ship propellers,are widely used in aviation,energy and defense industries.These products relate closely to the national economy and defense security,and reflect significant strategic demands of our country.The manufacturing level of such products represents the core competitiveness of the national manufacturing industry.Due to the advantages of large workspace,high flexibility and low cost,industrial robot is gradually becoming another important way to manufacture this kind of parts in addition to large multi-axis CNC machine tool.However,the low accuracy and weak stiffness of industrial robot will result in poor accuracy of the finished part and insufficient machining efficiency when it is used in milling applications.It is the research hotspot in the area of robot milling to resolve the contradictions between the low-accuracy and weak-stiffness of commercial standard industrial robot and the requirements of high-accuracy and high-efficiency machining of large complex-shaped parts.This dissertation aims to solve the common and difficult problems in robot milling.First,considering the error sources of robot machining,it guarantees the positioning accuracy of the robot in machining tasks via kinematic calibration and machining trajectory optimization,which will compensate the geometric errors and enhance the overall stiffness performance of the robot respectively.Then,it establishes a robotic machining-measurement-compensation closed-loop to improve the accuracy of the final finished part via directly measuring the machining errors.Finally,it improves the machining efficiency by adjusting the maximum milling forces between the robot and the part to be a constant allowable value of the machining system.The main contents and achievements of this dissertation are summarized as follows:(1)A method is presented to calibrate the POE parameters of the robot with optimal measurement poses.First,the robot forward kinematic model is established based on the POE formula.The model contains the robot base frame error and only needs the end effector positioning information.The analytical linear error model is derived accordingly by incorporating the enhanced partial pose measurement technique.Then,a combinatorial optimization model is established in terms of the D-Optimality theory to select an optimal set of measurement poses for calibration after the analysis of the effects of the measurement noise on the POE parameter identification process.An improved sequential forward floating search(ISFFS)algorithm is proposed to solve the model.Finally,the POE parameters are identified under the optimal measurement pose set,and the geometric errors are compensated via the inverse Jacobian iterative algorithm.Simulation examples and experiments show that the ISFFS algorithm has the advantage of obtaining an optimal solution closer to the global optimum compared with other algorithms in this area,and the absolute positioning accuracy of the robot can be improved significantly via the proposed calibration process.(2)A new approach is proposed to optimize the robot milling trajectory based on an overall stiffness performance index.First,the overall stiffness performance index is extracted from the Cartesian stiffness matrix of the robot after joint stiffness coefficients identification.The index has an explicit physical meaning and is independent of the machining process.Also,it is proved that it has the characteristic of frame invariance property.Then,in consideration of the joint limits,trajectory smoothness and motion dexterity constraints of the robot,a one-dimensional optimization model is established based on the machining redundant degree of freedom to improve the robot overall stiffness performance.The model is solved by a simple discrete search algorithm and an optimal robot pose corresponding to the specified NC cutter location can be obtained.Finally,the robot mition program for the milling task is converted according to these optimal poses.Simulation examples and experiments show that the trajectory optimization method proposed in this paper can effectively convert the NC cutter location data into robot milling trajectory,and it will enhance the robot overall stiffness performance in the process of offline programming.Thus this method can reduce the machining errors.(3)A systematic robotic machining-measurement-compensation closed-loop based error compensation method is developed.After a ruled surface or ruled-surface-like part is machined via robotic flank milling,this dissertation first measures the finished surface in situ by a laser tracker measuring system,and maps the sampled points into the part model reference coordinate system,then extracts the systematic components of the machining errors via a deterministic surface fitted based on the spatial statistical analysis technique.The compensation tool path is directly generated for the symmetry points of the only-systematic-error-contained measurement points about the design surface.The compensation machining is finally conducted accordingly.With the help of the semi-analytical point-to-surface distance functions and their differential properties,the problems of coordinate system alignment,surface fitting and compensation tool path generation are all formulated as distance function based nonlinear least square problems and solved by the sequential linear approximation algorithm in this dissertaion.Experiments show that the surface profile error is significantly reduced by the proposed error compensation method.(4)An adaptive control strategy integrated with optimal feedrates is proposed for milling force control.Inspired by the idea of digital twin and the fusion of process physical model and sensor feedback information,this dissertation first optimizes the feedrates according to the milling force model at several critical points,where the material removal amount increases abruptly,then designs an adaptive PI controller on the basis of the identification of robot feed-direction dynamics and milling process dynamics,and finally integrates the optimized feedrates into the online closed-loop controller to regulate the maximum milling force during the machining process.Experiments on the force control platform developed in this dissertation show that the proposed force control strategy can efficiently eliminate the large force overshoot caused by the low bandwidth characteristic of the robot feed servo system,thus can guarantee the maximal cutting forces to be around the expected value with small errors.