Research On Robotic Measurement And Manufacturing For Thin-Walled Workpieces Welding Processing

In the aerospace and defense industries,due to special needs such as weight reduction and aerodynamic performance,a large number of lightweight and thin-walled structures are used for design.In some applications,due to the large size of the workpiece,the overall thin-walled workpiece is usually welded by friction stir welding of multiple workpieces.A large amount of processing is required before and after welding of thin-walled workpieces,including the chamfering of the edge before welding and the removal of the welded seam after welding.However,such thin-walled workpieces usually have the characteristics of no clear positioning reference,large deformation during clamping and processing,which brings many difficulties to automatic processing,and manual processing is usually performed under existing conditions.Aiming at the needs of automation transformation for the manufacture of important parts in the aerospace and defense industries,and in order to break through the technical difficulties caused by the thin-wall characteristics of workpieces,this thesis focuses on the research of robotic automated measurement and processing methods for thin-wall workpieces before and after welding.A robot-line laser sensor hand-eye calibration method and evaluation index based on3 D point cloud reconstruction are proposed.The relationship between the absolute and relative errors of the 3D point cloud reconstruction and the robot hand-eye matrix error is analyzed,based on the matching error between the reconstructed point cloud and the standard point cloud and particle swarm-Gaussian process optimization solution method,this thesis proposes the hand-eye matrix rotation part calibration method.A calibration method of hand-eye matrix translation component based on multi-pose measurement is proposed.The correctness of the method is verified by simulation and experiment,and compared with the traditional methods.A robot measuring method based on 3D camera is proposed for the edge object before welding.For the problem of easy extraction of thin-wall interference point cloud,a rough edge extraction method based on a priori anchor point and a point cloud edge extraction and evaluation method based on spherical coordinate edge evaluation operator are proposed.For the problem of low-quality point cloud far away from the center of the field of view,based on the evaluation results of edge extraction,a method for robot shooting pose optimization and point cloud fusion based on edge quality analysis is proposed.The accuracy of the method is verified through measurement and analysis of thin-walled workpieces and comparative experiments.A robot-line laser sensor measurement method is proposed for the welded seam object after welding with more obvious local characteristics.Aiming at the problems of clamping deformation of thin-walled workpieces and irregular shapes of friction stir welded seam,an initialization method based on initial registration transformation and voxelization segmentation is proposed.Based on the initialization results,a method for estimating welded seam point cloud probability is proposed.The method for extracting welded seam point cloud is based on voxel-level and point cloudlevel Graph-Cut to achieve welded seam point cloud extraction.The accuracy of the method is verified through the measurement and analysis of the welded seam workpiece and the comparative analysis experiment.A thin-walled workpiece robot processing method based on intelligent path correction is proposed.The intelligent path correction includes intelligent fitting compensation and the fusion generation of the corrected path.It can obtain a reasonable processing pose by force-controlled fitting with the workpiece in the unprocessed state,avoiding the difficulty of force control compensation in online processing.This method is applied to edge chamfering before welding and welded seam removal after welding.For the edge chamfering before welding,based on the scraper chamfering processing tool,a robot multi-selected point intelligent fitting compensation method and a corrected path fusion generation method are proposed.For the welded seam grinding,a continuous intelligent fitting compensation and path fusion generation method based on abrasive belt grinding tools is proposed.In order to achieve the desired contact force tracking of the thin-walled workpiece in the variable position and variable stiffness environment in the continuous intelligent fitting,the defects of the traditional adaptive impedance method are analyzed,and a contact force control method based on super-spiral adaptive sliding mode impedance control is proposed,and the effectiveness of the method is verified by simulation and comparison experiments.Experimental researches on thin-walled workpieces chamfering before welding and welded seam grinding after welding are conducted.Set up a robot chamfering processing system for pre-weld chamfering,perform chamfering tool performance test and singlepoint intelligent fitting compensation experiment,conduct multi-selected point intelligent fitting compensation and path fusion experiment,and compare the the corrected path and measurement path.The corrected path is used to conduct robot chamfering experiments,and the results of chamfering are analyzed.Set up a robotic belt grinding system for welded seam grinding and removal,and conduct impedance control performance test for the compensation part of the belt grinding tool.In order to verify the proposed contact force control method,the manually loaded interfering environment and thin-walled workpieces are used for constant force experiment.Carry out two types of welded seam intelligent fitting compensation experiments,based on the intelligent fitting compensation results,welded seam point cloud and other information,the fusion calculation of the corrected grinding path is carried out,and use the fusion path to carry out the welded seam grinding experiment to verify the effectiveness of the method.