Robotic edge profiling of complex components

Jayaweera, Nirosh; Webb, Philip

doi:10.1108/01439911111097832

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…Numerous control laws and strategies have been proposed in order to compensate robot, process, and workpiece errors. The control methods can be categorized in two major approaches: impedance control and hybrid position/force control [128]. Examples of control applications are, for instance: the robot path generation method using hybrid force and visual servoing for reducing programming time [129]; tool-path modification based on direct teaching over the workpiece with a force control in normal/tangential direction [130]; the control strategy of mimicking human behavior during manual deburring [131].…”

Section: Deburringmentioning

confidence: 99%

Robots in machining

et al. 2019

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Robotic machining centers offer diverse advantages: large operation reach with large reorientation capability, and a low cost, to name a few. Many challenges have slowed down the adoption or sometimes inhibited the use of robots for machining tasks. This paper deals with the current usage and status of robots in machining, as well as the necessary modelling and identification for enabling optimization, process planning and process control. Recent research addressing deburring, milling, incremental forming, polishing or thin wall machining is presented. We discuss various processes in which robots need to deal with significant process forces while fulfilling their machining task.

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Section: Deburringmentioning

confidence: 99%

Robots in machining

et al. 2019

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“…Traditionally, surface finishing operations have been conducted predominantly through skilled workers, which results in inconsistencies in the finishing quality of the product, is inefficient, and poses health risks to the workers [1][2][3][4]. Industrial robots (6 degrees of freedom) have the ability to significantly improve and automate the surface finishing process [5], considering their flexibility, mechanical reconfigurability, and reliability as compared to other approaches [6][7][8][9]. Despite the high utilization of manipulator robots in industrial applications, such as palletizing and depalletizing, their usage is still very low in complex machining operations, such as surface finishing, due to challenges such as complex programming, poor accuracy, and insufficient rigidity [10].…”

Section: Introductionmentioning

confidence: 99%

Robot Pose Estimation and Normal Trajectory Generation on Curved Surface Using an Enhanced Non-Contact Approach

Shah

Lin

Tran

et al. 2023

Sensors

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The use of robots for machining operations has become very popular in the last few decades. However, the challenge of the robotic-based machining process, such as surface finishing on curved surfaces, still persists. Prior studies (non-contact- and contact-based) have their own limitations, such as fixture error and surface friction. To cope with these challenges, this study proposes an advanced technique for path correction and normal trajectory generation while tracking a curved workpiece’s surface. Initially, a key-point selection approach is used to estimate a reference workpiece’s coordinates using a depth measuring tool. This approach overcomes the fixture errors and enables the robot to track the desired path, i.e., where the surface normal trajectory is needed. Subsequently, this study employs an attached RGB-D camera on the end-effector of the robot for determining the depth and angle between the robot and the contact surface, which nullifies surface friction issues. The point cloud information of the contact surface is employed by the pose correction algorithm to guarantee the robot’s perpendicularity and constant contact with the surface. The efficiency of the proposed technique is analyzed by carrying out several experimental trials using a 6 DOF robot manipulator. The results reveal a better normal trajectory generation than previous state-of-the-art research, with an average angle and depth error of 1.8 degrees and 0.4 mm.

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“…Hong (2001) compensated the tool path points of the robot for fixture/placement error of the workpiece during robotic welding operations [4]. Jayaweera and Webb (2011) generated real-time robot tool path for deburring operations based on the unknown irregularities present on complex components [5]. The literature shows that the problems caused by the robot inaccuracies and the workpiece fixture error in industrial robotic applications have been addressed and solved.…”

Section: Introductionmentioning

confidence: 99%

A Framework of <i>In Situ</i> Model Error Compensation for Adaptive Robotic Task Execution

Pradeep

Zhou

Dharmawan

et al. 2015

AMM

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In precision robotic applications, inaccuracy in workpiece geometry has been a common problem to the precise processing of the workpiece. Due to manufacturing defects and workpiece deformation, the actual geometry of the workpiece deviates from its nominal 3D CAD model which is defined as model error. For many of the existing industrial robotic applications today, the robot path for processing the workpiece is planned based on the nominal 3D CAD model of the workpiece. Hence, the model error of the workpiece leads to error in the robot path planning eventually inducing inaccurate processing. To enhance the accuracy of the robot in processing the workpiece, a framework for in-situ model error compensation has been proposed. Prior to the processing of each workpiece, the proposed technique employs 3D optical laser scanning technology to capture the actual 3D model of the workpiece and compares it with the nominal model to establish the model errors. The nominal path of the robot initially created based on the nominal CAD model is then modified according to the model error. Thus, this step performs the in-situ model error compensation making the robotic task adaptive to the actual workpiece geometry. Experiments have been conducted to verify the proposed framework and an accuracy of up to 30 micrometers has been achieved in model error identification and compensation. The proposed technique can be employed in applications such as high precision robotic tasks, where accuracy of task execution is an important factor.

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Robotic edge profiling of complex components

Cited by 10 publications

References 23 publications

Robots in machining

Robots in machining

Robot Pose Estimation and Normal Trajectory Generation on Curved Surface Using an Enhanced Non-Contact Approach

A Framework of <i>In Situ</i> Model Error Compensation for Adaptive Robotic Task Execution

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