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Chen, X, Sufian, M and Yu, DL (2017) Investigating the Capability of Precision in Robotic Grinding. In: Proceedings of the 23rd International conference on Automation & Computing . (23rd International Conference on Automation & Computing, 7-8th September, 2017, Huddersfield, UK).
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Abstract
Most robotic grinding focus on the surface finish rather than accuracy and precision. However ever increased demand on complex component manufacture requires to advance robot grinding capability so that more practical and competitive accurate systems can be developed. The current study focuses on improving the level of accuracy of robotic grinding, which is a significant challenge in robot application because the kinematic accuracy of robot movement is much more complex than normal CNC machine tools. Aiming to improve accuracy and efficiency the work considers all quality of measures including surface roughness and the accuracy of size and form. For that to be done, a repeatability test is firstly preformed to observe the distributions of the joint positions and how well the robot responds to its programmed position using a dial gauge method and a circuit trigger method. After that, a datum setting method is performed to assess the datum alignment with the robot. Hence, a mathematical model based on regression analyses applies towards the collected data to observe closely any error correlation when setting up a datum to perform the grinding procedure.
| Item Type: | Conference or Workshop Item (Paper) |
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| Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Subjects: | T Technology > TJ Mechanical engineering and machinery |
| Divisions: | Engineering |
| Publisher: | IEEE |
| Date Deposited: | 20 Sep 2017 08:45 |
| Last Modified: | 16 May 2024 15:34 |
| DOI or ID number: | 10.23919/IConAC.2017.8081984 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/7128 |
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