Applicationof Computer Visionfor Polishing RobotinAutomotive Manufacturing Industries
Polishing is a highly skilled manufacturing process with a lot of constraints and interaction with environment. In general, the purpose of polishing is to get the uniform surface roughness distributed evenly throughout partâ€™s surface. In order to reduce the polishing time and cope with the shortage of skilled workers, robotic polishing technology has been investigated. This paper studies about vision system to measure surface defects that have been characterized to some level of surface roughness. The surface defects data have learned using artificial neural networks to give a decision in order to move the actuator of arm robot. Force and rotation time have chosen as output parameters of artificial neural networks. Results shows that although there is a considerable change in both parameter values acquired from vision data compared to real data, it is still possible to obtain surface defects characterization using vision sensor to a certain limit of accuracy. The overall results of this research would encourage further developments in this area to achieve robust computer vision based surface measurement systems for industrial robotic, especially in polishing process.
Keywords: polishing robot, vision sensor, surface defects, and artificial neural networks
Liao, L., Xi, F., Liu, K.: Modeling and control of automated polishingâ€deburring process using a dualâ€purpose compliant toolhead, International Journal of Machine Tools & Manufacture Vol.48, pp. 1454â€“1463 (2008).
Tam, H., Lui, O.C., Mok, A.C.K.: Robotic polishing of free form surfaces using scanning paths, Journal of Materials Processing Technology Vol. 95, pp.191â€200 (1999).
Zhang, H., Chen, H., Xi, N., Zhang, G., He, J., Onâ€Line Path Generation for Robotic Deburring of Cast Aluminum Wheels, Proceedings of the 2006 IEEE/RS International Conference on Intelligent Robots and Systems, pp.2400â€2405 (2006).
Li, D. Zhang, L., Zhao, J., Yang, X., and Ji, S., Research on Polishing Path Planning and Simulation of Small Mobile Robot, Proceedings of the 2009 IEEE International Conference on Mechatronics and Automation, pp.4941â€4945 (2009).
Nagata, F., Kusumoto, Y., Fujimoto, Y., Watanabe, K.: Robotic sanding system for new designed furniture with freeâ€formed surface, Robotics and Computerâ€Integrated Manufacturing Vol. 23, pp. 371â€379 (2007).
Zhao, Ji., Zhan, J., Jin, R. Tao, M.: An oblique ultrasonic polishing method by robot for free form surfaces. International Journal of Machine Tools & Manufacture Vol. 40, pp. 795â€808 (2000).
Yang, Z., Xi, F., Wu, B.: A shape adaptive motion control system with application to robotic polishing, Robotics and Computerâ€Integrated Manufacturing Vol.21, pp. 355â€367 (2005).
Li, L. Wang, N., Cai.: Machineâ€visionâ€based surface finish inspection for cutting tool replacement in production, International Journal of Production Research Vol. 42â€11, pp. 2279â€2287 (2004).
Lee, B.Y., Yu, S.F., Juan, H.: The model of surface roughness inspection. Mechatronics Vol.14, pp. 129â€141 (2004).
Kuo, R.J.: A robotic die polishing system through fuzzy neural networks, Computers in Industry 32, pp. 273â€280 (1997).
Tongâ€ying, G., Daoâ€kui, Q., Zaiâ€li, D.: Research of Path Planning for Polishing Robot Based on Improved Genetic Algoritm, Proceedings of the 2004 IEEE International Conference on Robotics and Biomimetics, pp.334â€338 (2004).
Yang, Z., Chen, F., Zhao, J., Wu, X.: A Novel Vision Localization Method of Automated Microâ€Polishing Robot, Journal of Bionic Engineering 6, pp.46â€54 (2009).
Lin, H.D., Computerâ€aided visual inspection of surface defects in ceramic capacitor chips, Journal of Materials Processing Technology 189, pp. 19â€25 (2007).
Egan, W.J., Angel, M., Morgan, S.L., Rapid Optimization and Minimal Complexity in Computational Neural Network Multivariate Calibration of Chlorinated Hydrocarbons using Raman Spectroscopy, Journal of Chemometrics Vol.15, pp. 29â€48 (2001).
Nagata, F., Watanahe, K., Kusumoto, K., Yasuda, K., Tsukamoto, O., Tsudai, K., Omoto, M., Hagas, Z., and Hase, T.: Generation of Normalized Tool Vector from 3â€Axis CL Data and Its Application to a Mold Polishing Robot, Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3971â€3976 (2004).
The copyright to this article is transferred to Politeknik Elektronika Negeri Surabaya(PENS) if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to PENS. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment. The copyright transfer form can be downloaded here .
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.
Plagiarism screening will be conducted by EMITTER Journal Editorial Board using iThenticate Plagiarism Checker and CrossCheck plagiarism screening service. Author should download and signing declaration of plagiarism form here and resubmit it with copyright transfer form via online submission.