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Parameter Prediction Using Machine Learning in Robot-Assisted Finishing Process

Bobby K Pappachan 1 and Tegoeh Tjahjowidodo 2
1. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
2. School of Mechanical and Aerospace Engineering

Abstract— In finishing processes equipped with real-time pro- cess monitoring, analyzing real-time data acquired is vital to ensure product quality and safety compliance. The quality and dimensions of a finished product is often times dictated by the process parameter set initially. However, changes in parameter occurs whenever an unexpected event such as an equipment failure or voltage fluctuations occurs. This could result in a finished product with a below par quality and subsequently delays in production due to rework or machine downtime. With an indirect monitoring method to continually monitor these parameters such as spindle speed, these occurrences can be minimized. Here lies in the benefit of an integrated parameter prediction model, which is able to detect deviation from normal operation early, hence enabling the capability of delivering actionable insights in a real-time basis to shop-floor engineers. This paper presents a parameter prediction method tested successfully on data acquired from a robot-assisted deburring process.

Index Terms—finishing, gradient descent, back propagation, actionable insights

Cite: Bobby K Pappachan and Tegoeh Tjahjowidodo, "Parameter Prediction Using Machine Learning in Robot-Assisted Finishing Process" International Journal of Mechanical Engineering and Robotics Research, Vol. 9, No. 3, pp. 435-440, March 2020. DOI: 10.18178/ijmerr.9.3.435-440

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.