Title: Predicting remaining useful life of cutting tools with regression and ANN analysis

Authors: J. Gokulachandran; K. Mohandas

Addresses: Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641105, Tamilnadu, India ' Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641105, Tamilnadu, India

Abstract: In manufacturing industry, cutting tools are often discarded when much of their potential life still remains. Predicting the remaining useful life of the partially degraded components and putting them to use will help to save natural resources to a great extent. This saves manufacturing cost and protects environment. The main objective of this research is to develop a comprehensive methodology to assess the reuse potential of carbide-tipped tools. In this work, based on Taguchi approach, experiments are conducted and tool life values are obtained. The analysis is carried out in two stages. In the first stage, a regression model is proposed for the prediction of remaining life of carbide-tipped tools. In the second stage, an artificial neural network model is developed for predicting tool life. The results of both models are compared.

Keywords: RUL; remaining useful life; reuse potential; manufacturing industry; degraded components; natural resources; manufacturing costs; statistical methods; Genichi Taguchi; orthogonal array; cutting tools; predictions; carbide-tipped tools; tool life values; regression models; ANN; artificial neural networks; partial degradation; environmental protection; productivity management; quality management.

DOI: 10.1504/IJPQM.2012.047195

International Journal of Productivity and Quality Management, 2012 Vol.9 No.4, pp.502 - 518

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 05 Jun 2012 *

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