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

J. Gokulachandran and K. Mohandas

International Journal of Productivity and Quality Management, 2012, vol. 9, issue 4, 502-518

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. (search for similar items in EconPapers)
Date: 2012
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=47195 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijpqma:v:9:y:2012:i:4:p:502-518

Access Statistics for this article

More articles in International Journal of Productivity and Quality Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().