Prediction of remaining useful life of cutting tools: a comparative study using soft computing methods

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 will reduce the overall cost, energy and protects environment. High productivity cutting tools used in manufacturing industry are generally expensive. As such, the accurate assessment of remaining useful life (for reuse) of any given tool is of great significance in any manufacturing industry. The main objective of this research is to develop a comprehensive methodology to assess the reuse potential of carbide-tipped tools. This paper presents the use of three soft computing methods, namely, artificial neural network, neuro fuzzy logic and support vector regression methods for the assessment of remaining useful life (RUL) of cutting tools. In this work, experiments are conducted based on Taguchi approach and tool life values are obtained. Tool life values are predicted using the aforesaid three soft computing methods and RUL obtained from these values are compared. It is found that the predictive neuro fuzzy method is capable of giving a better prediction of remaining useful tool life than the other methods.