Comparative study of two soft computing techniques for the prediction of remaining useful life of cutting tools

Reuse of partially worn-out materials and parts is a philosophy now being applied in all manufacturing industries to achieve the goal of green manufacturing. 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. This exercise will in turn reduce the overall cost and help achieve enhanced productivity. This paper reports the use of two soft computing techniques, namely, neuro fuzzy logic technique and support vector regression technique 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 two soft computing techniques and RUL obtained from these values are compared.