Data-driven prognostics of remaining useful life for milling machine cutting tools

Abstract

Remaining useful life (RUL) prediction is one of the most important concepts in prognostics and health management (PHM). In this study, the RUL of milling machine cutting tools is predicted through the methodology of autoregressive integrated moving average (ARIMA). This methodology is a data-driven model that has advantages of simple implementation and low cost. Results show that the cutting tool has an RUL of 35 min according to the prediction. The RUL indicated approximately 25% extra tool usage. To increase competitiveness in many industries, PHM technology offers a path toward smart manufacturing and upgrading to industry 4.0.