Time series forecasting in turning processes using ARIMA model

Abstract

A prediction model which is able to predict the tool life and the cutting edge replacement is tackled. The study is based on the spindle load during a turning process in order to optimize productivity and the cost of the turning processes. The methodology proposed to address the problem encompasses several steps. The main ones include filtering the signal, modeling of the normal behavior and forecasting. The forecasting approach is carried out by an Autoregressive Integrated Moving Average (ARIMA) model. Results are compared with a robust ARIMA model and show that the previous preprocessing steps are necessary to obtain greater accuracy in predicting future values of this specific process.