ARIMA-Based Time Series Model of Cutting Temperature in Facing Process

Abstract

During machining processes, cutting temperature directly affects cutting performances, such as surface quality, dimensional precision, tool life, etc. Thus, evaluation of cutting temperature rise in the tool–chip interface by reliable techniques can lead to improved cutting performances. In this paper, we present the modeling of cutting temperature during facing process by using time series approach. The experimental data were collected by conducting facing experiments on a Computer Numerical Control lathe and by measuring the cutting temperature by an infrared camera. The collected data were used to test several Autoregressive Integrated Moving Average (ARIMA) models by using Box–Jenkins time series procedure. Then, the adequate model was selected according to four performance criteria: Akaike criterion, Schwarz Bayesian criterion, maximum likelihood, and standard error. The selected model corresponded to the ARIMA (1, 1, 1) and it was tested by conducting a new facing operation under the same cutting conditions (spindle speed, feed rate, depth of cut, and nose radius). It was clearly seen that there is a good agreement between experimental and simulated temperatures, which reveals that this approach simulates the evolution of cutting temperature in facing process with high accuracy (average percentage error [Formula: see text] 0.57%).