Output Only Functional Series Time Dependent AutoRegressive Moving Average (FS-TARMA) Modelling of Tool Acceleration Signals for Wear Estimation

In this paper, tool vibration signals obtained from a turning process are used for tool wear estimation purposes. During the cutting process, tool acceleration signals are recorded for different levels of wear. Due to non-stationarity of tool/holder system's response, Time dependent time series model of Functional Series Time dependent AutoRegressive Moving Average (FS-TARMA) type is used for modelling the signals and extraction of wear sensitive features that will be exploited in a wear estimation algorithm. Results of the analysis through FS-TARMA, reveals its higher accuracy with respect to stationary type models, since it captures time dependent properties as well, which can be used in an online tool wear estimation algorithm