The paper proposes an SPC approach for automatic forecasting and monitoring of sensor signals in presence of cycle-based data (i.e. signal data which periodically repeat themselves) which can be observed in different machining processes, such as milling, forming or water-jet cutting. The monitoring system exploits an univariate time series analysis and monitoring technique based on an Exponentially Weighted Moving Average (EWMA) Control Chart for auto-correlated data coupled with the Holt-Winters exponential smoothing method, that is used to forecast future signal behaviour given its past history. The approach allows one to exploit only data coming from the on-going process and avoids the need for a-priori knowledge about signal pattern. Furthermore the control limits are automatically defined on-line by using only statistical moments of the currently monitored time-series. A case study is proposed to demonstrates the feasibility of monitoring the condition of the tool in milling processes by on-line analysis of cutting force signals.
On the Use of Statistical Process Control Approaches for Automated and Real-Time Monitoring of Machining Processes
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Colosimo, BM, Moroni, G, & Grasso, M. "On the Use of Statistical Process Control Approaches for Automated and Real-Time Monitoring of Machining Processes." Proceedings of the ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis. ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis, Volume 4. Istanbul, Turkey. July 12–14, 2010. pp. 741-750. ASME. https://doi.org/10.1115/ESDA2010-24923
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