Cutting tool temperature monitoring in circular sawing: measurement and multi-sensor feature fusion-based prediction

Abstract

In this study, contact thermocouple temperature measurements were used in the wood circular sawing process, and the saw temperature was monitored at two locations—one close to the cutting edge and the other close to the blade center. The effects of depth of cut, rotation speed, and feed speed on the tool temperature were analyzed and discussed. Also, due to the complexity of tool temperature measurement during the machining process, the study proposed a sensor fusion-based approach for indirect monitoring of the tool temperature. Accordingly, the cutting process was monitored with power, sound, vibration, and acoustic emission (AE) sensors, and the extracted sensory features were used to predict the saw temperature by training random forest models. The results showed that the cutting parameters significantly impact the tool temperature, and the rotation speed has the most complex and nonlinear effect on the blade temperature. The role of rotation speed on the tool temperature is governed by both the kinematics of the machining process and chip size along with the dynamic behavior of the saw blade and the proximity of the rotation speed to the tool critical and flutter speeds. Among the four investigated sensors, AE showed better performance for tool temperature monitoring and, combined with the vibration signals, yielded the best monitoring performance. The developed sensory models can handle the challenges associated with tool temperature measurement/prediction and, at the same time, can be used for monitoring other aspects of machining processes, including surface quality.