Cutting temperature measurement in turning using fiber-optic multi-spectral radiation thermometry and its application in tool wear status recognition. (July 2022) - Han, Jinghui; Liu, Zhiyong; Cao, Kaiwei; Xu, Long; Shi, Tielin; Liao, Guanglan
Highlights: A near-infrared fiber-optic spectrometer system for in-situ online cutting temperature measurement is proposed. Spectral analysis method and artificial neural network are introduced for spectral-temperature mapping. Online measurement of cutting temperatures in dry/wet cuttings of constant and varied regimes are realized. Tool wear status recognition is realized based on cutting temperature by sparse autoencoder and k -means clustering. The capability of the system in heavy-duty cutting is proved. Abstract: The cutting temperature is essential for phenomena understanding and quality improvement in metal cutting while its in-situ online measurement is still a challenge. This paper presents a near-infrared fiber-optic multi-spectral method for in-situ online cutting temperature measurement. Using thermal radiation spectrum for temperature measurement, the method optimizes the lower limit of temperature measurement to 150 °C while improving accuracy. The calibration shows that in the range of above 250 °C, the average relative error of temperature measurement is stable below 0.5%. The titanium alloy cutting experiments are carried out. In-situ online measurement of tool temperatures in dry/wet cuttings are realized using the self-developed system. The influence of cutting parameters on cutting temperature is studied, and the real-time response of the temperature measurement system to the cutting state is verified. As for industrial application, the capability of the system in heavy-duty turning is proved by railway wheelsets turning experiments. Tool wear experiments are conducted, and a positive correlation between the cutting temperature and tool wear is revealed. Tool wear status recognition is realized based on cutting temperature by sparse autoencoder and k -means clustering, and a recognition accuracy of 97.3% is achieved. These results indicate promising prospects in cutting mechanism research, machining status monitoring and industrial applications, empowering the advancement of intelligent manufacturing and industry 4.0.