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• 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. 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. [ABSTRACT FROM AUTHOR]

Measurement of the temperature at the tool-chip interface is still a significant challenge especially for machining difficult-to-machine material like titanium alloys. In this paper, a versatile PCBN tool, starting with the existing tool shape, was devised by embedding thin-film thermocouples (TFTCs) into tool interior to monitor the temperature at the tool-chip interface while machining titanium alloy bar. A simplified process was developed to fabricate TFTC array with a degree of spatial resolution (100 μm). A diffusion bonding technique was realized between PCBN plates to achieve reliable sensor protection with shorter sintering time and lower sintering temperature while metallic interlayer like titanium film was adopted. The sensors exhibit an excellent linear response with a sensitivity of about 13 μV/℃. Dry cutting including interrupted and continuous cutting was implemented with the fabricated PCBN insert. Results show that TFTCs embedded into PCBN insert not only have a good transient perception by comparing with commercial sensors but also can stably monitor cutting temperature as close as 300 μm to tool-chip interface under different cutting conditions. The devised PCBN tool presented in this paper will also be helpful for the improvement of machining processes and better understanding of the tool-chip contact phenomena. [ABSTRACT FROM AUTHOR]