Identification and Evaluation of Tool Tip Contact and Cutting State Using AE Sensing in Ultra-Precision Micro Lathes

The growing demand for miniature mechanical components has increased the importance of ultra-precision micro machine tools and real-time monitoring. This study examines acoustic emission (AE) sensing for the intelligent control of an ultra-precision micro lathe. AE signals were measured while brass and aluminum alloys were turned with cermet and diamond tools at different spindle speeds and cutting depths. Finite element simulations were performed to clarify the AE generation mechanisms. The AE waveform amplitude changed stepwise corresponding to tool–workpiece contact, elastoplastic deformation, and chip formation, enabling precise contact detection at the 0.1 μm level. The AE amplitude increased with increasing spindle speed and increasing depth of cut except during abnormal conditions (e.g., workpiece adhesion). Frequency analysis revealed a dominant peak near 0.2 MHz during normal cutting, as well as high-frequency (>1 MHz) components linked to built-up edge formation. Simulations confirmed that these AE features reflect variations in the strain rate in the shear zone and on the rake face. They also confirmed that cutting force spectra under high friction reproduce the experimentally observed high-frequency peaks. These findings demonstrate the feasibility of using AE sensing to identify the cutting state and support the development of self-optimizing micro machine tools.