Intelligent tool setting for vibration cutting process using machine vision and hearing

Vibration cutting has emerged as a promising method for creating surface functional microstructures. However, achieving precise tool setting is a time-consuming process that significantly impacts process efficiency. This study proposes an intelligent approach for tool setting in vibration cutting using machine vision and hearing, divided into two steps. In the first step, machine vision is employed to achieve rough precision in tool setting within tens of micrometers. Subsequently, in the second step, machine hearing utilizes sound pickup to capture vibration audio signals, enabling fine tool adjustment within 1 μm precision. The relationship between the spectral intensity of vibration audio and cutting depth is analyzed to establish criteria for tool–workpiece contact. Finally, the efficacy of this approach is validated on an ultra-precision platform, demonstrating that the automated tool-setting process takes no more than 74 s. The total cost of the vision and hearing sensors is less than $1500.