Surface sliding revealed by operando monitoring of high-pressure torsion by acoustic emission

Abstract

High-pressure torsion (HPT) is widely used as a key method for microstructure control through deformation processing across a broad range of materials. However, certain gaps in process control impact its efficacy. In this study, we investigate the acoustic emission (AE) signals generated during HPT by considering commercially pure molybdenum as an example. By employing the adaptive sequential k-means algorithm, we analyse the AE stream to categorise and identify its sources. By comparing the kinetics of AE signal evolution during HPT processing at pressures of 2 GPa and 5 GPa, two distinct signal types are identified: one linked to plastic deformation and the other to workpiece slippage over HPT anvil surfaces. This research demonstrates the potential of AE tools for operando monitoring of HPT stability and detection of workpiece slippage, thereby enhancing the processing efficiency.