Monitoring of surface roughness evolution during abrasive flow machining by acoustic emission

Abrasive flow machining (AFM) is very effective and widely adopted superfinishing process of internal channel surfaces in industry. There have been high demands for monitoring surface roughness evolution during AFM, as the sensitive to AFM medium variables, such abrasive grain size concentration, well duration. Acoustic emission (AE) known be a promising tool detect microscale deformation mechanisms arising from abrasion. This work has shown that there close correlation, with different media, between material removal AE root mean square (RMS) fast Fourier transform (FFT) signals. Moreover, signals are correlated wear mechanisms, plowing cutting mechanisms.