Practical Approaches for Acoustic Emission Attenuation Modelling to Enable the Process Monitoring of CFRP Machining

Abstract

Acoustic emission-based monitoring of the milling process holds the potential to detect undesired damages of fibre-reinforced plastic workpieces, such as delamination or matrix cracking. In addition, abrasive tool wear, tool breakage, or coating failures can be detected. As measurements of the acoustic emission are impacted by attenuation, dispersion, and reflection as it propagates from source to sensor, the waveforms, amplitudes, and frequency content of a wave packet differ depending on the propagation length in the workpiece. Since the distance between acoustic emission sources and a stationary sensor attached to the workpiece changes continually in circumferential milling, the extraction of meaningful information from the raw measurement data is challenging and requires appropriate signal processing and frequency-dependent amplification. In this paper, practical and robust approaches, namely experimentally identified transfer functions and frequency gain parameter tables for attenuation modelling, which in reverse enable the reconstruction of frequency spectra emitted at the acoustic emission source, are presented and discussed. From the results, it is concluded that linear signal processing can largely compensate for the influence of attenuation, dispersion, and reflection on the frequency spectra and can therefore enable acoustic emission based process monitoring.

Damage Degree Assessment of Steel Products Based on the Statistical Processing of Acoustic Emission Signal Flow

“…These characteristics of AE signals reflect the energy and temporal parameters of the possible destruction [4]. As a result of the AE data processing, the identification, localization and damage degree assessment of propagating defects are carried out [5][6][7][8][9][10].…”