Evaluating the sensitivity of acoustic emission signal features to the variation of cutting parameters in milling aluminum alloys: Part A: frequency domain analysis

Abstract

It is known that adequate knowledge of the sensitivity of acoustic emission signal parameters to various experimental parameters is indispensable. According to the review of the literature, a lack of knowledge was noticeable concerning the behavior of acoustic emission parameters under a broad range of machining parameters. This becomes more visible in milling operations that include sophisticated chip formation morphology and significant interaction effects and directional pressures and forces. To remedy the aforementioned lack of knowledge, the effect of the variation of cutting parameters on the time and frequency features of acoustic emission signals, extracted and computed from the milling operation, needs to be investigated in a wide aspect. The objective of this study is to investigate the effects of cutting parameters including the feed rate, cutting speed, depth of cut, material properties, as well as cutting tool coating/insert nose radius on computed acoustic emission signals featured in the frequency domain. Similar studies on time-domain signal features were already conducted. To conduct appropriate signal processing and feature extraction, a signal segmentation and processing approach is proposed based on dividing the recorded acoustic emission signals into three sections with specific signal durations associated with cutting tool movement within the work part. To define the sensitive acoustic emission parameters to the variation of cutting parameters, advanced signal processing and statistical approaches were used. Despite the time features of acoustic emission signals, frequency domain acoustic emission parameters seem to be insensitive to the variation of cutting parameters. Moreover, cutting factors governing the effectiveness of acoustic emission signal parameters are hinted. Among these, the cutting speed and feed rate seem to have the most noticeable effects on the variation of time–frequency domain acoustic emission signal information, respectively. The outcomes of this work, along with recently completed works in the time domain, can be integrated into advanced classification and artificial intelligence approaches for numerous applications, including real-time machining process monitoring.