The use of wavelet transform to evaluate the sensitivity of acoustic emission signals attributes to variation of cutting parameters in milling aluminum alloys

Abstract

Identifying the dominant acoustic emission (AE) signal attributes acquired under various experimental cutting conditions may provide significant insight to the process. Signal processing methods in time-frequency domain are more appropriate for such analysis due to their capabilities to cover the time and frequency transparency and transient phenomena. However, according to the literature, a lack of study was noticed on the sensitivity of AE signal attributes acquired by time-frequency domain analysis to various cutting conditions in the machining processes. Since milling is among the most widely used machining operations, this investigation aims to acquire adequate knowledge about interactions between cutting parameters and their direct and indirect effects on the obtained AE signal attributes from the milling process. To that end, this study investigates wavelet transform (WT) analysis, one of the most famous analyses in the time-frequency domain. WT signal processing was conducted with five models of mother wavelets, and appropriate decomposition numbers were deployed. The detail and approximate signal attributes obtained from each decomposition were assessed. According to WT analysis and statistical calculations, cutting speed, feed rate, and coating material significantly impacted the variation of AE signal attributes. Also, the most sensitive AE signal attributes and decompositions were rms, std, entropy and energy, and 2nd and 6th decompositions, respectively. The outcome of this research can be integrated into artificial intelligence (AI) methods to implement online monitoring and predictive system. Consequently, it may lead to better process control and optimization.