Acoustic emission monitoring in high-speed micro end-milling based on SVD-EEMD method

Abstract

In monitoring high-speed micro-milling, acoustic emission is used to explore the relationship between the machining parameters and the acoustic emission signal under different processing parameters. The acquired acoustic emission signal is denoised by singular value decomposition based on Hankel matrix, and the characteristic values of the denoising signal is calculated by ensemble empirical mode decomposition and the Hilbert-Huang transform. Results show that the characteristic values of the acoustic emission signal can represent the change in machining parameters, such as the spindle speed, and the acoustic emission signal is suitable for monitoring the micro-milling process.