Acoustic condition monitoring of milling tools using the Rayleigh-Ritz autoencoder

Abstract The condition of the milling tool and its cutting edges is crucial for the surface quality of high-performance components. In this paper, we propose a non-intrusive approach to condition monitoring by analyzing the acoustic emissions generated during the milling process, enabling tool condition monitoring without interfering with the milling operation. The acoustic data is recorded using a MEMS microphone and analyzed employing a hybrid machine learning framework. In the first step, the raw acoustic data is transformed to a phase space representation, where the time-series data of each lane is mapped to a rotational angle. Subsequently, the Rayleigh-Ritz autoencoder is applied to the phase space data. To incorporate process-specific knowledge, constraints are defined using trigonometric functions. This approach has demonstrated its effectiveness in detecting progressive tool wear using only acoustic data providing a reliable and non-invasive solution for tool condition monitoring.