Exploring material removal dynamics during femtosecond laser micromachining by in-situ acoustic emission monitoring with physics-based and data-driven analysis

Femtosecond laser micromachining (FLµM) has emerged as a transformative technology for precision microfabrication, offering minimal thermal damage and exceptional resolution across diverse materials, including metals, semiconductors, polymers, and ceramics. Despite its advantages, FLµM faces challenges in maintaining consistent quality and efficiency, especially in industrial applications. This study investigates the integration of acoustic emission (AE) monitoring with FLµM for in-situ quality assessment and process optimization. AE signals, captured at high frequencies (up to 1.5 MHz), are analysed using physics-based methods such as RMS, MARSE, and STFT, as well as machine learning (ML)-based approaches. The results reveal strong correlations between AE signal characteristics and laser parameters such as pulse energy, scanning speed and focal position. They also show the possibility to detect critical material removal regimes, including ablation and melting. Feature importance analysis using ML techniques can identify process-specific frequency bands—350, 469, 547, and 664 kHz—which are particularly relevant for detecting process instabilities and ensuring quality control. By combining AE monitoring with advanced signal analysis, this approach demonstrates a scalable, non-invasive solution for improving the precision and reliability of FLµM, with potential applicability across a wide range of materials and microfabrication processes.