Femtosecond Laser micromachining (FLμM) is an effective method for fabricating micromechanical components, moulds, and medical devices with high precision and negligible thermal effects. However, FLμM remains challenging due to a large number of process variables and complex ablation dynamics. To meet the demand for high-quality products and low process cycle time in flexible production environments, in-process sensing of FLμM is desired. However, an automated in-situ quality diagnosis for FLμM remains challenging due to the need for high sensitivity to critical defects and adaptability to process changes. A multi-sensor approach using optical and acoustical systems is a promising strategy for monitoring the ablation regimes and evaluating the microscale structures. This paper describes the development of a multi-sensor system for monitoring FLμM using a structure-borne acoustic emission (AE) sensor together with an off-axis optical emission (OE) sensor. This system acquires sensor signals at a high sampling rate and employs an appropriate statistical data analysis methodology. Additionally, this system proposes a traditional machine vision-based focus detection, surface inspection before and after the process, and beam monitoring with complementary metal–oxide–semiconductor (CMOS) sensors for high precision and robustness of FLμM. The results highlight a significant correlation between the monitoring signals, process parameters, and the machined groove morphology. This work underlines the feasibility of AE- and OE-based sensors for online monitoring of laser-material removal during FLμM. The proposed monitoring technique has the potential to facilitate process control for laser micromachining, which will ultimately result in increased productivity and product quality.

sponsorship: This work was partially funded by the KU Leuven C3 IOF project fs-SPR (C3/20/084) and the Research Foundation-Flanders (FWO-Vlaanderen) Medium-Scale Research Infrastructure project Femto Fac (I001120N). (KU Leuven C3 IOF project|C3/20/084, Research Foundation-Flanders (FWO-Vlaanderen) Medium-Scale Research Infrastructure project Femto Fac|I001120N)