Characterisation and dripping anomaly detection in co-axial wire laser-directed energy deposition using acoustic emission

Co-axial Wire Laser–Directed Energy Deposition (DED-LB/w) is an emerging metal additive manufacturing (AM) technique that combines near-zero material waste with high deposition efficiency. However, the co-axial wire-feeding configuration is prone to wire-tip dripping, which destabilises the melt pool and degrades build quality. Without timely in-situ intervention, dripping can lead to costly repairs, build interruptions and even tool damage, underscoring the critical need for robust process monitoring. Unlike oX-axis vision-based monitoring systems, which are constrained by line-of-sight and sensitive to geometric variation, acoustic sensing is flexible, low-cost and largely geometry-independent. In this study, acoustic emission (AE) signatures were systematically investigated in co-axial DED-LB/w of nano-treated Aluminium 7075 under three representative process regimes: Lack of Fusion, Conduction and Overheating. The findings reveal that the acoustic signal encodes both dripping-specific and process- and regime-specific features, which were then used to train supervised machine learning models for dripping detection and regime classification. The novelty of this work is an acoustic-emission-based, machine-learning-assisted monitoring framework for co-axial DED-LB/w, enabling in-process identification of dripping and the process regime with high accuracy. By providing geometry-independent sensing and real-time decision support, the framework enhances process stability and reduces unplanned interruptions, supporting wider industrial adoption of co-axial DED-LB/w technology.