Xie, Y., Chen, L., Wang, Z., Wu, X., Chen, P., Wei Wen, D. L., Chew, Y., & Senthil Kumar, A. (2026). Characterisation and dripping anomaly detection in co-axial wire laser-directed energy deposition using acoustic emission. Virtual and Physical Prototyping, 21(1). https://doi.org/10.1080/17452759.2026.2653361
Abstract:
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.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This work is supported by the Agency for Science, Technology, and Research (A*STAR), and the National University of Singapore (NUS). It is also supported by the A*STAR Graduate Scholarship (AGS) (Awardee: Yuxuan Xie) funded by A*STAR.
Description:
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent