Multisensor fusion-based digital twin for localized quality prediction in robotic laser-directed energy deposition

• Main Authors: Chen, Lequn, Bi, Guijun, Yao, Xiling, Tan, Chaolin, Su, Jinlong, Ng, Nicholas Poh Huat, Chew, Youxiang, Liu, Kui, Moon, Seung Ki
• 其他作者: School of Mechanical and Aerospace Engineering
• 格式: Article
• 語言: English
• 出版: 2023
• 主題: Engineering::Mechanical engineering; Additive Manufacturing; Multisensor Fusion
• 在線閱讀: https://hdl.handle.net/10356/172582
• 機構: Nanyang Technological University

Early detection of defects, such as keyhole pores and cracks is crucial in laser-directed energy deposition (L-DED) additive manufacturing (AM) to prevent build failures. However, the complex melt pool behaviour cannot be adequately captured by conventional single-modal process monitoring approaches...