Multisensor fusion-based digital twin in additive manufacturing for in-situ quality monitoring and defect correction
Year: 2023
Editor: Kevin Otto, Boris Eisenbart, Claudia Eckert, Benoit Eynard, Dieter Krause, Josef Oehmen, Nad
Author: Chen, Lequn (1,2); Yao, Xiling (1); Liu, Kui (1); Tan, Chaolin (1); Moon, Seung Ki (2)
Series: ICED
Institution: 1: Singapore Institute of Manufacturing Technology, A*STAR, Singapore;
2: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
Section: Design Methods
Page(s): 2755-2764
DOI number: https://doi.org/10.1017/pds.2023.276
ISBN: -
ISSN: -
Abstract
Early detection and correction of defects are critical in additive manufacturing (AM) to avoid build failures. In this paper, we present a multisensor fusion-based digital twin for in-situ quality monitoring and defect correction in a robotic laser-directed energy deposition process. Multisensor fusion sources consist of an acoustic sensor, an infrared thermal camera, a coaxial vision camera, and a laser line scanner. The key novelty and contribution of this work are to develop a spatiotemporal data fusion method that synchronizes and registers the multisensor features within the part’s 3D volume. The fused dataset can be used to predict location-specific quality using machine learning. On-the-fly identification of regions requiring material addition or removal is feasible. Robot toolpath and auto-tuned process parameters are generated for defect correction. In contrast to traditional single-sensor-based monitoring, multisensor fusion allows for a more in-depth understanding of underlying process physics, such as pore formation and laser-material interactions. The proposed methods pave the way for self-adaptation AM with higher efficiency, less waste, and cleaner production.
Keywords: Additive Manufacturing, Industry 4.0, Multisensor fusion, Digital twin, Decision making