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MULTISENSOR FUSION-BASED DIGITAL TWIN IN ADDITIVE MANUFACTURING FOR IN-SITU QUALITY MONITORING AND DEFECT CORRECTION

Published online by Cambridge University Press:  19 June 2023

Lequn Chen ,
Xiling Yao ,
Kui Liu ,
Chaolin Tan  and
Seung Ki Moon
Lequn Chen*
Affiliation:
Singapore Institute of Manufacturing Technology, A*STAR, Singapore; School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
Xiling Yao
Affiliation:
Singapore Institute of Manufacturing Technology, A*STAR, Singapore;
Kui Liu
Affiliation:
Singapore Institute of Manufacturing Technology, A*STAR, Singapore;
Chaolin Tan
Affiliation:
Singapore Institute of Manufacturing Technology, A*STAR, Singapore;
Seung Ki Moon
Affiliation:
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
*
Chen, Lequn, Nanyang Technological University, Singapore, [email protected]

Abstract

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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 ManufacturingIndustry 4.0Multisensor fusionDigital twinDecision making
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2755 - 2764
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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