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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, chen1470@e.ntu.edu.sg

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

Information

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

References

AbouelNour, Y. and Gupta, N. (2022), “In-situ monitoring of sub-surface and internal defects in additive manufacturing: A review”, Materials & Design, Vol. 222, p. 111063, https://dx.doi.org/10.1016/j.matdes.2022.111063.CrossRefGoogle Scholar
Bradski, G. (2000), “The OpenCV Library”, Dr. Dobb's Journal of Software Tools.Google Scholar
Chen, L., Yao, X., Chew, Y., Weng, F., Moon, S.K. and Bi, G. (2020), “Data-Driven Adaptive Control for Laser-Based Additive Manufacturing with Automatic Controller Tuning”, Applied Sciences, Multidisciplinary Digital Publishing Institute, Vol. 10 No. 22, p. 7967, https://dx.doi.org/10.3390/app10227967.Google Scholar
Chen, L., Yao, X. and Moon, S.K. (2022), “In-situ acoustic monitoring of direct energy deposition process with deep learning-assisted signal denoising”, Materials Today: Proceedings, https://dx.doi.org/10.1016/j.matpr.2022.09.008.Google Scholar
Chen, L., Yao, X., Ng, N.P.H. and Moon, S.K. (2022), “In-situ Melt Pool Monitoring of Laser Aided Additive Manufacturing using Infrared Thermal Imaging”, 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), presented at the 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 1478–1482, https://dx.doi.org/10.1109/IEEM55944.2022.9989715.CrossRefGoogle Scholar
Chen, L., Yao, X., Xu, P., Moon, S.K. and Bi, G. (2020a), “Surface Monitoring for Additive Manufacturing with in-situ Point Cloud Processing”, 2020 6th International Conference on Control, Automation and Robotics (ICCAR), pp. 196–201, https://dx.doi.org/10.1109/ICCAR49639.2020.9108092.CrossRefGoogle Scholar
Chen, L., Yao, X., Xu, P., Moon, S.K. and Bi, G. (2020b), “Rapid surface defect identification for additive manufacturing with in-situ point cloud processing and machine learning”, Virtual and Physical Prototyping, Taylor & Francis, Vol. 16 No. 1, pp. 50–67, https://dx.doi.org/10.1080/17452759.2020.1832695.CrossRefGoogle Scholar
Choong, Y.Y.C., Tan, H.W., Patel, D.C., Choong, W.T.N., Chen, C.-H., Low, H.Y., Tan, M.J., et al. (2020), “The global rise of 3D printing during the COVID-19 pandemic”, Nature Reviews Materials, Nature Publishing Group, Vol. 5 No. 9, pp. 637–639, https://dx.doi.org/10.1038/s41578-020-00234-3.CrossRefGoogle ScholarPubMed
Drissi-Daoudi, R., Pandiyan, V., Logé, R., Shevchik, S., Masinelli, G., Ghasemi-Tabasi, H., Parrilli, A., et al. (2022), “Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning”, Virtual and Physical Prototyping, Taylor & Francis, Vol. 0 No. 0, pp. 1–24, https://dx.doi.org/10.1080/17452759.2022.2028380.Google Scholar
Knaak, C., von Eßen, J., Kröger, M., Schulze, F., Abels, P. and Gillner, A. (2021), “A Spatio-Temporal Ensemble Deep Learning Architecture for Real-Time Defect Detection during Laser Welding on Low Power Embedded Computing Boards”, Sensors, Multidisciplinary Digital Publishing Institute, Vol. 21 No. 12, p. 4205, https://dx.doi.org/10.3390/s21124205.Google ScholarPubMed
Moon, S.K., Tan, Y.E., Hwang, J. and Yoon, Y.-J. (2014), “Application of 3D printing technology for designing light-weight unmanned aerial vehicle wing structures”, International Journal of Precision Engineering and Manufacturing-Green Technology, Vol. 1 No. 3, pp. 223–228, https://dx.doi.org/10.1007/s40684-014-0028-x.CrossRefGoogle Scholar
Muda, L., Begam, M. and Elamvazuthi, I. (2010), “Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques”, arXiv, 22 March, https://dx.doi.org/10.48550/arXiv.1003.4083.Google Scholar
Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., et al. (2009), “ROS: an open-source Robot Operating System”, ICRA Workshop on Open Source Software, Vol. 3, Kobe, Japan, p. 5.Google Scholar
Tan, C., Weng, F., Sui, S., Chew, Y. and Bi, G. (2021), “Progress and perspectives in laser additive manufacturing of key aeroengine materials”, International Journal of Machine Tools and Manufacture, Vol. 170, p. 103804, https://dx.doi.org/10.1016/j.ijmachtools.2021.103804.CrossRefGoogle Scholar
Xu, P., Yao, X., Chen, L., Zhao, C., Liu, K., Moon, S.K. and Bi, G. (2022), “In-process adaptive dimension correction strategy for laser aided additive manufacturing using laser line scanning”, Journal of Materials Processing Technology, Vol. 303, p. 117544, https://dx.doi.org/10.1016/j.jmatprotec.2022.117544.CrossRefGoogle Scholar
Yao, X., Moon, S.K. and Bi, G. (2015), “Additive manufacturing design feature selection for variable product platforms”, DS 80-4 Proceedings of the 20th International Conference on Engineering Design (ICED 15) Vol 4: Design for X, Design to X, Milan, Italy, 27-30.07.15, pp. 205–214.Google Scholar