Real-Time Geometry Assessment Using Laser Line Scanner During Laser Powder Directed Energy Deposition Additive Manufacturing of SS316L Component with Sharp Feature Liu Yang The Hong Kong University of Science and Technology, Hong Kong Boyu Wang The Hong Kong University of Science and Technology, Hong Kong Jack C. P. Cheng University of Hong KongThe Hong Kong University of Science and Technology, Hong Kong Peipei Liu Southeast University, China Hoon Sohn Korea Advanced Institute of Science Technology, Korea, Republic of This is a section of CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality (DOI: 10.36253/979-12-215-0289-3) by Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi Firenze University Press Florence 2023 https://doi.org/10.36253/10.36253/979-12-215-0289-3.97

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Directed energy deposition (DED) is a major metal additive manufacturing (AM) technology that is increasingly used in many industries due to its ability to manufacture complex components of arbitrary shapes and sizes. However, a lack of timely geometry assessment and the consequent geometry control hinders the development of DED towards zero defect manufacturing. In this study, a real-time geometry assessment methodology is developed for laser pow-der directed energy deposition (LP-DED). A geometry assessment system is developed using a laser line scanner capable of inspecting the melt pool area, the just solidified area, as well as layer-wise inspection. An image processing method with an encoder-decoder based profile completion network was developed to obtain accurate track profile in images from real-time inspection. Experiments have been conducted to validate the proposed methodology by depositing multi-layer X-shape objects

 Additive Manufacturing Directed energy deposition Real-time geometry assessment Laser line scanning

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References J. Xiong, G. Zhang, Adaptive control of deposited height in GMAW-based layer additive manufacturing, Journal of Materials Processing Technology. 214 (2014) 962–968. 10.1016/j.jmatprotec.2013.11.014 L. Tang, R.G. Landers, Melt Pool Temperature Control for Laser Metal Deposition Processes—Part II: Layer-to-Layer Temperature Control, Journal of Manufacturing Science and Engineering. 132 (2010) 011011. 10.1115/1.4000883 S.K. Everton, M. Hirsch, P. Stravroulakis, R.K. Leach, A.T. Clare, Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing, Materials & Design. 95 (2016) 431–445. 10.1016/j.matdes.2016.01.099 H.-W. Hsu, Y.-L. Lo, M.-H. Lee, Vision-based inspection system for cladding height measurement in Direct Energy Deposition (DED), Additive Manufacturing. 27 (2019) 372–378. 10.1016/j.addma.2019.03.017 N. Decker, Y. Wang, Q. Huang, Efficiently registering scan point clouds of 3D printed parts for shape accuracy assessment and modeling, Journal of Manufacturing Systems. 56 (2020) 587–597. 10.1016/j.jmsy.2020.04.001 C. Xia, Z. Pan, J. Polden, H. Li, Y. Xu, S. Chen, Y. Zhang, A review on wire arc additive manufacturing: Monitoring, control and a framework of automated system, Journal of Manufacturing Systems. 57 (2020) 31–45. 10.1016/j.jmsy.2020.08.008 Micro-Epsilon Messtechnik, Laser line triangulation | Micro-Epsilon, Micro-Epsilon Messtechnik. (n.d.). https://www.micro-epsilon.com (accessed December 20, 2022). L. Yang, K. Hsu, B. Baughman, D. Godfrey, F. Medina, M. Menon, S. Wiener, Additive Manufacturing of Metals: The Technology, Materials, Design and Production, Springer International Publishing, Cham, 2017. 10.1007/978-3-319-55128-9 I. Jeon, L. Yang, K. Ryu, H. Sohn, Online melt pool depth estimation during directed energy deposition using coaxial infrared camera, laser line scanner, and artificial neural network, Additive Manufacturing. 47 (2021) 102295. 10.1016/j.addma.2021.102295 D. Tyralla, H. Köhler, T. Seefeld, C. Thomy, R. Narita, A multi-parameter control of track geometry and melt pool size for laser metal deposition, Procedia CIRP. 94 (2020) 430–435. 10.1016/j.procir.2020.09.159 B.T. Gibson, Y.K. Bandari, B.S. Richardson, W.C. Henry, E.J. Vetland, T.W. Sundermann, L.J. Love, Melt pool size control through multiple closed-loop modalities in laser-wire directed energy deposition of Ti-6Al-4V, Additive Manufacturing. 32 (2020) 100993. 10.1016/j.addma.2019.100993 Y. Lu, G. Sun, X. Xiao, J. Mazumder, Online Stress Measurement During Laser-aided Metallic Additive Manufacturing, Sci Rep. 9 (2019) 7630. 10.1038/s41598-019-39849-0 S. Kim, I. Jeon, H. Sohn, Infrared thermographic imaging based real-time layer height estimation during directed energy deposition, Optics and Lasers in Engineering. 168 (2023) 107661. 10.1016/j.optlaseng.2023.107661 R. Sampson, R. Lancaster, M. Sutcliffe, D. Carswell, C. Hauser, J. Barras, The influence of key process parameters on melt pool geometry in direct energy deposition additive manufacturing systems, Optics & Laser Technology. 134 (2021) 106609. 10.1016/j.optlastec.2020.106609 M. Borish, B.K. Post, A. Roschli, P.C. Chesser, L.J. Love, K.T. Gaul, Defect Identification and Mitigation Via Visual Inspection in Large-Scale Additive Manufacturing, JOM. 71 (2019) 893–899. 10.1007/s11837-018-3220-6 M. Faes, F. Vogeler, K. Coppens, H. Valkenaers, W. Abbeloos, T. Goedeme, E. Ferraris, Process Monitoring of Extrusion Based 3D Printing via Laser Scanning, (2014). 10.13140/2.1.5175.0081 W. Lin, H. Shen, J. Fu, S. Wu, Online quality monitoring in material extrusion additive manufacturing processes based on laser scanning technology, Precision Engineering. 60 (2019) 76–84. 10.1016/j.precisioneng.2019.06.004 A. Vandone, S. Baraldo, A. Valente, Multisensor Data Fusion for Additive Manufacturing Process Control, IEEE Robot. Autom. Lett. 3 (2018) 3279–3284. 10.1109/LRA.2018.2851792 A. Heralić, A.-K. Christiansson, B. Lennartson, Height control of laser metal-wire deposition based on iterative learning control and 3D scanning, Optics and Lasers in Engineering. 50 (2012) 1230–1241. 10.1016/j.optlaseng.2012.03.016 R.C. Gonzalez, R.E. Woods, Digital image processing, Pearson, New York, NY, 2018. D. Deng, DBSCAN Clustering Algorithm Based on Density, in: 2020 7th International Forum on Electrical Engineering and Automation (IFEEA), 2020: pp. 949–953. 10.1109/IFEEA51475.2020.00199 P.G. Guest, P.G. Guest, Numerical Methods of Curve Fitting, Cambridge University Press, 2012. L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation, (2018). http://arxiv.org/abs/1802.02611 (accessed October 28, 2022). W. Yuan, T. Khot, D. Held, C. Mertz, M. Hebert, PCN: Point Completion Network, in: 2018 International Conference on 3D Vision (3DV), IEEE, Verona, 2018: pp. 728–737. 10.1109/3DV.2018.00088 X. Yu, Y. Rao, Z. Wang, Z. Liu, J. Lu, J. Zhou, PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers, in: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, Montreal, QC, Canada, 2021: pp. 12478–12487. 10.1109/ICCV48922.2021.01227 K. He, X. Zhang, S. Ren, J. Sun, Deep Residual Learning for Image Recognition, in: 2016: pp. 770–778. https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html (accessed December 5, 2022). L.-C. Chen, G. Papandreou, F. Schroff, H. Adam, Rethinking Atrous Convolution for Semantic Image Segmentation, (2017). 10.48550/arXiv.1706.05587 J. Santolaria, J.J. Pastor, F.J. Brosed, J.J. Aguilar, A one-step intrinsic and extrinsic calibration method for laser line scanner operation in coordinate measuring machines, Meas. Sci. Technol. 20 (2009) 045107. 10.1088/0957-0233/20/4/045107