Rapid surface defect identification for additive manufacturing with in-situ point cloud processing and machine learning

References

• Bartlett, Jamison L., Brendan P. Croom, Jeffrey Burdick, Daniel Henkel, and Xiaodong Li. 2018. “Revealing Mechanisms of Residual Stress Development in Additive Manufacturing via Digital Image Correlation.” Additive Manufacturing 22 (August): 1–12. doi:10.1016/j.addma.2018.04.025.
   Google Scholar
• Bartlett, Jamison L., and Xiaodong Li. 2019. “An Overview of Residual Stresses in Metal Powder Bed Fusion.” Additive Manufacturing 27 (May): 131–149. doi:10.1016/j.addma.2019.02.020.
   Google Scholar
• Bentley, Jon Louis. 1975. “Multidimensional Binary Search Trees Used for Associative Searching.” Communications of the ACM 18 (9): 509–517. doi:10.1145/361002.361007.
   Web of Science ®Google Scholar
• Bergstra, James, and Yoshua Bengio. 2012. “Random Search for Hyper-parameter Optimization.” Journal of Machine Learning Research 13 (10): 281–305.
   Google Scholar
• Bishop, Elizabeth G., and Simon James Leigh. 2020. “Using Large-Scale Additive Manufacturing as a Bridge Manufacturing Process in Response to Shortages in Personal Protective Equipment during the COVID-19 Outbreak.” International Journal of Bioprinting 6 (4). doi:10.18063/ijb.v6i4.281.
   PubMedGoogle Scholar
• Celik, H. Kursat, Ozkan Kose, Mihaela-Elena Ulmeanu, Allan E. W. Rennie, Thomas N. Abram, and Ibrahim Akinci. 2020. “Design and Additive Manufacturing of Medical Face Shield for Healthcare Workers Battling Coronavirus (COVID-19).” International Journal of Bioprinting 6 (4). doi:10.18063/ijb.v6i4.286.
   PubMed Web of Science ®Google Scholar
• Chen, Lequn, Xiling Yao, Peng Xu, Seung Ki Moon, and Guijun Bi. 2020. “Surface Monitoring for Additive Manufacturing with In-Situ Point Cloud Processing.” In 2020 6th International Conference on Control, Automation and Robotics (ICCAR), 196–201. doi:10.1109/ICCAR49639.2020.9108092.
   Google Scholar
• Choong, Yu Ying Clarrisa, Saeed Maleksaeedi, Hengky Eng, Suzhu Yu, Jun Wei, and Pei-Chen Su. 2020. “High Speed 4D Printing of Shape Memory Polymers with Nanosilica.” Applied Materials Today 18 (March). doi:10.1016/j.apmt.2019.100515.
   Google Scholar
• Choong, Yu Ying Clarrisa, Hong Wei Tan, Deven C. Patel, Wan Ting Natalie Choong, Chun-Hsien Chen, Hong Yee Low, Ming Jen Tan, Chandrakant D. Patel, and Chee Kai Chua. 2020. “The Global Rise of 3D Printing during the COVID-19 Pandemic.” Nature Reviews Materials 5 (9): 637–639. doi:10.1038/s41578-020-00234-3.
   PubMed Web of Science ®Google Scholar
• Chua, Zhong Yang, Il Hyuk Ahn, and Seung Ki Moon. 2017. “Process Monitoring and Inspection Systems in Metal Additive Manufacturing: Status and Applications.” International Journal of Precision Engineering and Manufacturing-Green Technology 4 (2): 235–245. doi:10.1007/s40684-017-0029-7.
   Web of Science ®Google Scholar
• Collins, P. C., D. A. Brice, P. Samimi, I. Ghamarian, and H. L. Fraser. 2016. “Microstructural Control of Additively Manufactured Metallic Materials.” Annual Review of Materials Research 46 (1): 63–91. doi:10.1146/annurev-matsci-070115-031816.
   Google Scholar
• Croom, Brendan P., Clifton Bumgardner, and Xiaodong Li. 2016. “Unveiling Residual Stresses in Air Plasma Spray Coatings by Digital Image Correlation.” Extreme Mechanics Letters 7 (June): 126–135. doi:10.1016/j.eml.2016.02.013.
   Google Scholar
• Ester, Martin, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. “A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.” In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 226–231. KDD’96. Portland, Oregon: AAAI Press.
   Google Scholar
• García-Díaz, Antón, Verónica Panadeiro, Baltasar Lodeiro, Jorge Rodríguez-Araújo, John Stavridis, Alexios Papacharalampopoulos, and Panagiotis Stavropoulos. 2018. “OpenLMD, an Open Source Middleware and Toolkit for Laser-Based Additive Manufacturing of Large Metal Parts.” Robotics and Computer-Integrated Manufacturing 53 (October): 153–161. doi:10.1016/j.rcim.2018.04.006.
   Google Scholar
• Grasso, Marco, and Bianca Maria Colosimo. 2017. “Process Defects and in Situ Monitoring Methods in Metal Powder Bed Fusion: A Review.” Measurement Science and Technology 28 (4), doi:10.1088/1361-6501/aa5c4f.
   Web of Science ®Google Scholar
• Hauke, Jan, and Tomasz Kossowski. 2011. “Comparison of Values of Pearson’s and Spearman’s Correlation Coefficients on the Same Sets of Data.” Quaestiones Geographicae 30 (2): 87–93. doi:https://doi.org/10.2478/v10117-011-0021-1.
   Google Scholar
• He, Haijun, Min Gao, Balázs Illés, and Kolos Molnar. 2020. “3D Printed and Electrospun, Transparent, Hierarchical Polylactic Acid Mask Nanoporous Filter.” International Journal of Bioprinting 6 (4). doi:10.18063/ijb.v6i4.278.
   PubMed Web of Science ®Google Scholar
• Holzmond, Oliver, and Xiaodong Li. 2017. “In Situ Real Time Defect Detection of 3D Printed Parts.” Additive Manufacturing 17 (October): 135–142. doi:10.1016/j.addma.2017.08.003.
   Google Scholar
• Jiang, Jingchao, and Yongsheng Ma. 2020. “Path Planning Strategies to Optimize Accuracy, Quality, Build Time and Material Use in Additive Manufacturing: A Review.” Micromachines 11 (7): 633. doi:10.3390/mi11070633.
   PubMed Web of Science ®Google Scholar
• Jiang, Jingchao, Fei Weng, Shiming Gao, Jonathan Stringer, Xun Xu, and Ping Guo. 2019. “A Support Interface Method for Easy Part Removal in Directed Energy Deposition.” Manufacturing Letters 20 (April): 30–33. doi:10.1016/j.mfglet.2019.04.002.
   Google Scholar
• Jiang, Jingchao, Chunling Yu, Xun Xu, Yongsheng Ma, and Jikai Liu. 2020. “Achieving Better Connections between Deposited Lines in Additive Manufacturing via Machine Learning.” Mathematical Biosciences and Engineering 17 (April): 3382–3394. doi:10.3934/mbe.2020191.
   PubMedGoogle Scholar
• Kam, Hyeong Ryeol, Sung-Ho Lee, Taejung Park, and Chang-Hun Kim. 2015. “RViz: A Toolkit for Real Domain Data Visualization.” Telecommunications Systems 60 (2): 337–345. doi:10.1007/s11235-015-0034-5.
   Web of Science ®Google Scholar
• Kuo, C. N., C. K. Chua, P. C. Peng, Y. W. Chen, S. L. Sing, S. Huang, and Y. L. Su. 2020. “Microstructure Evolution and Mechanical Property Response via 3D Printing Parameter Development of Al–Sc Alloy.” Virtual and Physical Prototyping 15 (1): 120–129. doi:10.1080/17452759.2019.1698967.
   Web of Science ®Google Scholar
• Lao, Wenxin, Mingyang Li, Teck Neng Wong, Ming Jen Tan, and Tegoeh Tjahjowidodo. 2020. “Improving Surface Finish Quality in Extrusion-Based 3D Concrete Printing Using Machine Learning-Based Extrudate Geometry Control.” Virtual and Physical Prototyping 15 (2): 178–193. doi:10.1080/17452759.2020.1713580.
   Web of Science ®Google Scholar
• Li, Zhongwei, Xingjian Liu, Shifeng Wen, Piyao He, Kai Zhong, Qingsong Wei, Yusheng Shi, and Sheng Liu. 2018. “In Situ 3D Monitoring of Geometric Signatures in the Powder-Bed-Fusion Additive Manufacturing Process via Vision Sensing Methods.” Sensors 18 (4): 1180. doi:10.3390/s18041180.
   PubMed Web of Science ®Google Scholar
• Lu, Q. Y., and C. H. Wong. 2018. “Additive Manufacturing Process Monitoring and Control by Non-Destructive Testing Techniques: Challenges and in-Process Monitoring.” Virtual and Physical Prototyping 13 (2): 39–48. doi:10.1080/17452759.2017.1351201.
   Web of Science ®Google Scholar
• Ng, Wei Long, Alvin Chan, Yew Soon Ong, and Chee Kai Chua. 2020. “Deep Learning for Fabrication and Maturation of 3D Bioprinted Tissues and Organs.” Virtual and Physical Prototyping 15 (3): 340–358. doi:10.1080/17452759.2020.1771741.
   Web of Science ®Google Scholar
• Ng, Wei Long, Chee Kai Chua, and Yu-Fang Shen. 2019. “Print Me An Organ! Why We Are Not There Yet.” Progress in Polymer Science 97 (October). doi:10.1016/j.progpolymsci.2019.101145.
   Google Scholar
• Okarma, Krzysztof, and Jarosław Fastowicz. 2020. “Computer Vision Methods for Non-Destructive Quality Assessment in Additive Manufacturing.” In Progress in Computer Recognition Systems, edited by Robert Burduk, Marek Kurzynski, and Michał Wozniak, 11–20. Advances in Intelligent Systems and Computing. Cham: Springer International Publishing. doi:10.1007/978-3-030-19738-4_2.
   Google Scholar
• Pedregosa, Fabian, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, et al. 2011. “Scikit-Learn: Machine Learning in Python.” Journal of Machine Learning Research 12 (October): 2825–2830.
   Google Scholar
• Quigley, Morgan, Ken Conley, Brian Gerkey, Josh Faust, Tully Foote, Jeremy Leibs, Rob Wheeler, and Andrew Y. Ng. 2009. “ROS: An Open-Source Robot Operating System.” In ICRA Workshop on Open Source Software, 3:5. Kobe.
   Google Scholar
• Rodríguez-Araújo, Jorge, and Juan J. Rodríguez-Andina. 2015. “ROS-Based 3D on-Line Monitoring of LMD Robotized Cells.” In 2015 IEEE 13th International Conference on Industrial Informatics (INDIN), 308–313. doi:10.1109/INDIN.2015.7281752.
   Google Scholar
• Rusu, Radu Bogdan, and Steve Cousins. 2011. “3D Is Here: Point Cloud Library (PCL).” In IEEE International Conference on Robotics and Automation (ICRA). Shanghai.
   Google Scholar
• Sabbaghi, Arman, Qiang Huang, and Tirthankar Dasgupta. 2018. “Bayesian Model Building From Small Samples of Disparate Data for Capturing In-Plane Deviation in Additive Manufacturing.” Technometrics 60 (4): 532–544. doi:10.1080/00401706.2017.1391715.
   Web of Science ®Google Scholar
• Shi, Tuo, Jianjun Shi, Zhixin Xia, Bingheng Lu, Shihong Shi, and Geyan Fu. 2020. “Precise Control of Variable-Height Laser Metal Deposition Using a Height Memory Strategy.” Journal of Manufacturing Processes 57 (September): 222–232. doi:10.1016/j.jmapro.2020.05.026.
   Google Scholar
• Singh, Archana, Avantika Yadav, and Ajay Rana. 2013. “K-Means with Three Different Distance Metrics.” International Journal of Computer Applications 67 (10): 13–17. doi:10.5120/11430-6785.
   Google Scholar
• Sohnius, Felix, Peter Schlegel, Max Ellerich, and Robert H. Schmitt. 2019. “Data-Driven Prediction of Surface Quality in Fused Deposition Modeling Using Machine Learning.” In Production at the Leading Edge of Technology, edited by Jens Peter Wulfsberg, Wolfgang Hintze, and Bernd-Arno Behrens, 473–481. Berlin: Springer. doi:10.1007/978-3-662-60417-5_47.
   Google Scholar
• Tan, Hong Wei, Jia An, Chee Kai Chua, and Tuan Tran. 2019. “Metallic Nanoparticle Inks for 3D Printing of Electronics.” Advanced Electronic Materials 5 (5). doi:10.1002/aelm.201800831.
   Web of Science ®Google Scholar
• Tan, Xipeng, Yihong Kok, Yu Jun Tan, Marion Descoins, Dominique Mangelinck, Shu Beng Tor, Kah Fai Leong, and Chee Kai Chua. 2015. “Graded Microstructure and Mechanical Properties of Additive Manufactured Ti–6Al–4 V via Electron Beam Melting.” Acta Materialia 97 (September): 1–16. doi:10.1016/j.actamat.2015.06.036.
   Google Scholar
• Tan, Hong Wei, Nitipon Saengchairat, Guo Liang Goh, Jia An, Chee Kai Chua, and Tuan Tran. 2020. “Induction Sintering of Silver Nanoparticle Inks on Polyimide Substrates.” Advanced Materials Technologies 5 (1). doi:10.1002/admt.201900897.
   Web of Science ®Google Scholar
• Tang, Shangyong, Guilan Wang, and Haiou Zhang. 2019. “In Situ 3D Monitoring and Control of Geometric Signatures in Wire and Arc Additive Manufacturing.” Surface Topography: Metrology and Properties 7 (2). doi:10.1088/2051-672X/ab1c98.
   Web of Science ®Google Scholar
• Tsai, R. Y., and R. K. Lenz. 1989. “A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/Eye Calibration.” IEEE Transactions on Robotics and Automation 5 (3): 345–358. doi:10.1109/70.34770.
   Google Scholar
• Wu, Dazhong, Yupeng Wei, and Janis Terpenny. 2018. “Surface Roughness Prediction in Additive Manufacturing Using Machine Learning.” In MSEC2018. Volume 3: Manufacturing Equipment and Systems. doi:10.1115/MSEC2018-6501.
   Google Scholar
• Yu, Chunling, and Jingchao Jiang. 2020. “A Perspective on Using Machine Learning in 3D Bioprinting.” International Journal of Bioprinting 6 (1). doi:10.18063/ijb.v6i1.253.
   Web of Science ®Google Scholar
• Yu, W. H., S. L. Sing, C. K. Chua, C. N. Kuo, and X. L. Tian. 2019. “Particle-Reinforced Metal Matrix Nanocomposites Fabricated by Selective Laser Melting: A State of the Art Review.” Progress in Materials Science 104 (July): 330–379. doi:10.1016/j.pmatsci.2019.04.006.
   Google Scholar
• Yu, Wenhui, Swee Leong Sing, Chee Kai Chua, and Xuelei Tian. 2019. “Influence of Re-Melting on Surface Roughness and Porosity of AlSi10Mg Parts Fabricated by Selective Laser Melting.” Journal of Alloys and Compounds 792 (July): 574–581. doi:10.1016/j.jallcom.2019.04.017.
   Google Scholar