Detection and quantitative evaluation of surface defects in wire and arc additive manufacturing based on 3D point cloud

References

• Chadha U, Abrol A, Vora NP, et al. Performance evaluation of 3D printing technologies: a review, recent advances, current challenges, and future directions. Progr Addit Manufact. 2022;7(5):853–886. doi:10.1007/s40964-021-00257-4
   Google Scholar
• Mehnen J, Ding J, Lockett H, et al. Design study for wire and arc additive manufacture. Intern J Prod Develop. 2014;19(1-3):2–20. doi:10.1504/IJPD.2014.060028
   Google Scholar
• Plocher J, Panesar A. Review on design and structural optimisation in additive manufacturing: Towards next-generation lightweight structures. Mater Des. 2019;183:108164), doi:10.1016/j.matdes.2019.108164
   Web of Science ®Google Scholar
• Taşdemir A, Nohut S. An overview of wire arc additive manufacturing (WAAM) in shipbuilding industry. Ships Offsh Struct. 2021;16(7):797–814. doi:10.1080/17445302.2020.1786232
   Web of Science ®Google Scholar
• Singh S, kumar Sharma S, Rathod DW. A review on process planning strategies and challenges of WAAM. Mater Today Proc. 2021;47:6564–6575. doi:10.1016/j.matpr.2021.02.632
   Google Scholar
• Mu H, He F, Yuan L, et al. Toward a smart wire arc additive manufacturing system: A review on current developments and a framework of digital twin. J Manufact Syst. 2023;67:174–189. doi:10.1016/j.jmsy.2023.01.012
   Web of Science ®Google Scholar
• Zhu X, Jiang F, Guo C, et al. Surface morphology inspection for directed energy deposition using small dataset with transfer learning. J Manuf Process. 2023;93:101–115. doi:10.1016/j.jmapro.2023.03.016
   Web of Science ®Google Scholar
• He F, Yuan L, Mu H, et al. Research and application of artificial intelligence techniques for wire arc additive manufacturing: a state-of-the-art review. Robot Comput Integr Manuf. 2023;82:102525), doi:10.1016/j.rcim.2023.102525
   Web of Science ®Google Scholar
• Rout A, Deepak B, Biswal BB. Advances in weld seam tracking techniques for robotic welding: A review. Robot Comput Integr Manufact. 2019;56:12–37. doi:10.1016/j.rcim.2018.08.003
   Web of Science ®Google Scholar
• Qin J, Hu F, Liu Y, et al. Research and application of machine learning for additive manufacturing. Addit Manufact. 2022: 102691), doi:10.1016/j.addma.2022.102691
   Google Scholar
• Shaloo M, Schnall M, Klein T, et al. A review of Non-destructive testing (NDT) techniques for defect detection: application to fusion welding and future wire Arc additive manufacturing processes. Materials (Basel). 2022;15(10):3697), doi:10.3390/ma15103697
   PubMed Web of Science ®Google Scholar
• Vavilov VP, Pawar SS. A novel approach for one-sided thermal nondestructive testing of composites by using infrared thermography. Polym Test. 2015;44:224–233. doi:10.1016/j.polymertesting.2015.04.013
   Web of Science ®Google Scholar
• Chen X, Zhang H, Hu J, et al. A passive on-line defect detection method for wire and arc additive manufacturing based on infrared thermography. International Solid Freeform Fabrication Symposium 2019. doi:10.26153/tsw/17375.
   Google Scholar
• Lopez A, Bacelar R, Pires I, et al. Non-destructive testing application of radiography and ultrasound for wire and arc additive manufacturing. Addit Manufact. 2018;21:298–306. doi:10.1016/j.addma.2018.03.020
   Web of Science ®Google Scholar
• Xiong J, Zhang Y, Pi Y. Control of deposition height in WAAM using visual inspection of previous and current layers. J Intell Manuf. 2021;32:2209–2217. doi:10.1007/s10845-020-01634-6
   Web of Science ®Google Scholar
• Xiong J, Pi Y, Chen H. Deposition height detection and feature point extraction in robotic GTA-based additive manufacturing using passive vision sensing. Robot Comput Integr Manuf. 2019;59:326–334. doi:10.1016/j.rcim.2019.05.006
   Web of Science ®Google Scholar
• Cho HW, Shin SJ, Seo GJ, et al. Real-time anomaly detection using convolutional neural network in wire arc additive manufacturing: molybdenum material. J Mater Process Technol. 2022;302:117495), doi:10.1016/j.jmatprotec.2022.117495
   Web of Science ®Google Scholar
• Wu J, Huang C, Li Z, et al. An in situ surface defect detection method based on improved you only look once algorithm for wire and arc additive manufacturing. Rapid Prototyp J. 2023;29(5):910–920. doi:10.1108/RPJ-06-2022-0211
   Web of Science ®Google Scholar
• Li W, Zhang H, Wang G, et al. Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing. Robot Comput Integr Manuf. 2023;80:102470), doi:10.1016/j.rcim.2022.102470
   Web of Science ®Google Scholar
• Tang S, Wang G, Zhang H. In situ 3D monitoring and control of geometric signatures in wire and arc additive manufacturing. Surf Topogr Metrol Propert. 2019;7(2):025013), doi:10.1088/2051-672X/ab1c98
   Web of Science ®Google Scholar
• Huang C, Wang G, Song H, et al. Rapid surface defects detection in wire and arc additive manufacturing based on laser profilometer. Measurement ( Mahwah N J). 2022;189:110503), doi:10.1016/j.measurement.2021.110503
   Google Scholar
• Chen X, Fu Y, Kong F, et al. An in-process multi-feature data fusion nondestructive testing approach for wire arc additive manufacturing. Rapid Prototyp J. 2021;28(3). doi:10.1108/RPJ-02-2021-0034
   Web of Science ®Google Scholar
• Chaekyo L, Gijeong S, Bong DK, et al. Development of defect detection AI model for wire + arc additive manufacturing using high dynamic range images. Appl Sci. 2022;28(3):573–584. doi:10.1108/RPJ-02-2021-0034
   Google Scholar
• Lyu J, Manoochehri S. Online convolutional neural network-based anomaly detection and quality control for fused filament fabrication process. Virtual Phys Prototyp. 2021;16(2):160–177. doi:10.1080/17452759.2021.1905858
   Web of Science ®Google Scholar
• Zhang Y, Yuan L, Liang W, et al. 3D-SWiM: 3D vision based seam width measurement for industrial composite fiber layup in-situ inspection. Robot Comput Integr Manuf. 2023;82:102546), doi:10.1016/j.rcim.2023.102546
   Web of Science ®Google Scholar
• Samie Tootooni M, Dsouza A, Donovan R, et al. Classifying the dimensional variation in additive manufactured parts from laser-scanned three-dimensional point cloud data using machine learning approaches. J Manufact Sci Eng. 2017;139(9):091005), doi:10.1115/1.4036641
   Web of Science ®Google Scholar
• Lin W, Shen H, Fu J, et al. Online quality monitoring in material extrusion additive manufacturing processes based on laser scanning technology. Precision Eng. 2019;60:76–84. doi:10.1016/j.precisioneng.2019.06.004
   Web of Science ®Google Scholar
• Chen L, Yao X, Xu P, et al. Rapid surface defect identification for additive manufacturing with in-situ point cloud processing and machine learning. Virtual Phys Prototyp. 2021;16(1):50–67. doi:10.1080/17452759.2020.1832695
   Web of Science ®Google Scholar
• Wu B, Pan Z, Ding D, et al. A review of the wire arc additive manufacturing of metals: properties, defects and quality improvement. J Manuf Process. 2018;35:127–139. doi:10.1016/j.jmapro.2018.08.001
   Web of Science ®Google Scholar
• Yuan L, Pan Z, Ding D, et al. Investigation of humping phenomenon for the multi-directional robotic wire and arc additive manufacturing. Robot Comput Integr Manuf. 2020;63:101916), doi:10.1016/j.rcim.2019.101916
   Web of Science ®Google Scholar
• Rusu RB, Cousins S. 3d is here: Point cloud library (PCL). IEEE international conference on robotics and automation. IEEE International Conference on Robotics and Automation. 2011: 1–4. doi:10.1109/ICRA.2011.5980567.
   Google Scholar
• Pauly M, Gross M, Kobbelt LP. Efficient simplification of point-sampled surfaces. Visualization (Los Alamitos Calif). 2002: 163–170. doi:10.1109/VISUAL.2002.1183771
   Google Scholar
• Zhang TY, Suen CY. A fast parallel algorithm for thinning digital patterns. Commun ACM. 1984;27(3):236–239. doi:10.1145/357994.358023
   Web of Science ®Google Scholar