Multi-class semantic segmentation for identification of silicate island defects

References

• Dilthey U, Stein L. Multimaterial car body design: challenge for welding and joining. Sci Technol Weld Joining. 2006;11(2):135–142.
   Web of Science ®Google Scholar
• Lancaster JF. The physics of welding. London: Elsevier Science; 1984.
   Google Scholar
• Lancaster JF. The physics of fusion welding part 1: the electric arc in welding. IEE Proc B Electr Power Appl. 1987;134(5):233–254.
   Google Scholar
• Grong O. Metallurgical modelling of welding. Inst. Mater. 1 Carlt. London (UK): House Terrace; 1997.
   Google Scholar
• Kanjilal P, Pal TK, Majumdar SK. Prediction of element transfer in submerged arc welding. Weld J. 2007;86:135–143.
   Web of Science ®Google Scholar
• Kim JH, Frost R, Olson D, et al. Effect of electrochemical reactions on submerged arc weld metal compositions. Weld J. 1990;5:4181–4186.
   Google Scholar
• Lee Y, Jang JH, Liu S. Effect of chemical composition of welding consumable on slag formation and corrosion resistance. Weld World. 2021;65(3):373–380.
   Web of Science ®Google Scholar
• Liu S. Pyrometallurgical studies of molten metal droplets for the characterization of gas metal arc welding. ASM proceedings of the international conference: trends in welding research. 2012.
   Google Scholar
• Valavanis J, Kosmopoulos D. Multiclass defect detection and classification in weld radiographic images using geometric and texture features. Expert Syst Appl. 2010;37(12):7606–7614.
   Web of Science ®Google Scholar
• Juan L, Li C, Wu X, et al. An effective method of weld defect detection and classification based on machine vision. IEEE Trans Ind Informat. 2019;15:6322–6333.
   Web of Science ®Google Scholar
• Carrasco M, Mery D. Segmentation of welding defects using a robust algorithm. Mater Eval. 2004;62:1142–1147.
   Google Scholar
• Wang X, Wong B. Radiographic image segmentation for weld inspection using a robust algorithm. Res Nondestr Eval. 2005;16(3):131–142.
   Web of Science ®Google Scholar
• Wang Z, Zhang C, Pan Z, et al. Image segmentation approaches for weld pool monitoring during robotic arc welding. Applied Sciences. 2018;8(12):2445.
   Google Scholar
• Hamami L, Nacereddine N, Tridi M, et al. Statistical tools for weld defect evaluation in radiographic testing. J softw eng appl. 2006;6(5):251–258.
   Google Scholar
• Aoki K, Suga Y. Detecting shape of weld defect image in x-ray film by image processing applied genetic algorithm. JSME Int J Ser C. 2002;45(2):534–542.
   Google Scholar
• Zhu Y, Yang R, He Y, et al. A lightweight multiscale attention semantic segmentation algorithm for detecting laser welding defects on safety vent of power battery. IEEE Access. 2021;9:39245–39254.
   Web of Science ®Google Scholar
• Ajmi C, Zapata J, Elferchichi S, et al. Deep learning technology for weld defects classification based on transfer learning and activation features. Adv Mater Sci Eng. 2020;2020:1–16.
   Web of Science ®Google Scholar
• Knaak C, Kolter G, Schulze F, et al. Deep learning-based semantic segmentation for in-process monitoring in laser welding applications. Proc SPIE. 2019;2:35–41.
   Google Scholar
• Wang Q, Jiao W, Yu R, et al. Detecting dynamic development of weld pool using machine learning from innovative composite images for adaptive welding. J Manuf Processes. 2020;56:908–915.
   Web of Science ®Google Scholar
• Yang Y, Pan L, Ma J, et al. A high-performance deep learning algorithm for the automated optical inspection of laser welding. Appl Sci. 2020;10(3):933.
   Google Scholar
• Kasuya T, Kim H, Inoue J. Unsupervised microstructure segmentation by mimicking metallurgists’ approach to pattern recognition. Sci Rep. 2020;1486:17835.
   Google Scholar
• Ferguson M, Ak R, Lee Y, et al. Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning. Smart Sustain Manuf Syst. 2018;2:10.
   Google Scholar
• Lei Y, Fan J, Liu Y, et al. Automatic detection and location of weld beads with deep convolutional neural networks. IEEE Trans Instrum Meas. 2020;70:1–12.
   PubMed Web of Science ®Google Scholar
• Yu R, Kershaw J, Wang P, et al. Real-time recognition of arc weld pool using image segmentation network. J Manuf Processes. 2021;72:159–167.
   Web of Science ®Google Scholar
• Haffner O, Kučera E, Drahoš P, et al. Using entropy for welds segmentation and evaluation. Entropy. 2019;21(12):1168.
   Web of Science ®Google Scholar
• Gu Z, Chen J, Wu CS. Three-dimensional reconstruction of welding pool surface by binocular vision. Chin J Mech Eng. 2020;34:47.
   Google Scholar
• Kah P, Gyasi EA, Handroos H. Survey on artificial intelligence (AI) applied in welding: a future scenario of the influence of AI on technological, economic, educational and social changes. Procedia Manuf. 2019;38:702–714.
   Google Scholar
• Scime L, Siddel D, Baird S, et al. Layer-wise anomaly detection and classification for powder bed additive manufacturing processes: a machine-agnostic algorithm for real-time pixel-wise semantic segmentation. Addit Manuf. 2020;36:101453.
   Web of Science ®Google Scholar
• Dong S, Sun X, Xie S, et al. Automatic defect identification technology of digital image of pipeline weld. Nat Gas Ind B. 2019;6(4):399–403.
   Google Scholar
• Yang Y, Yang R, Pan L, et al. A lightweight deep learning algorithm for inspection of laser welding defects on safety vent of power battery. Comput Ind. 2020;123:103306.
   Web of Science ®Google Scholar
• Oh S, Kim H, Nam K, et al. Deep-learning approach for predicting laserbeam absorptance in full-penetration laser keyhole welding. Opt Express. 2021;29(13):20010–20021.
   PubMed Web of Science ®Google Scholar
• Minaee S, Boykov Y, Porikli F, et al. Image segmentation using deep learning: a survey. IEEE Trans Pattern Anal Mach Intell. 2020;44(7):3523–3542.
   Google Scholar
• Perez IIG. Welding droplet segmentation using deep learning [thesis]. https://repositorio.uchile.cl., 2021.
   Google Scholar
• Fei X, Sheng F, Xiang D. Research on image segmentation of inner cylinder wall with annular weld based on deep learning. J Phys Conf Ser. 2020;1486:072084.
   Google Scholar
• Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. LNCS. 2015;9351:234–241.
   Google Scholar
• Diakogiannis F, Waldner F, Caccetta P, et al. Resunet-a: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS J Photogramm Remote Sens. 2020;162:94–114.
   Web of Science ®Google Scholar