Page 33 - Read Online
P. 33
Page 26 of 28 Zhang et al. J Mater Inf 2024;4:34 https://dx.doi.org/10.20517/jmi.2024.64
network. J Manuf Syst 2024;72:93-103. DOI
17. Cheng Y, Yu R, Zhou Q, Chen H, Yuan W, Zhang Y. Real-time sensing of gas metal arc welding process - a literature review and
analysis. J Manuf Process 2021;70:452-69. DOI
18. Hossain R, Lewis J, Moore AL. In situ infrared temperature sensing for real-time defect detection in additive manufacturing. Addit
Manuf 2021;47:102328. DOI
19. Chen C, Lv N, Chen S. Welding penetration monitoring for pulsed GTAW using visual sensor based on AAM and random forests. J
Manuf Process 2021;63:152-62. DOI
20. Liu T, Wang J, Huang X, Lu Y, Bao J. 3DSMDA-Net: an improved 3DCNN with separable structure and multi-dimensional attention
for welding status recognition. J Manuf Syst 2022;62:811-22. DOI
21. Bacioiu D, Melton G, Papaelias M, Shaw R. Automated defect classification of aluminium 5083 TIG welding using HDR camera and
neural networks. J Manuf Process 2019;45:603-13. DOI
22. Yang J, Li S, Wang Z, Dong H, Wang J, Tang S. Using deep learning to detect defects in manufacturing: a comprehensive survey and
current challenges. Materials 2020;13:5755. DOI PubMed PMC
23. Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16 × 16 words: transformers for image recognition at scale. arXiv
2021, arXiv.2010.11929. Available online: https://doi.org/10.48550/arXiv.2010.11929 (accessed 26 Dec 2024)
24. Springenberg M, Frommholz A, Wenzel M, Weicken E, Ma J, Strodthoff N. From modern CNNs to vision transformers: assessing the
performance, robustness, and classification strategies of deep learning models in histopathology. Med Image Anal 2023;87:102809.
DOI PubMed
25. Mehta S, Rastegari M. MobileViT: light-weight, general-purpose, and mobile-friendly vision transformer. arXiv 2021, arXiv.2110.
02178. Available online: https://doi.org/10.48550/arXiv.2110.02178 (accessed 26 Dec 2024)
26. Liu Z, Mao H, Wu CY, Feichtenhofer C, Darrell T, Xie S. A convnet for the 2020s. In: 2022 IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR); 2022 Jun 18-24; New Orleans, USA. IEEE; 2022. pp. 11976-86. DOI
27. Hou Q, Lu CZ, Cheng MM, Feng J. Conv2Former: a simple transformer-style ConvNet for visual recognition. IEEE Trans Pattern
Anal Mach Intell 2024;46:8274-83. DOI PubMed
28. Lin M, Wu J, Meng J, Wang W, Wu J. Screening of retired batteries with gramian angular difference fields and ConvNeXt. Eng Appl
Artif Intell 2023;123:106397. DOI
29. Lei Z, Shen J, Wang Q, Chen Y. Real-time weld geometry prediction based on multi-information using neural network optimized by
PCA and GA during thin-plate laser welding. J Manuf Process 2019;43:207-17. DOI
30. Moon H, Na S. A neuro-fuzzy approach to select welding conditions for welding quality improvement in horizontal fillet welding. J
Manuf Syst 1996;15:392-403. DOI
31. Ai Y, Shao X, Jiang P, Li P, Liu Y, Yue C. Process modeling and parameter optimization using radial basis function neural network
and genetic algorithm for laser welding of dissimilar materials. Appl Phys A 2015;121:555-69. DOI
32. Yu R, Huang Y, Peng Y, Wang K. Monitoring of butt weld penetration based on infrared sensing and improved histograms of oriented
gradients. J Mater Res Technol 2023;22:3280-93. DOI
33. Yang L, Liu Y, Peng J, Liang Z. A novel system for off-line 3D seam extraction and path planning based on point cloud segmentation
for arc welding robot. Robot Cim Int Manuf 2020;64:101929. DOI
34. Zhang K, Yan M, Huang T, Zheng J, Li Z. 3D reconstruction of complex spatial weld seam for autonomous welding by laser
structured light scanning. J Manuf Process 2019;39:200-7. DOI
35. Liu T, Zheng P, Bao J. Deep learning-based welding image recognition: a comprehensive review. J Manuf Syst 2023;68:601-25. DOI
36. Sahu PK, Pal S. Multi-response optimization of process parameters in friction stir welded AM20 magnesium alloy by Taguchi grey
relational analysis. J Magnes Alloy 2015;3:36-46. DOI
37. Kulal S, Pasupat P, Chandra K, et al. SPoC: search-based pseudocode to code. arXiv 2019, arXiv.1906.04908. Available online: https:/
/doi.org/10.48550/arXiv.1906.04908 (accessed 26 Dec 2024)
38. Fedorenko E, Ivanova A, Dhamala R, Bers MU. The language of programming: a cognitive perspective. Trends Cogn Sci
2019;23:525-8. DOI PubMed
39. Bobrow DG, Stefik MJ. Perspectives on artificial intelligence programming. Science 1986;231:951-7. DOI PubMed
40. Ma J, Cao B, Dong S, et al. MLMD: a programming-free AI platform to predict and design materials. npj Comput Mater
2024;10:1243. DOI
41. Nadeem M, Sohail SS, Javed L, Anwer F, Saudagar AKJ, Muhammad K. Vision-enabled large language and deep learning models for
image-based emotion recognition. Cogn Comput 2024;16:2566-79. DOI
42. Pei Z, Yin J, Neugebauer J, Jain A. Towards the holistic design of alloys with large language models. Nat Rev Mater 2024;9:840-1.
DOI
43. Mouret JB. Large language models help computer programs to evolve. Nature 2024;625:452-3. DOI PubMed
44. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. In: Proceedings of the 31st International Conference on Neural
Information Processing Systems; Long Beach, USA. Curran Associates Inc.; 2017, pp. 6000-10. DOI
45. Wong MF, Guo S, Hang CN, Ho SW, Tan CW. Natural language generation and understanding of big code for AI-assisted
programming: a review. Entropy 2023;25:888. DOI PubMed PMC
46. Chiarello F, Giordano V, Spada I, Barandoni S, Fantoni G. Future applications of generative large language models: a data-driven case
study on ChatGPT. Technovation 2024;133:103002. DOI

