REFERENCES
1. An, Y.; Xu, X.; Zhao, Y.; Hou, H. Nonequilibrium solidification velocity, recalescence degree and grain refinement of highly undercooled Ni-based single-phase alloys. J. Alloys. Compd. 2021, 881, 160658.
2. Cheng, Y.; Wang, G.; Qiu, Z.; et al. Multi-physics simulation of non-equilibrium solidification in Ti-Nb alloy during selective laser melting. Acta. Mater. 2024, 272, 119923.
3. Peng, Z.; Zhang, X.; Liu, L.; Xu, G.; Wang, G.; Zhao, M. Effect of high-speed ultrasonic vibration cutting on the microstructure, surface integrity, and wear behavior of titanium alloy. J. Mater. Res. Technol. 2023, 24, 3870-88.
4. Williams W. Development of structural titanium alloys for marine applications. Ocean. Eng. 1969, 1, 375-83.
5. Auwal, S. T.; Ramesh, S.; Yusof, F.; Manladan, S. M. A review on laser beam welding of titanium alloys. Int. J. Adv. Manuf. Technol. 2018, 97, 1071-98.
6. Su, Y.; Liang, C.; Wang, D. Composition- and temperature-dependence of β to ω phase transformation in Ti-Nb alloys. J. Mater. Inf. 2023, 3, 14.
7. Li, P.; Zhang, Y.; Wang, W. Y.; et al. Coupling effects of high magnetic field and annealing on the microstructure evolution and mechanical properties of additive manufactured Ti–6Al–4V. Mater. Sci. Eng. A. 2021, 824, 141815.
8. Park, H.; Rhee, S. Estimation of weld bead size in CO2 laser welding by using multiple regression and neural network. J. Laser. Appl. 1999, 11, 143-50.
9. Wang, W. Y.; Yin, J.; Chai, Z.; et al. Big data-assisted digital twins for the smart design and manufacturing of advanced materials: from atoms to products. J. Mater. Inf. 2022. DOI: 10.20517/jmi.2021.11.
10. Vasan, V.; Sridharan, N. V.; Balasundaram, R. J.; Vaithiyanathan, S. Ensemble-based deep learning model for welding defect detection and classification. Eng. Appl. Artif. Intell. 2024, 136, 108961.
11. Wang, B.; Hu, S. J.; Sun, L.; Freiheit, T. Intelligent welding system technologies: state-of-the-art review and perspectives. J. Manuf. Syst. 2020, 56, 373-91.
12. Feng, Y.; Chen, Z.; Wang, D.; Chen, J.; Feng, Z. DeepWelding: A deep learning enhanced approach to GTAW using multisource sensing images. IEEE. Trans. Ind. Inf. 2020, 16, 465-74.
13. Wang, S.; Zhang, S.; Wen, S.; Fernandez, C. An accurate state-of-charge estimation of lithium-ion batteries based on improved particle swarm optimization-adaptive square root cubature kalman filter. J. Power. Sources. 2024, 624, 235594.
14. Ma, D.; Shu, L.; Zhou, Q.; Cao, S.; Jiang, P. Online porosity defect detection based on convolutional neural network for Al alloy laser welding. J. Phys. Conf. Ser. 2021, 1884, 012008.
15. Zhang, Y.; You, D.; Gao, X.; Katayama, S. Online monitoring of welding status based on a DBN model during laser welding. Engineering 2019, 5, 671-8.
16. Huang, J.; Zhang, Z.; Qin, R.; et al. Interpretable real-time monitoring of pipeline weld crack leakage based on wavelet multi-kernel network. J. Manuf. Syst. 2024, 72, 93-103.
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.
18. Hossain, R.; Lewis, J.; Moore, A. L. In situ infrared temperature sensing for real-time defect detection in additive manufacturing. Addit. Manuf. 2021, 47, 102328.
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.
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.
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.
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.
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.
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)
27. Hou, Q.; Lu, C. Z.; Cheng, M. M.; Feng, J. Conv2Former: a simple transformer-style ConvNet for visual recognition. IEEE. Trans. Pattern. Anal. Mach. Intell. 2024, 46, 8274-83.
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.
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.
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.
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.
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.
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.
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.
35. Liu, T.; Zheng, P.; Bao, J. Deep learning-based welding image recognition: a comprehensive review. J. Manuf. Syst. 2023, 68, 601-25.
36. Sahu, P. K.; 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.
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, M. U. The language of programming: a cognitive perspective. Trends. Cogn. Sci. 2019, 23, 525-8.
39. Bobrow, D. G.; Stefik, M. J. Perspectives on artificial intelligence programming. Science 1986, 231, 951-7.
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.
41. Nadeem, M.; Sohail, S. S.; Javed, L.; Anwer, F.; Saudagar, A. K. J.; Muhammad, K. Vision-enabled large language and deep learning models for image-based emotion recognition. Cogn. Comput. 2024, 16, 2566-79.
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.
45. Wong, M. F.; Guo, S.; Hang, C. N.; Ho, S. W.; Tan, C. W. Natural language generation and understanding of big code for AI-assisted programming: a review. Entropy 2023, 25, 888.
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.
47. Fernandes, L. C. Programming computational electromagnetic applications assisted by large language models [Em Programmer’s Notebook]. IEEE. Antennas. Propag. Mag. 2024, 66, 63-71.
48. Peng, D.; Zheng, L.; Liu, D.; et al. Large-language models facilitate discovery of the molecular signatures regulating sleep and activity. Nat. Commun. 2024, 15, 3685.
49. Karnalim, O.; Toba, H.; Johan, M. C. Detecting AI assisted submissions in introductory programming via code anomaly. Educ. Inf. Technol. 2024, 29, 16841-66.
50. Zheng, Y. Optimization of computer programming based on mathematical models of artificial intelligence algorithms. Comput. Electr. Eng. 2023, 110, 108834.
51. Wang, W. Y.; Zhang, S.; Li, G.; et al. Artificial intelligence enabled smart design and manufacturing of advanced materials: the endless Frontier in AI+ era. MGE. Advances. 2024, 2, e56.
52. Chen, L.; Chen, Z.; Yao, X.; et al. High-entropy alloy catalysts: high-throughput and machine learning-driven design. J. Mater. Inf. 2022, 2, 19.
53. Song, G.; Diao, Z.; Lv, X.; Liu, L. TIG and laser–TIG hybrid filler wire welding of casting and wrought dissimilar magnesium alloy. J. Manuf. Process. 2018, 34, 204-14.
54. Weman, K.
55. Zhang, D.; Liu, Y.; Liu, R.; et al. Characterization of corrosion behavior of TA2 titanium alloy welded joints in seawater environment. Front. Chem. 2022, 10, 950768.
56. Lei, T.; Wu, C.; Rong, Y.; Huang, Y. The development of tube-to-tubesheet welding from automation to digitization. Int. J. Adv. Manuf. Technol. 2021, 116, 779-802.
57. Jin, Z.; Li, H.; Zhang, C.; Wang, Q.; Gao, H. Online welding path detection in automatic tube-to-tubesheet welding using passive vision. Int. J. Adv. Manuf. Technol. 2017, 90, 3075-84.
58. Mu, H.; He, F.; Yuan, L.; Commins, P.; Ding, D.; Pan, Z. A digital shadow approach for enhancing process monitoring in wire arc additive manufacturing using sensor fusion. J. Ind. Inf. Integr. 2024, 40, 100609.
59. Pires J, Smith JS, Balfour C. Real-time top-face vision based control of weld pool size. Ind. Robot. 2005, 32, 334-40.
60. Ugender, S. Influence of tool pin profile and rotational speed on the formation of friction stir welding zone in AZ31 magnesium alloy. J. Magnes. Alloy. 2018, 6, 205-13.
61. Lin, Q.; Yang, S.; Yang, R.; Wu, H. Transistor modeling based on LM-BPNN and CG-BPNN for the GaAs pHEMT. Int. J. Numerl. Model. EL. 2024, 37, e3268.
62. Wang, X.; Sun, L.; Bai, H.; Yu, K.; Wang, B. SCARA mechanical fault identification based on WPM-SE+BPNN method. Meas. Sci. Technol. 2022, 33, 085007.
63. Lee, S.; Kim, H.; Lieu, Q. X.; Lee, J. CNN-based image recognition for topology optimization. Knowl. Based. Syst. 2020, 198, 105887.
64. Zhang, Y.; You, D.; Gao, X.; Zhang, N.; Gao, P. P. Welding defects detection based on deep learning with multiple optical sensors during disk laser welding of thick plates. J. Manuf. Syst. 2019, 51, 87-94.
65. Liu, T.; Zheng, H.; Zheng, P.; et al. An expert knowledge-empowered CNN approach for welding radiographic image recognition. Adv. Eng. Inform. 2023, 56, 101963.
66. Wang, Z.; Cao, B.; Liu, J. Hyperspectral image classification via spatial shuffle-based convolutional neural network. Remote. Sens. 2023, 15, 3960.
67. Dai, F.; Wen, B.; Hu, Y.; Gu, X. A deep neural network potential model for transition metal diborides. J. Mater. Inf. 2024, 4, 10.
69. Zhu, H.; Sun, M.; Fu, H.; Du, N.; Zhang, J. Training a seismogram discriminator based on ResNet. IEEE. Trans. Geosci. Remote. Sens. 2021, 59, pp.7076-85.
70. Howard, A. G.; Zhu, M.; Chen, B.; et al. MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv 2017, arXiv.1704.04861. Available online: https://doi.org/10.48550/arXiv.1704.04861 (accessed 26 Dec 2024)
71. Liu, Z.; Zhang, X.; Shen, Z.; Wei, Y.; Cheng, K. T.; Sun, J. Joint multi-dimension pruning via numerical gradient update. IEEE. Trans. Image. Process. 2021, 30, 8034-45.
72. Ma, S.; Zhang, Q.; Li, T.; Song, H. Basic motion behavior recognition of single dairy cow based on improved Rexnet 3D network. Comput. Electron. Agr. 2022, 194, 106772.
73. Han, K.; Wang, Y.; Chen, H.; et al. A survey on vision transformer. IEEE. Trans. Pattern. Anal. Mach. Intell. 2023, 45, 87-110.
74. Sun, W.; Qin, Z.; Deng, H.; et al. Vicinity vision transformer. IEEE. Trans. Pattern. Anal. Mach. Intell. 2023, 45, 12635-49.
75. Lv, P.; Xu, H.; Zhang, Q.; et al. An improved lightweight ConvNeXt for rice classification. Alex. Eng. J. 2025, 112, 84-97.
76. Zhu, J.; Feng, Y.; Liu, Q.; et al. An improved ConvNeXt with multimodal transformer for physiological signal classification. IEEE Access 2024.
77. Hendrycks, D.; Gimpel, K. Gaussian error linear units (GELUs). arXiv , 2016, arXiv, 1606.08415. Available online:
78. Lee, M.; Wu, Q. Mathematical analysis and performance evaluation of the GELU activation function in deep learning. J. Math. 2023, 2023, 1-13.
79. Qiao, Y.; Zhang, Q.; Qi, Y.; Wan, T.; Yang, L.; Yu, X. A waste classification model in low-illumination scenes based on ConvNeXt. Resour. Conserv. Recy. 2023, 199, 107274.
80. Jablonka, K. M.; Ai, Q.; Al-Feghali, A.; et al. 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon. Digit. Discov. 2023, 2, 1233-50.
81. Choi, J.; Lee, B. Accelerating materials language processing with large language models. Commun. Mater. 2024, 5, 449.
82. Zang, Y.; Li, W.; Han, J.; Zhou, K.; Loy, C. C. Contextual object detection with multimodal large language models. Int J Comput Vis 2024.
83. Mahowald, K.; Ivanova, A. A.; Blank, I. A.; Kanwisher, N.; Tenenbaum, J. B.; Fedorenko, E. Dissociating language and thought in large language models. Trends. Cogn. Sci. 2024, 28, 517-40.
84. Wang, H.; Li, J.; Wu, H.; Hovy, E.; Sun, Y. Pre-trained language models and their applications. Engineering 2023, 25, 51-65.
85. Hu, L.; Liu, Z.; Zhao, Z.; Hou, L.; Nie, L.; Li, J. A survey of knowledge enhanced pre-trained language models. IEEE. Trans. Knowl. Data. Eng. 2024, 36, 1413-30.
86. Lai, Z.; Wu, T.; Fei, X.; Ling, Q. BERT4ST:: fine-tuning pre-trained large language model for wind power forecasting. Energ. Convers. Manage. 2024, 307, 118331.
87. Cook, A.; Karakuş, O. LLM-commentator: novel fine-tuning strategies of large language models for automatic commentary generation using football event data. Knowl. Based. Syst. 2024, 300, 112219.
88. Zhang, Z.; Wen, G.; Chen, S. Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding. J. Manuf. Process. 2019, 45, 208-16.
89. Yang, X.; Zhang, Y.; Lv, W.; Wang, D. Image recognition of wind turbine blade damage based on a deep learning model with transfer learning and an ensemble learning classifier. Renew. Energ. 2021, 163, 386-97.
90. Balado, J.; Sousa, R.; Díaz-Vilariño, L.; Arias, P. Transfer learning in urban object classification: online images to recognize point clouds. Automat. Constr. 2020, 111, 103058.
92. Husnain, M.; Missen, M. M. S.; Mumtaz, S.; Luqman, M. M.; Coustaty, M.; Ogier, J. Visualization of high-dimensional data by pairwise fusion matrices using t-SNE. Symmetry 2019, 11, 107.
93. Moon, H.; Na, S. Optimum design based on mathematical model and neural network to predict weld parameters for fillet joints. J. Manuf. Syst. 1997, 16, 13-23.
94. Gao, Y.; Hao, K.; Xu, L.; et al. Microstructure homogeneity and mechanical properties of laser-arc hybrid welded AZ31B magnesium alloy. J. Magnes. Alloy. 2024, 12, 1986-95.
95. Geng, P.; Ma, H.; Wang, M.; et al. Dissimilar linear friction welding of Ni-based superalloys. Int. J. Mach. Tool. Manu. 2023, 191, 104062.
96. Kim, I.; Son, K.; Yang, Y.; Yaragada, P. Sensitivity analysis for process parameters in GMA welding processes using a factorial design method. Int. J. Mach. Tool. Manu. 2003, 43, 763-9.
97. Cook, G. E. Robotic arc welding: research in sensory feedback control. IEEE. Trans. Ind. Electron. 1983, IE-30, 252-68.
