| Issue |
Mechanics & Industry
Volume 26, 2025
Robotic Process Automation for Smarter Devices in Manufacturing
|
|
|---|---|---|
| Article Number | 27 | |
| Number of page(s) | 14 | |
| DOI | https://doi.org/10.1051/meca/2025019 | |
| Published online | 17 September 2025 | |
- T. Taylor, A. Clough, Critical review of automotive hot-stamped sheet steel from an industrial perspective, Mater. Sci. Technol. 34, 809 (2018) [CrossRef] [Google Scholar]
- Q. Luo, X. Zhang, C. Zhang, L. Wang, Y. Chen, Automated visual defect detection for flat steel surface: a survey, IEEE Trans. Instrum. Meas. 69, 626 (2020) [CrossRef] [Google Scholar]
- X. Fang, H. Wu, J. Shen, Z. Zhou, Research progress of automated visual surface defect detection for industrial metal planar materials, Sensors 20, 5136 (2020) [Google Scholar]
- S.A. Singh, K.A. Desai, Automated surface defect detection framework using machine vision and convolutional neural networks, J. Intell. Manuf. 34, 1995 (2023) [Google Scholar]
- L. Geng, J. Yu, Y. Li, Machine vision detection method for surface defects of automobile stamping parts, Am. Sci. Res. J. Eng. Technol. Sci. 53, 128 (2019) [Google Scholar]
- X. Zheng, R. Liu, H. Yang, Recent advances in surface defect inspection of industrial products using deep learning techniques, Int. J. Adv. Manuf. Technol. 113, 35 (2021) [Google Scholar]
- H. Tian, X. Liu, Y. Wu, Surface defects detection of stamping and grinding flat parts based on machine vision, Sensors 20, 4531 (2020) [Google Scholar]
- M. Prunella, G. Bontempi, N. Di Mauro, Deep learning for automatic vision-based recognition of industrial surface defects: A survey, IEEE Access 11, 43370 (2023) [Google Scholar]
- S.B. Block, B.R. Costa, J.R. da Silva, Inspection of imprint defects in stamped metal surfaces using deep learning and tracking, IEEE Trans. Ind. Electron. 68, 4498 (2020) [Google Scholar]
- J. Bai, L. Chen, F. Liu, A comprehensive survey on machine learning-driven material defect detection: Challenges, solutions, and prospects. arXiv:2406.07880 (2024) [Google Scholar]
- X. Ma, M. Zhang, Y. Wang, Stamping part surface crack detection based on machine vision, Measurement 251, (2025) [Google Scholar]
- L. Wei, D. Huang, Y. Sun, Surface defects detection of cylindrical high-precision industrial parts based on deep learning algorithms: a review, Oper. Res. Forum 5, 58 (2024) [Google Scholar]
- J. Ribeiro, R. Lima, T. Eckhardt, S. Paiva, Robotic process automation and artificial intelligence in industry 4.0-a literature review, Proc. Comput. Sci. 181, 51–58 (2021) [Google Scholar]
- R.L. Gudivaka, R.K. Gudivaka, M. Karthick, Deep learning-based defect detection and optimization in IoRT using metaheuristic techniques and the flower pollination algorithm, Int. J. Eng. Res. Sci. Technol. 15, (2019) [Google Scholar]
- K. Dinesh, Enhanced fault diagnosis in IoT: Uniting data fusion with deep multi-scale fusion neural network, Internet of Things (2024) [Google Scholar]
- T. Kumar, S. Singh, N. Chauhan, Image data augmentation approaches: a comprehensive survey and future directions, IEEE Access (2024) [Google Scholar]
- M. Sohan, S.R. Thotakura, C.V.R. Reddy, A review on yolov8 and its advancements, Int. Conf. Data Intell. Cogn. Inform., Springer, Singapore (2024) [Google Scholar]
- X. Qin, Z. Wang, Y. Bai, FFA-Net: Feature fusion attention network for single image dehazing, Proc. AAAI Conf. Artif. Intell. 34, 11908 (2020) [Google Scholar]
- D.R. Natarajan, S. Peddi, D.T. Valivarthi, S. Narla, S.S. Kethu, D. Kurniadi, Hybrid LSTM-GA and HS-CS algorithms for automation-assisted defect identification in CNC milling robotics: enhancing global search and optimization, in Proc. Int. Conf. Comput. Sustain. Intell. Future (COMP-SIF) (2025) [Google Scholar]
- P. Li, Z. Chen, J. Zhang, Abnormal driving detection algorithm based on improved YOLO-v8 with self-attention and GOLD-YOLO mechanism, Int. Conf. Auton. Unmanned Syst., Springer Nature, Singapore (2024) [Google Scholar]
- A. Khan, M.A. Rahman, D. Park, CamoFocus: enhancing camouflage object detection with split-feature focal modulation and context refinement, Proc. IEEE/CVF Winter Conf. Appl. Comput. Vis. (2024) [Google Scholar]
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