Smart defect detection using transfer learning in injection molding: a comparative exploration study of deep learning architectures
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00170-024-13768-5.pdf
Reference32 articles.
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2. Chen JC, Guo G, Wang W-N (2020) Artificial neural network-based online defect detection system with in-mold temperature and pressure sensors for high precision injection molding. Int J Adv Manuf Technol 110(7–8):2023–2033. https://doi.org/10.1007/s00170-020-06011-4
3. Fu G et al (2019) A deep-learning-based approach for fast and robust steel surface defects classification. Opt Lasers Eng 121:397–405. https://doi.org/10.1016/j.optlaseng.2019.05.005
4. el Ghadoui M, Mouchtachi A, Majdoul R (2023) Intelligent surface roughness measurement using deep learning and computer vision: a promising approach for manufacturing quality control. Int J Adv Manuf Technol 129(7):3261–3268. https://doi.org/10.1007/s00170-023-12457-z
5. Kim G, Choi JG, Ku M, Cho H, Lim S (2021) A multimodal deep learning-based fault detection model for a plastic injection molding process. IEEE Access 9:132455–132467. https://doi.org/10.1109/ACCESS.2021.3115665
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