Abstract
The employment of deep learning architecture for defect detection in the manufacturing industry has gained due attention owing to the advancement of computational technology. Conventional means of defect detection by manual visual inspection by operators are often deemed laborious as well as prone to mistakes. In the present study, a feature-based transfer learning approach is used to classify surface defects. The KolektorSDD database is used in the present study. Two pipelines were developed to investigate its efficacy in detecting the defects, namely the VGG16-kNN and VGG16-SVM pipelines, respectively. It was demonstrated from the study that the VGG16-SVM pipeline was more superior compared to the VGG16-kNN pipeline as no misclassification transpired in either the test or the validation dataset. It could be concluded that the proposed pipeline is suitable for the classification of surface defects.
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References
Papageorgiou EI, Theodosiou T, Margetis G, Dimitriou N, Charalampous P, Tzovaras D, Samakovlis I (2021) Short survey of artificial intelligent technologies for defect detection in manufacturing. In: 2021 12th International Conference on Information, Intelligence, Systems and Applications (IISA). IEEE, pp 1–7
Gotmare A, Bokade S (2019) Internet of Things in Manufacturing: a review on applications, challenges and future directions. In: 61st 5th conference, p 431
Bures M, Cerny T, Ahmed BS (2018) Internet of things: Current challenges in the quality assurance and testing methods. In: Information Science and Applications 2018: ICISA 2018. Springer, pp 625–634
Tercan H, Meisen T (2022) Machine learning and deep learning based predictive quality in manufacturing: a systematic review. J Intell Manuf 33:1879–1905
Nalbach O, Linn C, Derouet M, Werth D (2018) Predictive quality: towards a new understanding of quality assurance using machine learning tools. In: Business Information Systems: 21st International Conference, BIS 2018, Berlin, Germany, Proceedings, vol 21. Springer, pp 30–42
Bhatt PM, Malhan RK, Rajendran P, Shah BC, Thakar S, Yoon YJ, Gupta SK (2021) Image-based surface defect detection using deep learning: a review. J Comput Inf Sci Eng 21:040801. https://doi.org/10.1115/1.4049535
Chandrayan B, Kumar R (2020) IoT integration in industry—a literature review. Recent Adv Mech Eng Select Proc NCAME 2019:9–17
Yang J, Li S, Wang Z, Dong H, Wang J, Tang S (2020) Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13:5755
Cheon S, Lee H, Kim CO, Lee SH (2019) Convolutional neural network for wafer surface defect classification and the detection of unknown defect class. IEEE Trans Semicond Manuf 32:163–170
Fadli VF, Herlistiono IO (2020) Steel surface defect detection using deep learning. Int J Innov Sci Res Technol 5:244–250
Scime L, Beuth J (2018) A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process. Addit Manuf 24:273–286
San-Payo G, Ferreira JC, Santos P, Martins AL (2020) Machine learning for quality control system. J Ambient Intell Humaniz Comput 11:4491–4500
Xuen LS, Mohd Khairuddin I, Mohd Razman MA, Mat Jizat JA, Yuen E, Jiang H, Yap EH, Abdul Majeed A (2023) The classification of wafer defects: a support vector machine with different densenet transfer learning models evaluation. In: Robot intelligence technology and applications 7: results from the 10th international conference on robot intelligence technology and applications. Springer, pp 304–309
Mat Jizat JA, Abdul Majeed APP, Ab. Nasir AF, Taha Z, Yuen E, Lim SX (2022) Evaluation of the transfer learning models in wafer defects classification. In: Recent trends in mechatronics towards industry 4.0: selected articles from iM3F 2020, Malaysia. Springer, pp 873–881
Tabernik D, Šela S, Skvarč J, Skočaj D (2020) Segmentation-based deep-learning approach for surface-defect detection. J Intell Manuf 31:759–776. https://doi.org/10.1007/s10845-019-01476-x
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Ateeq, M., P. P. Abdul Majeed, A., Hafizh, H., Mohd Razman, M., Mohd Khairuddin, I., Noordin, N. (2024). A Feature-Based Transfer Learning Method for Surface Defect Detection in Smart Manufacturing. In: Mohd. Isa, W.H., Khairuddin, I.M., Mohd. Razman, M.A., Saruchi, S.'., Teh, SH., Liu, P. (eds) Intelligent Manufacturing and Mechatronics. iM3F 2023. Lecture Notes in Networks and Systems, vol 850. Springer, Singapore. https://doi.org/10.1007/978-981-99-8819-8_37
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DOI: https://doi.org/10.1007/978-981-99-8819-8_37
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