Transfer Learning for Detection of Casting Defects Model In Scope of Industrial 4.0

Yıl 2023, , 45 - 51, 27.09.2023

Hayriye Tanyıldız , Canan Batur Şahin

https://doi.org/10.46810/tdfd.1236584

Öz

Casting represents a production process where a liquid material is poured into a mold with a hollow cavity, usually of the intended shape, following which its solidification is allowed. Numerous defect types are available, including blow holes, pin holes, burrs, mold material defects, shrinkage defects, metallurgical defects, casting metal defects, etc. All industries have quality control departments to eliminate the occurrence of this defective product. But the main problem is that this inspection process is done manually. This is a very time consuming process and due to human sensitivity this is not 100% accurate. In this study, we will verify whether the "manual inspection" bottleneck can be eliminated by automating the inspection process with transfer learning in the manufacturing process of casting products. In this study, we will verify whether the "manual inspection" bottleneck can be eliminated by automating the inspection process with transfer learning in the manufacturing process of casting products. In this study, the casting images were divided into two separate classes, and the classification process was carried out by applying deep learning architectures. The benefits of this proposed approach are discussed and proposed as a more efficient way to control the quality of final products under Industry 4.0.

Anahtar Kelimeler

Transfer Learning, Deep Learning, Prediction, Casting Defects, Industrial 4.0

Kaynakça

• [1] Lili Jiang, Yongxiong Wang, Zhenhui Tang, Yinlong Miao, Shuyi Chen, Casting defect detection in X-ray images using convolutional neural networks and attention-guided data augmentation,Measurement,Volume 170, 2021
• [2] C. Hu, Y. Wang, K. Chen, Y. Qin, H. Shao and J. Wang, "A CNN Model Based on Spatial Attention Modules for Casting Type Classification on Pseudo-color Digital Radiography Images," 2019 Chinese Automation Congress (CAC), Hangzhou, China, 2019, pp. 4585-4589
• [3] Dilliraj Ekambaram, Vijayakumar Ponnusamy. (2022). Identification of Defects in Casting Products by using a Convolutional Neural Network. IEIE Transactions on Smart Processing & Computing, 11(3), 149-155.
• [4] HABIBPOUR, Maryam, et al. An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products. arXiv preprint arXiv:2107.11643, 2021.
• [5] M Shanthalakshmi, Susmita mishra, V Jananee, P Narayana Perumal, S Manoj Jayakar5.(2022). Identification of Casting Product Surface Quality Using Alex net and Le-net CNN Models.
• [6] Suykens, J.A.K., Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3): 293-300.
• [7] Gürkan, H., Hanilçi, A. 2020. Evrişimli sinir ağı ve QRS imgeleri kullanarak EKG tabanlı biyometrik tanıma yöntemi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 26(2), 318-327.
• [8] Eryılmaz, F. & Karacan, H. (2021). Akciğer X-Ray Görüntülerinden COVID-19 Tespitinde Hafif ve Geleneksel Evrişimsel Sinir Ağ Mimarilerinin Karşılaştırılması . Düzce Üniversitesi Bilim ve Teknoloji Dergisi , ICAIAME 2021 , 26-39 . DOI: 10.29130/dubited.1011829
• [9] D. Theckedath and R. Sedamkar, “Detecting affect states using vgg16, resnet50 and se-resnet50 networks,” SN Computer Science, vol. 1, no. 2, pp. 1–7, 2020.
• [10] Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A. (2017). Inception-v4, inception-ResNet and the impact of residual connections on learning. Thirty-first AAAI Conference on Artificial Intelligence, pp. 4278-4284.
• [11] Özyurt, F., Sert, E., Avci, D. (2022). Ensemble residual network features and cubic-SVM based tomato leaves disease classification system. Traitement du Signal, 39(1): 71-77.
• [12] He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.
• [13] Yanfeng Gong, Jun Luo, Hongliang Shao, Zhixue Li, A transfer learning object detection model for defects detection in X-ray images of spacecraft composite structures, Composite Structures,Vol.284, 2022
• [14] Bolla, B. K., Kingam, M., & Ethiraj, S. (2022). Efficient Deep Learning Methods for Identification of Defective Casting Products. arXiv.
• [15] U. K. Lilhore, S. Simaiya, J. K. Sandhu, N. K. Trivedi, A. Garg and A. Moudgil, "Deep Learning-Based Predictive Model for Defect Detection and Classification in Industry 4.0," 2022 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, 2022, pp. 1-5, doi: 10.1109/ESCI53509.2022.9758280.
• [16] Mesbah, Mahmoud, Wang, Zixin,Gao, Jing,Zeng, Qingcheng, Sun, Yuhui, 2021, Multitype Damage Detection of Container Using CNN Based on Transfer Learning,Hindawi
• [17] İ. E. Parlak, E. Emel, “Deep learning-based detection of aluminum casting defects and their types”, Engineering Applications of Artificial Intelligence, Vol.118, 2023, ISSN 0952-1976
• [18] Z. Zhao, T. Wu, "Casting Defect Detection and Classification of Convolutional Neural Network Based on Recursive Attention Model", Scientific Programming, vol. 2022, Article ID 4385565, 11 pages, 2022
• [19] A. R.Dakak, V. Kaftandjian, P. Duvauchelle, P. Bouvet, Insight - Non-Destructive Testing and Condition Monitoring, Vol. 64, No. 11, 2022, pp. 647-658, The British Institute of Non-Destructive Testing
• [20] I. Raouf, P. Kumar, H. Lee, H.S. Kim, Transfer Learning-Based Intelligent Fault Detection Approach for the Industrial Robotic System. Mathematics 2023, 11, 945.
• [21] M. S. Azari, F. Flammini, S. Santini and M. Caporuscio, "A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0," in IEEE Access, vol. 11, pp. 12887-12910, 2023, doi: 10.1109/ACCESS.2023.3239784.