Detection of Defects in Rolled Stainless Steel Plates by Machine Learning Models

Year 2023, , 37 - 43, 31.03.2023

Ahmet Feyzioğlu , Yavuz Selim Taspınar

https://doi.org/10.18100/ijamec.1253191

Abstract

Iron metal is the most widely used metal type. This metal, which is used in countless sectors, is processed in different ways and turned into steel. Since steel has a brittle structure compared to iron, defects may occur in the plates during the rolling process. Detection of these defects at the production stage is of great importance in terms of commercial and safety. Machine learning methods can be used in such problems for fast and high accuracy detection. For this purpose, using a dataset obtained from stainless steel surface defects in this study, classification processes were carried out to detect defects with four different machine learning methods. Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) algorithms were used for classification processes. The highest classification accuracy was obtained from the 79.44% RF model. Correlation analysis was performed in order to analyze the effects of the features in the dataset on the classification results. It is thought that the classification accuracy of the proposed models is satisfactory for this challenging problem, but needs to be upgraded.

Keywords

Artificial intelligence, Detection, Steel, Machine learning, Defect

References

• [1] Chen, J., Z. Wang, Z. Liu, and S. Ju, Experimental investigation of mechanical properties of steel half-grouted sleeve splice with rebar bonding defects. Journal of Building Engineering, 2022. 50: p. 104113.
• [2] Molnár, D., X. Sun, S. Lu, W. Li, G. Engberg, and L. Vitos, Effect of temperature on the stacking fault energy and deformation behaviour in 316L austenitic stainless steel. Materials Science and Engineering: A, 2019. 759: p. 490-497.
• [3] Zhou, Z. and Z. Liu, Fault diagnosis of steel wire ropes based on magnetic flux leakage imaging under strong shaking and strand noises. IEEE Transactions on Industrial Electronics, 2020. 68(3): p. 2543-2553.
• [4] Liu, Y.-j., S. Wang, J.-b. Qi, and X.-q. Yan, Vibrations of tandem cold rolling mill: coupled excitation of rolling force and variable stiffness of fault-free back-up roll bearing. Journal of Iron and Steel Research International, 2023: p. 1-11.
• [5] Wang, Y., J. Xia, Z. Wang, and H. Shen, Design of a fault-tolerant output-feedback controller for thickness control in cold rolling mills. Applied Mathematics and Computation, 2020. 369: p. 124841.
• [6] ÖZAKIN, B., Sac Malzeme Üretiminde Hata Türleri ve Etkileri Analizi. Avrupa Bilim ve Teknoloji Dergisi, 2021(28): p. 1204-1209.
• [7] Başkaya, A., Soğuk haddeleme işleminin proses amaçlı eniyilenmesi. 2020, Sakarya Üniversitesi.
• [8] Wang, J., P. Fu, and R.X. Gao, Machine vision intelligence for product defect inspection based on deep learning and Hough transform. Journal of Manufacturing Systems, 2019. 51: p. 52-60.
• [9] Li, D., Q. Xie, X. Gong, Z. Yu, J. Xu, Y. Sun, and J. Wang, Automatic defect detection of metro tunnel surfaces using a vision-based inspection system. Advanced Engineering Informatics, 2021. 47: p. 101206.
• [10] Zhang, X., J. Saniie, and A. Heifetz, Detection of defects in additively manufactured stainless steel 316L with compact infrared camera and machine learning algorithms. JOM, 2020. 72(12): p. 4244-4253.
• [11] Elanangai, V. and K. Vasanth. An Efficient Technique for Identifying Defects in Stainless Steel (SS) Plate using Image Processing. in 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS). 2022. IEEE.
• [12] Vannocci, M., A. Ritacco, A. Castellano, F. Galli, M. Vannucci, V. Iannino, and V. Colla. Flatness defect detection and classification in hot rolled steel strips using convolutional neural networks. in Advances in Computational Intelligence: 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Gran Canaria, Spain, June 12-14, 2019, Proceedings, Part II 15. 2019. Springer.
• [13] Faulty Steel Plates. [cited 2023 February, 15]; Available from: https://www.kaggle.com/datasets/uciml/faulty-steel-plates.
• [14] Erbaş, N., G. Çınarer, and K. Kılıç, Classification of hazelnuts according to their quality using deep learning algorithms. Czech Journal of Food Sciences, 2022. 40(3): p. 240-248.
• [15] Koklu, M., R. Kursun, Y.S. Taspinar, and I. Cinar, Classification of date fruits into genetic varieties using image analysis. Mathematical Problems in Engineering, 2021. 2021: p. 1-13.
• [16] Koklu, M., H. Kahramanli, and N. Allahverdi, A new approach to classification rule extraction problem by the real value coding. International Journal of Innovative Computing, Information and Control, 2012. 8(9): p. 6303-6315.
• [17] Kahramanli, H. and N. Allahverdi, Extracting rules for classification problems: AIS based approach. Expert Systems with Applications, 2009. 36(7): p. 10494-10502.
• [18] Taspinar, Y.S., M. Koklu, and M. Altin, Classification of flame extinction based on acoustic oscillations using artificial intelligence methods. Case Studies in Thermal Engineering, 2021. 28: p. 101561.
• [19] Al-Doori, S.K.S., Y.S. Taspinar, and M. Koklu, Distracted Driving Detection with Machine Learning Methods by CNN Based Feature Extraction. International Journal of Applied Mathematics Electronics and Computers, 2021. 9(4): p. 116-121.
• [20] Kishore, B., A. Yasar, Y.S. Taspinar, R. Kursun, I. Cinar, V.G. Shankar, M. Koklu, and I. Ofori, Computer-aided multiclass classification of corn from corn images integrating deep feature extraction. Computational Intelligence and Neuroscience, 2022. 2022.
• [21] Butuner, R., I. Cinar, Y.S. Taspinar, R. Kursun, M.H. Calp, and M. Koklu, Classification of deep image features of lentil varieties with machine learning techniques. European Food Research and Technology, 2023: p. 1-14.
• [22] Koklu, M., S. Sarigil, and O. Ozbek, The use of machine learning methods in classification of pumpkin seeds (Cucurbita pepo L.). Genetic Resources and Crop Evolution, 2021. 68(7): p. 2713-2726.
• [23] Dogan, M., Y.S. Taspinar, I. Cinar, R. Kursun, I.A. Ozkan, and M. Koklu, Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine. Computers and Electronics in Agriculture, 2023. 204: p. 107575.
• [24] Singh, D., Y.S. Taspinar, R. Kursun, I. Cinar, M. Koklu, I.A. Ozkan, and H.-N. Lee, Classification and analysis of pistachio species with pre-trained deep learning models. Electronics, 2022. 11(7): p. 981.
• [25] Taspinar, Y.S., M. Koklu, and M. Altin, Fire Detection in Images Using Framework Based on Image Processing, Motion Detection and Convolutional Neural Network. International Journal of Intelligent Systems and Applications in Engineering, 2021. 9(4): p. 171-177.
• [26] Taspinar, Y.S., M. Dogan, I. Cinar, R. Kursun, I.A. Ozkan, and M. Koklu, Computer vision classification of dry beans (Phaseolus vulgaris L.) based on deep transfer learning techniques. European Food Research and Technology, 2022. 248(11): p. 2707-2725.
• [27] Yasar, A., Benchmarking analysis of CNN models for bread wheat varieties. European Food Research and Technology, 2022: p. 1-10.
• [28] Unal, Y., Y.S. Taspinar, I. Cinar, R. Kursun, and M. Koklu, Application of pre-trained deep convolutional neural networks for coffee beans species detection. Food Analytical Methods, 2022. 15(12): p. 3232-3243.
• [29] Feyzioglu, A., Y.S. Taspinar, Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types. Sensors, 2023. 23(4): p. 2222.