Enhancing Defect Detection in Steel Plate Manufacturing with Explainable Machine Learning and SMOTE for Imbalanced Data
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Imbalanced Data Classification Techniques · Industrial Vision Systems and Defect Detection · Infrastructure Maintenance and Monitoring
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (62)
Show source-provided references
1. P. Sawdatkar, D. Singh, K. Kundu (2023) Applications of Machine Learning in the Manufacturing Sector. Mach Learn Sustain Manuf Ind 40 Concept, Concerns Appl 20110.1201/9781003453567-11 ↗
2. M.A.A. Kazemi, S. Hajian, and N. Kiani, Quality Control and Classification of Steel Plates Faults Using Data Mining, Appl. Math. Inf. Sci. Lett., 2018, 6, p 59–67.10.18576/amisl/060202 ↗
3. E.C. Özkat, A Method to Classify Steel Plate Faults Based on Ensemble Learning, J. Mater. Mechatron. A, 2022, 3, p 240–256.10.55546/jmm.1161542 ↗
4. A. Kharal (2020) Explainable Artificial Intelligence Based Fault Diagnosis and Insight Harvesting for Steel Plates manufacturing. arXiv Prepr arXiv200804448
5. T. Czimmermann, G. Ciuti, M. Milazzo et al., Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A Survey, Sensors, 2020, 20, p 1459.10.3390/s20051459 ↗
6. F. Harrou, A. Dorbane, Y. Sun (2024) Automatic Sitting Posture Recognition in Wheelchair Users: An AI-driven Pilot Study. In: 2024 9th International Conference on Frontiers of Signal Processing (ICFSP). pp 10–1510.1109/ICFSP62546.2024.10785494 ↗
7. A. Dorbane, F. Harrou, and Y. Sun, Exploring Deep Learning Methods to Forecast Mechanical Behavior of FSW Aluminum Sheets, J. Mater. Eng. Perform., 2022 https://doi.org/10.1007/s11665-022-07376-110.1007/s11665-022-07376-1 ↗
8. S. Makhfi, A. Dorbane, F. Harrou, and Y. Sun, Prediction of Cutting Forces in Hard Turning Process Using Machine Learning Methods: A Case Study, J. Mater. Eng. Perform., 2023 https://doi.org/10.1007/s11665-023-08555-410.1007/s11665-023-08555-4 ↗
9. A. Dorbane, F. Harrou, D.-C. Anghel, and Y. Sun, Machine Learning Prediction of Aluminum Alloy Stress-Strain Curves at Variable Temperatures with Failure Analysis, J. Fail. Anal. Prev., 2023 https://doi.org/10.1007/s11668-023-01833-210.1007/s11668-023-01833-2 ↗
10. F. Harrou, A. Dairi, A. Dorbane, and Y. Sun, Energy Consumption Prediction in Water Treatment Plants Using Deep Learning with Data Augmentation, Results Eng, 2023, 20, p 101428. https://doi.org/10.1016/j.rineng.2023.10142810.1016/j.rineng.2023.101428 ↗
11. T.A.ŞA.R. Beyda, Comparison Analysis of Machine Learning Algorithms for Steel Plate Fault Detection, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 2022, 10(3), p 1578–1588. https://doi.org/10.29130/dubited.105846710.29130/dubited.1058467 ↗
12. I.D. Kordatos and P. Benardos, Comparative Analysis of Machine Learning Algorithms for Steel Plate Defect Classification, Int. J. Mechatronics Manuf. Syst., 2022, 15, p 246–263. https://doi.org/10.1504/IJMMS.2022.12721110.1504/IJMMS.2022.127211 ↗
13. T. Nkonyana, Y. Sun, B. Twala, and E. Dogo, Performance Evaluation of Data Mining techniques in Steel Manufacturing Industry, Procedia Manuf., 2019, 35, p 623–628. https://doi.org/10.1016/j.promfg.2019.06.00410.1016/j.promfg.2019.06.004 ↗
14. M. Shah, V. Vakharia, R. Chaudhari et al., Tool Wear Prediction in Face Milling of Stainless Steel Using Singular Generative Adversarial Network and LSTM Deep Learning Models, Int. J. Adv. Manuf. Technol., 2022, 121, p 723–736. https://doi.org/10.1007/s00170-022-09356-010.1007/s00170-022-09356-0 ↗
15. M. Shah, H. Borade, V. Sanghavi et al., Enhancing Tool Wear Prediction Accuracy Using Walsh-Hadamard Transform, DCGAN and Dragonfly Algorithm-Based Feature Selection, Sensors, 2023, 23, p 3833.10.3390/s23083833 ↗
16. F. Harrou, A. Dorbane, and Y. Sun (2024) Enhancing Photovoltaic System Performance Using Advanced Gradient Boosting Methods. In: 2024 International Conference on Control, Automation and Diagnosis (ICCAD). pp 1–610.1109/ICCAD60883.2024.10553919 ↗
17. A. Dorbane, F. Harrou, and Y. Sun (2022) A Tree-Driven Ensemble Learning Approach to Predict FS Welded Al-6061-T6 Material Behavior. In: 2022 7th International Conference on Frontiers of Signal Processing (ICFSP). IEEE, pp 184–18810.1109/ICFSP55781.2022.9924883 ↗
18. Z. Wu, W. Lin, and Y. Ji, An Integrated Ensemble Learning Model for Imbalanced Fault Diagnostics and Prognostics, IEEE Access, 2018, 6, p 8394–8402.10.1109/ACCESS.2018.2807121 ↗
19. Y. Sina, Y. Shen, and A.M. Gibran, Intelligent Fault Diagnosis of Manufacturing Processes using Extra Tree Classification Algorithm and Feature Selection Strategies, IEEE Open J Ind Electron Soc, 2023, 4, p 618–628.10.1109/OJIES.2023.3334429 ↗
20. S. Yousefi (2022) A Data-Driven Approach for Fault Classification of a Manufacturing Process
21. S.N. Somala, S. Chanda, M. AlHamaydeh, and S. Mangalathu, Explainable XGBoost–SHAP Machine-Learning Model for Prediction of Ground Motion Duration in New Zealand, Nat. Hazards Rev., 2024, 25, p 4024005.10.1061/NHREFO.NHENG-1837 ↗
22. A. Mansouri, M. Mansouri, and S. Mangalathu, Interpretable Machine Learning Model for Shear Strength Estimation of Circular Concrete-Filled Steel Tubes, Struct Des Tall Spec Build, 2024, 33, p e2111. https://doi.org/10.1002/tal.211110.1002/tal.2111 ↗
23. C. Cakiroglu, Y. Aydın, G. Bekdaş et al., Cooling Load Prediction of a Double-Story Terrace House Using Ensemble Learning Techniques and Genetic Programming with SHAP Approach, Energy Build, 2024, 313, p 114254. https://doi.org/10.1016/j.enbuild.2024.11425410.1016/j.enbuild.2024.114254 ↗
24. S.A. Ebiaredoh-Mienye (2021) Improved Machine Learning Methods for Classification of Imbalanced Data
25. G.U. Alaneme, K.A. Olonade, E. Esenogho et al., Artificial Intelligence Prediction of the Mechanical Properties of Banana Peel-Ash and Bagasse Blended Geopolymer Concrete, Sci. Rep., 2024, 14, p 26151. https://doi.org/10.1038/s41598-024-77144-910.1038/s41598-024-77144-9 ↗
26. G. Obaido, B. Ogbuokiri, T.G. Swart et al., An Interpretable Machine Learning Approach for Hepatitis B Diagnosis, Appl. Sci., 2022, 12, p 11127.10.3390/app122111127 ↗
27. E. Esenogho, I.D. Mienye, T.G. Swart et al., A Neural Network Ensemble With Feature Engineering for Improved Credit Card Fraud Detection, IEEE Access, 2022, 10, p 16400–16407. https://doi.org/10.1109/ACCESS.2022.314829810.1109/ACCESS.2022.3148298 ↗
28. S.A. Ebiaredoh-Mienye, E. Esenogho, and T.G. Swart, Artificial Neural Network Technique for Improving Prediction of Credit card Default: A Stacked Sparse Autoencoder Approach, Int. J. Electr. Comput. Eng. (IJECE), 2021, 11(5), p 4402. https://doi.org/10.11591/ijece.v11i5.pp4392-440210.11591/ijece.v11i5.pp4392-4402 ↗
29. S.A. Ebiaredoh-Mienye, E. Esenogho, and T.G. Swart, Integrating Enhanced Sparse Autoencoder-Based Artificial Neural Network Technique and Softmax Regression for Medical Diagnosis, Electronics, 2020, 9(11), p 1963. https://doi.org/10.3390/electronics911196310.3390/electronics9111963 ↗
30. T. Chen and C. Guestrin (2016) Xgboost: A Scalable Tree Boosting System. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining. pp 785–79410.1145/2939672.2939785 ↗
31. M. Zounemat-Kermani, O. Batelaan, M. Fadaee, and R. Hinkelmann, Ensemble Machine Learning Paradigms in Hydrology: A Review, J Hydrol, 2021, 598, p 126266. https://doi.org/10.1016/j.jhydrol.2021.12626610.1016/j.jhydrol.2021.126266 ↗
32. R. Szczepanek, Daily Streamflow Forecasting in Mountainous Catchment Using XGBoost, LightGBM and CatBoost. Hydrology, 2022, 9, p 226.10.3390/hydrology9120226 ↗
33. R. Piraei, S.H. Afzali, and M. Niazkar, Assessment of XGBoost to Estimate Total Sediment Loads in Rivers, Water Resour. Manag., 2023, 37, p 5289–5306. https://doi.org/10.1007/s11269-023-03606-w10.1007/s11269-023-03606-w ↗
34. Z.M. Omer and H. Shareef, Comparison of Decision Tree Based Ensemble Methods for Prediction of Photovoltaic Maximum Current, Energy Convers Manag X, 2022, 16, p 100333. https://doi.org/10.1016/j.ecmx.2022.10033310.1016/j.ecmx.2022.100333 ↗
35. A. Dorbane, F. Harrou, and Y. Sun, Detecting Faulty Steel Plates Using Machine Learning BT - Advances in Computing and Data Sciences, Singh M. V. Tyagi, P.K. Gupta et al., Ed., Springer Nature Switzerland, Cham, 2025, p 321–333
36. R. Zhang, Y. Qi, S. Kong et al., A Hybrid Artificial Intelligence Algorithm for Fault Diagnosis of Hot Rolled Strip Crown Imbalance, Eng. Appl. Artif. Intell., 2024, 130, p 107763. https://doi.org/10.1016/j.engappai.2023.10776310.1016/j.engappai.2023.107763 ↗
37. Y. Freund and R.E. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, J. Comput. Syst. Sci., 1997, 55, p 119–139. https://doi.org/10.1006/jcss.1997.150410.1006/jcss.1997.1504 ↗
38. T. Hastie, R. Tibshirani, J.H. Friedman, and J.H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009.10.1007/978-0-387-84858-7 ↗
39. J.H. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, Ann. Stat., 2001, 29, p 1189–1232. https://doi.org/10.1214/aos/101320345110.1214/aos/1013203451 ↗
40. T. Zhang, W. Lin, A.M. Vogelmann et al., Improving Convection Trigger Functions in Deep Convective Parameterization Schemes Using Machine Learning, J. Adv. Model Earth Syst., 2021, 13, p e2020MS002365. https://doi.org/10.1029/2020MS00236510.1029/2020MS002365 ↗
41. L. Prokhorenkova, G. Gusev, A. Vorobev, et al (2018) CatBoost: unbiased boosting with categorical features. In: Bengio S, Wallach H, Larochelle H, et al (eds) Advances in Neural Information Processing Systems. Curran Associates, Inc.
42. A.M. Guarnieri and S. Tebaldini, On the Exploitation of Target Statistics for SAR Interferometry Applications, IEEE Trans. Geosci. Remote Sens., 2008, 46, p 3436–3443. https://doi.org/10.1109/TGRS.2008.200175610.1109/TGRS.2008.2001756 ↗
43. A.V. Dorogush, V. Ershov, A. Gulin (2018) CatBoost: Gradient Boosting with Categorical Features Support. arXiv Prepr arXiv181011363
44. J.T. Hancock and T.M. Khoshgoftaar, CatBoost for Big Data: An Interdisciplinary Review, J Big Data, 2020, 7, p 94. https://doi.org/10.1186/s40537-020-00369-810.1186/s40537-020-00369-8 ↗
45. M. Pandey, M. Karbasi, M. Jamei et al., A Comprehensive Experimental and Computational Investigation on Estimation of Scour Depth at Bridge Abutment: Emerging Ensemble Intelligent Systems, Water Resour. Manag., 2023, 37, p 3745–3767. https://doi.org/10.1007/s11269-023-03525-w10.1007/s11269-023-03525-w ↗
46. L. Breiman, Random Forests, Mach. Learn., 2001, 45, p 5.10.1023/A:1010933404324 ↗
47. R. Jiang, W. Tang, X. Wu and W. Fu, A Random Forest Approach to the Detection of Epistatic Interactions in Case-Control Studies, BMC Bioinform., 2009, 10, p S65. https://doi.org/10.1186/1471-2105-10-S1-S6510.1186/1471-2105-10-S1-S65 ↗
48. R.E. Schapire (1999) A brief introduction to boosting. In: Ijcai. Citeseer, pp 1401–1406
49. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001. IEEE, pp I–I
50. P. Bartlett, Y. Freund, W.S. Lee, and R.E. Schapire, Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods, Ann. Stat., 1998, 26, p 1651–1686.10.1214/aos/1024691352 ↗
51. D.H. Wolpert, Stacked Generalization, Neural Netw., 1992, 5, p 241–259.10.1016/S0893-6080(05)80023-1 ↗
52. W. Wang, F Harrou, B. Bouyeddou et al. (2022) A stacked deep learning approach to cyber-attacks detection in industrial systems: application to power system and gas pipeline systems. Cluster Comput 1–1810.1007/s10586-021-03426-w ↗
53. W. Wang, F. Harrou, A. Dairi, and Y. Sun, Stacked Deep Learning Approach for Efficient SARS-CoV-2 Detection in Blood Samples, Artif. Intell. Med., 2024, 148, p 102767.10.1016/j.artmed.2024.102767 ↗
54. S.M. Lundberg, S.-I. Lee (2017) A Unified Approach to Interpreting Model Predictions. In: Guyon I, Luxburg U Von, Bengio S, et al (eds) Advances in Neural Information Processing Systems. Curran Associates, Inc
55. S.M. Lundberg, G. Erion, H. Chen et al., From Local Explanations to Global Understanding with Explainable AI for Trees, Nat Mach Intell, 2020, 2, p 56–67. https://doi.org/10.1038/s42256-019-0138-910.1038/s42256-019-0138-9 ↗
56. D. Abdelhakim, F. Harrou, Y. Sun, et al. Explainable Machine Learning for Enhancing Predictive Accuracy of Cutting Forces in Hard Turning Processes. Int. J. Adv. Manuf. Technol. (2024) 1–2310.1007/s00170-024-14470-2 ↗
57. M. Al Saleem, F. Harrou, and Y. Sun, Explainable Machine Learning Methods for Predicting Water Treatment Plant Features Under Varying Weather Conditions, Results Eng., 2024, 21, p 101930.10.1016/j.rineng.2024.101930 ↗
58. M.T. Ribeiro, S. Singh, C. Guestrin (2016) Model-agnostic interpretability of machine learning. arXiv Prepr arXiv160605386
59. M.T. Ribeiro, S. Singh, C. Guestrin (2016) Why should i trust you? Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. pp 1135–114410.1145/2939672.2939778 ↗
60. Tastle W (2010) Steel Plates Faults
61. F. Harrou, Y. Sun, A.S. Hering et al (2020) Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications. Elsevier
62. N.V. Chawla, K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer, SMOTE: Synthetic Minority Over-Sampling Technique, J. Artif. Intell. Res., 2002, 16, p 321–357.10.1613/jair.953 ↗