A Method to Classify Steel Plate Faults Based on Ensemble Learning
Yıl: 2022 Cilt: 3 Sayı: 2 Sayfa Aralığı: 240 - 256 Metin Dili: İngilizce DOI: 10.55546/jmm.1161542 İndeks Tarihi: 23-12-2022
A Method to Classify Steel Plate Faults Based on Ensemble Learning
Öz: With the industrial revolution 4.0, machine learning methods are widely used in all aspects of manufacturing to perform quality prediction, fault diagnosis, or maintenance. In the steel industry, it is important to precisely detect faults/defects in order to produce high-quality steel plates. However, determining the exact first-principal model between process parameters and mechanical properties is a challenging process. In addition, steel plate defects are detected through manual, costly, and less productive offline inspection in the traditional manufacturing process of steel. Therefore, it is a great necessity to enable the automatic detection of steel plate faults. To this end, this study explores the capabilities of the following three machine learning models Adaboost, Bagging, and Random Forest in detecting steel plate faults. The well-known steel plate failure dataset provided by Communication Sciences Research Centre Semeion was used in this study. The aim of many studies using this dataset is to correctly classify defects in steel plates using traditional machine learning models, ignoring the applicability of the developed models to real-world problems. Manufacturing is a dynamic process with constant adjustments and improvements. For this reason, it is necessary to establish a learning process that determines the best model based on the arrival of new information. Contrary to previous studies on the steel plate failure dataset, this article presents a systematic modelling approach that includes the normalization step in the data preparation stage to reduce the effects of outliers, the feature selection step in the dimension reduction stage to develop a machine learning model with fewer inputs, and hyperparameter optimization step in the model development stage to increase the accuracy of the machine learning model. The performances of the developed machine learning models were compared according to statistical metrics in terms of precision, recall, sensitivity, and accuracy. The results revealed that AdaBoost performed well on this dataset, achieving accuracy scores of 93.15% and 91.90% for the training and test datasets, respectively.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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| APA | Ozkat E (2022). A Method to Classify Steel Plate Faults Based on Ensemble Learning. , 240 - 256. 10.55546/jmm.1161542 |
| Chicago | Ozkat Erkan Caner A Method to Classify Steel Plate Faults Based on Ensemble Learning. (2022): 240 - 256. 10.55546/jmm.1161542 |
| MLA | Ozkat Erkan Caner A Method to Classify Steel Plate Faults Based on Ensemble Learning. , 2022, ss.240 - 256. 10.55546/jmm.1161542 |
| AMA | Ozkat E A Method to Classify Steel Plate Faults Based on Ensemble Learning. . 2022; 240 - 256. 10.55546/jmm.1161542 |
| Vancouver | Ozkat E A Method to Classify Steel Plate Faults Based on Ensemble Learning. . 2022; 240 - 256. 10.55546/jmm.1161542 |
| IEEE | Ozkat E "A Method to Classify Steel Plate Faults Based on Ensemble Learning." , ss.240 - 256, 2022. 10.55546/jmm.1161542 |
| ISNAD | Ozkat, Erkan Caner. "A Method to Classify Steel Plate Faults Based on Ensemble Learning". (2022), 240-256. https://doi.org/10.55546/jmm.1161542 |
| APA | Ozkat E (2022). A Method to Classify Steel Plate Faults Based on Ensemble Learning. Journal of Materials and Mechatronics:A (Online), 3(2), 240 - 256. 10.55546/jmm.1161542 |
| Chicago | Ozkat Erkan Caner A Method to Classify Steel Plate Faults Based on Ensemble Learning. Journal of Materials and Mechatronics:A (Online) 3, no.2 (2022): 240 - 256. 10.55546/jmm.1161542 |
| MLA | Ozkat Erkan Caner A Method to Classify Steel Plate Faults Based on Ensemble Learning. Journal of Materials and Mechatronics:A (Online), vol.3, no.2, 2022, ss.240 - 256. 10.55546/jmm.1161542 |
| AMA | Ozkat E A Method to Classify Steel Plate Faults Based on Ensemble Learning. Journal of Materials and Mechatronics:A (Online). 2022; 3(2): 240 - 256. 10.55546/jmm.1161542 |
| Vancouver | Ozkat E A Method to Classify Steel Plate Faults Based on Ensemble Learning. Journal of Materials and Mechatronics:A (Online). 2022; 3(2): 240 - 256. 10.55546/jmm.1161542 |
| IEEE | Ozkat E "A Method to Classify Steel Plate Faults Based on Ensemble Learning." Journal of Materials and Mechatronics:A (Online), 3, ss.240 - 256, 2022. 10.55546/jmm.1161542 |
| ISNAD | Ozkat, Erkan Caner. "A Method to Classify Steel Plate Faults Based on Ensemble Learning". Journal of Materials and Mechatronics:A (Online) 3/2 (2022), 240-256. https://doi.org/10.55546/jmm.1161542 |
