Comparison Analysis of Machine Learning Algorithms for Steel Plate Fault Detection

Beyda Taşar

doi:10.29130/dubited.1058467

EN TR

Comparison Analysis of Machine Learning Algorithms for Steel Plate Fault Detection

Abstract

Metals are one of the most important building materials of modern times. Especially the production and metalworking process of flat metal sheets is very sensitive. Control of the manufacturing process affects not only the intermediate products but also the quality of final products. Early detection of defects on steel plate surfaces is an important task in industrial production. Process control and mistake detection have traditionally been done manually by experts. However, this method is not proper in terms of both time and cost. With the industrial revolution IR 4.0, machine learning (ML) techniques have been developed to solve fault detection problems in products. This study focuses on developing basic machine learning methods for the detection of six different error classes that may occur during production on steel surfaces. Five standard ML models: LD, KNN, DT, SVM, RF, and deep learning (DNN) model: one-dimensional DNN was developed for the classification problem. The UCI steel plate deformation data set was used as the experimental data set. Five performance criteria: Accuracy, Sensitivity, Specificity, Precision, and F1 value were used to determine the success of the methods. The success rates of LD, KNN, DT, SVM, RF and DNN classification methods were 90.136%, 91.7880%, 93.013%, 93.287%, 95.479%, 96.986%, respectively. The results show the significant impact of the machine learning approach on the steel plate fault diagnosis problem.

Keywords

Steel Plate Defect, Fault Detection, Machine Learning

Çelik Levha Arıza Tespiti için Makine Öğrenimi Algoritmalarının Karşılaştırmalı Analizi

Abstract

Metaller, modern zamanların en önemli yapı malzemelerinden biridir. Özellikle yassı metal sacın üretim ve işleme süreci oldukça hassastır. Üretim sürecinin kontrolü sadece ara ürünlerin değil, aynı zamanda son ürünlerinde kalitesini etkiler. Çelik levha yüzeylerinde oluşan hataların erken tespiti, endüstriyel üretimde önemli bir görevdir. Geleneksel olarak süreç kontrolü ve hata tespiti uzman kişiler tarafından manuel olarak yapılmaktadır. Ancak bu yöntem hem zaman hem de maliyet açısından uygun değildir. Sanayi devrimi IR 4.0 ile ürünlerde hata tespit problemlerini çözmek için makine öğrenimi (ML) teknikleri geliştirilmiştir. Bu çalışma, çelik yüzeyde üretim esnasında oluşabilecek altı farklı hata sınıfının tespiti için temel makine öğrenme yöntemleri geliştirmeye odaklanmıştır. Sınıflandırma problemi için beş standart ML modeli: LD, KNN, DT, SVM, RF ve bir derin öğrenme (DNN) modeli: tek boyutlu DNN geliştirilmiştir. Deneysel veri seti olarak UCI çelik plaka deformasyon veri seti kullanılmıştır. Yöntemlerin başarısını tespit etmek için beş performans kriteri: Doğruluk, Duyarlılık, Özgüllük, Kesinlik, F1 değeri kullanılmıştır. LD, KNN, DT, SVM, RF ve DNN sınıflandırma yöntemlerinin başarı oranları sırasıyla 90.136%, 91.780%, 93.013%, 93.287%, 95.479%, 96.986% olarak elde edilmiştir. Sonuçlar, makina öğrenmesi yaklaşımının çelik levha arıza teşhis problemindeki önemli etkisini gösterilmiştir.

Keywords

Çelik Levha Arızası, Arıza Tespiti, Makine Öğrenimi

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Beyda Taşar ^*
0000-0002-4689-8579
Türkiye

Publication Date

July 31, 2022

Submission Date

January 16, 2022

Acceptance Date

April 20, 2022

Published in Issue

Year 2022 Volume: 10 Number: 3

DOI

https://doi.org/10.29130/dubited.1058467

IZ

https://izlik.org/JA55TZ69LW

APA

Taşar, B. (2022). Comparison Analysis of Machine Learning Algorithms for Steel Plate Fault Detection. Duzce University Journal of Science and Technology, 10(3), 1578-1588. https://doi.org/10.29130/dubited.1058467

AMA

1.Taşar B. Comparison Analysis of Machine Learning Algorithms for Steel Plate Fault Detection. DUBİTED. 2022;10(3):1578-1588. doi:10.29130/dubited.1058467

Chicago

Taşar, Beyda. 2022. “Comparison Analysis of Machine Learning Algorithms for Steel Plate Fault Detection”. Duzce University Journal of Science and Technology 10 (3): 1578-88. https://doi.org/10.29130/dubited.1058467.

EndNote

Taşar B (July 1, 2022) Comparison Analysis of Machine Learning Algorithms for Steel Plate Fault Detection. Duzce University Journal of Science and Technology 10 3 1578–1588.

IEEE

[1]B. Taşar, “Comparison Analysis of Machine Learning Algorithms for Steel Plate Fault Detection”, DUBİTED, vol. 10, no. 3, pp. 1578–1588, July 2022, doi: 10.29130/dubited.1058467.

ISNAD

Taşar, Beyda. “Comparison Analysis of Machine Learning Algorithms for Steel Plate Fault Detection”. Duzce University Journal of Science and Technology 10/3 (July 1, 2022): 1578-1588. https://doi.org/10.29130/dubited.1058467.

JAMA

1.Taşar B. Comparison Analysis of Machine Learning Algorithms for Steel Plate Fault Detection. DUBİTED. 2022;10:1578–1588.

MLA

Taşar, Beyda. “Comparison Analysis of Machine Learning Algorithms for Steel Plate Fault Detection”. Duzce University Journal of Science and Technology, vol. 10, no. 3, July 2022, pp. 1578-8, doi:10.29130/dubited.1058467.

Vancouver

1.Beyda Taşar. Comparison Analysis of Machine Learning Algorithms for Steel Plate Fault Detection. DUBİTED. 2022 Jul. 1;10(3):1578-8. doi:10.29130/dubited.1058467

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Düzce Üniversitesi Bilim ve Teknoloji Dergisi

https://doi.org/10.29130/dubited.1628311

Comparison Analysis of Machine Learning Algorithms for Steel Plate Fault Detection

Comparison Analysis of Machine Learning Algorithms for Steel Plate Fault Detection

Abstract

Keywords

Çelik Levha Arıza Tespiti için Makine Öğrenimi Algoritmalarının Karşılaştırmalı Analizi

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

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