Çelik Levha Arıza Tespiti için Makine Öğrenimi Algoritmalarının Karşılaştırmalı Analizi

TAŞAR, Beyda

doi:10.29130/dubited.1058467

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…Deep learning-based SPSD technology not only improves surface quality but also supports subsequent maintenance and upgrading of steel plate production and equipment. The data shows that it can achieve defects classification and location detection, and the location coordinates and area size of defects can be obtained in real-time and saved in a specified document [4][5][6]. Deep learning technology can not only provide a reliable basis for the production and maintenance of steel plates but also support technicians in upgrading steel plate production equipment, further improving production efficiency and increasing revenue generation.…”

Section: Introductionmentioning

confidence: 99%

Application of AI intelligent vision detection technology using deep learning algorithm

Huang

2023

J. meas. eng.

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This study aims to design efficient and reliable artificial intelligence vision detection models to improve detection efficiency and accuracy. The study filters defect-free images by image preprocessing and region of interest detection techniques. AlexNet network is enhanced by introducing attention mechanism modules, deep separable convolutions, and more to effectively boost the network's feature extraction capacity. An area convolutional neural network is developed to rapidly identify and locate defects on steel plate surfaces, utilizing an enhanced AlexNet network for feature extraction. Results demonstrated that the algorithm attained an average detection rate of 98 % and can identify defects in a minimal time of only 0.0011 seconds. For the detection of six types of steel plate defects, the average accuracy of the optimized fast regional convolutional neural network reached more than 0.9, especially for the detection of small-size defects with excellent performance. This improved AlexNet network has a great advantage in F1 value. The conclusion of the study shows that the designed artificial intelligence vision detection model has high detection accuracy, speed, and performance stability in steel plate surface defect detection and has a wide range of application prospects.

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Section: Introductionmentioning

confidence: 99%

Application of AI intelligent vision detection technology using deep learning algorithm

Huang

2023

J. meas. eng.

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A Survey of Deep learning in Advancing Steel Industry Standards

Aghapanah,

Rad,

Rasti

2024

2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP)

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A Method to Classify Steel Plate Faults Based on Ensemble Learning

Özkat

2022

Journal of Materials and Mechatronics: A

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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 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 Center 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 learning process that determine 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 less 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.

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Çelik Levha Arıza Tespiti için Makine Öğrenimi Algoritmalarının Karşılaştırmalı Analizi

Cited by 3 publications

References 20 publications

Application of AI intelligent vision detection technology using deep learning algorithm

Application of AI intelligent vision detection technology using deep learning algorithm

A Survey of Deep learning in Advancing Steel Industry Standards

A Method to Classify Steel Plate Faults Based on Ensemble Learning

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