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Tabatabaei M S, Yazdian-Dehkordi M, Jahangard Rafsanjani A. Predicting Dimensional Deviation of Ceramic Tiles using Machine Learning Methods. Journal of Iranian Association of Electrical and Electronics Engineers 2022; 19 (2) :201-208
URL: http://jiaeee.com/article-1-1035-en.html
Department of Computer Engineering, Faculty of Computer Engineering, Yazd University
Abstract:   (1274 Views)
In recent years, machine learning approaches play an important role in quality identification of  manufactured products including tiles and ceramics. Deviation of tile dimensions is one the main challenge in ceramic and tile industry. Prediction of this deformation will be beneficial if it can be predicted before producing the tile. In this paper, an automatic system has been proposed to predict the deviation of the ceramic tiles. Besides, a machine learning approach is utilized to identify the most effective parameters that leads to tiles’ defect. In this way, three different classification approaches including logistic regression, random forest, and support vector machine have been studied and the best solution is determined for this purpose. Moreover, several feature sets and forward feature selection method have been employed to select more effective variables on our decision making. The experimental results conducted on real-world dataset show that, random forest approach achieves better performance than others, and the results illustrate that improper temperature parameters has more effect on tile deviation.
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Type of Article: Research | Subject: Control
Received: 2019/12/10 | Accepted: 2021/02/20 | Published: 2022/06/24

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