TY  - JOUR
T1  - ENHANCED PRODUCTION QUALITY PREDICTION IN COLD ROLLING PROCESSES USING TABTRANSFORMER AND MACHINE LEARNING ALGORITHMS
AU  - Balcıoğlu, Yavuz Selim
AU  - Göksu, Semih
AU  - Sezen, Bülent
PY  - 2025
DA  - June
Y2  - 2025
DO  - 10.18038/estubtda.1590581
JF  - Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering
JO  - Estuscience - Se
PB  - Eskisehir Technical University
WT  - DergiPark
SN  - 2667-4211
SP  - 112
EP  - 131
VL  - 26
IS  - 2
LA  - en
AB  - In this study, the impact of production parameters on product quality in cold rolling processes was examined, and the qualitative status of products was predicted using machine learning algorithms. While existing literature focuses on production efficiency, this study stands out by systematically comparing eight different machine learning algorithms: Decision Tree, KNN, Naive Bayes, Logistic Regression, Random Forest, XGBoost, Support Vector Machines, and TabTransformer. The results reveal that TabTransformer, a transformer-based model designed for tabular data, outperforms the other algorithms in terms of accuracy and generalization capability, making significant contributions to the automation of quality control in production processes. Additionally, feature importance analysis provides critical insights into parameter optimization, making this study a valuable addition to the literature on industrial quality prediction.
KW  - Quality prediction
KW  - Cold rolling
KW  - Tabtransformer
KW  - Machine learning
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UR  - https://doi.org/10.18038/estubtda.1590581
L1  - https://dergipark.org.tr/en/download/article-file/4390434
ER  -