Title: Improving production quality of a hot-rolling industrial process via genetic programming model
Authors: Alaa F. Sheta; Hossam Faris; Ertan Öznergiz
Addresses: Computers and Systems Department, Electronics Research Institute (ERI), El-Tahrir Street, Dokky, Giza 12622, Egypt ' Business Information Technology, The University of Jordan, Amman, Jordan ' Faculty of Naval Architecture and Maritime, Marine Engineering Operations Department, Yildiz Technical University, Istanbul, Turkey
Abstract: Satisfying the customers' need for manufacturing plants and the demand for high-quality products becomes more challenging nowadays. Manufacturers need to retain advanced attributes of their products by applying high-quality automation process. In this paper, a genetic programming (GP) approach is applied in order to develop three mathematical models for the force, torque and slab temperature in the hot-rolling industrial process. A frequency-based analysis using GP is performed to provide an insight into the process significant factors. The performance of the GP developed models is evaluated with respect to the known soft computing models explored in the literature. Experimental data were collected from the Ereğli Iron and Steel Factory in Turkey and used to test the performance of the GP models. Genetic programming shows better performance modelling capabilities compared with models-based artificial neural networks and fuzzy logic.
Keywords: production quality; hot rolling; genetic programming; neural networks; fuzzy logic; mathematical modelling; force; torque; slab temperature; artificial neural networks; ANNs.
International Journal of Computer Applications in Technology, 2014 Vol.49 No.3/4, pp.239 - 250
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 05 Jun 2014 *