20.500.12556/DKUM-87711 Reduction of surface defects by optimization of casting speed using genetic programming an industrial case study Štore Steel Ltd. produces more than 200 different types of steel with a continuous caster installed in 2016. Several defects, mostly related to thermomechanical behaviour in the mould, originate from the continuous casting process. The same casting speed of 1.6 m/min was used for all steel grades. In May 2023, a project was launched to adjust the casting speed according to the casting temperature. This adjustment included the steel grades with the highest number of surface defects and different carbon content: 16MnCrS5, C22, 30MnVS5, and 46MnVS5. For every 10 °C deviation from the prescribed casting temperature, the speed was changed by 0.02 m/min. During the 2-month period, the ratio of rolled bars with detected surface defects (inspected by an automatic control line) decreased for the mentioned steel grades. The decreases were from 11.27 % to 7.93 %, from 12.73 % to 4.11 %, from 16.28 % to 13.40 %, and from 25.52 % to 16.99 % for 16MnCrS5, C22, 30MnVS5, and 46MnVS5, respectively. Based on the collected chemical composition and casting parameters from these two months, models were obtained using linear regression and genetic programming. These models predict the ratio of rolled bars with detected surface defects and the length of detected surface defects. According to the modelling results, the ratio of rolled bars with detected surface defects and the length of detected surface defects could be minimally reduced by 14 % and 189 %, respectively, using casting speed adjustments. A similar result was achieved from July to November 2023 by adjusting the casting speed for the other 27 types of steel. The same was predicted with the already obtained models. Genetic programming outperformed linear regression. continuous casting of steel surface defects automatic control machine learning modelling optimisation prediction linear regression genetic programming kontinuirano litje jekla površinske napake avtomatska kontrola strojno učenje modeliranje optimizacija napoved linearna regresija genetsko programiranje true false true Chair of Production Engineering (CPE), University of Maribor, Faculty of Mechanical Engineering Angleški jezik Slovenski jezik https://apem-journal.org/ Content from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Članek v reviji 2024-03-25 12:59:50 2024-03-25 12:59:51 2026-02-19 07:12:52 0000-00-00 00:00:00 2023 0 0 str. 501-511 no. 4 Vol. 18 Dec. 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2023-11-03 2023-12-21 2023-12-28 681.5:004.42 182007555 10.14743/apem2023.4.488 1854-6250 APEM18-4_501-511.pdf APEM18-4_501-511.pdf 1 8625823A90B4D8547F97A9EF4945E35E 8f9c69c6569b933870f62f4b7275188af914c24dd74b34959262998b1889627c 23194afc-eaa0-11ee-affd-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=177371 https://apem-journal.org/Archives/2023/APEM18-4_501-511.pdf 0 80709f0c-eb40-11ee-adef-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=177398 Fakulteta za strojništvo Advances in production engineering & management 0 0 0