{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:12:36Z","timestamp":1778346756180,"version":"3.51.4"},"reference-count":87,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T00:00:00Z","timestamp":1681862400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T00:00:00Z","timestamp":1681862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["EXC 2023 Internet of Production \u2013 390621612"],"award-info":[{"award-number":["EXC 2023 Internet of Production \u2013 390621612"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007210","name":"RWTH Aachen University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100007210","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Rolling is a well-established forming process employed in many industrial sectors. Although highly optimized, process disruptions can still lead to undesired final mechanical properties. This paper demonstrates advances in pass schedule design based on reinforcement learning and analytical rolling models to guarantee sound product quality. Integrating an established physical strengthening model into an analytical rolling model allows tracking the microstructure evolution throughout the process, and furthermore the prediction of the yield strength and ultimate tensile strength of the rolled sheet. The trained reinforcement learning algorithm Deep Deterministic Policy Gradient (DDPG) automatically proposes pass schedules by drawing upon established scheduling rules combined with novel rule sets to maximize the final mechanical properties. The designed pass schedule is trialed using a laboratory rolling mill while the predicted properties are confirmed using micrographs and materials testing. Due to its fast calculation time, prospectively this technique can be extended to also account for significant process disruptions such as longer inter-pass times by adapting the pass schedule online to still reach the desired mechanical properties and avoid scrapping of the material.<\/jats:p>","DOI":"10.1007\/s10845-023-02115-2","type":"journal-article","created":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T08:02:39Z","timestamp":1681891359000},"page":"1469-1490","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Coupling of an analytical rolling model and reinforcement learning to design pass schedules: towards properties controlled hot rolling"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6460-2850","authenticated-orcid":false,"given":"C.","family":"Idzik","sequence":"first","affiliation":[]},{"given":"A.","family":"Kr\u00e4mer","sequence":"additional","affiliation":[]},{"given":"G.","family":"Hirt","sequence":"additional","affiliation":[]},{"given":"J.","family":"Lohmar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,19]]},"reference":[{"key":"2115_CR1","volume-title":"Sustainable materials: With both eyes open; [future buildings, vehicles, products and equipment - made efficiently and made with less new material]","author":"JM Allwood","year":"2012","unstructured":"Allwood, J. M., Cullen, J. M., & Carruth, M. A. (2012). Sustainable materials: With both eyes open; [future buildings, vehicles, products and equipment - made efficiently and made with less new material]. UIT Cambridge."},{"key":"2115_CR2","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1063\/1.1750380","volume":"7","author":"M Avrami","year":"1939","unstructured":"Avrami, M. (1939). Kinetics of phase change: General theory. The Journal of Chemical Physics, 7, 1103\u20131112. https:\/\/doi.org\/10.1063\/1.1750380","journal-title":"The Journal of Chemical Physics"},{"issue":"3","key":"2115_CR3","doi-asserted-by":"publisher","first-page":"359","DOI":"10.2355\/isijinternational.32.359","volume":"32","author":"JH Beynon","year":"1992","unstructured":"Beynon, J. H., & Sellars, C. M. (1992). Modelling microstructure and its effects during multipass hot rolling. The Iron and Steel Institute of Japan, 32(3), 359\u2013367.","journal-title":"The Iron and Steel Institute of Japan"},{"key":"2115_CR4","unstructured":"Buchholz, F.-G. (1976). Berechnung und Optimierung von Stichpl\u00e4nen f\u00fcr den station\u00e4ren Betrieb kontinuierlicher Kalt- und Warmwalzstra\u00dfen. Dissertation. Technische Hochschule, M\u00fcnchen."},{"key":"2115_CR5","first-page":"34","volume-title":"Hamilton, Ontario, Canada, 26\u201329 August","author":"P Choquet","year":"1990","unstructured":"Choquet, P., Fabr\u00e8gue, P., Giusti, J., Chamont, B., Pezant, J. N., & Blanchet, F. (1990). Modelling of forces, structure and final properties during the hot rolling process on the hot strip mill. In S. Yue (Ed.), Hamilton, Ontario, Canada, 26\u201329 August (pp. 34\u201343). Montreal, Canada: Canadian Institute of Mining and Metallurgy."},{"key":"2115_CR6","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.optlaseng.2019.01.011","volume":"117","author":"H Di","year":"2019","unstructured":"Di, H., Ke, X., Peng, Z., & Dongdong, Z. (2019). Surface defect classification of steels with a new semi-supervised learning method. Optics and Lasers in Engineering, 117, 40\u201348. https:\/\/doi.org\/10.1016\/j.optlaseng.2019.01.011","journal-title":"Optics and Lasers in Engineering"},{"key":"2115_CR7","first-page":"1","volume-title":"Multiobjective reinforcement learning for reconfigurable adaptive optimal control of manufacturing processes","author":"J Dornheim","year":"2018","unstructured":"Dornheim, J., & Link, N. (2018). Multiobjective reinforcement learning for reconfigurable adaptive optimal control of manufacturing processes (pp. 1\u20135). IEEE."},{"key":"2115_CR8","doi-asserted-by":"publisher","first-page":"1593","DOI":"10.1007\/s12555-019-0120-7","volume":"18","author":"J Dornheim","year":"2019","unstructured":"Dornheim, J., Link, N., & Gumbsch, P. (2019). Model-free adaptive optimal control of episodic fixed-horizon manufacturing processes using reinforcement learning. International Journal of Control, Automation and Systems, 18, 1593\u20131604. https:\/\/doi.org\/10.1007\/s12555-019-0120-7","journal-title":"International Journal of Control, Automation and Systems"},{"key":"2115_CR9","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1007\/BF02672567","volume":"21A","author":"DV Edmonds","year":"1990","unstructured":"Edmonds, D. V., & Cochrane, R. C. (1990). Structure-property relationships in Bainitic steels. Metallurgical Transactions A, 21A, 1527\u20131540.","journal-title":"Metallurgical Transactions A"},{"key":"2115_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-6670(17)67672-9","volume-title":"A new mathematical model for plate mill control: Construction department","author":"S Fujii","year":"1975","unstructured":"Fujii, S., & Saito, M. (1975). A new mathematical model for plate mill control: Construction department. Nippon Kokan K.K."},{"key":"2115_CR11","doi-asserted-by":"publisher","DOI":"10.18178\/ijmerr.10.7.349-356","author":"O Gamal","year":"2021","unstructured":"Gamal, O., Mohamed, M. I. P., Patel, C. G., & Roth, H. (2021). Data-driven model-free intelligent roll gap control of bar and wire hot rolling process using reinforcement learning. International Journal of Mechanical Engineering and Robotics Research. https:\/\/doi.org\/10.18178\/ijmerr.10.7.349-356","journal-title":"International Journal of Mechanical Engineering and Robotics Research"},{"key":"2115_CR12","first-page":"916","volume":"210","author":"T Gladman","year":"1972","unstructured":"Gladman, T., McIvor, I. D., & Pickering, F. B. (1972). Some aspects of the structure-property relationships in high-carbon ferrite-pearlite steels. Journal of the Iron and Steel Institute, 210, 916\u2013930.","journal-title":"Journal of the Iron and Steel Institute"},{"key":"2115_CR13","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1016\/j.protcy.2014.09.007","volume":"15","author":"J G\u00fcnther","year":"2014","unstructured":"G\u00fcnther, J., Pilarski, P. M., Helfrich, G., Shen, H., & Diepold, K. (2014). First steps towards an intelligent laser welding architecture using deep neural networks and reinforcement learning. Procedia Technology, 15, 474\u2013483. https:\/\/doi.org\/10.1016\/j.protcy.2014.09.007","journal-title":"Procedia Technology"},{"key":"2115_CR14","doi-asserted-by":"publisher","unstructured":"Guo, P., & Yu, J. (2019). Optimal control of blank holder force based on deep reinforcement learning. In 2019 IEEE international conference on industrial engineering and engineering management (IEEM), Macao, Macao, 15.12.2019\u201318.12.2019 (pp. 1466\u20131470). IEEE. https:\/\/doi.org\/10.1109\/IEEM44572.2019.8978743.","DOI":"10.1109\/IEEM44572.2019.8978743"},{"key":"2115_CR15","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1088\/0370-1301\/64\/9\/303","volume":"64","author":"EO Hall","year":"1951","unstructured":"Hall, E. O. (1951). The deformation and ageing of mild steel: III discussion of results. Proceedings of the Physical Society: Section B, 64, 747\u2013753. https:\/\/doi.org\/10.1088\/0370-1301\/64\/9\/303","journal-title":"Proceedings of the Physical Society: Section B"},{"key":"2115_CR16","volume-title":"Kraft- und Arbeitsbedarf bildsamer Formgebungsverfahren","author":"A Hensel","year":"1978","unstructured":"Hensel, A., & Spittel, T. (1978). Kraft- und Arbeitsbedarf bildsamer Formgebungsverfahren. VEB Deutscher Verlag f\u00fcr Grundstoffindustrie."},{"key":"2115_CR17","doi-asserted-by":"publisher","first-page":"3373","DOI":"10.1557\/adv.2019.436","volume":"4","author":"CA Hern\u00e1ndez Carre\u00f3n","year":"2019","unstructured":"Hern\u00e1ndez Carre\u00f3n, C. A., Mancilla Tolama, J. E., Castilla Valdez, G., & Hern\u00e1ndez Gonz\u00e1lez, I. (2019). Multi-objective optimization of the hot rolling scheduling of steel using a genetic algorithm. MRS Advances, 4, 3373\u20133380. https:\/\/doi.org\/10.1557\/adv.2019.436","journal-title":"MRS Advances"},{"key":"2115_CR18","doi-asserted-by":"publisher","first-page":"1329","DOI":"10.2355\/isijinternational.32.1329","volume":"32","author":"PD Hodgson","year":"1992","unstructured":"Hodgson, P. D., & Gibbs, R. K. (1992). A mathematical model to predict the mechanical properties of hot rolled C-Mn and microalloyed steels. ISIJ International, 32, 1329\u20131338. https:\/\/doi.org\/10.2355\/isijinternational.32.1329","journal-title":"ISIJ International"},{"key":"2115_CR19","doi-asserted-by":"publisher","first-page":"153123","DOI":"10.1109\/ACCESS.2020.3016725","volume":"8","author":"R Hwang","year":"2020","unstructured":"Hwang, R., Jo, H., Kim, K. S., & Hwang, H. J. (2020). Hybrid model of mathematical and neural network formulations for rolling force and temperature prediction in hot rolling processes. IEEE Access, 8, 153123\u2013153133. https:\/\/doi.org\/10.1109\/ACCESS.2020.3016725","journal-title":"IEEE Access"},{"key":"2115_CR20","doi-asserted-by":"publisher","unstructured":"Jakubowski, J., Stanisz, P., Bobek, S., & Nalepa, G. J. (2021\u20132021). Explainable anomaly detection for hot-rolling industrial process. In 2021 IEEE 8th international conference on data science and advanced analytics (DSAA), Porto, Portugal, 06.10.2021\u201309.10.2021 (pp. 1\u201310). IEEE. https:\/\/doi.org\/10.1109\/DSAA53316.2021.9564228.","DOI":"10.1109\/DSAA53316.2021.9564228"},{"issue":"8","key":"2115_CR21","first-page":"396","volume":"135","author":"WA Johnson","year":"1939","unstructured":"Johnson, W. A., & Mehl, R. F. (1939). Reaction kinetics in processes of nucleation and growth. Transactions of the Metallurgical Society of AIME, 135(8), 396\u2013415.","journal-title":"Transactions of the Metallurgical Society of AIME"},{"issue":"1","key":"2115_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1179\/mtlr.1969.14.1.1","volume":"14","author":"JJ Jonas","year":"1969","unstructured":"Jonas, J. J., Sellars, C. M., & Tegart, W. J. (1969). Strength and structure under hot-working conditions: Review 130. Metallurgical Reviews, 14(1), 1\u201324.","journal-title":"Metallurgical Reviews"},{"key":"2115_CR23","unstructured":"Jonsson, N.-G., & M\u00e4ntyl\u00e4, P. (1985). On-line control system for profile, shape and temperature in 4-high mills. Proceedings of the 27th mechanical working & steel processings conference, 129\u2013136."},{"key":"2115_CR24","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1007\/s40192-018-0116-9","volume":"7","author":"AR Kitahara","year":"2018","unstructured":"Kitahara, A. R., & Holm, E. A. (2018). Microstructure cluster analysis with transfer learning and unsupervised learning. Integrating Materials and Manufacturing Innovation, 7, 148\u2013156. https:\/\/doi.org\/10.1007\/s40192-018-0116-9","journal-title":"Integrating Materials and Manufacturing Innovation"},{"key":"2115_CR25","first-page":"355","volume":"1","author":"VL Kolmogorov","year":"1937","unstructured":"Kolmogorov, V. L. (1937). On the statistical theory of the crystallization of metals. Bulletin of the Russian Academy of Sciences, 1, 355\u2013359.","journal-title":"Bulletin of the Russian Academy of Sciences"},{"key":"2115_CR26","unstructured":"Konda, V., & Tsitsiklis, J. (2001). Actor-critic algorithms. Society for Industrial and Applied Mathematics, 42."},{"key":"2115_CR27","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1016\/S0924-0136(98)00151-4","volume":"80\u201381","author":"P Korczak","year":"1998","unstructured":"Korczak, P., Dyja, H., & \u0141abuda, E. (1998). Using neural network models for predicting mechanical properties after hot plate rolling processes. Journal of Materials Processing Technology, 80\u201381, 481\u2013486. https:\/\/doi.org\/10.1016\/S0924-0136(98)00151-4","journal-title":"Journal of Materials Processing Technology"},{"key":"2115_CR28","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1179\/030634582790427433","volume":"16","author":"CAN Lanzillotto","year":"2013","unstructured":"Lanzillotto, C. A. N., & Pickering, F. B. (2013). Structure\u2013property relationships in dual-phase steels. Metal Science, 16, 371\u2013382. https:\/\/doi.org\/10.1179\/030634582790427433","journal-title":"Metal Science"},{"key":"2115_CR29","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/S0924-0136(98)00206-4","volume":"80\u201381","author":"J Larkiola","year":"1998","unstructured":"Larkiola, J., Myllykoski, P., Korhonen, A. S., & Cser, L. (1998). The role of neural networks in the optimisation of rolling processes. Journal of Materials Processing Technology, 80\u201381, 16\u201323. https:\/\/doi.org\/10.1016\/S0924-0136(98)00206-4","journal-title":"Journal of Materials Processing Technology"},{"key":"2115_CR30","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1016\/j.engappai.2004.03.008","volume":"17","author":"DM Lee","year":"2004","unstructured":"Lee, D. M., & Choi, S. (2004). Application of on-line adaptable neural network for the rolling force set-up of a plate mill. Engineering Applications of Artificial Intelligence, 17, 557\u2013565. https:\/\/doi.org\/10.1016\/j.engappai.2004.03.008","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"2115_CR31","volume-title":"Mathematical and physical simulation of the properties of hot rolled products","author":"JG Lenard","year":"1999","unstructured":"Lenard, J. G., Pietrzyk, M., & Cser, L. (1999). Mathematical and physical simulation of the properties of hot rolled products. Elsevier."},{"key":"2115_CR32","doi-asserted-by":"publisher","first-page":"3069","DOI":"10.1007\/s11771-012-1380-z","volume":"19","author":"W Li","year":"2012","unstructured":"Li, W., Liu, X., & Guo, Z. (2012). Multi-objective optimization for draft scheduling of hot strip mill. Journal of Central South University, 19, 3069\u20133078. https:\/\/doi.org\/10.1007\/s11771-012-1380-z","journal-title":"Journal of Central South University"},{"key":"2115_CR33","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.procir.2013.05.033","volume":"7","author":"D Lieber","year":"2013","unstructured":"Lieber, D., Stolpe, M., Konrad, B., Deuse, J., & Morik, K. (2013). Quality prediction in interlinked manufacturing processes based on supervised & unsupervised machine learning. Procedia CIRP, 7, 193\u2013198. https:\/\/doi.org\/10.1016\/j.procir.2013.05.033","journal-title":"Procedia CIRP"},{"key":"2115_CR34","unstructured":"Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y. et al. (2016). Continuous control with deep reinforcement learning. http:\/\/arxiv.org\/pdf\/1509.02971v6."},{"key":"2115_CR35","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/BF00992699","volume":"8","author":"L-J Lin","year":"1992","unstructured":"Lin, L.-J. (1992). Self-improving reactive agents based on reinforcement learning, planning and teaching. Machine Learning, 8, 293\u2013321. https:\/\/doi.org\/10.1007\/BF00992699","journal-title":"Machine Learning"},{"key":"2115_CR36","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1007\/s12652-018-0944-7","volume":"10","author":"L-L Liu","year":"2019","unstructured":"Liu, L.-L., Wan, X., Gao, Z., Li, X., & Feng, B. (2019). Research on modelling and optimization of hot rolling scheduling. Journal of Ambient Intelligence and Humanized Computing, 10, 1201\u20131216. https:\/\/doi.org\/10.1007\/s12652-018-0944-7","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"key":"2115_CR37","unstructured":"Lohmar, J., Bambach, M., Hirt, G., Kiefer, T., Kotliba, D., Jochum, M., et al. (2014a). Fast and accurate force prediction for high quality heavy plates by a state of the art rolling model calibrated from mill data via inverse techniques. In P. Darmayan & C. Lerouge (Eds.), ESTAD2014a, Paris, France, 07\u201308 April."},{"key":"2115_CR38","unstructured":"Lohmar, J., Seuren, S., Bambach, M., & Hirt, G. (2014b). Design and application of an advanced fast rolling model with through thickness resolution for heavy plate rolling. In J. Guzzoni & M. Manning (Eds.), Ingot casting, rolling & forging, Milan, Italy, 07\u201309 May."},{"key":"2115_CR40","unstructured":"Mahadevan, S., & Theocharous, G. (1998). Optimizing production manufacturing using reinforcement learning. In (Vol. 372, p. 377)."},{"key":"2115_CR41","unstructured":"M\u00e4ntyl\u00e4, P., Myllykoski, L., & Jonsson, N.-G. (1989). Rolling wide thin plates using the profile and shape vector method. Iron and Steel Engineer(November), 48\u201354."},{"key":"2115_CR42","unstructured":"Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., et al. (2013). Playing atari with deep reinforcement learning. http:\/\/arxiv.org\/pdf\/1312.5602v1."},{"key":"2115_CR43","doi-asserted-by":"publisher","first-page":"1159","DOI":"10.1243\/09544054JEM1417","volume":"223","author":"CH Moon","year":"2009","unstructured":"Moon, C. H., & Lee, Y. (2009). Methodology for draft schedule design of plate rolling process with peening effect considered. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223, 1159\u20131169. https:\/\/doi.org\/10.1243\/09544054JEM1417","journal-title":"Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture"},{"key":"2115_CR44","doi-asserted-by":"publisher","first-page":"1885","DOI":"10.3844\/ajassp.2006.1885.1889","volume":"3","author":"A Moussaoui","year":"2006","unstructured":"Moussaoui, A., Selaimia, Y., & Abbassi, H. A. (2006). Hybrid hot strip rolling force prediction using a Bayesian trained artificial neural network and analytical models. American Journal of Applied Sciences, 3, 1885\u20131889. https:\/\/doi.org\/10.3844\/ajassp.2006.1885.1889","journal-title":"American Journal of Applied Sciences"},{"key":"2115_CR45","doi-asserted-by":"publisher","first-page":"284","DOI":"10.2355\/isijinternational1966.24.284","volume":"24","author":"K Nakajima","year":"1984","unstructured":"Nakajima, K., Asamura, T., Kikuma, T., Matsumoto, H., Awazuhara, H., Kimura, T., et al. (1984). Hot strip crown control by six-high mill. Transactions ISIJ, 24, 284\u2013291.","journal-title":"Transactions ISIJ"},{"key":"2115_CR46","doi-asserted-by":"publisher","first-page":"212","DOI":"10.2355\/isijinternational1966.25.212","volume":"25","author":"K Nakajima","year":"1985","unstructured":"Nakajima, K., Kokai, K., Koike, M., Kikuma, T., Ataka, M., & Kako, Y. (1985). New plate mill draft scheduling system for crown and flatness control. Transactions ISIJ, 25, 212\u2013218.","journal-title":"Transactions ISIJ"},{"key":"2115_CR47","first-page":"422","volume":"6","author":"T Okamoto","year":"1975","unstructured":"Okamoto, T., Misaka, Y., Yokoi, T., Kise, K., & Fujimaki, I. (1975). The new advanced system of plate mill computer control. Proceedings World Congress of the International Federation of Automatic Controll 6th, 6, 422\u2013429.","journal-title":"Proceedings World Congress of the International Federation of Automatic Controll 6th"},{"key":"2115_CR48","doi-asserted-by":"publisher","first-page":"106606","DOI":"10.1016\/j.cie.2020.106606","volume":"151","author":"A \u00d6zg\u00fcr","year":"2021","unstructured":"\u00d6zg\u00fcr, A., Uygun, Y., & H\u00fctt, M.-T. (2021). A review of planning and scheduling methods for hot rolling mills in steel production. Computers & Industrial Engineering, 151, 106606. https:\/\/doi.org\/10.1016\/j.cie.2020.106606","journal-title":"Computers & Industrial Engineering"},{"key":"2115_CR49","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1179\/cmq.1992.31.3.217","volume":"31","author":"IC Ozsoy","year":"2013","unstructured":"Ozsoy, I. C., Ruddle, G. E., & Crawley, A. F. (2013). Optimum scheduling of a hot rolling process by nonlinear programming. Canadian Metallurgical Quarterly, 31, 217\u2013224. https:\/\/doi.org\/10.1179\/cmq.1992.31.3.217","journal-title":"Canadian Metallurgical Quarterly"},{"key":"2115_CR50","unstructured":"Pandey, V., Rao, P. S., Singh, S., & Pandey, M. (2020). A calculation procedure and optimization for pass scheduling in rolling process: A review, 126\u2013130."},{"key":"2115_CR51","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1080\/21642583.2021.1967220","volume":"9","author":"G Peng","year":"2021","unstructured":"Peng, G., Huang, K., & Wang, H. (2021). Dynamic multimode process monitoring using recursive GMM and KPCA in a hot rolling mill process. Systems Science & Control Engineering, 9, 592\u2013601. https:\/\/doi.org\/10.1080\/21642583.2021.1967220","journal-title":"Systems Science & Control Engineering"},{"key":"2115_CR52","first-page":"25","volume":"174","author":"NJ Petch","year":"1953","unstructured":"Petch, N. J. (1953). The cleavage strength of polycrystals. J. Iron Steel Inst., 174, 25\u201328.","journal-title":"J. Iron Steel Inst."},{"issue":"8","key":"2115_CR53","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1002\/srin.199000362","volume":"61","author":"M Pietrzyk","year":"1990","unstructured":"Pietrzyk, M., Kusiak, J., & Glowacki, M. (1990). Some aspects of development of models for automatic control of rolling mills. Steel Research International, 61(8), 359\u2013364.","journal-title":"Steel Research International"},{"key":"2115_CR54","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/S1006-706X(12)60135-6","volume":"19","author":"X Qi","year":"2012","unstructured":"Qi, X., Wang, T., & Xiao, H. (2012). Optimization of pass schedule in hot strip rolling. Journal of Iron and Steel Research International, 19, 25\u201328. https:\/\/doi.org\/10.1016\/S1006-706X(12)60135-6","journal-title":"Journal of Iron and Steel Research International"},{"key":"2115_CR55","unstructured":"Rath, S., Thakur, S. K., Mohapatra, S., & Karmakar, D. (2019). Application of machine learning in rolling mills: Case studies."},{"key":"2115_CR56","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.3390\/pr9071084","volume":"9","author":"N Reinisch","year":"2021","unstructured":"Reinisch, N., Rudolph, F., G\u00fcnther, S., Bailly, D., & Hirt, G. (2021). Successful pass schedule design in open-die forging using double deep Q-learning. Processes, 9, 1084. https:\/\/doi.org\/10.3390\/pr9071084","journal-title":"Processes"},{"key":"2115_CR57","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1037\/h0042519","volume":"65","author":"F Rosenblatt","year":"1958","unstructured":"Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65, 386\u2013408. https:\/\/doi.org\/10.1037\/h0042519","journal-title":"Psychological Review"},{"key":"2115_CR58","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533\u2013536. https:\/\/doi.org\/10.1038\/323533a0","journal-title":"Nature"},{"key":"2115_CR59","doi-asserted-by":"publisher","first-page":"3418","DOI":"10.1016\/j.proeng.2012.06.395","volume":"38","author":"P Saravanakumar","year":"2012","unstructured":"Saravanakumar, P., Jothimani, V., Sureshbabu, L., Ayyappan, S., Noorullah, D., & Venkatakrishnan, P. G. (2012). Prediction of mechanical properties of low carbon steel in hot rolling process using artificial neural network model. Procedia Engineering, 38, 3418\u20133425. https:\/\/doi.org\/10.1016\/j.proeng.2012.06.395","journal-title":"Procedia Engineering"},{"key":"2115_CR60","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1016\/j.promfg.2020.10.126","volume":"51","author":"C Scheiderer","year":"2020","unstructured":"Scheiderer, C., Thun, T., Idzik, C., Posada-Moreno, A. F., Kr\u00e4mer, A., Lohmar, J., et al. (2020). Simulation-as-a-service for reinforcement learning applications by example of heavy plate rolling processes. Procedia Manufacturing, 51, 897\u2013903. https:\/\/doi.org\/10.1016\/j.promfg.2020.10.126","journal-title":"Procedia Manufacturing"},{"key":"2115_CR61","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1002\/srin.201600047","volume":"87","author":"M Schmidtchen","year":"2016","unstructured":"Schmidtchen, M., & Kawalla, R. (2016). Fast Numerical simulation of symmetric flat rolling processes for inhomogeneous materials using a layer model\u2014part I: Basic theory. Steel Research International, 87, 1065\u20131081. https:\/\/doi.org\/10.1002\/srin.201600047","journal-title":"Steel Research International"},{"key":"2115_CR62","unstructured":"Sellars, C. M. (1979). The physical metallurgy of hot working. In C. M. Sellars & G. J. Davies (Eds.), Sheffield, England, 17\u201320 July (pp. 3\u201315). London: The Society."},{"key":"2115_CR63","unstructured":"Sellars, C. M., & Beynon, J. (1985). Conference on high strength low alloy steels, 142."},{"issue":"9","key":"2115_CR64","doi-asserted-by":"publisher","first-page":"1136","DOI":"10.1016\/0001-6160(66)90207-0","volume":"14","author":"CM Sellars","year":"1966","unstructured":"Sellars, C. M., & Tegart, W. J. (1966). On the mechanisms of hot deformation. Acta Metallurgica, 14(9), 1136\u20131138.","journal-title":"Acta Metallurgica"},{"key":"2115_CR65","unstructured":"Seuren, S., Bambach, M., Hirt, G., Heeg, R., & Philipp, M. (2010). Geometric factors for fast calculation of roll force in plate rolling. In Z-J.-Xuehui (Ed.), Peking, 15\u201317 September. Beijing: Metallurgical Industry Press."},{"key":"2115_CR66","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s11740-013-0500-4","volume":"1","author":"S Seuren","year":"2014","unstructured":"Seuren, S., Seitz, J., Kraemer, A. M., Bambach, M., & Hirt, G. (2014). Accounting for shear deformation in fast models for plate rolling. Production Engineering, 1, 17\u201324. https:\/\/doi.org\/10.1007\/s11740-013-0500-4","journal-title":"Production Engineering"},{"key":"2115_CR67","doi-asserted-by":"publisher","first-page":"100245","DOI":"10.1016\/j.mlwa.2021.100245","volume":"7","author":"S Shen","year":"2022","unstructured":"Shen, S., Guye, D., Ma, X., Yue, S., & Armanfard, N. (2022). Multistep networks for roll force prediction in hot strip rolling mill. Machine Learning with Applications, 7, 100245. https:\/\/doi.org\/10.1016\/j.mlwa.2021.100245","journal-title":"Machine Learning with Applications"},{"key":"2115_CR68","unstructured":"Shohet, K. N., & Townsend, N. A. (1968). Roll bending methods of crown control in four-high plate mills. Journal of the Iron and Steel Institute, 1088\u20131098."},{"issue":"37","key":"2115_CR69","first-page":"1563","volume":"45","author":"E Siebel","year":"1925","unstructured":"Siebel, E. (1925). Kr\u00e4fte und Materialfluss bei der bildsamen Formgebung. Stahl Und Eisen, 45(37), 1563\u20131566.","journal-title":"Stahl Und Eisen"},{"key":"2115_CR70","unstructured":"Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., & Riedmiller, M. (2014). Deterministic policy gradient algorithms. In Proceedings of the 31st international conference on machine learning (32nd ed., pp. 387\u2013395). Beijing, China."},{"key":"2115_CR71","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1038\/nature24270","volume":"550","author":"D Silver","year":"2017","unstructured":"Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., et al. (2017). Mastering the game of Go without human knowledge. Nature, 550, 354\u2013359. https:\/\/doi.org\/10.1038\/nature24270","journal-title":"Nature"},{"issue":"1","key":"2115_CR72","first-page":"191","volume":"168","author":"RB Sims","year":"1954","unstructured":"Sims, R. B. (1954). The calculation of roll force and torque in hot rolling mills. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 168(1), 191\u2013200.","journal-title":"Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science"},{"issue":"5","key":"2115_CR73","first-page":"261","volume":"3","author":"RB Sims","year":"1963","unstructured":"Sims, R. B., & Wright, H. (1963). Roll force and torque in hot rolling mills. Journal of the Iron and Steel Institute, 3(5), 261\u2013269.","journal-title":"Journal of the Iron and Steel Institute"},{"key":"2115_CR74","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1179\/026708304225022296","volume":"20","author":"AP Singh","year":"2013","unstructured":"Singh, A. P., Sengupta, D., Jha, S., Yallasiri, M. P., & Mishra, N. S. (2013). Predicting microstructural evolution and yield strength of microalloyed hot rolled steel plate. Materials Science and Technology, 20, 1317\u20131325. https:\/\/doi.org\/10.1179\/026708304225022296","journal-title":"Materials Science and Technology"},{"key":"2115_CR75","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1007\/s00170-016-9424-4","volume":"91","author":"S Spuzic","year":"2017","unstructured":"Spuzic, S., Narayanan, R., Kovacic, Z., Hapu Arachchige, D., & Abhary, K. (2017). Roll pass design optimisation. The International Journal of Advanced Manufacturing Technology, 91, 999\u20131005. https:\/\/doi.org\/10.1007\/s00170-016-9424-4","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"2115_CR76","volume-title":"Reinforcement learning: An introduction (Adaptive computation and machine learning)","author":"RS Sutton","year":"2018","unstructured":"Sutton, R. S., & Barto, A. (2018). Reinforcement learning: An introduction (Adaptive computation and machine learning). The MIT Press."},{"issue":"7","key":"2115_CR77","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1002\/srin.198405349","volume":"55","author":"I Szerenyi","year":"1984","unstructured":"Szerenyi, I. (1984). Schedule pass planning with dialogic computer program for reversing hot strip rolls. Arch. Eisenh\u00fcttenwesen, 55(7), 313\u2013320.","journal-title":"Arch. Eisenh\u00fcttenwesen"},{"key":"2115_CR78","first-page":"1633","volume":"10","author":"ME Taylor","year":"2009","unstructured":"Taylor, M. E., & Stone, P. (2009). Transfer learning for reinforcement learning domains: A survey. Journal of Machine Learning Research, 10, 1633\u20131685.","journal-title":"Journal of Machine Learning Research"},{"key":"2115_CR79","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1103\/PhysRev.36.823","volume":"36","author":"GE Uhlenbeck","year":"1930","unstructured":"Uhlenbeck, G. E., & Ornstein, L. S. (1930). On the theory of the brownian motion. Physical Review, 36, 823\u2013841. https:\/\/doi.org\/10.1103\/PhysRev.36.823","journal-title":"Physical Review"},{"issue":"2","key":"2115_CR80","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1002\/zamm.19250050213","volume":"5","author":"T von K\u00e1rm\u00e1n","year":"1925","unstructured":"von K\u00e1rm\u00e1n, T. (1925). Beitrag zur Theorie des Walzvorganges. Zeitschrift F\u00fcr Angewandte Mathematik Und Mechanik, 5(2), 139\u2013141.","journal-title":"Zeitschrift F\u00fcr Angewandte Mathematik Und Mechanik"},{"key":"2115_CR81","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1016\/S0952-1976(00)00016-6","volume":"13","author":"DD Wang","year":"2000","unstructured":"Wang, D. D., Tieu, A. K., de Boer, F. G., Ma, B., & Yuen, W. (2000). Toward a heuristic optimum design of rolling schedules for tandem cold rolling mills. Engineering Applications of Artificial Intelligence, 13, 397\u2013406.","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"2115_CR82","first-page":"805","volume":"33","author":"SR Wang","year":"1996","unstructured":"Wang, S. R., & Tseng, A. A. (1996). ISS Mech. Work Steel Processing Conference Proceedings, 33, 805\u2013818.","journal-title":"Work Steel Processing Conference Proceedings"},{"key":"2115_CR83","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1007\/s42243-018-0101-8","volume":"25","author":"S Wu","year":"2018","unstructured":"Wu, S., Zhou, X., Ren, J., Cao, G., Liu, Z., & Shi, N. (2018). Optimal design of hot rolling process for C-Mn steel by combining industrial data-driven model and multi-objective optimization algorithm. Journal of Iron and Steel Research International, 25, 700\u2013705. https:\/\/doi.org\/10.1007\/s42243-018-0101-8","journal-title":"Journal of Iron and Steel Research International"},{"key":"2115_CR84","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1080\/21693277.2016.1192517","volume":"4","author":"T Wuest","year":"2016","unstructured":"Wuest, T., Weimer, D., Irgens, C., & Thoben, K.-D. (2016). Machine learning in manufacturing: Advantages, challenges, and applications. Production & Manufacturing Research, 4, 23\u201345. https:\/\/doi.org\/10.1080\/21693277.2016.1192517","journal-title":"Production & Manufacturing Research"},{"key":"2115_CR85","doi-asserted-by":"publisher","first-page":"109201","DOI":"10.1016\/j.matdes.2020.109201","volume":"197","author":"Q Xie","year":"2021","unstructured":"Xie, Q., Suvarna, M., Li, J., Zhu, X., Cai, J., & Wang, X. (2021). Online prediction of mechanical properties of hot rolled steel plate using machine learning. Materials & Design, 197, 109201. https:\/\/doi.org\/10.1016\/j.matdes.2020.109201","journal-title":"Materials & Design"},{"key":"2115_CR86","doi-asserted-by":"publisher","unstructured":"Youkachen, S., Ruchanurucks, M., Phatrapomnant, T., & Kaneko, H. (2019 - 2019). Defect segmentation of hot-rolled steel strip surface by using convolutional auto-encoder and conventional image processing. In 2019 10th international conference of information and communication technology for embedded systems (IC-ICTES), Bangkok, Thailand, 25.03.2019\u201327.03.2019 (pp. 1\u20135). IEEE. doi:https:\/\/doi.org\/10.1109\/ICTEmSys.2019.8695928.","DOI":"10.1109\/ICTEmSys.2019.8695928"},{"key":"2115_CR87","doi-asserted-by":"publisher","first-page":"1848","DOI":"10.3390\/pr9101848","volume":"9","author":"D Zhang","year":"2021","unstructured":"Zhang, D., Du, L., & Gao, Z. (2021). Real-time parameter identification for forging machine using reinforcement learning. Processes, 9, 1848. https:\/\/doi.org\/10.3390\/pr9101848","journal-title":"Processes"},{"key":"2115_CR88","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/6473137","volume":"2016","author":"F Zhang","year":"2016","unstructured":"Zhang, F., Zhao, Y., & Shao, J. (2016). Rolling force prediction in heavy plate rolling based on uniform differential neural network. Journal of Control Science and Engineering, 2016, 1\u20139. https:\/\/doi.org\/10.1155\/2016\/6473137","journal-title":"Journal of Control Science and Engineering"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02115-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-023-02115-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02115-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T22:03:27Z","timestamp":1711404207000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-023-02115-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,19]]},"references-count":87,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["2115"],"URL":"https:\/\/doi.org\/10.1007\/s10845-023-02115-2","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,19]]},"assertion":[{"value":"31 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 April 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}