{"doi":"10.1177/03019233251357114","title":"Prediction of transverse thickness difference in medium- and high-carbon steels based on transformer and optimisation of rolling schedules","abstract":"<jats:p>\n                    The cross-sectional profile quality of medium- and high-carbon steels is crucial in high-end manufacturing. Machine learning algorithms are employed to predict transverse thickness differences in cold rolling. Facing variable-length sequences in single-stand mills, the predictive abilities of multi-layer perceptron, random forest, long short-term memory and transformer models are compared, with the transformer model showing the highest accuracy, with an\n                    <jats:italic>R</jats:italic>\n                    <jats:sup>2</jats:sup>\n                    of 0.9783. Then, machine learning is integrated with particle swarm optimisation, a cold rolling mechanism model and stable rolling requirements to create a rolling schedule optimisation model aiming to minimise transverse thickness differences. A finite-element model of the S6-High cold rolling mill (CRM) is established via ABAQUS to interpret optimisation results. Field applications reveal that this research reduces the average transverse thickness difference in rolling medium- and high-carbon steels from 26.6 to 17.5 μm, greatly improving the control level in the S6-High CRM.\n                  </jats:p>","journal":"Ironmaking &amp; Steelmaking: Processes, Products and Applications","year":2025,"id":618422,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1595392,"name":"Tingsong Yang","orcid":null,"position":1,"is_corresponding":false},{"id":1595394,"name":"Anrui He","orcid":"0000-0002-5811-0128","position":2,"is_corresponding":false},{"id":1213708,"name":"Yang Liu","orcid":"0000-0001-6235-200X","position":3,"is_corresponding":false},{"id":1595395,"name":"Wenquan Sun","orcid":null,"position":4,"is_corresponding":false},{"id":282061,"name":"Chao Liu","orcid":"0000-0003-2184-9102","position":5,"is_corresponding":false},{"id":1595397,"name":"Zhiping Fu","orcid":null,"position":6,"is_corresponding":false},{"id":1595398,"name":"Luzhen Chen","orcid":null,"position":7,"is_corresponding":false},{"id":1595389,"name":"Tieheng Yuan","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Prediction of transverse thickness difference in medium- and high-carbon steels based on transformer and optimisation of rolling schedules","abstract":"<jats:p>\n                    The cross-sectional profile quality of medium- and high-carbon steels is crucial in high-end manufacturing. Machine learning algorithms are employed to predict transverse thickness differences in cold rolling. Facing variable-length sequences in single-stand mills, the predictive abilities of multi-layer perceptron, random forest, long short-term memory and transformer models are compared, with the transformer model showing the highest accuracy, with an\n                    <jats:italic>R</jats:italic>\n                    <jats:sup>2</jats:sup>\n                    of 0.9783. Then, machine learning is integrated with particle swarm optimisation, a cold rolling mechanism model and stable rolling requirements to create a rolling schedule optimisation model aiming to minimise transverse thickness differences. A finite-element model of the S6-High cold rolling mill (CRM) is established via ABAQUS to interpret optimisation results. Field applications reveal that this research reduces the average transverse thickness difference in rolling medium- and high-carbon steels from 26.6 to 17.5 μm, greatly improving the control level in the S6-High CRM.\n                  </jats:p>","is_dataset_classified":null,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W7128268889","authors":[],"funders":[{"funder_name":"Key Technologies Research and Development Program","grant_id":"2023YFB3812602","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"52004029","title":null}],"total_grants":2,"fwci":0.5378,"citation_percentile":0.69095492,"influential_citations":0,"citation_trend":[{"year":2026,"count":1}],"oa_status":"closed","license":"https://journals.sagepub.com/page/policies/text-and-data-mining-license","oa_locations":[{"url":"https://journals.sagepub.com/doi/pdf/10.1177/03019233251357114","host_type":"publisher"},{"url":"https://journals.sagepub.com/doi/full-xml/10.1177/03019233251357114","host_type":"publisher"},{"url":"https://doi.org/10.1177/03019233251357114","host_type":"journal"}],"fields_of_study":["Metallurgy and Material Forming","Microstructure and Mechanical Properties of Steels","Machine Learning in Materials Science"],"mesh_terms":[],"keywords":["Transverse plane","Particle swarm optimization","Rolling mill","Transformer","Schedule","Transverse field"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-03T04:21:45.228186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}