A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels
Stainless steel is a cold-work-hardened material. The degree and mechanism of hardening depend on the grade and family of the steel. This characteristic has a direct effect on the mechanical behaviour of stainless steel when it is cold-formed. Since cold rolling is one of the most widespread process...
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MDPI AG
2023-12-01
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| Online Access: | https://www.mdpi.com/1996-1944/17/1/147 |
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| author | Julia Contreras-Fortes M. Inmaculada Rodríguez-García David L. Sales Rocío Sánchez-Miranda Juan F. Almagro Ignacio Turias |
| author_facet | Julia Contreras-Fortes M. Inmaculada Rodríguez-García David L. Sales Rocío Sánchez-Miranda Juan F. Almagro Ignacio Turias |
| author_sort | Julia Contreras-Fortes |
| collection | DOAJ |
| description | Stainless steel is a cold-work-hardened material. The degree and mechanism of hardening depend on the grade and family of the steel. This characteristic has a direct effect on the mechanical behaviour of stainless steel when it is cold-formed. Since cold rolling is one of the most widespread processes for manufacturing flat stainless steel products, the prediction of their strain-hardening mechanical properties is of great importance to materials engineering. This work uses artificial neural networks (ANNs) to forecast the mechanical properties of the stainless steel as a function of the chemical composition and the applied cold thickness reduction. Multiple linear regression (MLR) is also used as a benchmark model. To achieve this, both traditional and new-generation austenitic, ferritic, and duplex stainless steel sheets are cold-rolled at a laboratory scale with different thickness reductions after the industrial intermediate annealing stage. Subsequently, the mechanical properties of the cold-rolled sheets are determined by tensile tests, and the experimental cold-rolling curves are drawn based on those results. A database is created from these curves to generate a model applying machine learning techniques to predict the values of the tensile strength (<i>Rm</i>), yield strength (<i>Rp</i>), hardness (<i>H</i>), and elongation (<i>A</i>) based on the chemical composition and the applied cold thickness reduction. These models can be used as supporting tools for designing and developing new stainless steel grades and/or adjusting cold-forming processes. |
| first_indexed | 2024-03-08T15:02:39Z |
| format | Article |
| id | doaj.art-a251885a5da14b958b21852fbaa9a0c5 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-08T15:02:39Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-a251885a5da14b958b21852fbaa9a0c52024-01-10T15:02:46ZengMDPI AGMaterials1996-19442023-12-0117114710.3390/ma17010147A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless SteelsJulia Contreras-Fortes0M. Inmaculada Rodríguez-García1David L. Sales2Rocío Sánchez-Miranda3Juan F. Almagro4Ignacio Turias5Laboratory and Research Section, Technical Department Acerinox Europa S.A.U., 11379 Los Barrios, SpainMIS Group, Department of Computer Science Engineering, Algeciras School of Engineering and Technology, University of Cádiz, Ramón Puyol Ave., 11202 Algeciras, SpainDepartment of Materials Science Metallurgical Engineering and Inorganic Chemistry, Algeciras School of Engineering and Technology, Universidad de Cádiz, INNANOMAT, IMEYMAT, Ramón Puyol Ave., 11202 Algeciras, SpainLaboratory and Research Section, Technical Department Acerinox Europa S.A.U., 11379 Los Barrios, SpainLaboratory and Research Section, Technical Department Acerinox Europa S.A.U., 11379 Los Barrios, SpainMIS Group, Department of Computer Science Engineering, Algeciras School of Engineering and Technology, University of Cádiz, Ramón Puyol Ave., 11202 Algeciras, SpainStainless steel is a cold-work-hardened material. The degree and mechanism of hardening depend on the grade and family of the steel. This characteristic has a direct effect on the mechanical behaviour of stainless steel when it is cold-formed. Since cold rolling is one of the most widespread processes for manufacturing flat stainless steel products, the prediction of their strain-hardening mechanical properties is of great importance to materials engineering. This work uses artificial neural networks (ANNs) to forecast the mechanical properties of the stainless steel as a function of the chemical composition and the applied cold thickness reduction. Multiple linear regression (MLR) is also used as a benchmark model. To achieve this, both traditional and new-generation austenitic, ferritic, and duplex stainless steel sheets are cold-rolled at a laboratory scale with different thickness reductions after the industrial intermediate annealing stage. Subsequently, the mechanical properties of the cold-rolled sheets are determined by tensile tests, and the experimental cold-rolling curves are drawn based on those results. A database is created from these curves to generate a model applying machine learning techniques to predict the values of the tensile strength (<i>Rm</i>), yield strength (<i>Rp</i>), hardness (<i>H</i>), and elongation (<i>A</i>) based on the chemical composition and the applied cold thickness reduction. These models can be used as supporting tools for designing and developing new stainless steel grades and/or adjusting cold-forming processes.https://www.mdpi.com/1996-1944/17/1/147stainless steelstrain hardeningcold-rolling curvesmachine learningintelligent modellingartificial neural networks |
| spellingShingle | Julia Contreras-Fortes M. Inmaculada Rodríguez-García David L. Sales Rocío Sánchez-Miranda Juan F. Almagro Ignacio Turias A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels Materials stainless steel strain hardening cold-rolling curves machine learning intelligent modelling artificial neural networks |
| title | A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels |
| title_full | A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels |
| title_fullStr | A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels |
| title_full_unstemmed | A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels |
| title_short | A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels |
| title_sort | machine learning approach for modelling cold rolling curves for various stainless steels |
| topic | stainless steel strain hardening cold-rolling curves machine learning intelligent modelling artificial neural networks |
| url | https://www.mdpi.com/1996-1944/17/1/147 |
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