Rationalisation of steel grades and specifications using machine learning techniques

Abstract

There are an excessive number of steel grades currently in use. However, many of them are used in the same application despite differences in chemical composition and processing conditions, and in some cases shows equivalent ranges of properties. These huge number of grades poses challenges for sustainable recycling and increases production complexity and cost. This study introduces a multi-phase, application-driven framework to simplify the steel grade system and reduce the number of grades by proposing a novel approach called K-Means Reduction Process (KMRP). The framework was applied to 148 carbon and 288 stainless steel grades, including chemical composition, processing conditions, and mechanical properties (hardness, UTS, YS, and elongation). Machine learning models were first used to quantify the influence of alloying elements and processing conditions on mechanical performance. K-Means clustering was then applied to group grades based on performance to identify steels that shared equivalent property profiles, with four distinct clusters identified including ferritic/low-carbon steels, medium-carbon and martensitic steels, high-carbon steels, and austenitic steels. These clusters revealed significant redundancy, with multiple grades from existing steel classifications occupying the same mechanical property space. In the reduction phase, KMRP identified the minimal set of grades required to preserve full mechanical property coverage within the generated clusters. Two sustainability-driven strategies were implemented: (1) tramp-element avoidance, favouring grades with low Cu and Sn, and (2) tramp-element tolerance, prioritising grades compatible with scrap-based recycling. While both approaches reduced reliance on critical raw materials (Mo, Ni, V, Ti), this study focused on the tramp-tolerance strategy as the most relevant for advancing circular economy objectives. Under this approach, the number of carbon steel grades were reduced by 38.4% (from 146 to 90) and stainless steel grades by 52.8% (from 288 to 136), while fully preserving the original mechanical property ranges, including UTS ranges of 295–2450 MPa for carbon steels and 120–1970 MPa for stainless steels, and elongation ranges of 7–41% and 2–55%, respectively. These results demonstrated that KMRP can successfully simplify the steel grade system while supporting circularity, reducing dependency on critical elements, and improving the sustainability of future steel production. Moreover, the methodology is generalisable and can be applied to other domains where reducing redundant options is essential, such as pharmaceutical applications.