Machine learning approaches for predicting ultimate tensile strength in 9% Cr steels
Seza DINIBUTUN, Yousef ALSHAMMARI, Jafarali PAROL, Leandro BOLZONI
Abstract. This paper applies machine learning techniques to analyze a dataset featuring the initial elemental composition and mechanical properties of various 9% chromium steels. Mechanical properties in the dataset are yield stress and tensile strength, elongation, and reduction in area. The primary objective is to predict the ultimate tensile strength of these steel alloys using different machine learning algorithms. The study involves preparing the dataset to make it suitable for machine learning algorithms, selecting relevant features, and implementing three different machine learning models to evaluate their performance. The performance of these models, namely linear regression, random forest, and support vector machines model, were compared to determine the most accurate and reliable method for predicting the ultimate tensile strength. Spearman’s rank correlation coefficient was performed to understand the influence of elemental composition on the ultimate tensile strength. This study was conducted using Python version 3.12.2. The objective is to demonstrate the potential of machine learning in decision-making in material science.
Keywords
Machine Learning, Linear Regression, Random Forest, Correlation Analysis, Chromium Steels, Tensile Strength
Published online 2/25/2025, 9 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Seza DINIBUTUN, Yousef ALSHAMMARI, Jafarali PAROL, Leandro BOLZONI, Machine learning approaches for predicting ultimate tensile strength in 9% Cr steels, Materials Research Proceedings, Vol. 48, pp 501-509, 2025
DOI: https://doi.org/10.21741/9781644903414-55
The article was published as article 55 of the book Civil and Environmental Engineering for Resilient, Smart and Sustainable Solutions
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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