Machine learning-driven multi-objective alloy selection framework for mechanical property criteria

Aug 3, 2026

Upload by Quinn

This study presents a machine learning framework to support multi-objective alloy selection for mechanical property requirements. It targets tensile strength, elongation, hardness, and Charpy energy, addressing limitations of traditional selection methods that struggle with large datasets and complex non-linear relationships between alloy composition, processing parameters, and properties. XGBoost, fine-tuned stacking, and ensemble models are trained on a comprehensive stainless-steel dataset and used to filter alloys meeting predefined performance criteria. The ensemble model achieves the strongest performance with precision 0.98 and recall 0.93, improving engineering decision accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Introduction

Motivation and challenges in multi-objective alloy selection

Machine learning approaches for predicting steel properties

Evolution of multi-objective selection models

Citation for published version: Tur, E, Betts, J, Perge, L & Shokrani, A 2025, 'Machine learning-driven multi-objective alloy selection framework for mechanical property criteria', Procedia CIRP, vol. 134, pp. 61-66. [https://doi.org/10.1016/j.procir.2025.03.056](https://doi.org/10.1016/j.procir.2025.03.056) DOI: 10.1016/j.procir.2025.03.056 Publication date: 2025 Document Version Publisher's PDF, also known as Version of record Link to publication Publisher Rights CC BY University of Bath Alternative formats If you require this document in an alternative format, please contact: [openaccess@bath.ac.uk](openaccess@bath.ac.uk) General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Download date: 16. Jul. 2025 [Available online at www.sciencedirect.com](Available online at www.sciencedirect.com) ScienceDirect Procedia CIRP 134 (2025) 61–66 Proceedings of the 58th CIRP Conference on Manufacturing Systems 2025 Machine learning-driven multi-objective alloy selection framework for mechanical property criteria Erkan Tura *, Joseph Bettsa, Laurent Pergea, Alborz Shokrania a Univerisity of Bath, Bath, BA2 7AY, United Kindom * Corresponding author. Tel.: +44 1225 38 6588;. E-mail address: [et902@bath.ac.uk](et902@bath.ac.uk) Abstract This study presents a new machine learning framework for multi-objective alloy selection, focusing on key mechanical properties such as tensile strength, elongation, hardness, and Charpy energy. Traditional tools are often limited in their ability to manage large datasets and the complex, non-linear relationships between alloy composition, process parameters, and mechanical properties. In contrast, machine learning models such as XGBoost, Fine-Tuned Stacking, and Ensemble methods provide a scalable solution, allowing for the simultaneous consideration of multiple mechanical property objectives. The models were trained on a comprehensive dataset of stainless steel alloys, filtering materials that meet predefined performance criteria. Among the models, the Ensemble approach achieved the best results, with a precision of 0.98 and recall of 0.93. The findings show that integrating machine learning into the alloy selection process has the potential to improve decision-making accuracy for practical engineering applications. © 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)) Peer-review under responsibility of the scientific committee of the International Programme committee of the 58th CIRP Conference on Manufacturing Systems Keywords: Stainless steel; Machine learning; Material selection. 1. Introduction Stainless steel alloys are widely used in industries such as aerospace, automotive, and construction, due to their superior mechanical properties, including tensile strength, corrosion resistance, and durability. In these industries, selecting the right alloy for specific applications is essential to ensure product performance and longevity. However, identifying materials that meet multiple performance objectives simultaneously, such as tensile strength, elongation, hardness, and impact resistance, presents a significant challenge for manufacturers . Traditional selection methods, which often involve trial-anderror testing and expert judgment, are time-consuming and resource-intensive, especially when multiple mechanical properties must be balanced. Recent advancements in machine learni

FAQs

What mechanical properties does the framework consider for alloy selection?

It focuses on tensile strength, elongation, hardness, and Charpy energy as key mechanical property objectives for selection.

Why are traditional alloy selection tools limited in this context?

Traditional approaches are often insufficient for managing large datasets and capturing complex non-linear relationships between composition, processing parameters, and mechanical outcomes, making balanced multi-property decisions time-consuming.

Which machine learning model performs best in the study, and what are its results?

The Ensemble approach delivers the best results, with precision 0.98 and recall 0.93 when filtering stainless-steel alloys that meet predefined criteria.

Aug 3, 2026

Upload by Quinn