Excellent Balance of Ultimate Tensile Strength and Ductility in a Ti–6Al–2Sn–4Zr–2Mo–Si Alloy Having Duplex α + α′ Microstructure: Effect of Microstructural Factors from Experimental Study and Machine Learning - Archive ouverte HAL Access content directly
Journal Articles Materials Transactions Year : 2023

Excellent Balance of Ultimate Tensile Strength and Ductility in a Ti–6Al–2Sn–4Zr–2Mo–Si Alloy Having Duplex α + α′ Microstructure: Effect of Microstructural Factors from Experimental Study and Machine Learning

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Abstract

This research focuses on the systematic study of a Ti–6Al–2Sn–4Zr–2Mo–Si titanium alloy and the characterization of α + β (equiaxed and bimodal) and α + α′ (duplex) microstructures. It provides more insights on the outstanding advantages of the duplex (α + α′) microstructure, especially on its exceptional work hardening and strength-ductility balance. The heat treatment conditions required to form equiaxed, bimodal and duplex microstructures and their effects on the grain size and the phase proportion are discussed. It shows how the microstructural parameters can be controlled thanks to the heat treatment temperatures, the holding times and possible aging processes. The influence of such microstructural factors on the tensile properties of each alloy is investigated, especially on strength (proof stress, ultimate tensile strength), ductility (plastic elongation) and work hardening properties. The duplex (α + α′) microstructure is compared with the equiaxed and bimodal microstructures and its advantages are displayed, highlighting the better strength-ductility balance and superior work hardening properties of the duplex microstructure. Indeed, the deformed microstructure of the duplex (α + α′) microstructure reveals more homogeneous strain partitioning than that of the bimodal (α + β) microstructure. Thus, this work proved the potential of an optimized duplex (α + α′) microstructure for the enhanced tensile properties at room temperature. Finally, a machine learning model using gradient boosting regression trees is used to quantify the importance of the microstructural factors (type of microstructure, grain size and phase ratio) on the mechanical properties.
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Dates and versions

hal-03918839 , version 1 (02-01-2023)

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Cite

Irvin Séchepée, Paul Paulain, Yuka Nagasaki, Riku Tanaka, Hiroaki Matsumoto, et al.. Excellent Balance of Ultimate Tensile Strength and Ductility in a Ti–6Al–2Sn–4Zr–2Mo–Si Alloy Having Duplex α + α′ Microstructure: Effect of Microstructural Factors from Experimental Study and Machine Learning. Materials Transactions, 2023, 64 (1), pp.111-120. ⟨10.2320/matertrans.MT-MLA2022009⟩. ⟨hal-03918839⟩
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