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Machine learning model to predict tensile properties of annealed Ti6Al4V parts prepared by selective laser melting

Published online by Cambridge University Press:  29 September 2022

Zhaotong Yang ,
Mei Yang ,
Richard Sisson ,
Yanhua Li  and
Jianyu Liang Open the ORCID record for Jianyu Liang [Opens in a new window]
Zhaotong Yang
Affiliation:
Department of Mechanical and Materials Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USA
Mei Yang
Affiliation:
Department of Mechanical and Materials Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USA
Richard Sisson
Affiliation:
Department of Mechanical and Materials Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USA
Yanhua Li*
Affiliation:
Department of Computer Science, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USA
Jianyu Liang*
Affiliation:
Department of Mechanical and Materials Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USA
*
Author for correspondence: Jianyu Liang, E-mail: jianyul@wpi.edu
Author for correspondence: Jianyu Liang, E-mail: jianyul@wpi.edu
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Abstract

In this work, an artificial neural network model is established to understand the relationship among the tensile properties of as-printed Ti6Al4V parts, annealing parameters, and the tensile properties of annealed Ti6Al4V parts. The database was established by collecting published reports on the annealing treatment of selective laser melting (SLM) Ti6Al4V, from 2006 to 2020. Using the established model, it is possible to prescribe annealing parameters and predict properties after annealing for SLM Ti-6Al-4V parts with high confidence. The model shows high accuracy in the prediction of yield strength (YS) and ultimate tensile strength (UTS). It is found that the YS and UTS are sensitive to the annealing parameters, including temperature and holding time. The YS and UTS are also sensitive to initial YS and UTS of as-printed parts. The model suggests that an annealing process of the holding time of fewer than 4 h and the holding temperature lower than 850°C is desirable for as-printed Ti6Al4V parts to reach the YS required by the ASTM standard. By studying the collected data of microstructure and tensile properties of annealed Ti6Al4V, a new Hall-Petch relationship is proposed to correlate grain size and YS for annealed SLM Ti6Al4V parts in this work. The prediction of strain to failure shows lower accuracy compared with the predictions of YS and UTS due to the large scattering of the experimental data collected from the published reports.

Keywords

annealingartificial neural networkselective laser meltingtensile propertiesTi-6Al-4V
Type
Research Article
Information
AI EDAM , Volume 36 , 2022 , e30
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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