Laser powder bed fusion of Ti-6Al-2Sn-4Zr-6Mo alloy and properties prediction using deep learning approaches

Research output: Contribution to journalArticlepeer-review

Authors

  • Hany Hassanin
  • Yahya Zweiri
  • Laurane Finet
  • Chunlei Qiu

Colleges, School and Institutes

External organisations

  • Kingston University
  • Canterbury Christ Church University
  • Khalifa University of Science and Technology
  • University of Birmingham
  • Beihang University

Abstract

Ti-6Al-2Sn-4Zr-6Mo is one of the most important titanium alloys characterised by its high strength, fatigue, and toughness properties, making it a popular material for aerospace and biomedical applications. However, no studies have been reported on processing this alloy using laser powder bed fusion. In this paper, a deep learning neural network (DLNN) was introduced to rationalise and predict the densification and hardness due to Laser Powder Bed Fusion of Ti-6Al-2Sn-4Zr-6Mo alloy. The process optimisation results showed that near-full densification is achieved in Ti-6Al-2Sn-4Zr-6Mo alloy samples fabricated using an energy density of 77-113 J/mm3. Furthermore, the hardness of the builds was found to increase with increasing the laser energy density. Porosity and the hardness measurements were found to be sensitive to the island size, especially at high energy density. Hot isostatic pressing (HIP) was able to eliminate the porosity, increase the hardness, and achieve the desirable α and β phases. The developed model was validated and used to produce process maps. The trained deep learning neural network model showed the highest accuracy with a mean percentage error of 3% and 0.2% for the porosity and hardness. The results showed that deep learning neural networks could be an efficient tool for predicting materials properties using small data.

Bibliographic note

Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Details

Original languageEnglish
Article number2056
Number of pages18
JournalMaterials
Volume14
Issue number8
Publication statusPublished - 19 Apr 2021

Keywords

  • Deep learning, Additive Manufacturing, porosity, powder bed fusion