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

Journal article

Hassanin, H., Zweiri, Y., Finet, L., Essa, K., Qiu, C. and Attallah, M. 2021. Laser powder bed fusion of Ti-6Al-2Sn-4Zr-6Mo alloy and properties prediction using deep learning approaches. Materials. 14 (8), p. 2056. https://doi.org/10.3390/ma14082056
AuthorsHassanin, H., Zweiri, Y., Finet, L., Essa, K., Qiu, C. and Attallah, M.
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.

KeywordsDeep learning; Additive manufacturing; Porosity; Powder bed fusion
Year2021
JournalMaterials
Journal citation14 (8), p. 2056
PublisherMDPI
ISSN1996-1944
Digital Object Identifier (DOI)https://doi.org/10.3390/ma14082056
Official URLhttps://www.mdpi.com/1996-1944/14/8/2056
Publication dates
Print19 Apr 2021
Publication process dates
Accepted14 Apr 2021
Deposited22 Apr 2021
Publisher's version
File Access Level
Open
Output statusPublished
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