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Jung, Im Doo
Intelligent Manufacturing and Materials Lab
Research Interests
  • 인공지능, 3D 프린팅 기반 생산 및 소재 개발

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Virtual surface morphology generation of Ti-6Al-4V directed energy deposition via conditional generative adversarial network

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Title
Virtual surface morphology generation of Ti-6Al-4V directed energy deposition via conditional generative adversarial network
Author
Kim, TaekyeongKim, Jung GiPark, SangeunKim, Hyoung SeopKim, NamhunHa, HyunjongChoi, Seung-KyumTucker, ConradSung, HyokyungJung, Im Doo
Issue Date
2023-01
Publisher
TAYLOR & FRANCIS LTD
Citation
VIRTUAL AND PHYSICAL PROTOTYPING, v.18, no.1, pp.e2124921
Abstract
The core challenge in directed energy deposition is to obtain high surface quality through process optimisation, which directly affects the mechanical properties of fabricated parts. However, for expensive materials like Ti-6Al-4V, the cost and time required to optimise process parameters can be excessive in inducing good surface quality. To mitigate these challenges, we propose a novel method with artificial intelligence to generate virtual surface morphology of Ti-6Al-4V parts by given process parameters. A high-resolution surface morphology image generation system has been developed by optimising conditional generative adversarial networks. The developed virtual surface matches experimental cases well with an Frechet inception distance score of 174, in the range of accurate matching. Microstructural analysis with parts fabricated with artificial intelligence guidance exhibited less textured microstructural behaviour on the surface which reduces the anisotropy in the columnar structure. This artificial intelligence guidance of virtual surface morphology can help to obtain high-quality parts cost-effectively.
URI
https://scholarworks.unist.ac.kr/handle/201301/59868
DOI
10.1080/17452759.2022.2124921
ISSN
1745-2759
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