Optimizing laser powder bed fusion of Ti-5Al-5V-5Mo-3Cr by artificial intelligence
SCIE
SCOPUS
- Title
- Optimizing laser powder bed fusion of Ti-5Al-5V-5Mo-3Cr by artificial intelligence
- Authors
- SHIN, DA SEUL; Chi Hun Lee; Uta Kühn; LEE, SEUNG CHUL; Seong Jin Park; Holger Schwab; Sergio Scudino; Konrad Kosiba
- Date Issued
- 2021-05
- Publisher
- ELSEVIER SCIENCE SA
- Abstract
- The prerequisite for exploiting the full potential of additive manufacturing (AM) is the rapid and cost-effective fabrication of defect-free components. However, each newly processed material usually requires the identification of the optimal parameter set, a cost and time-consuming process, mostly conducted by trial and error. Here, an optimization strategy based on artificial intelligence (AI) is developed for predicting the density of additively manufactured Ti-5Al-5V-5Mo-3Cr components from experimental data. The present approach opens the way to a faster identification of the optimum set of processing parameters via AI. (C) 2020 Elsevier B.V. All rights reserved.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/104687
- DOI
- 10.1016/j.jallcom.2020.158018
- ISSN
- 0925-8388
- Article Type
- Article
- Citation
- JOURNAL OF ALLOYS AND COMPOUNDS, vol. 862, 2021-05
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- There are no files associated with this item.
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