About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Now On-Demand Only - Defect Minimization in Additive Manufacturing Through a Customized High-Throughput Experimental Methodology and Machine Learning Approach |
Author(s) |
Baldur Steingrimsson, Benjamin Adam, Michael Gao, Graham Tewksbury, E-Wen Huang, Peter Liaw |
On-Site Speaker (Planned) |
Benjamin Adam |
Abstract Scope |
This presentation addresses defect mitigation in additively manufactured (AM) parts, in particular hot cracking, through a customized approach combining high-throughput experimental methodology with machine learning (HTEML). Nickel-base superalloys behave quite different, when fabricated with AM compared to the manufacturing methods that they were originally designed for. Defects in AM superalloy parts can partially be attributed to the fact that these alloys were not specifically designed for AM. Cracking in AM parts represents particularly deleterious failure mode that can result in unexpected catastrophic failures. To address this problem, we target high-strength Ni-base superalloys with high γ’ fraction, but prone to hot cracking, such as CM247. One innovative aspect of the presentation relates to trade-off between high throughput (HT) and measurement accuracy in HTEM screening. As opposed to sole emphasis on HT, we will investigate HT in context with the associated measurement accuracy and what one is looking to measure or detect. |
Proceedings Inclusion? |
Undecided |