About this Abstract |
| Meeting |
2026 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2026)
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| Symposium
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2026 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2026)
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| Presentation Title |
Modeling surface roughness on fatigue performance of additively manufactured Ti-6Al-4V with machine learning |
| Author(s) |
Shehzaib Irfan, Nabeel Ahmad, Daniel F. Silva, Alexander Vinel, Shuai Shao, Jia Liu |
| On-Site Speaker (Planned) |
Jia Liu |
| Abstract Scope |
Fatigue life in as-built additively manufactured parts exhibits inherent variability due to surface roughness. This uncertainty of fatigue life poses a significant challenge in the widespread adoption of additively manufactured parts in mission-critical components. In this study, we investigate how the deep notches on the surface of additively manufactured Ti-6Al-4V affect the fatigue life of the specimens and their spread. We quantify the impact of surface roughness as a stochastic process and incorporate it into a fatigue prediction model via machine learning. The approach uses a physics-based model as its foundation to describe the relationship between stress and fatigue life, and incorporates Gaussian processes to model the random effects of surface-finish features, thereby quantifying the scatter in fatigue life. It has also been benchmarked against another recently proposed method, resulting in an improvement in the prediction mean absolute percentage error from 39% to 22%. |
| Proceedings Inclusion? |
Undecided |