The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
A machine-learning fatigue life prediction approach of additively manufactured metals
Open accessAbstract
Abstract The defects retained during laser powder bed fusion determine the poor fatigue performance and pronounced lifetime scatter of the fabricated metallic components. In this work, a machine learning method was adopted to explore the influence of defect location, size, and morphology on the fatigue life of a selective laser melted Ti-6Al-4 V alloy. Both the high cycle fatigue post-mortem examination and synchrotron X-ray tomography were combined to acquire the geometric features of the critical defects, which were trained using a support vector machine (SVM). To accelerate the optimization process, the grid search approach with cross validation was selected for fitting the model parameters. It is found that the coefficient of determination between the predicted and experimental fatigue lives can reach up to 0.99, indicating that the SVM model shows strong training ability.
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Citations
Cites
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
Additive manufacturing of metals
Herzog, D., Seyda, V., Wycisk, E., & Emmelmann, C. (2016) · Acta Materialia · doi:10.1016/j.actamat.2016.07.019 · Cited by 4,759
What is a support vector machine?
Noble, W. S. (2006) · Nature Biotechnology · doi:10.1038/nbt1206-1565 · Cited by 4,301
Additive manufacturing of Ti6Al4V alloy: A review
Liu, S., & Shin, Y. C. (2019) · Materials & Design · doi:10.1016/j.matdes.2018.107552 · Cited by 2,472
Leuders, S., Thöne, M., Riemer, A., Niendorf, T., Tröster, T., Richard, H. A., & Maier, H. J. (2013) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2012.11.011 · Cited by 1,475
Metal fatigue : effects of small defects and nonmetallic inclusions
村上, 敬. (2002) · Elsevier eBooks · Cited by 1,457
Metal Fatigue: Effects of Small Defects and Nonmetallic Inclusions
MURAKAMI, Y. (2002) · Cited by 1,150
Rafi, H. K., Karthik, N. V., Gong, H., Starr, T. L., & Stucker, B. E. (2013) · Journal of Materials Engineering and Performance · doi:10.1007/s11665-013-0658-0 · Cited by 987
Fatigue performance evaluation of selective laser melted Ti–6Al–4V
Edwards, P., & Ramulu, M. (2014) · Materials Science and Engineering: A · doi:10.1016/j.msea.2014.01.041 · Cited by 874
Accelerated search for materials with targeted properties by adaptive design
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Cited by
Wang, H., Li, B., Gong, J., & Xuan, F.-Z. (2023) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2023.109242 · Cited by 281
Metadata
- Authors
- Bao, H., Wu, S., Wu, Z., Kang, G., Peng, X., & Withers, P. J.
- Venue
- Engineering Fracture Mechanics
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 242
- Pages
- 107508
- ISSN
- 0013-7944 (ISSN-L)
- OpenAlex
- W3114692006
- Cited by
- 336
- References
- 36
- FWCI
- 17.26
- Type
- journal article
- Language
- en
- Open access
- green
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