PSI - Issue 42
Iryna Didych et al. / Procedia Structural Integrity 42 (2022) 1344–1349 Author name / Structural Integrity Procedia 00 (2019) 000 – 000
1349
6
Pujol, J. C. F., Pinto, J. M. A., 2011. A neural network approach to fatigue life prediction, International Journal of Fatigue 33, 313 – 322. Richard, D. N., 1998. Applied regression analysis, John Wiley & Sons, New York. Rovinelli, A., Sangid, M. D., Proudhon, H., Ludwig, W., 2018. Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials, Npj Computational Materials 4. Seed, G. M., Murphy, G. S., 1998. The applicability of neural networks in modeling the growth of short fatigue cracks, Fatigue Fracture Engng Mater. Struct. 21, 183 – 190. Wang, H., Zhang, W., Sun, F., Zhang ,W., 2017. A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation, Materials 10, 543. Weaver, J. S., Khosravani, A., Castillo, A., Kalidindi, S.R., 2016. High throughput exploration of process-property linkages in Al-6061 using instrumented spherical microindentation and microstructurally graded samples, Integrating Materials and Manufacturing Innovation 5, 192 − 211. Yasniy, O., Didych, I., Fedak, S., Lapusta, Yu., 2020. Modeling of AMg6 aluminum alloy jump-like deformation properties by machine learning methods. Procedia Structural Integrity 28, 1392 – 1398. Yasniy, O., Didych, I., Lapusta, Yu., 2020. Prediction of fatigue crack growth diagrams by methods of machine learning under constant amplitude loading. Acta Metallurgica Slovaca 26, 31 − 33. Yasnii , О. P., Pastukh, O . А., Pyndus, Yu . І., Lutsyk, N. S., Didych, I. S., 2018. Prediction of the Diagrams of Fatigue Fracture of D16T Aluminum Alloy by the Methods of Machine Learning. Materials Science 54, 333 – 338.
Made with FlippingBook - Online catalogs