Data-Driven Modeling of Mechanical Properties for 17-4 PH Stainless Steel Built by Additive Manufacturing
Author:
Publisher
Springer Science and Business Media LLC
Subject
Industrial and Manufacturing Engineering,General Materials Science
Link
https://link.springer.com/content/pdf/10.1007/s40192-022-00261-8.pdf
Reference24 articles.
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3. DebRoy T, Mukherjee T, Wei HL, Elmer JW, Milewski JO (2020) Metallurgy, mechanistic models and machine learning in metal printing. Nat Rev Mater 6(1):48–68. https://doi.org/10.1038/s41578-020-00236-1
4. Popova E, Rodgers TM, Gong X, Cecen A, Madison JD, Kalidindi SR (2017) Process-structure linkages using a data science approach: application to simulated additive manufacturing data. Integr Mater Manuf Innov 6(1):54–68. https://doi.org/10.1007/s40192-017-0088-1
5. Herriot C, Spear AD (2020) Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine- and deep-learning methods. Comput Mater Sci. https://doi.org/10.1016/j.commatsci.2020.109599
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1. Characterization of Microstructural and Mechanical Properties of 17-4 PH Stainless Steel by Cold Rolled and Machining vs. DMLS Additive Manufacturing;Journal of Manufacturing and Materials Processing;2024-03-01
2. Control of grain structure, phases, and defects in additive manufacturing of high-performance metallic components;Progress in Materials Science;2023-09
3. Effects of Printing Layer Orientation on the High-Frequency Bending-Fatigue Life and Tensile Strength of Additively Manufactured 17-4 PH Stainless Steel;Materials;2023-01-04
4. Effect of print parameters on additive manufacturing of metallic parts: performance and sustainability aspects;Scientific Reports;2022-11-11
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