On the efficiency of machine learning for fatigue assessment of post-processed additively manufactured AlSi10Mg

Autor:	E. Maleki, S. Bagherifard, S.M.J. Razavi, M. Bandini, A. du Plessis, F. Berto, M. Guagliano
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	Notch deep learning heat treatment morphology neural networks residual stresses shot peening silicon surface roughness tensile strength yield stress complex geometries fatigue assessments fatigue behaviour fatigue performance laser powder bed fusion laser powders notch post-processing powder bed shot-peening fatigue of materials fatigue machine learning Heat treatment Industrial and Manufacturing Engineering fatigue of materials Machine learning General Materials Science Fatigue complex geometries Mechanical Engineering Shot peening yield stress Mechanics of Materials Modeling and Simulation Laser powder bed fusion shot-peening
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::136452d43d19f62b02ce005dc8aaee4b https://hdl.handle.net/11311/1232073 Zobrazit plný text záznamu