Mechanistic machine learning for metamaterial fatigue strength design from first principles in additive manufacturing

Mechanistic machine learning for metamaterial fatigue strength design from first principles in additive manufacturing

Digital control in manufacturing processes produces significant amounts of metadata. The production process metadata, such as thermal and optical measurements, enables a higher degree of property grading than uninstrumented manufacturing and feedback for fault detection. This study explores how meta...

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Bibliographic Details
Main Authors: Mustafa Awd, Lobna Saeed, Sebastian Münstermann, Matthias Faes, Frank Walther
Format: Article
Language:English
Published: Elsevier 2024-05-01
Series:Materials & Design
Subjects:
Density functional theory (DFT)
Functional grading
Machine learning (ML)
Fatigue strength
Cohesion energy
Additive manufacturing (AM)
Online Access:http://www.sciencedirect.com/science/article/pii/S0264127524002624

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http://www.sciencedirect.com/science/article/pii/S0264127524002624

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