Overview
Part of the book series: Werkstofftechnische Berichte │ Reports of Materials Science and Engineering (WBRMSE)
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Table of contents (6 chapters)
Keywords
About this book
Fatigue failure of structures used in transportation, industry, medical equipment, and electronic components needs to build a link between cutting-edge experimental characterization and probabilistically grounded numerical and artificially intelligent tools. The physics involved in this process chain is computationally prohibitive to comprehend using traditional computation methods. Using machine learning and Bayesian statistics, a defect-correlated estimate of fatigue strength was developed. Fatigue, which is a random variable, is studied in a Bayesian-based machine learning algorithm. The stress-life model was used based on the compatibility condition of life and load distributions. The defect-correlated assessment of fatigue strength was established using the proposed machine learning and Bayesian statistics algorithms. It enabled the mapping of structural and process-induced fatigue characteristics into a geometry-independent load density chart across a wide range of fatigue regimes.
Authors and Affiliations
About the author
Mustafa Mamduh Mustafa Awd heads the Workgroup Modeling and Simulation at the Chair of Materials Test Engineering (WPT). He deals with the problem of multiscale numerical analysis of the effect of microstructural heterogeneities on fatigue strength by adapting quantum mechanical methods and data-driven algorithms alongside numerical optimization. The developed general-purpose models help increase the structural stability and production efficiency of modern manufacturing processes.
Bibliographic Information
Book Title: Machine Learning Algorithm for Fatigue Fields in Additive Manufacturing
Authors: Mustafa Mamduh Mustafa Awd
Series Title: Werkstofftechnische Berichte │ Reports of Materials Science and Engineering
DOI: https://doi.org/10.1007/978-3-658-40237-2
Publisher: Springer Vieweg Wiesbaden
eBook Packages: Life Science and Basic Disciplines (German Language)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022
Softcover ISBN: 978-3-658-40236-5Published: 02 January 2023
eBook ISBN: 978-3-658-40237-2Published: 01 January 2023
Series ISSN: 2524-4809
Series E-ISSN: 2524-4817
Edition Number: 1
Number of Pages: XXXVIII, 255
Number of Illustrations: 143 b/w illustrations
Topics: Machine Learning