Machine Learning Algorithm for Fatigue Fields in Additive Manufacturing
Mustafa Mamduh Mustafa Awd
ISBN 10: 3658402369 ISBN 13: 9783658402365
Published by Springer VS, 2023
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Machine Learning Algorithm for Fatigue Fields in Additive Manufacturing | Mustafa Mamduh Mustafa Awd | Taschenbuch | Werkstofftechnische Berichte ¿ Reports of Materials Science and Engineering | xxxviii | Englisch | 2023 | Springer VS | EAN 9783658402365 | Verantwortliche Person für die EU: Springer Vieweg in Springer Science + Business Media, Abraham-Lincoln-Str. 46, 65189 Wiesbaden, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Seller Inventory # 125818496
Synopsis:
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.
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.
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Title: Machine Learning Algorithm for Fatigue ...
Publisher: Springer VS
Publication Date: 2023
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Paperback. Condition: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9783658402365
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 296 pp. Englisch. Seller Inventory # 9783658402365