Machine Learning Algorithm for Fatigue Fields in Additive Manufacturing (Werkstofftechnische Berichte │ Reports of Materials Science and Engineering) - Tapa blanda

Mamduh Mustafa Awd, Mustafa

 
9783658402365: Machine Learning Algorithm for Fatigue Fields in Additive Manufacturing (Werkstofftechnische Berichte │ Reports of Materials Science and Engineering)
Tapa blanda
ISBN 10: 3658402369 ISBN 13: 9783658402365
Editorial: Springer Vieweg, 2023

Sinopsis

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.

"Sinopsis" puede pertenecer a otra edición de este libro.

Acerca del autor

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.

De la contraportada

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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Editorial
Springer Vieweg
Año de publicación
2023
Idioma
Inglés
ISBN 10 
3658402369
ISBN 13 
9783658402365
Encuadernación
Tapa blanda
Número de edición
1
Número de páginas
296
Contacto del fabricante
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