Optimization of Fiber-Reinforced Laminates for a Maximum Fatigue Life by Using the Particle Swarm Optimization. Part II

An improved methodology, with considering experimental results and numerical models, is proposed to find globally optimum designs of composite laminates subject to given in-plane loads for a maximum fatigue life. For this purpose, the Fawaz–Ellyin parametric fatigue-life prediction model was coupled...

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Veröffentlicht in:Mechanics of composite materials Jg. 49; H. 1; S. 107 - 116
1. Verfasser: Ertas, Ahmet H.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.03.2013
Springer
Schlagworte:
Algorithms
Analysis
Ceramics
Characterization and Evaluation of Materials
Chemistry and Materials Science
Classical Mechanics
Composites
Fatigue
Fatigue life
Fatigue testing machines
Fiber orientation
Glass
Laminated materials
Laminates
Materials
Materials Science
Mathematical analysis
Mathematical models
Mathematical optimization
Natural Materials
Optimization
Particulate composites
Solid Mechanics
composites
particle swarm optimization
fatigue life
Fawaz–Ellyin model
ISSN:0191-5665, 1573-8922
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:An improved methodology, with considering experimental results and numerical models, is proposed to find globally optimum designs of composite laminates subject to given in-plane loads for a maximum fatigue life. For this purpose, the Fawaz–Ellyin parametric fatigue-life prediction model was coupled with a global optimization technique, the particle swarm optimization, which is a metaheuristic optimization algorithm. The number of distinct fiber orientation angles and the number of plies in each lamina were predetermined. The fiber orientation angles in each layer were taken as design variables and determined in the design process. A number of problems were solved to demonstrate the efficiency and reliability of the method proposed.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0191-5665
1573-8922
DOI:10.1007/s11029-013-9326-6