Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/6407
Title: Optimum design of fatigue-resistant composite laminates using hybrid algorithm
Authors: Deveci, Hamza Arda
Artem, Hatice Seçil
Keywords: Fatigue
Hybrid algorithm
Laminated composites
Life prediction
Optimization
Issue Date: May-2017
Publisher: Elsevier Ltd.
Source: Deveci, H. A., and Artem, H. S. (2017). Optimum design of fatigue-resistant composite laminates using hybrid algorithm. Composite Structures, 168, 178-188. doi:10.1016/j.compstruct.2017.01.064
Abstract: In this study, a fatigue life prediction model termed as Failure Tensor Polynomial in Fatigue (FTPF) is applied to the optimum stacking sequence design of laminated composites under various in-plane cyclic loadings to obtain maximum fatigue life. The validity of the model is investigated with an experimental correlation using the data available in the literature. The correlation study indicates the reliability of FTPF, and its applicability to different composite materials and multidirectional laminates. In the optimization, a hybrid algorithm combining genetic algorithm and generalized pattern search algorithm is used. It is found by test problems that the hybrid algorithm shows superior performance in finding global optima compared to the so far best results in the literature. After the verifications, a number of problems including different design cases are solved, and the optimum designs constituted of discrete fiber angles which give the maximum possible fatigue lives are proposed to discuss. A comparison study is also performed with selected design cases to demonstrate potential advantages of using non-conventional fiber angles in design.
URI: http://doi.org/10.1016/j.compstruct.2017.01.064
http://hdl.handle.net/11147/6407
ISSN: 0263-8223
Appears in Collections:Mechanical Engineering / Makina Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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