2008
DOI: 10.1016/j.compstruc.2008.05.003
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Optimum design of composite laminates for minimum thickness

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Cited by 65 publications
(46 citation statements)
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“…There are mainly two approaches used to predict failure load of composite laminates: One is the first ply failure criterion (Akbulut and Sonmez 2008;Akbulut and Sonmez 2011), according to which, failure occurs at the instant a damage initiates. This may be true for uniform plates under a uniform stress state.…”
Section: Introductionmentioning
confidence: 99%
“…There are mainly two approaches used to predict failure load of composite laminates: One is the first ply failure criterion (Akbulut and Sonmez 2008;Akbulut and Sonmez 2011), according to which, failure occurs at the instant a damage initiates. This may be true for uniform plates under a uniform stress state.…”
Section: Introductionmentioning
confidence: 99%
“…29. An illustration of composite laminate (Akbulut & Sonmez, 2008 Table 1. The optimum lay-ups obtained using two distinct fibre angles for various biaxial loading cases (Akbulut & Sonmez, 2008).…”
Section: Composites Optimizationmentioning
confidence: 99%
“…Locating the globally optimal material design with a local search algorithm is almost a hopeless enterprise. For this reason, many researchers preferred global search algorithms like genetic algorithms (Soremekun et al, 2001;Todoroki & Tetsuya, 2004;Kang & Kim, 2005), simulated annealing algorithm (Soares et al, 1995;Jayatheertha et al, 1996;Sciuva et al, 2003;Correia et al, 2003;Erdal & Sonmez, 2005;Moita et al, 2006;Akbulut & Sonmez, 2008), improving hit-and run (Savic et al, 2001). …”
Section: Composites Optimizationmentioning
confidence: 99%
“…Genetic algorithms (GA) have been examined to solve these kinds of problems by different researchers in many studies [6,[9][10][11][12][13]15]. Also, other techniques such as fractal branch and bound method were used by Terada et al [14] and simulated annealing by Akbulut and Sonmez [2]. Recently, a new class of algorithms, the ant colony optimization (ACO), was developed to solve combinatorial optimization problems.…”
Section: Introductionmentioning
confidence: 99%