Stacking sequence design of composite laminates for maximum strength using genetic algorithms

• Published in: Composite structures Vol. 52; no. 2; pp. 217 - 231
• Main Authors: Park, J.H., Hwang, J.H., Lee, C.S., Hwang, W.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.05.2001; Elsevier Science
• Subjects: Composite laminates; Composite materials; Condensed matter: electronic structure, electrical, magnetic, and optical properties; Condensed matter: structure, mechanical and thermal properties; Cross-disciplinary physics: materials science; rheology; Deformation and plasticity (including yield, ductility, and superplasticity); Dielectric, piezoelectric, ferroelectric and antiferroelectric materials; Dielectrics, piezoelectrics, and ferroelectrics and their properties; Exact sciences and technology; Failure index; Fatigue, brittleness, fracture, and cracks; Finite element method; Genetic algorithm; Load-carrying efficiency index; Materials science; Materials testing; Mechanical and acoustical properties of condensed matter; Mechanical properties of solids; Mechanical testing, impact tests, static and dynamic loads; Optimum design; Physics; Strength; Finite element method; Composite laminates; Optimum design; Genetic algorithm; Failure index; Load-carrying efficiency index; Strength; Creep; Stacking sequence; Mechanical strength; Theoretical study; Boundary conditions; Layered materials; Fracture criterion; Failure (mechanical); Genetic algorithms; Optimal design; Composite materials; Rupture strength; Strength of materials
• ISSN: 0263-8223
• DOI: 10.1016/S0263-8223(00)00170-7

This paper uses genetic algorithms (GAs) for the optimal design of symmetric composite laminates subject to various loading and boundary conditions. To analyze these laminates, the finite element method based on shear deformation theory is used. The Tsai–Hill failure criterion is taken as the fitness function, and the ply orientation angles are the design variables. In the GA, tournament selection and the uniform crossover method are used. The elitist model is also used for an effective evolution strategy and the creeping random search method is adopted in order to approach the solution with high accuracy. Optimization results are given for various loading and boundary conditions. The results show that optimization via a GA can find the global optimal solution leading to a substantial decrease in the failure index.