Material properties and critical buckling pressure of the baseline design (
Structural applications of composite materials are increasing in several engineering areas where high stiffness and strength-to-weight ratios, long fatigue life, superior thermal properties, and corrosive resistance are most beneficial [1, 2, 3, 4]. Common types include laminated composites , functionally graded material (
This introductory chapter provides a brief review on the optimum design of composite structures and the relevant optimization techniques that are capable of finding the needed optimal solutions. Several problems can be addressed, including the structural design for maximum stability, maximum natural frequencies, and minimum mass or maximum stiffness subject to limits on strength, deflections, and side constraints. The relevant design variables include geometrical dimensions and material properties as well. A numerical example is given at the end of this chapter to demonstrate a real and practical application of the optimum composite structures.
2. The optimal design problem
Several research papers and text books exist in the field of optimal design of composite structures with a variety of valuable applications in civil, mechanical, ocean, and aerospace engineering. An important stage has now been reached at which an investigation of such developments and their practical possibilities should be made and presented. Two distinct review papers have been published covering the development of the optimum design of composites over more than 40 years. The first paper by Sonmez  presented a comprehensive survey for more than 1000 journal papers, conference papers, textbooks, and web links from the year 1969 to 2009. Sonmez classified the papers according to the type of the composite structure, loading conditions, optimization model, failure criteria, and the utilized search algorithm. The second paper by Ganguli  covered a historical review from 1973 to 2013. It provides the growth of the field by including more than 90 references dealing with a variety of optimization methods utilized for tailoring composites to achieve certain design objectives. Applications of several optimization techniques were presented, including feasible direction methods, sequential quadratic programming, and stochastic optimization such as particle swarm and ant colony algorithms. Ganguli classified the published work into five categories named pioneering research for the work published in the 1970s, early research in the 1980s, moving toward design in the 1990s, the new century in the 2010s, and the current research for papers published after 2010.
In general, design optimization seeks the best values of
Find the set of design variables
Figure 1 shows the overall structure of an optimization approach to design. Major objectives in mechanical and structural engineering involve minimum fabrication cost, maximum product reliability, maximum stiffness/weight ratio, minimum aerodynamic drag, maximum natural frequencies, maximum critical shaft speeds, etc. Design variables describe configuration, dimensions and sizes of elements, and material properties as well. In the design of structural components, such as those of an automobile structure, the main design variables represent the thickness of the covering skin panels and the spacing, size, and shape of the transverse and longitudinal stiffeners. The sizes of the constituent elements of the system are measured by such properties as the cross-sectional dimensions, section areas, area moments of inertia, torsional constants, plate’s thicknesses, etc. If the skin and/or stiffeners are made of layered composites, the orientation of the fibers and their proportion can become additional variables. If one optimizes for configuration, the design variables will include spatial coordinates. Also, in dynamic problems, the location of nonstructural masses and their magnitudes can be additional design variables.
3. Optimization techniques
The class of optimization problems described by Eqs. (1)–(3) may be thought of as a search in an n-dimensional space for a point corresponding to the minimum value of the overall objective function and such that it lies within the region bounded by the subspaces representing the constraint functions. Iterative techniques are usually used for solving such optimization problems in which a series of directed design changes (moves) are made between successive points in the design space. Several optimization techniques are classified according to the way of selecting the search direction . The most commonly used approaches are the random search, conjugate directions, and conjugate gradients methods. Other algorithms for solving global optimization problems may be classified into heuristic methods that find the global optimum only with high probability and methods that guarantee to find a global optimum with some accuracy. The simulated annealing technique and the genetic algorithms (GAs) belong to the former type, where analogies to physics and biology to approach the global optimum are utilized. The simulated annealing technique is an iterative search method based on the simulation of thermal annealing of critically heated solids. Hasancebi et al.  applied it to find the optimum design of fiber composite structures as an efficient method to solve multi-objective optimization models. On the other hand, the GAs [11, 12] are based on the principles of natural genetics and natural selection. GAs do not utilize any gradient information during the searching process. Narayana Naik et al.  used GA and various failure mechanisms based on different failure criteria to reach an optimal composite structure. Another robust algorithm in solving complex problems of optimal structural design is named particle swarm optimization algorithm (PSOA). This algorithm is based on the behavior of a colony of living things, such as a swarm of insects like ants, bees, and wasps, a folk of birds, or a school of fish. Omkar et al.  applied PSOA to achieve a specified strength with minimizing weight and total cost of a composite structure under different failure criteria. To the author’s knowledge, GA has been the most efficient stochastic method for obtaining the global optimum design of composite structures.
4. Application: buckling optimization of anisotropic cylindrical shells
Structural buckling failure due to high external hydrostatic pressure is a major consideration in designing cylindrical shell-type structures. This section presents a direct approach for enhancing buckling stability limits of thin-walled long cylinders that are fabricated from multi-angle fibrous laminated composite lay-ups. The mathematical formulation employs the classical lamination theory for calculating the critical buckling pressure, where an analytical solution that accounts for the effective axial and flexural stiffness separately as well as the inclusion of the coupling stiffness terms is presented. The associated design optimization problem of maximizing the critical buckling pressure has been formulated in a standard nonlinear mathematical programming problem with the design variables encompassing the fiber orientation angles and the ply thicknesses as well. The physical and mechanical properties of the composite material are taken as preassigned parameters. The proposed model deals with dimensionless quantities in order to be valid for thin shells having different thickness-to-radius ratios. Results have been obtained for cases of filament wound cylinders fabricated from different types of composite materials.
The basic analysis and analytical formulation presented in this chapter are based on the work given by Maalawi , which provides good sensitivity to lamination parameters and allows the search for the needed optimal stacking sequences in a reasonable computational time. Referring to the structural model depicted in Figure 2, the significant strain components are the hoop strain () and the circumferential curvature (
4.1. Analytical buckling model
The governing differential equations of anisotropic long cylinders subjected to external pressure are cast in the following:
4.2. Definition of the baseline design
It is convenient first to normalize all variables and parameters with respect to a baseline design, which has been selected to be a unidirectional orthotropic laminated cylinder with the fibers parallel to the shell axis
|Material type||Orthotropic mechanical properties* (GPa)|
4.3. Optimization model
The search for the optimized lamination can be performed by coupling the analytical buckling shell model to a standard nonlinear mathematical programming procedure. The resulting optimization problem may be cast in the following standard form to
where is the dimensionless critical buckling pressure, and (
According to the filament-winding manufacturing process, each ply is characterized by its angle
4.4. Optimal solutions
The functional behavior of the candidate objective function, as represented by maximization of the dimensionless buckling pressure , is thoroughly investigated in order to see how it is changed with the optimization variables in the selected design space. The final optimum designs recommended by the model will directly depend on the mathematical form and behavior of the objective function.
4.4.1. Two-layer anisotropic long cylinder
The first case study to be considered herein is a long thin-walled cylindrical shell fabricated from E-glass/vinyl ester composites with the lay-up composed of only two plies (
|Baseline [||Helically wound [||Hoop plies [|
| = |
For a two-ply long cylinder fabricated from graphite/epoxy composites, Figure 3 shows the developed level curves of the dimensionless buckling pressure, (also named isomerits or isobars) in the (
|Baseline [||Helically wound [||Hoop plies [|
| = ||5|
4.4.2. Three-layer anisotropic long cylinder
Results for a cylinder constructed from three, equal-thickness layers with stacking sequence denoted by [
| = |
4.4.3. Four-layer sandwiched anisotropic cylinder
The same graphite/epoxy cylinder is reconsidered here with changing the stacking sequence to become ±20° equal-thickness layers sandwiched in between outer and inner 90° hoop layers with unequal thicknesses, i.e., () and ( ≠ ), such that the thickness equality constraint is always satisfied. Figure 4 shows the developed -isomerits in the () design space. The contours inside the feasible domain, which is bounded by the three lines and and (i.e.,
Finally, the obtained results have indicated that the optimized laminations induce significant increases, always exceeding several tens of percent, of the buckling pressures with respect to the reference or baseline design. It is assumed that the volume fractions of the composite material constituents do not significantly change during optimization, so that the total structural mass remains constant. It has been shown that the overall stability level of the laminated composite shell structures under considerations can be substantially improved by finding the optimal stacking sequence without violating any imposed side constraints. The stability limits of the optimized shells have been substantially enhanced as compared with those of the reference or baseline designs.
Daniel I, Ishai O. Engineering Mechanics of Composite Materials. 2nd ed. Oxford University Press, New York; 2006. 432p. ISBN: 978-0195150971
Kollar LP, Springer GS. Mechanics of Composite Structures. 1st ed. United Kingdom: Cambridge University Press; 2009. 500p. ISBN: 978- 0521126908
Niu MCY. Composite Airframe Structures. 3rd ed. Hong Kong: Hong Kong Conmilit Press Ltd; 2011. 500p. ISBN: 978-9627128069
Kassapoglou C. Design and Analysis of Composite Structures: With Applications to Aerospace Structures. 2nd ed. New Jersey: Wiley; 2013. 410p. ISBN: 978-1118401606
Ye J. Laminated Composite Plates and Shells, 3D Modelling. 1st ed. London: Springer-Verlag; 2003. DOI: 10.1007/978-1447100959. 273p. ISBN: 978-1447100959
Kumar SS. Smart Composite Structures. 1st ed. United States: Lambert Academic Publishing; 2013. 180p. ISBN: 978-3659322532
Sonmez FO. Optimum design of composite structures: A literature survey (1969–2009). Journal of Reinforced Plastics and Composites. 2017; 36(1):3-39. DOI: 10.1177/0731684416668262
Ganguli R. Optimal design of composite structures: A historical review. Journal of the Indian Institute of Science. 2013; 93(4):557-570. ISSN: 0970-4140 Coden-JIISAD
Maalawi K, Badr M. Design optimization of mechanical elements and structures: A review with application. Journal of Applied Sciences Research. 2009; 5(2):221-231
Hasancebi O, Carba S, Saka M. Improving the performance of simulated annealing in structural optimization. Structural and Multidisciplinary Optimization. 2010; 41:189-203
Walker M, Smith RE. A technique for the multi-objective optimization of laminated composite structures using genetic algorithms and finite element analysis. Composite Structures. 2003; 62:123-128
Narayana Naik G, Gopalakrishnan S, Ganguli R. Design optimization of composites using genetic algorithms and failure mechanism based failure criterion. Composite Structures. 2008; 83:354-367
Omkar SN, Senthilnath J, Khandelwal R, Narayana Naik G, Gopalakrishnan S. Artificial bee colony (ABC) for multi-objective design optimization of composite structures. Applied Soft Computing. 2011; 11(1):489-499
Maalawi KY. Optimal buckling design of anisotropic rings/long cylinders under external pressure. Journal of Mechanics of Materials and Structures. 2008; 3(4):775-793