RESEARCH ARTICLE
Layup Optimization of Laminated Composites Using a Modified Branch and Bound Method
Giacomo Canale^{1}, Paul M. Weaver^{2}, Felice Rubino^{3, *}, Angelo Maligno^{3}, Roberto Citarella^{4}
Article Information
Identifiers and Pagination:
Year: 2018Volume: 12
First Page: 138
Last Page: 150
Publisher Id: TOMEJ12138
DOI: 10.2174/1874155X01812010138
Article History:
Received Date: 01/02/2018Revision Received Date: 15/05/2018
Acceptance Date: 21/05/2018
Electronic publication date: 20/06/2018
Collection year: 2018
openaccess license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CCBY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background:
Composite materials are widely used in the aerospace, marine and automotive industries. One of their main advantages is that their stacking sequence can be tailored to maximise/minimise a specific structural performance. Efficient and noncomputationalexpensive algorithms are always needed to find the optimum stacking sequence of a composite laminate whose thickness is either to be minimised or may be kept constant (i.e. the thickness and the plies orientation percentages are predetermined; the problem of the optimisation is therefore permutational).
Objective:
A modified branch and bound algorithm is proposed here and used to determine the stacking sequence for single and multiobjective optimisation problems. Laminate thickness and orientation percentages are either variables or determined a priori (the optimisation problem is therefore permutational). Computational time is drastically reduced when compared with other metaheuristic techniques.
Methods:
The proposed method is a branch and bound algorithm, modified from the original work proposed by Kim and Hwang [10]. The main novelty is the starting point of the optimisation sequence: a laminate formed by “Ideal” layers, described in this paper.
Results:
The modified branch and bound has been first tested with a laminate having fixed thickness and a fixed percentage of layer orientation. Three different problems have been investigated: maximisation of natural frequencies, minimisation of tip deflection and maximisation of buckling critical load. The algorithm has been also tested, secondly, for a problem of weight minimisation subjected to buckling and strength constraints.
Conclusion:
The MBB has been shown to give good fidelity and significant computational advantages compared with a GA. Despite the simplicity of the structures in the numerical examples, it is anticipated that the MBB can be used to determine layups in multipart structures. The method was used to determine stacking sequences for several problems. The modified branch and bound method was shown to determine good laminate designs and offer significant efficiency savings.
A “Good Design” is here defined as a solution producing “Near Global Optima” fitness values by minimising the computational effort. It was shown that for a single objective without ply competition, global optima were obtained.
1. INTRODUCTION
The success of composite materials has been well documented in recent years [1, 2]. Despite their success and insertion in high profile aircraft, significant efficiency and functionality gains may be obtained by undertaking stacking sequence (layup) optimization. Studies have shown that significant weight savings and performance gains can be achieved via layup optimization [35].
In layup optimization, the design variables are generally ply thickness and ply orientation. In many practical applications, ply thickness is fixed and ply orientations take a range of discrete values. If ply orientations are used as design variables, the resulting response surface is nonconvex. As such, gradientbased methods are not entirely reliable optimizers. Motivated by this deficiency, several heuristic and deterministic methods have been used to determine laminate stacking sequences. With respect to heuristic optimizers, the success of a Genetic Algorithm (GA) has been well documented [4, 5]. Bloomfield et al. [6] compared three popular techniques for stacking sequence optimization, GA, Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). The research indicated that all three methods were suitable optimizers, yet for different problems, different optimizers may be more appropriate. Furthermore, Bloomfield et al. [6] observed that ACO and PSO could offer significant efficiency savings over a GA. With respect to deterministic optimizers, a branch and bound method has been proposed, chiefly by Todoroki et al. [7, 8]. Todoroki et al. [7, 8] successfully used the branch and bound approach (where the laminate thickness is fixed) for a variety of problems using a single level optimization routine. Despite the apparent success of branch and bound method [9], the algorithm is known to be computationally expensive. This becomes apparent when the set of possible ply orientations or number of plies increases.
It is often desirable to consider the effect of each ply in the layup independent of the other plies in the stacking sequence. That is, identification of the i^{th} ply orientation independent of the other i1 plies. To achieve this, Kim and Hwang [10] introduced the concept of an idealized laminate. An idealized laminate is artificially enhanced, which cannot be realized with a real stacking sequence. It is noted, that by utilizing idealized laminae, the effect of each ply in the stacking sequence on the objective function can be quantified. Building upon the concept of an idealized laminate and the branch and bound approach, a twolevel optimization is proposed. At the first level of the optimization lamination parameters and plate thickness are used to minimize the mass of the composite plate subject to a set of design constraints, i.e. strength, buckling or aeroelastic constraints. The first level of the optimization is not the focus of the current work, but is detailed in [3] as part of a twolevel approach for layup optimisation. However, it is noted that by representing laminate stiffness in terms of lamination parameters, a convex response surface is created which is ably optimised, for lamination parameters and thicknesses, using a gradientbased method. The first level of the optimization established the potential optimal mass (total thickness) and guarantees the existence of a real stacking sequence which satisfies the set of design constraints, and it takes into account conflicting objective or constraint tradeoffs. At the second level, a stacking sequence is determined using a discrete optimizer which seeks to maximize (or minimize) a given function or satisfy design constraints depending upon the nature of the problem. Motivated by the efficiency problems of the standard branch and bound algorithm and the work of Kim and Hwang [10], a Modified Branch and Bound (MBB) is now proposed. The MBB approach expands the formulation by Kim and Hwan [10] to include anisotropic laminates. Furthermore, a modified pruning technique is used to select the ply orientation of each position in the layup. This approach yields good designs and offers significant efficiency savings over the conventional branch and bound and other methods such as GA, PSO and ACO. It is shown that by using the MBB with a uniobjective function then global optima are obtained. The novelty of the current work lies in expanding the concept of an idealized laminate and in the introduction of a new and simplified branching and pruning technique compared to the original formulation of Kim and Hwan. This new formulation helps reducing the computational time of more than 10 times when compared to the original algorithm. The proposed modified branch and bound algorithm has been applied to a wide variety of stacking sequence dependent problems in this paper. In addition, a comparison of MBB with GA is provided and discussed to demonstrate the efficacy of the proposed approach.
2. MATERIALS AND METHODS
Kim and Hwang [10] defined an ideal lamina to be one with enhanced stiffness, that is practically unachievable, yet relate to the inherent basic ply stiffness. It should be mentioned that the authors restricted their study of ideal laminae to symmetric orthotropic/isotropic laminates. Firstly, the inplane, coupling and outofplane stiffness matrices are defined:
(1) 
Where:
n is the number of plies in the layup
z is the through thickness coordinate, the distance of each layer from the middle plane of the laminate as per classical lamination theory.
Q_{ij} are the terms of the reduced inplane stiffness matrix, characterising the planar behaviour of each ply of the laminate. These terms can be expressed in terms of material invariants as follows:
(2) 
 The fitness (using a defined fitness function) of a potential laminate is maximized for the ideal laminate.
 When a real layer replaces an ideal layer in the ideal layup, a lower value of the fitness function is obtained, simply because a real layer is not as stiff as the ideal one. When each ideal layer is subsequently replaced with a real layer (from the outermost ply inwards), the fitness function decreases monotonically with ply position. It is noted that a monotonically sequence which is bounded below, is necessarily convergent. Therefore, the resulting algorithm converges to stable points.
 The change in the fitness function should be quantifiable on a plybyply basis. If the ideal stiffness constants are too large. i.e. excessively values are arbitrarily chosen, changing one ply may not significantly alter the fitness of the laminate. Using Eq. (2) prevents this potential illconditioning problem.
 The gradient of the objective function evaluated at the ideal stacking sequence is zero (implying at least local optima).
It is observed that there are potentially different (nonunique) idealizations for different objective functions. This is evident when considering that the optimal angles for a buckling driven problem (typically 45^{o}) differ from the optimum angles from a strength driven problem (typically 0^{o} for axial loading). Therefore, an acceptable laminate idealization must satisfy the aforementioned conditions. This implies that the analysis may have to perform different “Trials and Errors” simulations before finding an ideal layer definition for his specific problem. For complex problems, such like multidirection loading, the ideal layer may be an “Isotropic” layer whose stiffness and strength cannot be achieved by any existing material or orientation of the available material.
Next, the branch and bound algorithm presented by Kim and Hwang [10] is discussed. The first step of their algorithm establishes a random stacking sequence S whose objective function is evaluated. The fitness of S is denoted by F*. Next, the fitness of the ideal stacking sequence, denoted by ID, is evaluated and denoted by F^{ID}. Following this step, all of the layers in the ideal laminate are sequentially replaced (from the outermost ply inwards) with real layers until the optimal stacking sequence is determined. Starting from the outermost ply, the i^{th} ideal layer is replaced with the real layer of ply angle θ_{i}_{.} During this process, an obtained fitness F_{i} which is worse than F* need not be branched further. In fact, by replacing ideal layers with real layers, the fitness function necessarily decreases. The branch and bound approach adopted by Kim and Hwang [10] is shown in Fig. (1).
Fig. (1). Scheme of Branch and Bound used by Kim and Hwang [10]. 
It is observed that whilst the full algorithm, proposed by Kim and Hwang [10], is able to determine good designs, the resulting algorithm is extremely inefficient. The algorithm proposed by Kim and Hwang [10] uses a random initial stacking sequence. In contrast, by using an idealized stacking sequence as a starting point, and changing the ply orientation from the outermost ply inwards, this ensures that the fitness of the laminate monotonically decreases with position. Each ideal layer is replaced with the best real layer with respect to position. Effectively, all branches are pruned except the best and the algorithm continues to the next layer. This pruning mechanism, essentially identifies the largest branch, and is demonstrated in Fig. (2).
Fig. (2). Scheme of modified branch and bound with 3 possible ply orientations [90/45/0]. 
In other words, the MBB here proposed:
 Does not make any use of the initial random stacking sequence but branches the ideal laminate, as the fitness function decreases in value.
 Only the larger branch is identified and computed (each level in which a layer orientation is selected has only 1 solution that will be branched further). If n is the number of layers and m the number of possible orientations, m x n computations are performed.
Using the new branching technique, it is observed that significant efficiency savings can be achieved. Consider, a laminate of n plies, formed from m different ply orientations, there are possible layups. As such, enumeration quickly becomes unviable with a large number of layers, n, due to the combinatorial explosion of possible layups with increasing thickness. If a heuristic search method (such as a GA) is used, the number of evaluations would be r x s, where r is the population size and s is the number of iterations that have taken place until a solution is determined. Using a heuristic approach, r × s < m^{n}. In contrast, the MBB approach evaluates m x n designs. It is observed that this process represents a significant reduction in the number of evaluations.
If there is more than one objective, different ply orientations may compete for the same position. Therefore, additional criteria must be used, in that case, to select the 'Best' ply at each position. Nonetheless, by selecting the 'Best' ply at each position, in multiobjective problems there may be an abundance of possible combinations of stacking sequences which are missed and may yield better results. Despite this potential shortfall, it will be shown, in the next section, that the method is able to determine good designs (near global optima, with a strong reduction of computational time).
3. RESULTS AND DISCUSSION
In this section, several numerical examples are considered. The first set of numerical examples aims to determine a stacking sequence which maximises or minimizes a given objective function for a laminate of fixed thickness and fixed percentages of ply orientations. Take into account that fixing the percentages is equivalent to fixing the inplane lamination parameters. The following three objectives are considered:
 Maximize the natural frequency (first mode) of a simply supported rectangular plate. It has been shown [11], that such analysis could be important in the design of laminates which limit resonance due to external excitation.
 Maximize the compression buckling load of a simply supported rectangular plate.
 Minimize the vertical displacements of a clamped laminate subjected to a cantilever tip vertical load.
The second set of numerical examples aims to determine stacking sequences of minimum thickness which satisfy strength and buckling constraints. For each numerical example detailed in this section, the MBB algorithm is used to determine laminate stacking sequences and the results are compared with a permutative GA.
3.1. Maximising the Natural Frequency, Buckling Load and Minimising Vertical Displacement
In the following numerical examples, it is assumed that the laminate is formed from 80 plies. The laminate is assumed to be symmetric with respect to the midplane and hence not exhibit any bendingextension coupling. As such, the stacking sequence of 40 plies needs to be determined. Furthermore, the laminate is considered to be balanced in order to not obtain extensionshear coupling. Additionally, the membrane properties of the laminate are fixed, where the laminate has 44% percent of 0 degrees fibres, 22% of 45 degrees fibres, 22% of 45 degrees fibres and 12% of 90 degrees fibres. Hence, there are 16 layers with 0 degrees fibres, 8 layers with 45 degrees fibres, 8 layers with 45 degrees fibres and 8 layers with 90 degrees fibres. The rectangular plate dimensions are 120mm x 40 mm. The material properties of the laminate are shown in Table 1.
E_{11}  181 GPa 
E_{22}  10.3 GPa 
G_{12}  4.55 GPa 
ν_{12}  0.28 
p  1600 kg/m^{3} 
ply thickness  0.0002 m 
The MBB requires the definition of an ideal lamina that satisfies the elastic properties described in section 2. Furthermore, it is assumed that the density of the ideal layer is the same as a real layer. Natural frequencies and compressive buckling loads are evaluated by using a close form solution [12]. The fitness function for the problem of the vertical displacement minimisation under a tip bending load is evaluated by interfacing MATLAB [13] and MSC Nastran [14]. For the permutative GA [15] three tuning parameters must be refined in order to ensure efficient convergence. The following parameters were used in the GA for the maximisation of the compressive buckling load and for the maximisation of the first natural frequency:
 Minimum number of generation before terminations: 80
 Size of initial population: 50
 Number of mutations (permutation of two layers randomly selected): one in a single string of each generation.
Such parameters, for the problem of the minimisation of vertical displacement, since interfacing FEM with Matlab requires more CPU running time, are:
 Minimum number of generation before terminations: 30
 Size of initial population: 20
 Number of mutations (permutation of two layers randomly selected): one in a single string of each generation.
In comparison, it can be observed that the MBB approach requires no such parameters. It is, in fact, a completely deterministic method. A useful way to understand completely how the MBB works is shown in Fig. (3). The ideal laminate is characterized by the highest value of fitness. As the algorithm proceeds, the value of the fitness decreases monotonically, until the last layer is replaced. With respect to the GA, it was observed that whilst the GA obtained good results, it was only able to find local solutions which may or may not be local minima. For practical considerations, it is useful to include the analysis with the empirical four ply rule [16]. Results obtained using MBB with and without the four plies rule are presented, along with results obtained using a GA.
Fig. (3). Natural frequency of the first mode against the number of ideal layers replaced. 
The results for the frequency maximization problem are detailed in Tables 2a and 2b.
Method  Stacking Sequence 

GA  
GA with 4 ply rule  
MBB/Original BB  
MBB with 4 ply rule 
GA 
GA With 4 Ply Rule 
MBB 
Original BB 
MBB With Four Ply Rule 


Value of Fitness (Frequency [rad\s])  2.51 x 10^{5}  2.42 x 10^{5}  2.54 x 10^{5}  2.54 x 10^{5}  2.43 x 10^{5} 
CPU Running Time [s]  10.21  10.23  0.30  3.67  0.31 
Number of stacking sequences evaluated  4000  4001  88  291  89 
The mean fitness of each generation of the population of the GA is shown in Fig. (4). Such population is evolving through the generations because the mean value generally increases, even if some oscillations are observed. It proves that the algorithm works correctly.
Fig. (4). Evolution of the mean fitness of the GA population. 
Moreover, a number of 80 generations has been chosen because the algorithm does not need more to reach its convergence: A larger number of cycles will not correspond to any improvement in the population.
Next, both optimization techniques were used to maximize the buckling compression load of a simply supported plate. Results are shown in Tables 3a and 3b.
Method  Stacking Sequence 

GA  
GA with 4 ply rule  
MBB/Original BB  
MBB with 4 ply rule 
GA 
GA with 4 Plies Rule 
MBB 
Original BB 
MBB With Four Plies Rule 


Buckling Load [N]  2.79 x 10^{7}  2.78 x 10^{7}  2.90 x 10^{7}  2.90 x 10^{7}  2.89 x 10^{7} 
CPU Running Time [s]  14.26  14.27  0.30  4.20  0.31 
Number of Stacking Sequences Evaluated  4000  4001  88  318  89 
It is observed that also for this particular problem the MBB gives better results. Moreover, the MBB is quicker and, hence, offers significant efficiency savings. In both cases, the implementation of the four plies rule does not affect the value of the fitness function.
Next, for the vertical displacement minimization problem under a unitary tip load, the obtained stacking sequences are shown in Table (4a) and a comparison of results is presented in Table 4b.
Method  Stacking Sequence 

GA  
GA with 4 ply rule  
MBB/Original BB  
MBB with 4 ply rule 
GA 
GA with 4 ply rule 
MBB 
Original BB 
MBB with 4 ply rule 


Tip vertical displacement of the point at the middle of the plate [m]  2.70 x 10^{9}  3.01 x 10^{9}  2.55 10^{9}  2.55 10^{9}  2.93 x 10^{9} 
CPU Running Time [s]  580  581  91  819  92 
Number of stacking sequences evaluated  600  601  88  156  89 
It is observed that the MBB obtains better layups than the GA. The MBB offers also significant efficiency savings over the GA.
As already remarked in this paper, the MBB offers computational time advantage when compared to the original BB proposed in Reference 10. The optimum stacking sequence is however the same Tables 2a and 2b, 3a and 3b, 4a and 4b.
3.2. Layup Optimisation for Minimum Thickness Subject to Strength and Buckling Constraints
In this section, a different optimization problem is considered. That is, a stacking sequence of minimum thickness is sought which satisfies strength and buckling constraints. To achieve this objective, a twolevel optimization approach is used. At the first level, lamination parameters and plate thicknesses are used to minimise the mass of the composite plate subject to strength and buckling constraints [3].The first level proves the existence of a real stacking sequence and establishes the minimal (continuous) thickness taking into account buckling and strength tradeoffs. At the second level, a discrete optimizer is used to determine the laminate stacking sequence which satisfies the set of design constraints. For reasons of brevity, the details concerning the first level of the optimization are not presented here, but can be found in [3]. In the following numerical examples, several load cases are considered. Furthermore, the set of possible ply orientations are expanded to include 0, 90, 45, 45, 30, 30, 60, 60 degrees. For each case study, four structural problems are considered – each being a simply supported composite plate of 1200mm x 200mm with different loading conditions. The following load cases will be considered:
i) N_{x} = 0, N_{xy} = 800 N / mm
ii) N_{x} = 750, N_{xy} = 0 N / mm
iii) N_{x} = 600, N_{xy} = 300 N / mm
iv) N_{x} = 200, N_{xy} = 500 N / mm
It can be observed that a) the loads are representative of those found in a cover skin for an aircraft wing structure and b) all aspect ratios are sufficiently large to allow use of the buckling closed form solutions outlined in [3]. The shear deformation effects are not considered. Additionally, the material properties used in the optimization are shown in Table 5.
E_{11}  130 GPa 
E_{22}  7 GPa 
G_{12}  4 GPa 
ν_{12}  0.28 
p  1600 kg/m^{3} 
ply thickness  0.00125 m 
For each load case, the MBB and a GA [6] were used to determine laminate stacking sequences. In Tables 6a, and 6b stacking sequences and critical reserve factors, determined via the two methods are detailed. Furthermore, the CPU time is shown. As can be seen, the selected idealization is an isotropic laminate which satisfies the criteria for an idealised layer, defined in section 1. Interestingly, by fixing the two outermost plies and assume the inner n2 plies to be ideal layers, all possible combinations of the first two plies are considered and the objective function evaluated. The results, for an example problem, are visualized in Fig. (5).
Load Case (i)  GA  BB 

Minimum Critical Reserve Factor  0.99  0.99 
CPU Running Time s  39  0.86 
Fig. (5). Response surface of the fitness function for the two outermost plies. 
In Fig. (4), negative values for F show designs which satisfy the strength and buckling constraints. Therefore, lower values of F correspond to better designs. From Fig. (4), it can be seen that the best outer two positions correspond to approximately 60 degrees respectively. The highly nonconvex nature of the response surface implies that gradientbased methods would have difficulty in obtaining stacking sequences which satisfy the strength and buckling constraints.
GA  
BB 
Load Case (ii)  GA  BB 

Minimum Critical Reserve Factor  1  0.97 
CPU Running Time s  8.3  0.8 
GA  
MBB 
Load Case (iii)  GA  BB 

Minimum Critical Reserve Factor  1  0.99 
CPU Running Time s  0.9  0.8 
GA  
MBB 
Load Case (iv)  GA  BB 

Minimum Critical Reserve Factor  1  1 
CPU Running Time s  3.1  0.7 
From the Tables 6a6h, it is clear that both algorithms performed well. Generally, the GA obtains slightly higher reserve factors but with a higher associated CPU penalty. When the MBB did not satisfy the design constraints, i.e. for multiobjective problems where ply orientations were competing for the same position, integrating the algorithm with a heuristic optimiser may yield better reserve factors and still reduce the overall running time of the optimization (compared to a single heuristic based approach).
GA  
BB 
CONCLUSION
The MBB has been shown to give good fidelity and significant computational advantages compared with a GA. Despite the simplicity of the structures in the numerical examples, it is anticipated that the MBB can be used to determine layups in multipart structures.
The method was used to determine stacking sequences for several problems. The modified branch and bound method was shown to determine good laminate designs and offer significant efficiency savings. It was shown that for a single objective without ply competition, global optima were obtained. However, for multiobjective or multicriteria problems, different ply orientations may compete for the same ply position. This competition is evident when considering the optimal angles for buckling or the optimum angles for strength driven designs. By selecting only the best ply for a given position there may be an abundance of possible combinations of angles missed which may give improved designs.Obtained designs, which do not have reserve factors greater or equal to one or satisfy some given criteria, could form a part of an initial population of a heuristic method. In addition, it is believed that the method can be applied to determine layups in multipart structures, a topic of future interest.
NOMENCLATURE
A_{ij}  = Components of The InPlane Stiffness Matrix 
B_{ij}  = Components of The Coupling Stiffness Matrix 
D_{ij}  = Components of The OutofPlane Stiffness Matrix 
E_{11}, E_{22}  = Longitudinal and Transverse Young's Moduli 
F  = Fitness Function 
G_{12}  = Shear Modulus 
Nx  = Axial Force Per Unit of Length 
N_{xy}  = Shear Force Per Unit of Length 
Q_{ij}  = Components of the Reduced Stiffness Matrix 
s  = Symmetric 
U_{i}  = Material Invariants 
ρ  = Density of Material 
ν_{12}  = Poisson’s Ratio 
CONSENT FOR PUBLICATION
Not applicable.
CONFLICT OF INTEREST
The authors declare no conflict of interest, financial or otherwise.
ACKNOWLEDGEMENTS
Declared none.