Application of design of experiments and artificial neural networks for stacking sequence optimizations of laminated composite plates

Abstract

This paper discusses the use of Distance based optimal designs in the design of experiments (DOE) and artificial neural networks (ANN) in optimizing the stacking sequence for simply supported laminated composite plate under uniformly distributed load (UDL) for minimizing the deflections and stresses. A number of finite element analyses have been carried out using Distance-based optimal design, for training and testing of the ANN model. The deflections and stresses were found by analyses which were done by finite element analysis software. The ANN model has been developed using multilayer perceptron (MLP) back propagation algorithm. The adequacy of the developed model is verified by coefficient of determination (R). The sensitivity analysis has been performed to study the behavior of the laminated composite plate. The results obtained from the ANN model are compared with the finite element results. For various fibre orientations, deflections and stresses analyses are performed to get the optimal fibre orientations. A verification tests are also performed to prove the effectiveness of the ANN technique after the optimum levels of fibre orientations are determined. The confirmation experimental results show that deflections and stresses are very good agreed with the finite element (FE) results. Consequently, the Distance-based optimal set of laminates and ANN are shown to be effective for optimization of stacking sequence of laminated composite plates.

Nonlinear low‐velocity impact analysis of functionally graded nanotube‐reinforced composite cylindrical shells in thermal environments

“…Modifying Sander's shell theory, Reddy and Liu [43] developed a higher-order shear deformation shell theory. The transverse shear strains are assumed to be parabolically distributed across the shell thickness; therefore, no shear correction factors are required in this theory.…”

“…where a 11 and a 22 are longitudinal and transverse thermal expansion coefficients, respectively; and Q ij are the transformed elastic constants which are given in details in [43]. Clearly for an FG-CNTRC layer (i.e.…”

Laminated Composites Buckling Analysis Using Lamination Parameters, Neural Networks and Support Vector Regression

“…Artificial neural networks have been used by Bezerra et al (2007) to approximate the shear mechanical properties of reinforced composites. Reddy et al (2011) optimized the stacking sequence of laminated composite plates applying neural network. Their work included experimental and finite element analysis for minimizing the deflections and stresses.…”

Spectrophotometric methods based on charge transfer complexation reaction for the determination of some antifungal drugs in pure and dosage forms

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