Laminated composite plate structures find numerous applications in aerospace, military and automotive industries. The role of  transverse shear is very important in  composites, as the material is weak in shear due to its low shear modulus compared to extensional rigidity. Hence, an accurate understanding of their structural behaviour is required, such as deflections and stresses. In this paper, a number of finite element analyses have been carried out for various side-to-thickness ratios, aspect ratios and modulus ratios to study the effect of transverse shear deformation on deflection and stresses of laminated composite plates  subjected to uniformly distributed load. The numerical results showed, on the deflections and stresses, that the effect of coupling is to decrease the deflections with the increase in the aspect ratio and modulus ratio and increase the stresses with the increase in the side-to-thickness ratio and modulus ratio.Keywords: Laminated composite plate, bending analysis, uniformly distributed load, finite element method.

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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.

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Pre-hardened steel (P20) is a widely used material in the production of moulds/dies due to less wear resistance and used for large components. In this study, minimization of surface roughness has been investigated by integrating design of experiment method, Response surface methodology (RSM) and genetic algorithm. To achieve the minimum surface roughness optimal conditions are determined. The experiments were conducted using Taguchi's L 50 orthogonal array in the design of experiments (DOE) by considering the machining parameters such as Nose radius (R), Cutting speed (V), feed (f), axial depth of cut (d) and radial depth of cut(rd). A predictive response surface model for surface roughness is developed using RSM. The response surface (RS) model is interfaced with the genetic algorithm (GA) to find the optimum machining parameter values.