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

2012 ◽  
Vol 3 (6) ◽  
Author(s):  
AR Reddy ◽  
BS Reddy ◽  
KVK Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Of Experiments ◽  
Laminated Composite ◽  
Composite Plates ◽  
Stacking Sequence ◽  
Laminated Composite Plates ◽  
Artificial Neural

scholarly journals Prediction of Natural Frequency of Laminated Composite Plates Using Artificial Neural Networks

Engineering ◽  
2012 ◽  
Vol 04 (06) ◽  
pp. 329-337 ◽  
Author(s):  
Mutra Raja Sekhara Reddy ◽  
Bathini Sidda Reddy ◽  
Vanguru Nageswara Reddy ◽  
Surisetty Sreenivasulu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Natural Frequency ◽  
Laminated Composite ◽  
Composite Plates ◽  
Laminated Composite Plates ◽  
Artificial Neural

scholarly journals Experimental and Numerical Buckling Analyses of Laminated Composite Plates under Temperature Effects

2010 ◽  
Vol 19 (4) ◽  
pp. 096369351001900 ◽  
Author(s):  
Emin Ergun
Keyword(s):  
Composite Plate ◽  
Numerical Solutions ◽  
Laminated Composite ◽  
Composite Plates ◽  
Buckling Load ◽  
Fibre Reinforcement ◽  
Stacking Sequence ◽  
Laminated Composite Plates ◽  
Different Temperatures ◽  
Good Agreement

The aim of this study is to investigate, experimentally and numerically, the change of critical buckling load in composite plates with different ply numbers, orientation angles, stacking sequences and boundary conditions as a function of temperature. Buckling specimens have been removed from the composite plate with glass-fibre reinforcement at [0°]i and [45°]i (i= number of ply). First, the mechanical properties of the composite material were determined at different temperatures, and after that, buckling experiments were done for those temperatures. Then, numerical solutions were obtained by modelling the specimens used in the experiment in the Ansys10 finite elements package software. The experimental and numerical results are in very good agreement with each other. It was found that the values of the buckling load at [0°] on the composite plates are higher than those of other angles. Besides, symmetrical and anti-symmetrical conditions were examined to see the effect of the stacking sequence on buckling and only numerical solutions were obtained. It is seen that the buckling load reaches the highest value when it is symmetrical in the cross-ply stacking sequence and it is anti-symmetrical in the angle-ply stacking sequence.

Application of Operator Libraries on Laminate Strength Optimization of Composites

2013 ◽  
Vol 853 ◽  
pp. 686-692
Author(s):  
Gong Dong Wang ◽  
Jun Wang ◽  
Hao Chen
Keyword(s):  
Memetic Algorithm ◽  
Laminated Composite ◽  
Object Oriented ◽  
Composite Plates ◽  
Object Oriented Programming ◽  
Local Operator ◽  
Stacking Sequence ◽  
Laminated Composite Plates ◽  
Practical Design ◽  
Improved Memetic Algorithm

An improved Memetic algorithm is applied in this article. Mathematical model is proposed to optimize the laminate strength. The composites laminate strength optimization system with local operator library and rule operator library has been developed by the object-oriented programming with C++. The local operator has contributed to increase the convergence rate and the rule operators have contributed to implement practical design aspects in optimization of laminated composite plates. A numerical example demonstrates the validity of optimization model and practical applicability of the improved Memetic algorithm; hence, exhibiting the improvement of the method in tackling the stacking sequence.

Performance evaluation of artificial neural networks for identification of failure modes in composite plates

2021 ◽  
Vol 63 (6) ◽  
pp. 565-570
Author(s):  
Serkan Balli ◽  
Faruk Sen
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Failure Modes ◽  
Experimental Studies ◽  
Composite Plates ◽  
Geometric Parameters ◽  
Sandwich Composite ◽  
Geometrical Parameters ◽  
Training Algorithms ◽  
Artificial Neural

Abstract The aim of this work is to identify failure modes of double pinned sandwich composite plates by using artificial neural networks learning algorithms and then analyze their accuracies for identification. Mechanically pinned specimens with two serial pins/bolts for sandwich composite plates were used for recognition of failure modes which were obtained in previous experimental studies. In addition, the empirical data of the preceding work was determined with various geometric parameters for various applied preload moments. In this study, these geometric parameters and fastened/bolted joint forms were used for training by artificial neural networks. Consequently, ten different backpropagation training algorithms of artificial neural network were applied for classification by using one hundred data values containing three geometrical parameters. According to obtained results, it was seen that the Levenberg-Marquardt backpropagation training algorithm was the most successful algorithm with 93 % accuracy rate and it was appropriate for modeling of this problem. Additionally, performances of all backpropagation training algorithms were discussed taking into account accuracy and error ratios.

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