REVIEW OF ARTIFICIAL NEURAL NETWORK AND TAGUCHI APPLICATION IN POLYMER MATRIX COMPOSITES N.Venkateshwaran1, A.Elayaperumal1,A.Alavudeen2 and M.Thiruchitrambalam3
نویسندگان
چکیده
This paper reviews the application of two analytical tools namely Artificial Neural Network (ANN) and Taguchi based design of experiment in the field of polymer matrix composite. These two tools are used extensively to predict the tool wear, fatigue life etc., during machining operation.
منابع مشابه
Application of statistical techniques and artificial neural network to estimate force from sEMG signals
This paper presents an application of design of experiments techniques to determine the optimized parameters of artificial neural network (ANN), which are used to estimate force from Electromyogram (sEMG) signals. The accuracy of ANN model is highly dependent on the network parameters settings. There are plenty of algorithms that are used to obtain the optimal ANN setting. However, to the best ...
متن کاملStatistical Analysis and Predictive Learning of Mechanical Parameters for TiO2 Filled GFRP Composite
The new, polymer composites consisting of e-glass fiber reinforcement with titanium oxide filler in the double bonded unsaturated polyester resin matrix were made. The glass fiber and titanium oxide reinforcement composites were made in three different fiber lengths (3cm, 5cm, and 7cm), filler content (2 wt%, 4 wt%, and 6 wt%) and fiber content (20 wt%, 40 wt%, and 60 wt%). 27 different composi...
متن کاملUsing the hybrid Taguchi experimental design method – TOPSIS to identify the most suitable artificial neural networks used in energy forecasting
The use of artificial neural networks (ANN) in forecasting has many applications. Appropriate design of ANN parameters enhances the performance and accuracy of neural network models. Most studies use a trial and error approach in setting the value of ANN parameters. Other methods used to determine the best structure of a neural network only use a single evaluation criterion to determine the ap...
متن کاملAn Evaluation of Mahalanobis-Taguchi System and Neural Network for Multivariate Pattern Recognition
The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis- Taguchi System and a neural-network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The...
متن کاملOptimum Design of Liquified Natural Gas Bi-lobe Tanks using Finite Element, Genetic Algorithm and Neural Network
A comprehensive set of ten artificial neural networks is developed to suggest optimal dimensions of type ‘C’ Bi-lobe tanks used in the shipping of liquefied natural gas. Multi-objective optimization technique considering the maximum capacity and minimum cost of vessels are implemented for determining optimum vessel dimensions. Generated populations from a genet...
متن کامل