Open Access Open Access  Restricted Access Subscription or Fee Access

Implementation of Radial Basis Function Neural Network for Estimation of Strain of Blade

R.I. Rajidap Neshtar, S. Purushothaman

Abstract


This paper presents estimation of stress and strain of a Rapid prototype product using artificial neural network (ANN). Radial basis function network is used to train the ANN topology. 3D model of blade is developed by using PROE. The model is analyzed using ANSYS to find the Von Mises stress and equivalent strain. The algorithm is trained using 15 values in the input layer of the ANN topology and two values in the output layer: stress and strain that are to be estimated during the testing stage of RBF algorithm.

Keywords


Radial Basis Function Network, Finite Element Method, Structural Analysis, and Blade.

Full Text:

PDF

References


Craddock R.J., and Warwick K., 1996, Multi-Layer Radial Basis Function Networks. An Extension to the Radial Basis Function, IEEE International Conference on Neural Networks, Vol.2, No.1, pp.700-705.

Hacib T., Mekideche MR., and Ferkha N., Computational Investigation on the Use of FEM and RBF Neural Network in the Inverse Electromagnetic Problem of Parameter Identification, IAENG International Journal of Computer Science, 33:2.

Hyuntae Na, Seung-Yub Lee, Ersan Üstündag, Sarah L. Ross, Halil Ceylan, and Kasthurirangan Gopalakrishnan,Development of a Neural Network Simulator for Studying the Constitutive Behavior of Structural Composite Materials, ISRN Materials Science, Vol.2013, Article ID 147086, 10 pages.

Javier Toraño, Isidro Diego, Mario Menéndez and Malcolm Gent, 2008, Finite element method (FEM) – Fuzzy logic (Soft Computing) – virtual reality model approach in a coalface long wall mining simulation, Automation in Construction, Vol.17, pp.413–424.

John Moody and Christian J Darken, 1989, Fast Learning in Networks of Locally-Tuned Processing Units, Neural Computation, Vol.1, No.2, pp.281-294.

Larry Manevitz, Akram Bitar and Dan Givoli, 2005, Neural network time series forecasting of finite-element mesh adaptation, Neurocomputing, Vol.63, pp.447–463.

Mohsen Ostad Shabani and Ali Mazahery, 2011, The ANN application in FEM modeling of mechanical properties of Al–Si alloy, Applied Mathematical Modelling, Vol.35, pp.5707–5713.

Mohsen Ostad Shabani, Ali Mazahery, Mohammad Reza Rahimipour and Mansour Razavi, 2012, FEM and ANN investigation of A356 composites reinforced with B4C particulates, Journal of King Saud University – Engineering Sciences, Vol.24, pp.107–113.

REN Xiao-qiang, CHEN Wu-jun, DONG Shi-lin and WANG Feng, 2006, Neural network method for solving elastoplastic finite element problems, Journal of Zhejiang University SCIENCE A, Vol.7, No.3, pp.378-382.

Scott Kessler B., Sherif El-Gizawy A., and Douglas E. Smith, 2007, Incorporating Neural Network Material Models Within Finite Element Analysis for Rheological Behavior Prediction, Journal of Pressure Vessel Technology, Vol. 129, 58-65.

Wenbin Song, Andy Keane, Janet Rees, Atul Bhaskar, and Steven Bagnall, 2002, Turbine blade fir-tree root design optimisation using intelligent CAD and finite element analysis, Computers and Structures, Vol.80, pp.1853–1867.


Refbacks

  • There are currently no refbacks.