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Solidification Simulations Coupled With Neural Networks for Prediction of Secondary Dendrite Arm Spacings in Sand Castings

D. Hanumantha Rao*, G.R.N. Tagore**, G. Ranga Janardhana***

*Associate professor of Mechanical Engineering, Deccan college of Engineering and technology, Hyderabad.

**Professor & Head, Dept. of Meeh. Engg. at National Institute of Technology, WARANGAL

***Professor of Mechanical Engineering, Jawaharlal Nehru Technological University, Hyderabad.

Periodicity:August - October'2006
DOI : https://doi.org/10.26634/jfet.2.1.970

Abstract

Systematic knowledge accumulation regarding casting process is essential in order to obtain optimal process conditions. A number of factors define the metallurgical structure in castings of which primary importance is secondary dendrite arm spacing. Controlling the rate of solidification can control dendrite structure. The objective of the present paper is to study the influence of casting process parameters like pouring temperature, insulation on riser, chill thickness and chill contact area on the microstructure parameter secondary dendrite arm spacing. A component of material aluminum alloy A356 is used in the present study. Neural networks are widely used in solving complicated mathematical problems whose analytical description is very difficult and computationally demanding. Design of experiments are used for experimental simulations using finite difference method. The developed neural network models are trained using the response obtained from solidification simulation. The predicted secondary dendrite arm spacing values from neural network are found to be in reasonable agreement with the simulation results and the effect of varied input values on output response is also obtained through sensitivity analysis.

Keywords

Solidification, Secondary Dendrite Arm Spacing, Finite Difference Method, Neural Networks and Sensitivity Analysis.

How to Cite this Article?

D. Hanumantha Rao, G.R.N. Tagore and G.Ranga Janardhana (2006). Solidification Simulations Coupled With Neural Networks for Prediction of Secondary Dendrite Arm Spacings in Sand Castings. i-manager’s Journal on Future Engineering and Technology, 2(1), 42-49. https://doi.org/10.26634/jfet.2.1.970

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