A prediction model for the lost wax process through fuzzy-based artificial neural network

Abstract

The application of investment casting process is rapidly increasing, specifically for near net shape manufacturing of complex and small engineering components. The process begins with making of wax patterns, thereafter employing a precision mould, dewaxing, pouring molten alloy and knocking the shell, followed by minor finishing operations. This study is about predicting the quality of responses of the wax patterns namely linear shrinkage and surface roughness using fuzzy-based artificial neural network. The process parameters considered are injection temperature, injection pressure and holding time, and experiments have been performed as per Taguchi’s L₁₈ orthogonal array. As optimum parameter levels were different for both the responses, fuzzy logic reasoning has been used to combine all the objectives and transform the experimental results into single performance index known as multi-response performance index. Later, modelling of the process has been done using artificial neural network with experimental process parameters as inputs and multi-response performance index as output obtained from fuzzy modelling. Further, experiments have been conducted at random combination of parameter levels to validate the developed model, and it has been found that the actual results agreed well with that of the predicted value on the basis of mean absolute percentage error and correlation plots.

Keywords

Investment casting neural network orthogonal array fuzzy logic wax patterns

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