Rapid cost estimation of metallic components for the aerospace industry
This paper illustrates and compares the results of the application of two different approaches--non-parametric and artificial neural network techniques--for the rapid cost estimation of turbine components. This technique is a simple and automatic way for the estimation of the cost of a piece with no expert intervention. Three methods of estimation are compared: the projection pursuit method (PPR), the local polynomial approach (LOESS) and adaptive neural networks (ANNs). This comparative analysis serves to enhance current work that seeks to choose the optimum predictor model. The results confirm the validity of the neural network theory in this field of application, but not a clear superiority as compared with the non-parametric approach. The present research provides a new tool to avoid inadequate piece budgeting strategies. The use of these methods contributes to the minimisation of errors in the budgeting of new items.
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