Cost model development using artificial neural networks

Abstract

In order for the aerospace industry to achieve success in export markets through the provision of high levels of product choice, it will need to develop and economically use many new materials and manufacturing processes. Examines how the constraints imposed by changing market trends affect the identification of “cost estimating relationships” and investigates their relative benefits and limitations in terms of their effects on the overall cost model development process. A method of establishing cost estimating relationships that appears to offer benefits to the cost modelling process is that of artificial neural networks (ANNs). Using the Taguchi method, a series of experiments have been undertaken to select an appropriate network for the “turning process”. The estimation accuracy and robustness of cost models developed using suitable ANN structures have then been examined under varying conditions in order to identify guidelines.