Using Neural Networks with Limited Data to Estimate Manufacturing Cost
This paper investigates the ability of a neural network to estimate the cost of jet engine components, specifically shafts and cases. Even with limited data the neural network is able to produce a superior cost estimate in a fraction of the time required by the current cost estimation process.
Due to the complex nature of the parts and the limited amount of data available, data expansion techniques such as doubling data, and data creation were examined. Sensitivity analysis is produced in order to gain an understanding of the underlying functions used by the neural network when generating the cost estimate.
School Location:USA - Ohio
Source Type:Master's Thesis
Keywords:neural network cost estimation limited data jet engine
Date of Publication:01/01/2008