DMI-9733747 Twomey Artificial Neural Networks (ANN) require large numbers of observations to ensure good generalization, and a large number of independent observations (or data) to evaluate the networks generalization performance. ANN are potentially one of the most important data processing technologies in a truly intelligent system for the monitoring and control of manufacturing processes. However, manufacturers are often reluctant, if not resistant, to use an ANN approach because of the associated costs of user expertise, long development times, and the need for large amounts of training data. Very little research has been performed on evaluation methods, and the problems of training and evaluation concurrently, when data is sparse, have been overlooked. The research objective of this CAREER project is to develop an ANN training and evaluation strategy for manufacturing situations where data is sparse. The approach in the project seeks the simultaneous use of sparse data for both network training and evaluation. The results of this research will be applied to two manufacturing processes: drilling and electrochemical machining. The utilization of ANN and other information processing technologies will be developed into courses for the undergraduate curriculum. The research will increase the utility of ANN through the development of an ANN training and validation strategy in cases where data is sparse. Together with the development of curriculum in information processing, the project will provide a means for attracting talented students to careers in research, and will embody practices that encourage women and ethnic minorities to see themselves as tomorrow's engineers.
