The main objective of this research is to develop and demonstrate an intelligent machining monitoring and diagnostic system for quality assurance in end-milling. This will be based on the integration of multiple sensors outputs, such as acoustic emission, vibration, cutting force, and cutting temperature, with cutting conditions via artificial neural networks in conjunction with an expert system. Product quality characteristics such as surface finish and tolerance will be estimated and, therefore, eventually maintained by the system during end-milling. The research team will conduct machining experiments to acquire data and determine the best sensor feature that correlates to product quality, develop multi-sensor integrated neural networks for estimating product quality, develop trend classification neural network for identifying trends and train and evaluate the performance of these networks. Product quality in manufacturing is of paramount importance especially in view of the tremendous competition that we face in the globalized marketplace. Pioneering work of this nature, where the latest developments in artificial intelligence are applied to basic problems in manufacturing and in close cooperation with a United States machine tool manufacturer, will help domestic manufacturing compete successfully.
