IN-PROCESS REGRESSIONS AND ADAPTIVE MULTICRITERIA NEURAL NETWORKS FORMONITORING AND SUPERVISING MACHINING OPERATIONS

The authors develop a monitoring and supervising system for machining operations using in-process regressions (for monitoring) and adaptive feedforward artificial neural networks (for supervising). The system i s designed for: (1) in-process tool life measurement and prediction; ( 2) supervision of machining operations in terms of the best machining setup; and (3) catastrophic tool failure monitoring. The monitoring sy stem predicts tool life by using different sensors for gathering infor mation based on a regression model that allows for the variations betw een tools and different machine setups. The regression model makes its prediction by using the history of other tools and combining it with the information obtained about the tool under consideration. The super vision system identifies the best parameters for the machine setup pro blem within the framework of multiple criteria decision making. The de cision maker (operator) considers several criteria, such as cutting qu ality, production rate and tool life. To make the optimal decision wit h several criteria, an adaptive feedforward artificial neural network is used to assess the decision maker's preferences. The authors' neura l network approach learns from the decision maker's complex behavior a nd hence, in automatic mode, can make decisions for the decision maker . The approach is not computationally demanding, and experiments demon strate that its predictions are accurate.