On-line wear estimation using neural networks

In this paper, a neural network based system for 'on-line' estimation of tool wear in turning operations is introduced. The system monitors the cutting force components and extracts the tool wear information from the changes occurring over the cutting process. A hierarchical structure using multilayered feedforward static and dynamic neural networks is used as a specialized subsystem, for each wear component to be monitored. These subsystems share information about the tool wear components they are monitoring and their error in estimating the cutting force components is used to update the dynamic neural networks. The adaptability property of neural networks ensures that changes in machining parameters can be accommodated. Simulation studies are undertaken using experimental data available from manufacturing literature. The results are promising and show good estimation ability.