Tool wear sensing plays an important role in the optimisation of tool exchange and tip geometry compensation during automated machining in flexible manufacturing systems. The focus of this work is to develop a reliable method to predict flank wear during a turning process. A neural network scheme is applied to perform one-step-ahead prediction of flank wear from cutting force signals obtained from a tool dynamometer. Machining experiments conducted using the method presented in this paper indicate that using an appropriate force ratio, the flank wear can be predicted to within 8 per cent of the actual wear for various turning conditions.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Signal Processing
- Civil and Structural Engineering
- Aerospace Engineering
- Mechanical Engineering
- Computer Science Applications