TOOL WEAR ESTIMATION BY GROUP METHOD OF DATA HANDLING IN TURNING

Tool wear monitoring and estimation are essential for improved product ivity of manufacturing systems. Multi-sensory approaches based on forc e, vibration and Acoustic Emission (AE) signals have been recognized a s potential methods for tool wear monitoring. In the present work, ste ady-state components of force, dynamics of the main cutting force and vibration in the direction of the main cutting force have been used fo r on-line tool wear estimation in a turning process. The group method of data handling (GMDH), a heuristic self-organizing method of modelli ng, has been used to integrate information from different sensors and the cutting conditions to obtain estimates of tool wear. Different met hods of preprocessing the forces have been attempted to determine the best method to suit the data. Various heuristics of GMDH have been ana lysed to obtain the appropriate models for tool wear estimation. The r esults show that GMDH can be effectively used to integrate sensor info rmation and obtain reliable estimates of tool wear.