Abstract
Tool wear monitoring and estimation are essential for improved productivity of manufacturing systems. Multi-sensory approaches based on force, vibration and Acoustic Emission (AE) signals have been recognized as potential methods for tool wear monitoring. In the present work, steady-state components of force, dynamics of the main cutting force and vibration in the direction of the main cutting force have been used for on-line tool wear estimation in a turning process. The group method of data handling (GMDH), a heuristic self-organizing method of modelling, has been used to integrate information from different sensors and the cutting conditions to obtain estimates of tool wear. Different methods of preprocessing the forces have been attempted to determine the best method to suit the data. Various heuristics of GMDH have been analysed to obtain the appropriate models for tool wear estimation. The results show that GMDH can be effectively used to integrate sensor information and obtain reliable estimates of...
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