Multiple sensor monitoring in nickel alloy turning for tool wear assessment via sensor fusion
Abstract
A multiple sensor monitoring system comprising cutting force, acoustic emission and vibration sensing units was employed for tool state assessment during turning of Inconel 718 nickel alloy. Feature extraction was realised by processing the detected sensor signals in order to reduce the high dimensionality of the sensorial data. The extracted features were fused to realise a sensor fusion methodology based on neural network pattern recognition for decision making on tool wear condition.
