Comparing orthogonal force and unidirectional strain component processing for tool condition monitoring
Journal of Intelligent Manufacturing
Dynamic Systems Group, Department of Mechanical and Aeronautical Engineering, University of Pretoria, 0002 Pretoria, South Africa
Signal processing using orthogonal cutting force components for tool condition monitoring has established itself in literature. In the application of single axis strain sensors however a linear combination of cutting force components has to be processed in order to monitor tool wear. This situation may arise when a single axis piezoelectric actuator is simultaneously used as an actuator and a sensor, e.g. its vibration control feedback signal exploited for monitoring purposes. The current paper therefore compares processing of a linear combination of cutting force components to the reference case of processing orthogonal components. Reconstruction of the dynamic force acting at the tool tip from signals obtained during measurements using a strain gauge instrumented tool holder in a turning process is described. An application of this dynamic force signal was simulated on a filter-model of that tool holder that would carry a self-sensing actuator. For comparison of the orthogonal and unidirectional force component tool wear monitoring strategies the same time-delay neural network structure has been applied. Wear-sensitive features are determined by wavelet packet analysis to provide information for tool wear estimation. The probability of a difference less than 5 percentage points between the flank wear estimation errors of above mentioned two processing strategies is at least 95 %. This suggests the viability of simultaneous monitoring and control by using a self-sensing actuator. © 2012 Springer Science+Business Media New York.
Computer simulation; Cutting; Neural networks; Piezoelectric actuators; Sensors; Signal processing; Orthogonal components; Self-sensing actuators; Simultaneous monitoring; Structure dynamics; Time delay neural networks; Tool condition monitoring; Tool wear estimations; Wavelet Packet Analysis; Wear of materials