Journal
JOURNAL OF INTELLIGENT MANUFACTURING
Volume 25, Issue 3, Pages 473-487Publisher
SPRINGER
DOI: 10.1007/s10845-012-0698-6
Keywords
Tool wear-monitoring; Self-sensing actuator; Structure dynamic modelling; Neural network; Wavelet packet analysis
Funding
- National Research Foundation of South Africa
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available