Comparing orthogonal force and unidirectional strain component processing for tool condition monitoring
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Date
Authors
Freyer, Burkhard Heinrich
Heyns, P.S. (Philippus Stephanus)
Theron, Nicolaas J.
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Abstract
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 thatwould 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.Wearsensitive
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.
Description
Keywords
Tool wear-monitoring, Self-sensing actuator, Structure dynamic modelling, Neural network, Wavelet packet analysis
Sustainable Development Goals
Citation
Freyer, BH, Heyns, PS & Theron, NJ 2014, 'Comparing orthogonal force and unidirectional strain component processing for tool condition monitoring', Journal of Intelligent Manufacturing, vol. 25, no. 3, pp. 473-487.