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Comparing orthogonal force and unidirectional strain component processing for tool condition monitoring

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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 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.

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Authors and Affiliations

  1. Dynamic Systems Group, Department of Mechanical and Aeronautical Engineering, University of Pretoria, 0002, Pretoria, South Africa

    Burkhard H. Freyer, P. Stephan Heyns & Nico J. Theron

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  1. Burkhard H. Freyer
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  2. P. Stephan Heyns
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  3. Nico J. Theron
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Correspondence to Burkhard H. Freyer.

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Freyer, B.H., Heyns, P.S. & Theron, N.J. Comparing orthogonal force and unidirectional strain component processing for tool condition monitoring. J Intell Manuf 25, 473–487 (2014). https://doi.org/10.1007/s10845-012-0698-6

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  • DOI: https://doi.org/10.1007/s10845-012-0698-6

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