Freyer, Burkhard HeinrichHeyns, P.S. (Philippus Stephanus)Theron, Nicolaas J.2014-07-212014-07-212014-06Freyer, 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. 473-487.0956-5515 (print)1572-8145 (online)10.1007/s10845-012-0698-6http://hdl.handle.net/2263/40896Signal 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.en© Springer Science+Business Media New York 2012. The original publication is available at : http://link.springer.com/journal/10845.Tool wear-monitoringSelf-sensing actuatorStructure dynamic modellingNeural networkWavelet packet analysisPostprint Article