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

Abstract

Signal processing using orthogonal cutting force components for tool condition monitoring has established itself in literature. In the application of single axis strain sen sors however a linear combination of cutting force compo nents 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. Recon struction 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 ltermodel of that tool holder that would carry a selfsensing actuator. For comparison of the orthogonal and unidirectional force component tool wear monitoring strategies the same timedelay neural network structure has been applied. Wear sensitive features are determined by wavelet packet analysis to provide information for tool wear estimation. The proba bility of a difference less than 5 percentage points between the ank wear estimation errors of above mentioned two process ing strategies is at least 95 %. This suggests the viability of simultaneous monitoring and control by using a selfsensing actuator.

Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing

“…Taking into account the noise with slack variables ξ i and error penalty C, Eq. (19) can be rewritten as [40]:…”

“…that indirectly indicate tool conditions. Tool wear causes the increases of friction and heat generation, thus consequently causes the changes of in-process parameters, such as cutting force [16], vibration [17], acoustic emission [18], strain [19], eddy-current displacement [20], and spindle motor current [21], etc. Comparing to direct sensing, indirect sensing methods are less costly and enables continuous detection of all changes (e.g.…”

Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations

“…Gao et al [ 14 ] introduced a data-driven model framework for TCM in a machining operations based on the statistics of cutting forces. The orthogonal force and unidirectional strain components were compared, resulting in a probability of a difference less than 5% points between the flank wear estimation errors of the two processing strategies was at least 95% [ 15 ]. Kaya et al [ 16 ] proposed an online TCM system based on the cutting force and torque signals, and a high correlation and low error ratio between the actual and predicted values of flank wear were obtained.…”

Analysis of the Suitability of Signal Features for Individual Sensor Types in the Diagnosis of Gradual Tool Wear in Turning

“…Since, in contrast to a signal in the frequency domain represented as a spectrum, the result of the WPT resembles a series of time waveforms, similar SFs as for signals in the time domain are usually used as SFs of individual packets (bandpass signals). Most often RMS is used as the only feature [29,47,48]. However, some publications also use other statistical features like standard deviation [22,27,32] kurtosis [35,36], skewness [35,36], peak-to-peak value [32], crest factor [27] and others [49].…”