Application of Artificial neural network and wavelet packet transform for vibration signal based monitoring in mechanical micro drilling

Ranjan, Jitesh; Patra, Karali; Szalay, Tibor

doi:10.1109/ablaze.2015.7154957

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…Among different time-frequency domain signal processing methods, the most common is the wavelet packet transform which can overcome the poor time localization of a fast Fourier transform (FFT) and loss of important information due to not consideration of the detail part in case of a discrete wavelet transform (DWT). In this work, the wavelet packet transform is performed to decompose the vibration signal (up to 5 kHz) into 8 frequency bands (8 wavelet packets) up to level 3 [53]. These packets are denoted as packet (3,0), packet (3,1), packet (3,2), packet (3,3), packet (3,4) Wavelet packet coefficients are estimated using Daubechies 8 wavelet function.…”

Section: Wavelet Packet Features Of Vibration Signalsmentioning

confidence: 99%

Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors

Ranjan

Patra

Szalay

et al. 2020

Sensors

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The prevalence of micro-holes is widespread in mechanical, electronic, optical, ornaments, micro-fluidic devices, etc. However, monitoring and detection tool wear and tool breakage are imperative to achieve improved hole quality and high productivity in micro-drilling. The various multi-sensor signals are used to monitor the condition of the tool. In this work, the vibration signals and cutting force signals have been applied individually as well as in combination to determine their effectiveness for tool-condition monitoring applications. Moreover, they have been used to determine the best strategies for tool-condition monitoring by prediction of hole quality during micro-drilling operations with 0.4 mm micro-drills. Furthermore, this work also developed an adaptive neuro fuzzy inference system (ANFIS) model using different time domains and wavelet packet features of these sensor signals for the prediction of the hole quality. The best prediction of hole quality was obtained by a combination of different sensor features in wavelet domain of vibration signal. The model’s predicted results were found to exert a good agreement with the experimental results.

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Section: Wavelet Packet Features Of Vibration Signalsmentioning

confidence: 99%