Version 1
: Received: 12 June 2024 / Approved: 12 June 2024 / Online: 12 June 2024 (16:03:17 CEST)
How to cite:
Rajakannu, A.; KP, R.; K, V. Condition Monitoring of Drill Bit for Manufacturing Sector Using Wavelet Analysis and Artificial Neural Network (ANN). Preprints2024, 2024060859. https://doi.org/10.20944/preprints202406.0859.v1
Rajakannu, A.; KP, R.; K, V. Condition Monitoring of Drill Bit for Manufacturing Sector Using Wavelet Analysis and Artificial Neural Network (ANN). Preprints 2024, 2024060859. https://doi.org/10.20944/preprints202406.0859.v1
Rajakannu, A.; KP, R.; K, V. Condition Monitoring of Drill Bit for Manufacturing Sector Using Wavelet Analysis and Artificial Neural Network (ANN). Preprints2024, 2024060859. https://doi.org/10.20944/preprints202406.0859.v1
APA Style
Rajakannu, A., KP, R., & K, V. (2024). Condition Monitoring of Drill Bit for Manufacturing Sector Using Wavelet Analysis and Artificial Neural Network (ANN). Preprints. https://doi.org/10.20944/preprints202406.0859.v1
Chicago/Turabian Style
Rajakannu, A., Ramachandran KP and Vijayalakshmi K. 2024 "Condition Monitoring of Drill Bit for Manufacturing Sector Using Wavelet Analysis and Artificial Neural Network (ANN)" Preprints. https://doi.org/10.20944/preprints202406.0859.v1
Abstract
Real time condition monitoring and precision health assessment system is a mandatory need for effective maintenance program in industrial sector. Rapid advancement in the information technology and other engineering technologies have invited more proactive attention from research and development in industrial sectors and particularly in condition monitoring of machines and related Industrial processes. In this work, the drill bit condition monitoring techniques have been developed based on the wavelet analysis and artificial neural network (ANN) as automatic drill bit fault detection and classification. An experimental work has been conducted to capture the vibration signals for analysis. In this experiment, the CNC drill machine is used with high carbon steel drill bit and mild steel material as work piece. The cutting condition parameters are kept constant and the wear level is changed. The Data Acquisition system (DAQ) with Lab VIEW software is used to capture the vibration signals for drill bit with different wear conditions. The captured vibrations data are analyzed using continuous wavelet transform (CWT) with Morlet wavelet and Daubechies wavelet as a prime function. In general, the CWT coefficient is used to generate the inputs features to ANN for automatic tool condition classification, with two outputs (0, 1) for healthy and (1, 0) for faulty. The results show the effectiveness of the combed WT and ANN for automatic classification of tool wear conditions with high success rate.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
