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Applicant:
Indian Institute of Technology (IIT) Patna 
Author:
J. Ranjan, K. Patra, T. Szalay, M. Mia, M. K. Gupta, Q. Song, V. A. Pashnyov, D. Y. Pimenov 
Corresponding Authors:
Karali Patra 
DOI #:
https://doi.org/10.3390/s20030885 
Title:
Artificial intelligence based hole quality prediction in micro-drilling using multiple sensors 
Journal:
Sensors 
Year:
2020 
Volume:
20(3) 
Page:
885 
Keywords:
micro drilling; vibration; cutting force; wavelet packet; adaptive neuro fuzzy inference system 
Abstract:
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.  
Entered by:
Venkata Dantham on 2020-08-04 
 
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