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Drilling wear detection and classification using vibration signals and artificial neural network
Issam Abu-Mahfouz
School of Science, Engineering & Technology (Harrisburg)
Research output
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Contribution to journal
›
Article
›
peer-review
197
Scopus citations
Overview
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Dive into the research topics of 'Drilling wear detection and classification using vibration signals and artificial neural network'. Together they form a unique fingerprint.
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Engineering
Time Domain
100%
Frequency Domain
100%
Artificial Neural Network
100%
Harmonics
50%
Feedforward
50%
Condition Monitoring
50%
Domain Feature
50%
Statistical Moment
50%
Maximum Entropy
50%
Flexible Manufacturing System
50%
Network Model
50%
Backpropagation
50%
Keyphrases
Drill Wear
100%
Wear Detection
100%
Twist Drill
33%
Harmonic Wavelet
33%
Drilling Tool
33%
Maximum Entropy Spectrum
33%
Multilayer Feedforward Neural Network
33%
Tool Condition Monitoring System
33%
Frequency Domain Features
33%
Material Science
Machining
100%
Multilayer
100%
Chemical Engineering
Feedforward Neural Network
20%