Author:
Jovic Srdjan,Anicic Obrad,Jovanovic Milivoje
Abstract
Purpose
Acoustic emission (AE) could be used for prevention and detection of tool errors in Computer Numerical Control (CNC) machining. The purpose of this study is to analyze the AE form of CNC machining operations.
Design/methodology/approach
Experimental measurements were performed with three sensors on the CNC lathe to collect the data of the CNC machining. Adaptive neuro-fuzzy inference system (ANFIS) was applied for the fusion from the sensors’ signals to determine the strength of the signal periodic component among the sensors.
Findings
There were three inputs, namely, spindle speed, feed rate and depth of cut. ANFIS was also used to determine the inputs’ influence on the prediction of strength of the signal periodic component. Variable selection process was used to select the most dominant factors which affect the prediction of strength of the signal periodic component.
Originality/value
Results were shown that the spindle speed has the most dominant effect on the strength of the signal periodic component.
Subject
Electrical and Electronic Engineering,Industrial and Manufacturing Engineering
Reference18 articles.
1. Acoustic emission-based condition monitoring methods: review and application for low speed slew bearing;Mechanical Systems and Signal Processing,2016
2. One sensor acoustic emission localization in plates;Ultrasonics,2016
3. Surface finish monitoring in taper turning CNC using artificial neural network and multiple regression methods;Procedia Engineering,2013
4. Real-time acoustic emission monitoring for surface damage in hard machining;International Journal of Machine Tools & Manufacture,2005
5. Low speed bearing fault diagnosis using acoustic emission sensors;Applied Acoustics,2016
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