Online tool condition monitoring in turning titanium (grade 5) using acoustic emission: modeling

Online tool condition monitoring in turning titanium (grade 5) using acoustic emission: modeling

This paper presents an online prediction of tool wear using acoustic emission (AE) in turning titanium (grade 5) with PVD-coated carbide tools. In the present work, the root mean square value of AE at the chip–tool contact was used to detect the progression of flank wear in carbide tools. In particu...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology Vol. 67; no. 5-8; pp. 1947 - 1954
Main Authors: Kosaraju, Satyanarayana, Anne, Venu Gopal, Popuri, Bangaru Babu
Format: Journal Article
Language:English
Published: London Springer London 01-07-2013
Springer Nature B.V
Subjects:
Acoustic emission
CAE) and Design
Carbide tools
Computer-Aided Engineering (CAD
Condition monitoring
Cutting parameters
Cutting speed
Cutting tools
Cutting wear
Energy dispersive X ray spectroscopy
Engineering
Industrial and Production Engineering
Mathematical models
Mechanical Engineering
Media Management
Original Article
Predictions
Process parameters
Production planning
Signal processing
Titanium alloys
Titanium base alloys
Tool wear
Turning (machining)
Acoustic emission
Turning
Flank wear
Energy-dispersive X-ray spectroscopy
Online tool wear monitoring
Modeling
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Summary:This paper presents an online prediction of tool wear using acoustic emission (AE) in turning titanium (grade 5) with PVD-coated carbide tools. In the present work, the root mean square value of AE at the chip–tool contact was used to detect the progression of flank wear in carbide tools. In particular, the effect of cutting speed, feed, and depth of cut on tool wear has been investigated. The flank surface of the cutting tools used for machining tests was analyzed using energy-dispersive X-ray spectroscopy technique to determine the nature of wear. A mathematical model for the prediction of AE signal was developed using process parameters such as speed, feed, and depth of cut along with the progressive flank wear. A confirmation test was also conducted in order to verify the correctness of the model. Experimental results have shown that the AE signal in turning titanium alloy can be predicted with a reasonable accuracy within the range of process parameters considered in this study.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-012-4621-2