Investigation of acoustic emission as an indicator for process condition monitoring

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Investigation of acoustic emission as an indicator for process condition monitoring

 

Author: Lee, Chi-kai
Title: Investigation of acoustic emission as an indicator for process condition monitoring
Degree: M.Sc.
Year: 1999
Subject: Drilling and boring machinery -- Testing
Machine-tools -- Monitoring
Acoustic emission testing
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Dept. of Mechanical Engineering
Pages: vii, 92, [6] leaves : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1485295
URI: http://theses.lib.polyu.edu.hk/handle/200/5007
Abstract: With increasing cost of production, manufacturing automation is very important for low cost production. At the same time the quality of product cannot be sacrificed. With efficiency, productivity and quality of prime importance in manufacturing automation, tool failure is a significant element in the machining process. So monitoring of cutting tool condition is essential for consistent quality of production. When working on different materials, the characteristics of tool wear will be different. For example, the tool life for a twist drill will be different when used for drilling different kinds of materials. In this project, acoustic emission (AE) signal is studied for use as an indicator for condition monitoring in drilling. The root-mean square value of the AE signal is collected for drilling of different drill sizes, drilling speeds, feed rates and hole depths. High speed steel drills of diameter 8mm and 10mm will be used and working materials are aluminium alloy and copper respectively. After collecting the signal, they are plotted and compared to see if there exist any correlation between the tool condition and the AE signal. It is found that the AE root mean square signal increases while drilling and it is sensitive to the cutting parameters (drill speeds, feed rates, hole depths). In addition, the repeatable nature of the AE root mean square signal is demonstrated. Based on the above findings, it is concluded that acoustic emission can be an appropriate method for tool condition monitoring.

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