An investigation of tool wear using acoustic emission and genetic algorithm. (November 2015)

• Record Type: Journal Article
• Authors: Vetrichelvan, G; Sundaram, S; Kumaran, S Senthil; Velmurugan, P
• Is Part Of: Journal of vibration and control. Volume 21:Number 15(2015)
• Page Start: 3061
• Page End: 3066
• Publication Date: 2015-11
• Subjects: Acoustic emission technique -- genetic algorithm -- tool failure -- tool wear rate; Vibration -- Periodicals; Damping (Mechanics) -- Periodicals; 620.3
• DOI: 10.1177/1077546314520835
• Languages: English
• ISSNs: 1077-5463

Abstract

Numerous developments have been witnessing in manufacturing industries the ability of the machines to change the tools automatically during their wear or damage. In general, tool failure contributes about 7% to the down time of machine centers. Therefore, online monitoring of tool wear is an important phenomenon in producing quality products at reasonable cost. This also increases the production rate in the industries. Tool condition monitoring using the acoustic emission technique (AET) are real methods identified by researchers for online quality assessment of machine tools. The genetic algorithm (GA) is used to optimize the tool wear rate parameters. The practical significance of applying GA to tool wear rate has been validated by means of computing the deviation between predicted and experimentally obtained process parameters. Based on this research work, an experimental setup has been developed for online monitoring of a single point cutting tool using AETs. The experimental tool wear rate results are compared with online measurements using mean acoustic emission parameters (average value, root mean square value and area).