Brezak, Danko and Staroveški, Tomislav and Stiperski, Ivan and Klaić, Miho and Majetić, Dubravko (2015) Artificial neural network model for tool condition monitoring in stone drilling. = Artificial neural network model for tool condition monitoring in stone drilling. Applied Mechanics and Materials, 772. pp. 268-273. ISSN 1660-9336. Vrsta rada: ["eprint_fieldopt_article_type_article" not defined]. . Točan broj autora: 5.
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Abstract
This paper explores the possibility of tool wear classification in stone drilling. Wear model is based on Radial Basis Function Neural Network which links tool wear features extracted from motor drive current signals and acoustic emission signals with two wear levels – sharp and worn drill. Signals were measured during stone drilling under different cutting conditions, and then filtered before tool wear features extraction. Features were obtained from time and frequency domain. They have been analyzed individually and in combinations. The results indicate tool wear monitoring capacity of the proposed model in stone drilling, and its potential for simple and cost-effective integration with CNC machine tools.
| Item Type: | Article (["eprint_fieldopt_article_type_article" not defined]) |
|---|---|
| Keywords (Croatian): | stone drilling, tool wear classification, neural networks |
| Subjects: | TECHNICAL SCIENCE > Mechanical Engineering |
| Divisions: | 1200 Department of Technology > 1230 Chair of Machine Tools 900 Department of Robotics and Production System Automation > 910 Chair of Engineering Automation |
| Indexed in Web of Science: | No |
| Indexed in Current Contents: | No |
| Date Deposited: | 26 Oct 2015 16:46 |
| Last Modified: | 20 Sep 2018 11:12 |
| URI: | http://repozitorij.fsb.hr/id/eprint/4830 |
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