Drill wear monitoring in cortical bone drilling

Staroveški, Tomislav and Brezak, Danko and Udiljak, Toma (2015) Drill wear monitoring in cortical bone drilling. = Drill wear monitoring in cortical bone drilling. Medical Engineering and Physics, 37 (6). pp. 560-566. ISSN 1350-4533. Vrsta rada: ["eprint_fieldopt_article_type_article" not defined]. Kvartili JCR: Q3 (2015). Točan broj autora: 3.

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Official URL: https://doi.org/10.1016/j.medengphy.2015.03.014


Medical drills are subject to intensive wear due to mechanical factors which occur during the bone drilling process, and potential thermal and chemical factors related to the sterilisation process. Intensive wear increases friction between the drill and the surrounding bone tissue, resulting in higher drilling temperatures and cutting forces. Therefore, the goal of this experimental research was to develop a drill wear classification model based on multi-sensor approach and artificial neural network algorithm. A required set of tool wear features were extracted from the following three types of signals: cutting forces, servomotor drive currents and acoustic emission. Their capacity to classify precisely one of three predefined drill wear levels has been established using a pattern recognition type of the Radial Basis Function Neural Network algorithm. Experiments were performed on a custom-made test bed system using fresh bovine bones and standard medical drills. Results have shown high classification success rate, together with the model robustness and insensitivity to variations of bone mechanical properties. Features extracted from acoustic emission and servomotor drive signals achieved the highest precision in drill wear level classification (92.8%), thus indicating their potential in the design of a new type of medical drilling machine with process monitoring capabilities.

Item Type: Article (["eprint_fieldopt_article_type_article" not defined])
Keywords (Croatian): medical drill wear, thermal osteonecrosis, neural networks, computational modelling, medical devices
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: Yes
Indexed in Current Contents: Yes
Quartiles: Q3 (2015)
Date Deposited: 22 Sep 2016 08:14
Last Modified: 15 Nov 2018 13:50
URI: http://repozitorij.fsb.hr/id/eprint/6818

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