Drill wear monitoring in cortical bone drilling
•Multi-sensor medical drill wear monitoring system was experimentally analysed.•Drill wear increases drilling temperature and cutting forces.•The best combination of drill wear features is proposed.•Features insensitive to variations in bone mechanical properties are established. Medical drills are...
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| Published in: | Medical engineering & physics Vol. 37; no. 6; pp. 560 - 566 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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England
Elsevier Ltd
01.06.2015
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| ISSN: | 1350-4533, 1873-4030 |
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| Abstract | •Multi-sensor medical drill wear monitoring system was experimentally analysed.•Drill wear increases drilling temperature and cutting forces.•The best combination of drill wear features is proposed.•Features insensitive to variations in bone mechanical properties are established.
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. |
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| AbstractList | Highlights • Multi-sensor medical drill wear monitoring system was experimentally analysed. • Drill wear increases drilling temperature and cutting forces. • The best combination of drill wear features is proposed. • Features insensitive to variations in bone mechanical properties are established. 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. •Multi-sensor medical drill wear monitoring system was experimentally analysed.•Drill wear increases drilling temperature and cutting forces.•The best combination of drill wear features is proposed.•Features insensitive to variations in bone mechanical properties are established. 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. |
| Author | Brezak, Danko Udiljak, Toma Staroveski, Tomislav |
| Author_xml | – sequence: 1 givenname: Tomislav surname: Staroveski fullname: Staroveski, Tomislav email: tstaroveski@fsb.hr, tstaroveski@gmail.com organization: Department of Technology, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lucica 5, Zagreb, Croatia – sequence: 2 givenname: Danko surname: Brezak fullname: Brezak, Danko organization: Department of Robotics and Production System Automation, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lucica 5, Zagreb, Croatia – sequence: 3 givenname: Toma surname: Udiljak fullname: Udiljak, Toma organization: Department of Technology, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lucica 5, Zagreb, Croatia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25922212$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1302/0301-620X.78B3.0780357 10.1016/j.medengphy.2010.10.003 10.1016/j.cuor.2005.09.011 10.1016/S0890-6955(98)00020-0 10.4028/www.scientific.net/KEM.321-323.1044 10.1007/s00402-007-0427-3 10.1088/1742-6596/181/1/012014 10.1034/j.1600-0501.1997.080305.x 10.1016/S1350-4533(01)00016-9 10.1016/j.joms.2007.12.037 10.1016/j.bjoms.2004.11.007 10.1016/S0890-6955(03)00110-X 10.1016/j.joms.2005.10.011 10.1016/S0890-6955(02)00040-8 10.1016/j.clinbiomech.2011.10.010 10.1016/0022-3913(83)90174-9 10.1016/j.medengphy.2011.05.014 10.5099/aj090400312 10.1016/S1350-4533(01)00003-0 |
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| Keywords | Medical devices Thermal osteonecrosis Neural networks Medical drill wear Computational modelling |
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| Snippet | •Multi-sensor medical drill wear monitoring system was experimentally analysed.•Drill wear increases drilling temperature and cutting forces.•The best... Highlights • Multi-sensor medical drill wear monitoring system was experimentally analysed. • Drill wear increases drilling temperature and cutting forces. •... Medical drills are subject to intensive wear due to mechanical factors which occur during the bone drilling process, and potential thermal and chemical factors... |
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| SubjectTerms | Acoustics Algorithms Animals Bone and Bones - surgery Cattle Computational modelling Computer Simulation Equipment Failure Friction Medical devices Medical drill wear Neural networks Neural Networks (Computer) Orthopedic Procedures - adverse effects Orthopedic Procedures - instrumentation Radiology Temperature Thermal osteonecrosis |
| Title | Drill wear monitoring in cortical bone drilling |
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