Medical Drill Wear Classification Using Servomotor Drive Signals and Neural Networks (CROSBI ID 613633)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Staroveški, Tomislav ; Brezak, Danko ; Grđan, Vinko ; Baček, Tomislav
engleski
Medical Drill Wear Classification Using Servomotor Drive Signals and Neural Networks
Medical drills are subject to intensive wear due to the influence of different mechanical, chemical and thermal factors characteristic for drilling and sterilization process. Wear progress increases friction in the cutting zone, which consequently leads to higher temperatures and cutting forces, i.e., possible thermal and mechanical damages of the bone tissue. Therefore, the presented study aimed to analyze the possibility of drill wear monitoring using electric servomotor drive signals and neural network algorithm. Experimental work has been performed with adequately designed testbed machining system and using prepared bovine bone samples. Drill wear features were extracted from time and frequency domain of the process signals, and then analyzed separately and in combinations.
medical drill; wear; thermal osteonecrosis; neural networks; modeling
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Podaci o prilogu
2014.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of the World Congress on Engineering 2014 Vol I
S. I. Ao, Len Gelman, David WL Hukins, Andrew Hunter and A. M. Korsunsky
Hong Kong: Newswood Limited
978-988-19252-7-5
2078-0958
Podaci o skupu
World Congress on Engineering - WCE 2014
predavanje
02.07.2014-04.07.2014
London, Ujedinjeno Kraljevstvo