Naslov
Medical Drill Wear Classification Using Servomotor Drive Signals and Neural Networks

Autori
Staroveški, Tomislav ; Brezak, Danko ; Grđan, Vinko ; Baček, Tomislav

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
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, 2014

ISBN
978-988-19252-7-5

Skup
World Congress on Engineering - WCE 2014

Mjesto i datum
London, Ujedinjeno Kraljevstvo, 02.07.2014. - 04.07.2014

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
medical drill; wear; thermal osteonecrosis; neural networks; modeling

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo, Kliničke medicinske znanosti

POVEZANOST RADA