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A model-based bone milling state identification method via force sensing for a robotic surgical system
journal contribution
posted on 2019-06-01, 00:00 authored by K I Al-Abdullah, Chee Peng LimChee Peng Lim, Zoran NajdovskiZoran Najdovski, W YassinBackground: This paper presents a model-based bone milling state identification method that provides intraoperative bone quality information during robotic bone milling. The method helps surgeons identify bone layer transitions during bone milling. Methods: On the basis of a series of bone milling experiments with commercial artificial bones, an artificial neural network force model is developed to estimate the milling force of different bone densities as a function of the milling feed rate and spindle speed. The model estimations are used to identify the bone density at the cutting zone by comparing the actual milling force with the estimated one. Results: The verification experiments indicate the ability of the proposed method to distinguish between one cortical and two cancellous bone densities. Conclusions: The significance of the proposed method is that it can be used to discriminate a set of different bone density layers for a range of the milling feed rate and spindle speed.
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Journal
International journal of medical robotics and computer assisted surgeryVolume
15Issue
3Article number
e1989Pagination
1 - 16Publisher
John Wiley & SonsLocation
Chichester, Eng.Publisher DOI
ISSN
1478-5951eISSN
1478-596XLanguage
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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