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A model-based bone milling state identification method via force sensing for a robotic surgical system

Version 2 2024-06-06, 08:10
Version 1 2019-02-26, 16:07
journal contribution
posted on 2024-06-06, 08:10 authored by KI Al-Abdullah, Chee Peng LimChee Peng Lim, Zoran NajdovskiZoran Najdovski, W Yassin
Background: 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.

History

Journal

International Journal of Medical Robotics and Computer Assisted Surgery

Volume

15

Article number

e1989

Pagination

1 - 16

Location

England

ISSN

1478-5951

eISSN

1478-596X

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Issue

3

Publisher

WILEY