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A model‐based bone milling state identification method via force sensing for a robotic surgical system
Author(s) -
AlAbdullah Kais I.,
Lim Chee Peng,
Najdovski Zoran,
Yassin Wisam
Publication year - 2019
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.1989
Subject(s) - cancellous bone , artificial neural network , computer science , materials science , identification (biology) , biomedical engineering , cortical bone , artificial bone , biological system , artificial intelligence , composite material , engineering , medicine , botany , pathology , anatomy , biology
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.