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Optimization of the milling parameters of a robotic-based bone milling system
conference contribution
posted on 2019-07-01, 00:00 authored by Kais Ibrahim Abdul-lateef Al-Abdullah, Chee Peng LimChee Peng Lim, Zoran NajdovskiZoran Najdovski, W Yassin© 2019 IEEE. Using a surgical robot for bone milling can reduce bone tissue damage due to the cutting process. Robotic systems can regulate bone cutting parameters at an optimized setting to reduce excessive bone milling force and temperature. This study investigates the possibility to minimize cortical bone milling force and temperature by optimizing the milling parameters. An artificial neural network (ANN) is constructed to estimate the mean milling force and bone temperature as a function of milling feed rate and spindle speed. The ANN outputs are then used as the objective functions in a genetic algorithm (GA) for optimizing two conflicting objectives, i.e. the milling force and bone temperature. The results indicate that minimizing the bone temperature requires increasing the feed rate that increases the milling force.
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Event
Industrial Technology. International Conference ( 2019 : Melbourne, Victoria)Volume
2019-FebruaryPagination
163 - 168Publisher
IEEELocation
Melbourne, VictoriaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2019-02-13End date
2019-02-15ISBN-13
9781538663769Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2019, IEEETitle of proceedings
ICIT 2019 : Proceedings of the IEEE International Conference on Industrial TechnologyUsage metrics
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