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Force and temperature modelling of bone milling using artificial neural networks
journal contribution
posted on 2018-02-01, 00:00 authored by Kais Ibrahim Abdul-lateef Al-Abdullah, Hamid AbdiHamid Abdi, Chee Peng LimChee Peng Lim, W YassinThe force and temperature of bone milling depends on a large number of parameters pertaining to the bone tissue and cutting tools. In the current literature, there is a lack of information on bone milling for cancellous tissues. In this paper, we use the artificial neural network (ANN) methodology to develop appropriate force and temperature models based on real experimental measurement data of bone milling on artificial tissues with cancellous properties. The models estimate the milling force and temperature as a function of feed rate and spindle speed. Two temperature models are considered, the bur temperature and the fresh-milled bone surface temperature. A full factorial design of experiment (DOE) is used to collect the necessary data for developing and validating the models. A very good agreement between the estimated and the experimental milling forces and temperature is found. The established models are useful for real-time bone milling optimization and control.
History
Journal
Measurement: Journal of the International Measurement ConfederationVolume
116Pagination
25 - 37Publisher DOI
ISSN
0263-2241Publication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2017, ElsevierUsage metrics
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Keywords
Science & TechnologyTechnologyEngineering, MultidisciplinaryInstruments & InstrumentationEngineeringBone millingMilling forceMilling temperatureArtificial neural networks modellingBREAKTHROUGH DETECTIONHAPTIC SIMULATIONSTATE RECOGNITIONOPTIMIZATIONPERFORMANCEROBOTMechanical EngineeringArtificial Intelligence and Image Processing
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