Predicting temperature in orthopaedic drilling using back propagation neural network

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Present work deals with the prediction of temperature in orthopaedic drilling using back propagation neural network. Drilling of bone is common to prepare an implant site during orthopaedic surgery. The increase in temperature during such a procedure increases the chances of thermal invasion of bone which can cause thermal osteonecrosis. Drilling operations have been performed in polymethylmethacrylate (PMMA) (as a substitute for bone) work-piece by high- speed steel (HSS) drill bits over a wide range of cutting conditions. Drill diameter, feed rate and spindle speed are used as input for the back propagation neural network whereas temperature is taken as output. The performance of the trained neural network has been tested with the experimental results. Good agreement is observed between the predictive model values and experimental values.

Original languageEnglish (US)
Title of host publicationChemical, Civil and Mechanical Engineering Tracks of 3rd Nirma University International Conference on Engineering, NUiCONE 2012
PublisherElsevier Ltd.
Pages676-682
Number of pages7
Volume51
ISBN (Print)9781627486330
DOIs
StatePublished - 2013
Event3rd Nirma University International Conference on Engineering, NUiCONE 2012 - Ahmedabad, Gujarat, India
Duration: Dec 6 2012Dec 8 2012

Conference

Conference3rd Nirma University International Conference on Engineering, NUiCONE 2012
CountryIndia
CityAhmedabad, Gujarat
Period12/6/1212/8/12

Keywords

  • Neural network
  • Orthopaedic drilling
  • Osteonecrosis

ASJC Scopus subject areas

  • Engineering(all)

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