Predicting temperature in orthopaedic drilling using back propagation neural network

S. S. Panda

doi:10.1016/j.proeng.2013.01.096

Predicting temperature in orthopaedic drilling using back propagation neural network

S. S. Panda

Chemistry and Physics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 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 language	English (US)
Title of host publication	Chemical, Civil and Mechanical Engineering Tracks of 3rd Nirma University International Conference on Engineering, NUiCONE 2012
Publisher	Elsevier Ltd.
Pages	676-682
Number of pages	7
Volume	51
ISBN (Print)	9781627486330
DOIs	https://doi.org/10.1016/j.proeng.2013.01.096
State	Published - 2013
Event	3rd Nirma University International Conference on Engineering, NUiCONE 2012 - Ahmedabad, Gujarat, India Duration: Dec 6 2012 → Dec 8 2012

Conference

Conference	3rd Nirma University International Conference on Engineering, NUiCONE 2012
Country/Territory	India
City	Ahmedabad, Gujarat
Period	12/6/12 → 12/8/12

Keywords

Neural network
Orthopaedic drilling
Osteonecrosis

ASJC Scopus subject areas

Engineering(all)

Access to Document

10.1016/j.proeng.2013.01.096

Cite this

Panda, SS 2013, Predicting temperature in orthopaedic drilling using back propagation neural network. in Chemical, Civil and Mechanical Engineering Tracks of 3rd Nirma University International Conference on Engineering, NUiCONE 2012. vol. 51, Elsevier Ltd., pp. 676-682, 3rd Nirma University International Conference on Engineering, NUiCONE 2012, Ahmedabad, Gujarat, India, 12/6/12. https://doi.org/10.1016/j.proeng.2013.01.096

@inproceedings{6972fd108ec3417787bcf1a77219a5ce,

title = "Predicting temperature in orthopaedic drilling using back propagation neural network",

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.",

keywords = "Neural network, Orthopaedic drilling, Osteonecrosis",

author = "Panda, {S. S.}",

year = "2013",

doi = "10.1016/j.proeng.2013.01.096",

language = "English (US)",

isbn = "9781627486330",

volume = "51",

pages = "676--682",

booktitle = "Chemical, Civil and Mechanical Engineering Tracks of 3rd Nirma University International Conference on Engineering, NUiCONE 2012",

publisher = "Elsevier Ltd.",

note = "3rd Nirma University International Conference on Engineering, NUiCONE 2012 ; Conference date: 06-12-2012 Through 08-12-2012",

}

TY - GEN

T1 - Predicting temperature in orthopaedic drilling using back propagation neural network

AU - Panda, S. S.

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Neural network

KW - Orthopaedic drilling

KW - Osteonecrosis

UR - http://www.scopus.com/inward/record.url?scp=84891715155&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84891715155&partnerID=8YFLogxK

U2 - 10.1016/j.proeng.2013.01.096

DO - 10.1016/j.proeng.2013.01.096

M3 - Conference contribution

AN - SCOPUS:84891715155

SN - 9781627486330

VL - 51

SP - 676

EP - 682

BT - Chemical, Civil and Mechanical Engineering Tracks of 3rd Nirma University International Conference on Engineering, NUiCONE 2012

PB - Elsevier Ltd.

T2 - 3rd Nirma University International Conference on Engineering, NUiCONE 2012

Y2 - 6 December 2012 through 8 December 2012

ER -

Predicting temperature in orthopaedic drilling using back propagation neural network

Abstract

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this