International Journal of Advanced Manufacturing Technology, volume 73, issue 1-4, pages 509-519

Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE)

Farzad Rayegani 1
Godfrey C. Onwubolu 1
1
 
School of Mechanical and Electrical Engineering and Technology, Faculty of Applied Science and Technology, Sheridan Institute of Technology and Advanced Learning, Brampton, Canada
Publication typeJournal Article
Publication date2014-04-25
Quartile SCImago
Q2
Quartile WOS
Q2
Impact factor2.9
ISSN02683768, 14333015
Computer Science Applications
Mechanical Engineering
Industrial and Manufacturing Engineering
Software
Control and Systems Engineering
Abstract
This paper presents the research done to determine the functional relationship between process parameters and tensile strength for the fused deposition modelling (FDM) process using the group method for data modelling for prediction purposes. An initial test was carried out to determine whether part orientation and raster angle variations affect the tensile strength. It was found that both process parameters affect tensile strength response. Further experimentations were carried out in which the process parameters considered were part orientation, raster angle, raster width and air gap. The process parameters and the experimental results were submitted to the group method of data handling (GMDH), resulting in predicted output, in which the predicted output values were found to correlate very closely with the measured values. Using differential evolution (DE), optimal process parameters have been found to achieve good strength simultaneously for the response. The mathematical model of the response of the tensile strength with respect to the process parameters comprising part orientation, raster angle, raster width and air gap has been developed based on GMDH, and it has been found that the functionality of the additive manufacturing part produced is improved by optimizing the process parameters. The results obtained are very promising, and hence, the approach presented in this paper has practical application for the design and manufacture of parts using additive manufacturing technologies.

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Rayegani F., Onwubolu G. C. Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE) // International Journal of Advanced Manufacturing Technology. 2014. Vol. 73. No. 1-4. pp. 509-519.
GOST all authors (up to 50) Copy
Rayegani F., Onwubolu G. C. Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE) // International Journal of Advanced Manufacturing Technology. 2014. Vol. 73. No. 1-4. pp. 509-519.
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.1007/s00170-014-5835-2
UR - https://doi.org/10.1007/s00170-014-5835-2
TI - Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE)
T2 - International Journal of Advanced Manufacturing Technology
AU - Rayegani, Farzad
AU - Onwubolu, Godfrey C.
PY - 2014
DA - 2014/04/25
PB - Springer Nature
SP - 509-519
IS - 1-4
VL - 73
SN - 0268-3768
SN - 1433-3015
ER -
BibTex |
Cite this
BibTex Copy
@article{2014_Rayegani,
author = {Farzad Rayegani and Godfrey C. Onwubolu},
title = {Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE)},
journal = {International Journal of Advanced Manufacturing Technology},
year = {2014},
volume = {73},
publisher = {Springer Nature},
month = {apr},
url = {https://doi.org/10.1007/s00170-014-5835-2},
number = {1-4},
pages = {509--519},
doi = {10.1007/s00170-014-5835-2}
}
MLA
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MLA Copy
Rayegani, Farzad, and Godfrey C. Onwubolu. “Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE).” International Journal of Advanced Manufacturing Technology, vol. 73, no. 1-4, Apr. 2014, pp. 509-519. https://doi.org/10.1007/s00170-014-5835-2.
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