Finite element analysis (FEA) of bolted flange connections is the common methodology for the analysis of bolted flange connections. However, it requires high computational power for model preparation and nonlinear analysis due to contact definitions used between the mating parts. Design of an optimum bolted flange connection requires many costly finite element analyses to be performed to decide on the optimum bolt configuration and minimum flange and casing thicknesses. In this study, very fast responding and accurate artificial neural network-based bolted flange design tool is developed. Artificial neural network is established using the database which is generated by the results of more than 10,000 nonlinear finite element analyses of the bolted flange connection of a typical aircraft engine. The FEA database is created by taking permutations of the parametric geometric design variables of the bolted flange connection and input load parameters. In order to decrease the number of FEA points, the significance of each design variable is evaluated by performing a parameter correlation study beforehand, and the number of design points between the lower and upper and bounds of the design variables is decided accordingly. The prediction of the artificial neural network based design tool is then compared with the FEA results. The results show excellent agreement between the artificial neural network-based design tool and the nonlinear FEA results within the training limits of the artificial neural network.
Skip Nav Destination
Article navigation
October 2019
Research-Article
Development of Bolted Flange Design Tool Based on Artificial Neural Network
Ahmet Arda Akay,
Ahmet Arda Akay
Department of Aerospace Engineering,
Middle East Technical University,
Ankara 06800, Turkey
e-mail: aakay@metu.edu.tr
Middle East Technical University,
Ankara 06800, Turkey
e-mail: aakay@metu.edu.tr
Search for other works by this author on:
Hasan Gülaşık,
Hasan Gülaşık
Department of Aerospace Engineering,
Middle East Technical University,
Ankara 06800, Turkey
e-mail: hasangulasik@gmail.com
Middle East Technical University,
Ankara 06800, Turkey
e-mail: hasangulasik@gmail.com
Search for other works by this author on:
Demirkan Çoker,
Demirkan Çoker
Department of Aerospace Engineering,
Middle East Technical University,
Ankara 06800, Turkey
e-mail: coker@metu.edu.tr
Middle East Technical University,
Ankara 06800, Turkey
e-mail: coker@metu.edu.tr
Search for other works by this author on:
Ercan Gürses,
Ercan Gürses
Department of Aerospace Engineering,
Middle East Technical University,
Ankara 06800, Turkey
e-mail: gurses@metu.edu.tr
Middle East Technical University,
Ankara 06800, Turkey
e-mail: gurses@metu.edu.tr
Search for other works by this author on:
Altan Kayran
Altan Kayran
Department of Aerospace Engineering,
Middle East Technical University,
Ankara 06800, Turkey
e-mail: akayran@metu.edu.tr
Middle East Technical University,
Ankara 06800, Turkey
e-mail: akayran@metu.edu.tr
1Corresponding author.
Search for other works by this author on:
Alper Yıldırım
Ahmet Arda Akay
Department of Aerospace Engineering,
Middle East Technical University,
Ankara 06800, Turkey
e-mail: aakay@metu.edu.tr
Middle East Technical University,
Ankara 06800, Turkey
e-mail: aakay@metu.edu.tr
Hasan Gülaşık
Department of Aerospace Engineering,
Middle East Technical University,
Ankara 06800, Turkey
e-mail: hasangulasik@gmail.com
Middle East Technical University,
Ankara 06800, Turkey
e-mail: hasangulasik@gmail.com
Demirkan Çoker
Department of Aerospace Engineering,
Middle East Technical University,
Ankara 06800, Turkey
e-mail: coker@metu.edu.tr
Middle East Technical University,
Ankara 06800, Turkey
e-mail: coker@metu.edu.tr
Ercan Gürses
Department of Aerospace Engineering,
Middle East Technical University,
Ankara 06800, Turkey
e-mail: gurses@metu.edu.tr
Middle East Technical University,
Ankara 06800, Turkey
e-mail: gurses@metu.edu.tr
Altan Kayran
Department of Aerospace Engineering,
Middle East Technical University,
Ankara 06800, Turkey
e-mail: akayran@metu.edu.tr
Middle East Technical University,
Ankara 06800, Turkey
e-mail: akayran@metu.edu.tr
1Corresponding author.
Contributed by the Pressure Vessel and Piping Division of ASME for publication in the JOURNAL OF PRESSURE VESSEL TECHNOLOGY. Manuscript received September 19, 2017; final manuscript received May 28, 2019; published online July 17, 2019. Assoc. Editor: Sayed Nassar.
J. Pressure Vessel Technol. Oct 2019, 141(5): 051203 (11 pages)
Published Online: July 17, 2019
Article history
Received:
September 19, 2017
Revised:
May 28, 2019
Citation
Yıldırım, A., Akay, A. A., Gülaşık, H., Çoker, D., Gürses, E., and Kayran, A. (July 17, 2019). "Development of Bolted Flange Design Tool Based on Artificial Neural Network." ASME. J. Pressure Vessel Technol. October 2019; 141(5): 051203. https://doi.org/10.1115/1.4043915
Download citation file:
Get Email Alerts
Cited By
The Behavior of Elbow Elements at Pure Bending Applications Compared to Beam and Shell Element Models
J. Pressure Vessel Technol (February 2025)
Related Articles
On the Effect of External Bending Loads in Bolted Flange Joints
J. Pressure Vessel Technol (April,2009)
Effect of Bolt Spacing on the Circumferential Distribution of the Gasket Contact Stress in Bolted Flange Joints
J. Pressure Vessel Technol (August,2011)
Elastic Interaction in Bolted Flange Joints: An Analytical Model to Predict and Optimize Bolt Load
J. Pressure Vessel Technol (August,2018)
Nonlinear Deformation Behavior of Bolted Flanges Under Tensile, Torsional, and Bending Loads
J. Pressure Vessel Technol (December,2014)
Related Proceedings Papers
Related Chapters
Background InformatIon
Guidebook for the Design of ASME Section VIII Pressure Vessels
Openings
Guidebook for the Design of ASME Section VIII Pressure Vessels
Background Information
Guidebook for the Design of ASME Section VIII Pressure Vessels, Third Edition