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.
Development of Bolted Flange Design Tool Based on Artificial Neural Network
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.
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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
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