Development of Artificial Neural Network Based Design Tool for Aircraft Engine Bolted Flange Connection Subject to Combined Axial and Moment Load

Abstract

Bolted flange connections are one of the most commonly used joint types in aircraft structures. Typically, bolted flange connections are used in aircraft engines. The main duty of a bolted flange connection in an aircraft engine is to serve as the load transfer interface from one part of the engine to the other part of the engine. In aircraft structures, weight is a very critical parameter which has to be minimized while having the required margin of safety for the structural integrity. Therefore, optimum design of the bolted flange connection is crucial to minimize the weight. In the preliminary design stage of the bolted flange connection, many repetitive analyses have to be made in order to decide on the optimum design parameters of the bolted flange connection. Two main methods used for analyzing bolted flange connections are the hand calculations based on simplified approaches and finite element analysis (FEA). While hand calculations lack achieving optimum weight as they tend to give over safe results, finite element analysis is computationally expensive because of the non-linear feature of the problem due to contact definitions between the mating parts. In this study, a fast but very accurate design tool based on artificial neural network (ANN) is developed for the cylindrical bolted flange connection of a typical aircraft engine under combined axial and bending moment load. ANN uses the FEA database generated by taking permutations of the parametric design variables of the bolted flange connection. The selected parameters are the number of bolts, the bolt size, the flange thickness, the web thickness, the preload level of the bolt and the external combined loads of bending moment and axial force. The bolt reaction force and the average flange stress are taken as the output variables and the results of 12000 different finite element analyses are gathered to form a database for the training of the ANN. Results of the trained ANN are then compared with the finite element analysis results and it is shown that an excellent agreement exists between the ANN and the non-linear finite element analysis results within the training limits of the artificial neural network. We believe that the ANN established can be a very robust and accurate approximate model replacing the non-linear finite element solver in the optimization of the bolted flange connection of the aircraft engine to achieve weight reduction.