Development of Bolted Flange Design Tool Based on Artificial Neural Network

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Alper Yıldırım ◽  
Ahmet Arda Akay ◽  
Hasan Gülaşık ◽  
Demirkan Çoker ◽  
Ercan Gürses ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Design Tool ◽  
Finite Element Analyses ◽  
Element Analysis ◽  
Flange Connection ◽  
Design Variables ◽  
Artificial Neural ◽  
Bolted Flange

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.

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

2017 ◽  
Author(s):  
T. Volkan Sanli ◽  
Ercan Gürses ◽  
Demirkan Çöker ◽  
Altan Kayran
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Aircraft Engine ◽  
Element Analysis ◽  
Flange Connection ◽  
Non Linear ◽  
Artificial Neural ◽  
Bolted Flange

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.

scholarly journals Development of Bolted Flange Design Tool Based on Finite Element Analysis and Artificial Neural Network

2015 ◽  
Author(s):  
Alper Yildirim ◽  
Ahmet Arda Akay ◽  
Hasan Gulasik ◽  
Demirkan Coker ◽  
Ercan Gurses ◽  
...  
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Structural Analysis ◽  
Design Tool ◽  
Design Parameters ◽  
Element Analysis ◽  
Non Linear ◽  
Bolted Flange

In bolted flange connections, commonly utilized in aircraft engine designs, structural integrity and minimization of the weight are achieved by the optimum combination of the design parameters utilizing the outcome of many structural analyses. Bolt size, the number of bolts, bolt locations, casing thickness, flange thickness, bolt preload, and axial external force are some of the critical design parameters in bolted flange connections. Theoretical analysis and finite element analysis (FEA) are two main approaches to perform structural analysis of bolted flange connections. Theoretical approaches require the simplification of the geometry and are generally oversafe. In contrast, finite element analysis is more reliable but at the cost of high computational power. In this paper, a methodology is developed for iterative analyses of bolted flange that utilizes artificial neural network approximation of a database formed with more than ten thousand non-linear analyses with contact algorithm. In the design tool, a structural analysis database is created by taking permutations of the parametric variables. The number of intervals for each variable in the upper and lower range of the variables is determined with the parameters correlation study in which the significance of parameters are evaluated. The prediction of the ANN based design tool is then compared with FEA results and the theoretical approach of ESDU. The results show excellent agreement of the ANN based design tool with the actual non-linear finite element analysis results within the training limits of the ANN.

Vibration–Based Damage Detection Of Slab Structure Using Artificial Neural Network

2012 ◽  
Author(s):  
Norhisham Bakhary
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Damage Detection ◽  
Ann Model ◽  
Element Analysis ◽  
Severity Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Kertas kerja ini memaparkan kajian berkenaan keberkesanan Artificial Neural Network (ANN) dalam mengenal pasti kerosakan di dalam struktur. Data dari getaran seperti frekuensi semula jadi dan mod bentuk digunakan sebagai data masukan bagi ANN untuk meramalkan lokasi dan tahap kerosakan bagi struktur lantai. Analisis unsur terhingga (Finite Element Analysis) telah digunakan untuk memperoleh ciri–ciri dinamik bagi struktur–struktur rosak dan tidak rosak untuk ‘melatih’ model ‘neural network’. Senario kerosakan yang berbeza disimulasikan dengan mengurangkan kekukuhan elemen pada lokasi yang berbeza sepanjang struktur tersebut. Berbagai kombinasi data masukan bagi mod yang berbeza telah digunakan untuk memperolehi model ANN yang terbaik. Hasil kajian ini menunjukkan ANN mampu memberikan keputusan yang baik dalam meramal kerosakan pada struktur lantai tersebut. Kata kunci: Ramalan kerosakan struktur, Artificial Neural Network This paper investigates the effectiveness of artificial neural network (ANN) in identifying damages in structures. Global (natural frequencies) and local (mode shapes) vibration–based data has been used as the input to ANN for location and severity prediction of damages in a slab–like structure. A finite element analysis has been used to obtain the dynamic characteristics of intact and damaged structure to train the neural network model. Different damage scenarios have been introduced by reducing the local stiffness of the selected elements at different locations along the structure. Several combinations of input variables in different modes have been used in order to obtain a reliable ANN model. The trained ANN model is validated using laboratory test data. The results show that ANN is capable of providing acceptable result on damage prediction of tested slab structure. Key words: Structural damage detection, artificial neural network

The Reinforcement Rib Performance Prediction Based on the BP Algorithm and the Finite Element Analysis

2011 ◽  
Vol 101-102 ◽  
pp. 212-215
Author(s):  
Liang Yao Su ◽  
Xiang Sheng Li ◽  
Xiong Fei Yin ◽  
Xiao Yan Feng ◽  
Shang Wen Ruan
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Performance Prediction ◽  
Bp Algorithm ◽  
Prediction System ◽  
Element Analysis ◽  
The Finite Element Analysis ◽  
Artificial Neural

The reinforcement rib design is one of the key parts in entire bottle design. This paper presents the rib performance prediction system based on the BP algorithm and the finite element analysis, which adopts the finite element analysis results as its learning samples, sets up the rib performance prediction system with BP artificial neural network. The results show that the artificial neural network plays an important role in rib performance prediction; meanwhile it can guide the bottle design in practical terms.

Biomechanics of the Healthy and Keratoconic Corneas: A Combination of the Clinical Data, Finite Element Analysis, and Artificial Neural Network

2019 ◽  
Vol 24 (37) ◽  
pp. 4474-4483 ◽  
Author(s):  
Alireza Karimi ◽  
Najme Meimani ◽  
Reza Razaghi ◽  
Seyed Mohammadali Rahmati ◽  
Khosrow Jadidi ◽  
...  
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Clinical Data ◽  
Element Analysis ◽  
Artificial Neural
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