Optimization of a thin-walled element geometry using a system integrating neural networks and finite element method

Artificial neural networks [ANNs] are an effective method for predicting and classifying variables. This article presents the application of an integrated system based on artificial neural networks and calculations by the finite element method [FEM] for the optimization of geometry of a thin-walled element of an air structure. To ensure optimal structure, the structure’s geometry was modified by creating side holes and ribs, also with holes. The main criterion of optimization was to reduce the structure’s weight at the lowest possible deformation of the tested object. The numerical tests concerned a fragment of an elevator used in the ”Bryza” aircraft. The tests were conducted for networks with radial basis functions [RBF] and multilayer perceptrons [MLP]. The calculations described in the paper are an attempt at testing the FEM – ANN system with respect to design optimization.

[1]  T. Sadowski,et al.  The influence of quantity and distribution of cooling channels of turbine elements on level of stresses in the protective layer TBC and the efficiency of cooling , 2012 .

[2]  Grzegorz Litak,et al.  Quantitative estimation of the tool wear effects in a ripping head by recurrence plots , 2008 .

[3]  O. Kisi,et al.  Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches , 2015 .

[4]  Tomasz Sadowski,et al.  Multidisciplinary analysis of the operational temperature increase of turbine blades in combustion engines by application of the ceramic thermal barrier coatings (TBC) , 2011 .

[5]  Jarosław Bieniaś,et al.  Analysis of microstructure damage in carbon/epoxy composites using FEM , 2012 .

[6]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[7]  T. Sadowski,et al.  Sensitivity analysis of crack propagation in pavement bituminous layered structures using a hybrid system integrating Artificial Neural Networks and Finite Element Method , 2014 .

[8]  Hany El Kadi,et al.  Modeling the mechanical behavior of fiber-reinforced polymeric composite materials using artificial neural networks—A review , 2006 .

[9]  Józef Jonak,et al.  Towards the identification of worn picks on cutterdrums based on torque and power signals using Artificial Neural Networks , 2011 .

[10]  Zhiping Lin,et al.  Composite function wavelet neural networks with extreme learning machine , 2010, Neurocomputing.

[11]  Lin Ye,et al.  Lamb wave-based quantitative identification of delamination in CF/EP composite structures using artificial neural algorithm , 2004 .

[12]  Ricardo Perera,et al.  Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement , 2010 .

[13]  Chenglie Du,et al.  An integrated micromechanical model and BP neural network for predicting elastic modulus of 3-D multi-phase and multi-layer braided composite , 2015 .

[14]  Bin Wang,et al.  Application of artificial neural network in prediction of abrasion of rubber composites , 2013 .

[15]  A. Gibson,et al.  A novel vibration based non-destructive testing for predicting glass fibre/matrix volume fraction in composites using a neural network model , 2016 .

[16]  Xinwei Fu,et al.  Minimum-weight design for three dimensional woven composite stiffened panels using neural networks and genetic algorithms , 2015 .

[17]  Hosein Naderpour,et al.  Prediction of FRP-confined compressive strength of concrete using artificial neural networks , 2010 .

[18]  Assunta Sorrentino,et al.  Application of Artificial Neural Networks and Probability Ellipse methods for damage detection using Lamb waves , 2015 .

[19]  Józef Jonak,et al.  Identification of ripping tool types with the use of characteristic statistical parameters of time graphs , 2008 .

[20]  Józef Jonak,et al.  Identifying the cutting tool type used in excavations using neural networks , 2006 .

[21]  L. H. Yam,et al.  Vibration-based damage detection for composite structures using wavelet transform and neural network identification , 2003 .

[22]  Reza Eslami Farsani,et al.  Artificial Neural Network prediction of Cu–Al2O3 composite properties prepared by powder metallurgy method , 2013 .

[23]  S. Sampathkumar,et al.  Prediction of impact damage tolerance of drop impacted WGFRP composite by artificial neural network using acoustic emission parameters , 2014 .

[24]  Hu Zhang,et al.  Improvement of fracture toughness of directionally solidified Nb-silicide in situ composites using artificial neural network , 2014 .

[25]  Mahmoud Shakeri,et al.  Multi-objective stacking sequence optimization of laminated cylindrical panels using a genetic algorithm and neural networks , 2007 .

[26]  Jerzy Podgórski,et al.  Numerical simulation of brittle rock loosening during mining process , 2008 .

[27]  Tomasz Sadowski,et al.  Detection and numerical analysis of the most efforted places in turbine blades under real working conditions , 2012 .

[28]  Józef Jonak,et al.  Classification of wear level of mining tools with the use of fuzzy neural network , 2013 .

[29]  Gangadhara B Prusty,et al.  Neural network modelling for damage behaviour of composites using full-field strain measurements , 2011 .