Title

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

Journal title

Archives of Metallurgy and Materials

Yearbook

2017

Volume

vol. 62

Issue

No 1

Authors

Sadowski, T. ; Golewski, P. ; Gajewski, J.

Divisions of PAS

Nauki Techniczne

Publisher

Institute of Metallurgy and Materials Science of Polish Academy of Sciences ; Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences

Date

2017

Identifier

DOI: 10.1515/amm-2017-0067 ; e-ISSN 2300-1909

Source

Archives of Metallurgy and Materials; 2017; vol. 62; No 1

References

Farhana (2016), A novel vibration based non - destructive testing for predicting glass fibre / matrix volume fraction in composites using a neural Network model, Composite Structures, 144, 96, doi.org/10.1016/j.compstruct.2016.02.066 ; Gajewski (2008), Numerical simulation of brittle rock loosening during mining process, Computational Materials Science, 115, doi.org/10.1016/j.commatsci.2007.07.044 ; Litak (2008), Quantitative estimation of the tool wear effects in a ripping head by recurrence plots of Theoretical and, Journal Applied Mechanics, 521. ; Perera (2010), Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement, Composite Structures, 1169, doi.org/10.1016/j.compstruct.2009.10.027 ; Guan (2014), Fei Ding Improvement of fracture toughness of directionally solidified Nb - silicide in situ composites using artificial neural network, Materials Science Engineering, 605. ; Sadowski (2012), 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, Computational Materials Science, 293, doi.org/10.1016/j.commatsci.2011.02.027 ; Naderpour (2010), Prediction of FRP - confined compressive strength of concrete Rusing artificial neural networks, Composite Structures, 2817, doi.org/10.1016/j.compstruct.2010.04.008 ; El (2006), Kadi Modeling the mechanical behavior of fiber - reinforced polymeric composite materials using artificial neural networks - A review, Composite Structures, 1. ; Sadowski (2011), Multidisciplinary analysis of the operational temperature increase of turbine blades in combustion engines by application of the ceramic thermal barrier coatings ( TBC ), Computational Materials Science, 1326, doi.org/10.1016/j.commatsci.2010.05.032 ; Gajewski (2014), Sensitivity analysis of crack propagation in pavement bituminous layered structures using a hybrid system integrating Artificial Neural Networks and Finite Element Method, Computational Materials Science, 114, doi.org/10.1016/j.commatsci.2013.09.025 ; Jonak (2006), Identifying the cutting tool type used in excavations using neural networks Space, Technol, 21, 185. ; Gajewski (2013), Classification of wear level of mining tools with the use of fuzzy neural network Tunn Undergr Space, Technol, 35, 30. ; Bieniaś (2012), Analysis of microstructure damage in carbon / epoxy composites using FEM, Computational Materials Science, 168, doi.org/10.1016/j.commatsci.2012.03.033 ; Ramasamy (2014), Prediction of impact damage tolerance of drop impacted WGFRP composite by artificial neural network using acoustic emission parameters Part, Composites, 457, doi.org/10.1016/j.compositesb.2013.12.028 ; Mansouri (2015), Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches Part, Composites, 247, doi.org/10.1016/j.compositesb.2014.11.023 ; Man (2011), Neural network modelling for damage behaviour of composites using full - field strain measurements, Composite Structures, 383, doi.org/10.1016/j.compstruct.2010.09.003 ; Abouhamze (2007), Multi - objective stacking sequence optimization of laminated cylindrical panels using a genetic algorithm and neural networks, Composite Structures, 253, doi.org/10.1016/j.compstruct.2006.08.015 ; Jonak (2008), Identification of ripping tool types with the use of characteristic statistical parameters of time graphs Space, Technol, 23, 18. ; Sadowski (2012), Detection and numerical analysis of the most efforted places in turbine blades under real working conditions, Computational Materials Science, 285, doi.org/10.1016/j.commatsci.2012.02.048 ; Su (2004), Lamb wave - based quantitative identification of delamination in CF / EP composite structures using artificial neural algorithm, Composite Structures, 627, doi.org/10.1016/j.compstruct.2004.05.011 ; Cao Jiuwen (2010), bin Composite function wavelet neural networks with extreme learning machine, Neurocomputing, 1405. ; De Fenza (2015), Application of Artificial Neural Networks and Probability Ellipse methods for damage detection using Lamb waves, Composite Structures, 390, doi.org/10.1016/j.compstruct.2015.07.089 ; Gajewski (2011), Towards the identification of worn picks on cutterdrumsbased on torque and power signals using Artificial Neural Networks Space, Technol, 26, 22. ; Amirjan (2013), Artificial Neural Network prediction of Cu - composite properties prepared by powder metallurgy method, Mater Res Technol, 2, 351, doi.org/10.1016/j.jmrt.2013.08.001 ; Fu (2015), Bisagni Minimum - weight design for three dimensional woven composite stiffened panels using neural networks and genetic algorithms, Composite Structures, 708, doi.org/10.1016/j.compstruct.2015.08.077 ; Yam (2003), Vibration - based damage detection for composite structures using wavelet transform and neural network identification, Composite Structures, 403, doi.org/10.1016/S0263-8223(03)00023-0