Estimation of Critical Buckling Loads of Hybrid Composites in Different Environments using Artificial Neural Networks

Yıl 2023, , 869 - 887, 31.12.2023

Ayla Tekin , Ayşe Öndürücü , Hüseyin Fırat Kayıran

https://doi.org/10.18185/erzifbed.1253159

Öz

The primary objective of this study is to estimate the buckling behaviors of hybrid composite plate using test data on the effects of different environmental conditions. These estimations were made using an Artificial Neural Network (ANN). The MATLAB software was used to develop the artificial neural network. The data from the buckling tests were used to train the ANN model that used a multilayer, feedforward and backprop algorithm. The input parameters for the ANN modeling were determined as waiting times of the samples, ambient temperatures, ambient conditions, and material arrangement angles. This modeling was used to estimate critical buckling loads. The values obtained after the training and testing of ANN were examined by performing statistical analyses commonly used in ANN models, revealing that the designed model was applied successfully and the results were very close to the real test results.

Anahtar Kelimeler

Artificial Neural Networks,, Critical Buckling Load Estimation,, Hybrid Composites,

Kaynakça

• [1] Madenci, E, Shkarayev, S, Sergeev, B, Opliger, D.W., Shyprykevich, P. (1998). Analysis of composite laminates with multiple fasteners. International Journal of Solids and Structures, 35(15),1793-1811.
• [2] Okutan, Baba B. (2007). Buckling behavior of laminated composite plates. Journal of Reinforced Plastics & Composites, 26(16),1637-1655.
• [3] Davalos, J. E., Qiao, P., Salim, H. A.(1997). Flexural-torsional buckling of pultruded fiber reinforced plastic composite I - Beams: experimental and analytical evaluations. Composite Structure, 38(1-4), 241-250.
• [4] Ray, C. (2004). An investigation on the effect of fiber weight fraction on buckling of laminated composite plates. Journal of Reinforced Plastics & Composites, 23(9),951–957.
• [5] Erklig, A, Yeter, E. (2012). The effects of cutouts on buckling behavior of composite plates. Science and Engineering of Composite Materials, 19, 323–330.
• [6] Hamani, N., Ouinasa, D., Taghezoutb, N., et al. (2013). Effect of fiber orientation on the critical buckling load of symmetric composite laminated plates. Advanced Materials Research, 629,95-99.
• [7] Yang, B, Fu, K, Lee, J, Li, Y. (2021). Artificial Neural Network (ANN)-based residual strength prediction of carbon fiber reinforced composites after impact. Applied Composite Materials,1-25.
• [8] Xu, X., Elgamal, M., Doddamani, M., Gupta, N. (2021). Tailoring composite materials for nonlinear viscoelastic properties using artificial neural networks. Journal of Composite Materials, 55(11),1547-1560.
• [9] Zhang, Z., Friedrich, K. (2003). Artificial neural networks applied to polymer composites: A review. Composites Science and Technology, 63, 2029-2044.
• [10] Kadi, H. (2006). Modeling the mechanical behavior of fiber-reinforced polymeric composite materials using artificial neural networks. Composite Structures, 73(1),1-23.
• [11] Haj-Ali, R., Kim, H. (2007). Nonlinear constitutive models for FRP composites using artificial neural networks. Mechanics of Materials, 39(12), 1035-1042.
• [12] Albuquerque, V.H.C., Tavares, J., Durao, L. (2009). Evaluation of delamination damage on composite plates using an artificial neural network for the radiographic image analysis. Journal of Composite Materials, 44(9), 1139-1159.
• [13] Tekin, A., Öndürücü, A., Esendemir, Ü. (2017). ANN evaluation of bearing strength on pin-loaded composite plates in different environmental conditions. Material Testing, 59(7-8), 696-702.
• [14] Islak, S., Akkaş, M., Kaya, Ü., Güleç, H. G. (2017). Estimation of mechanical and physical properties of Cu-Ti composites by artificial neural networks (ANN) model. Journal of Applied Science and Technology, 12(3),122–129.
• [15] Kim, M., Kang, S., Kim, C., Kong, C. (2007). Tensile response of graphite-epoxy composites at low temperatures. Composite Structures, 79(1),84-89.
• [16] Torabizadeh, M. A. (2013). Tensile, compressive, and shear properties of unidirectional glass epoxy composites subjected to mechanical loading and low-temperature services. Indian Journal of Engineering and Materials Sciences, 20,299-309.
• [17] Jiangbo, B., Junjiang, X. (2014). Temperature effect on buckling properties of ultra-thin- walled lenticular collapsible composite tube subjected to axial compression. Chinese Journal of Aeronautics, 27(5),1312-1317.
• [18] Gu, H. (2009). Behaviors of glass fiber-unsaturated polyester composites under seawater environment. Materials & Design, 30(4),1337–1340.
• [19] Öndürücü, A. (2012). The Effects of seawater immersion on the bearing strength of woven glass-epoxy prepreg pin-loaded joints. International Journal of Damage Mechanics, 21, 153–170.
• [20] Karakuzu, R., Kanlıoğlu, H., Deniz, M. E. (2018). Effect of seawater on pin-loaded laminated composites. Material Testing, 60(1), 85–92.
• [21] Öndürücü, A., Kayıran, H. F. (2019). Effect of seawater on the buckling behavior of hybrid composite plates. Journal of Composite Materials, 53(9),1135-1144.
• [22] Kayıran, H. F. (2018). Investigation of buckling behavior in hybrid composite plates subjected to different environmental conditions. Süleyman Demirel University, Graduate School of Natural and Applied Sciences, Ph.D. Thesis, Isparta.
• [23] Karataş, Ç., Sözen, A., Dülek, E. (2009). Modeling of Residual stresses in shot-peened material C-1020 by artificial neural networks. Expert Systems with Applications, 36(2),3514-3521.
• [24] Eker, A.M., Dikmen, M., Cambazoğlu, S., et al. (2012). Application of artificial neural networks and logistic regression methods to landslide susceptibility mapping and comparison of the results for the Ulus district, Bartın. Journal of the Faculty of Engineering and Architecture of Gazi University, 27(1),163-173.
• [25] Sarali, D.S., V.A.I., Pandiyan, K. (2019). An improved design for neural-network-based model predictive control of three-phase inverters. In Proceedings of the 2019 IEEE International Conference on Clean Energy and Energy Efficient Electronics Circuit for Sustainable Development (INCCES), Krishnankoil, India, 18-20 December, 1-5.
• [26] Tiryaki, S., Bardak, S., Bardak, T. (2015). Experimental investigation and prediction of bonding strength of Oriental beech (Fagus orientalis Lipsky) bonded with polyvinyl acetate adhesive. Journal of Adhesion Science and Technology, 29 (23), 2521-2536.