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Evaluation of the Performance of ANN Algorithms with the Bidirectional Functionally Graded Circular Plate Problem

Year 2022, Volume: 6 Issue: 2, 103 - 115, 31.12.2022
https://doi.org/10.47897/bilmes.1207256

Abstract

Functionally graded materials (FGMs) are materials composed of metals and ceramics in which the distribution of material components varies according to a particular volumetric function. FGMs are often used in high-temperature applications. In our study, models were created in the Artificial Neural Network depending on the equivalent stress levels in the compositional gradient exponent, which is the most important parameter in determining the thermo-mechanical behavior of circular plates functionally staggered in two directions, and the performances of these models were evaluated. These models were obtained with four different training algorithms: Levenberg-Marquardt, Backpropagation Algorithm, Resilient Propagation Algorithm, Conjugate Gradient Backpropagation with Powell-Beale Restarts To train the ANN, equivalent stress levels were obtained by performing numerical analyzes at different compositional gradient upper values. The data sets were created by considering the largest value of the equivalent stress levels, the smallest value of the largest value, the largest value of the smallest value, and the smallest value of the smallest value. In this study, training stages and performance values were examined and interpreted with 4 training algorithms in detail.

References

  • [1] Kakac S., Pramuanjaroenkij A., Zhou X.Y., “A review of numerical modeling of solid oxide fuel cells,” International Journal of Hydrogen Energy, 32(7).761-786, 2007, doi.org/10.1016/j.ijhydene.2006.11.028
  • [2] Ruys A., Popov E., Sun D., Russell J., Murray C., “Functionally graded electrical/thermal ceramic systems,” Journal of the European Ceramic Society, 21(10-11). 2025-2029, 2001, doi.org/10.1016/S0955-2219(01)00165-0
  • [3] Koizumi M., Niino M., “Overview of FGM research in Japan”, MRS Bulletin, 20(1).19-21, 1995.
  • [4] Cho J.R., Ha D.Y., “Optimal tailoring of 2D volume-fraction distributions for heat-resisting functionally graded materials using FDM.” Computer Methods in Applied Mechanics and Engineering. 191 (29-30). 3195-3211, 2002. doi: 10.1557/S0883769400048867
  • [5] Nemat-Alla M., “Reduction of thermal stresses by developing two-dimensional functionally graded materials,” International Journal of Solids and Structures, 40(26). 7339-7356, 2003. doi: 10.1016/j.ijsolstr.2003.08.017
  • [6] Ootao Y., Tanigawa Y., Nakamura T., “Optimization of material composition of FGM hollow circular cylinder under thermal loading a neural network approach.” Composites Part B: Engineering. 30(4). 415-422 1999. doi: 10.1016/S1359-8368(99)00003- 7
  • [7] Khoshnoodi H.,Yas M.H.,Samadinejad A., “Dynamic Analysis of Multi-Drectional Functionally Graded Panels and Comparetive Modeling by ANN.” Journal of Solid Mechabics, 8(3).482-494, 2016. Retrieved from http://jsm.iauarak.ac.ir/article_524265.html
  • [8] Xu.Y., You.T., “Minimizing thermal residual stresses in ceramic matrix composites by using Iterative MapReduce guided particle swarm optimization algorithm.” Composite Structures. 99. 388-396. 2013. doi: 10.1016/j.compstruct.2012.11.027
  • [9] Singh A.K., Siddhartha, Hussain S., “Wear peculiarity of TiO2 filled polyester-based Homogeneous composites and their Functionally Graded Materials using Taguchi methodology and ANN.” Materials Today: Proceedings. 2. 2718 – 2727, 2015. doi: 10.1016/j.matpr.2015.07.239
  • [10] Ghatage P.S.,Kar V.R., P., Sudhagara E., “On the numerical modelling and analysis of multi-directional functionally graded composite structures: A review’’,Composite Structures , .236 ,111837, 2020. doi.org/10.1016/j.compstruct.2019.111837
  • [11] Na K.S., Kim J.H., “Volume fraction optimization for step-formed functionally graded plates considering stress and critical temperature”, Composite Structures, 92(6):1283-129092:1283-1290, 2010. doi.org/10.1016/j.compstruct.2009.11.004
  • [12] Demirbaş M.D., Çakır D., “Thermal stress control in functionally graded plates with artificial neural network.” ISVOS Journal. 2(1). 39-55, 2018.
  • [13] Khayat M., Baghlani A., Najafgholipour M.A., “A hybrid algorithm for modeling and studying of the effect of material and mechanical uncertainties on stability of sandwich FGM materials under thermal shock,” Composite Structures 293, 115657, 2022. doi.org/10.1016/j.compstruct.2022.115657
  • [14] Demirbaş M.D., Çakır D., Ozturk C., Arslan S., “Stress Analysis of 2D-FG Rectangular Plates with Multi-Gene Genetic Programming,” applied sciences, 12,8198, 2022.
  • [15] Demirbaş M.D. “Düzlem içi ısı yüküne maruz iki yönlü işlevsel kademelendirilmiş dikdörtgen ve dairesel plakanın ısıl gerilme analizi.” Erciyes Üniversitesi Fen Bilimleri Enstitüsü, Makine Mühendisliği. 2012.
  • [16] Mori, T., Tanaka, K., ‘‘ Average stress in matrix and average elastic energy of materials with misfittings inclusions.’’ Acta Metallurgica, 21(5): 517-574. 1973. doi.org/10.1016/0001-6160(73)90064-3
  • [17] Apalak M.K. , Demirbaş M.D., “Thermal Residual Stresses İn İn-Plane Functionally Graded Clamped Hollow Circular Plates’’, Subjected To An Edge Heat Flux Proceedıngs Of The Instıtutıon Of Mechanıcal Engıneers Part L-Journal Of Materıals-Desıgn And Applıcatıons,229:.236-260. 2015. doi.org/10.1177/1464420713509
  • [18] Apalak M.K. , Demirbaş M.D., “Thermal residual stresses in adhesively bonded in-plane functionally graded clamped circular hollow Plates’’, Journal Of Adhesıon Scıence And Technology, 27:1590-1623, 2013. doı:10.1080/01694243.2012.747732
  • [19] Öztürk C. 2011. “Yapay Sinir Ağlarının Yapay Arı Kolonisi Algoritması ile eğitilmesi.” Erciyes Üniversitesi Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği. 2011.
  • [20] Hagan M.T., Menhaj M.,.‘‘Training feed-forward networks with the Marquardt algorithm’’, IEEE Transactions on Neural Networks, 5(6)989-993. 1994.
  • [21] Ümütlü R.C. Fault “Diagnosis of A Power Transmission System Using Artificial Neural Networks,” Dokuz Eylül Ege Üniversitesi Fen Bilimleri Enstitüsü. Yüksek Lisans, İzmir,104s. 2016.
  • [22] Metrotra K., Mohan C.K., Ranka S.,.“Elements of artificial neural networks.”1997.
  • [23] Haykin. S., “Neural networks”. Prentice Hall. New Jersey. 2005.
  • [24] MATLAB.Mathematical software, version 2009a, “TheMathWorks.” Retrieved from http://www.mathworks.com.
  • [25] Demirbaş M.D.,Çakır D., “Modeling of 2D Functionally Graded Circular Plates with Artificial Neural Network”, ISVOS Journal, 2020, 4(2): 97-110, 2020.

Evaluation of the Performance of ANN Algorithms with the Bidirectional Functionally Graded Circular Plate Problem

Year 2022, Volume: 6 Issue: 2, 103 - 115, 31.12.2022
https://doi.org/10.47897/bilmes.1207256

Abstract

Functionally graded materials (FGMs) are materials composed of metals and ceramics in which the distribution of material components varies according to a particular volumetric function. FGMs are often used in high-temperature applications. In our study, models were created in the Artificial Neural Network depending on the equivalent stress levels in the compositional gradient exponent, which is the most important parameter in determining the thermo-mechanical behavior of circular plates functionally staggered in two directions, and the performances of these models were evaluated. These models were obtained with four different training algorithms: Levenberg-Marquardt, Backpropagation Algorithm, Resilient Propagation Algorithm, Conjugate Gradient Backpropagation with Powell-Beale Restarts To train the ANN, equivalent stress levels were obtained by performing numerical analyzes at different compositional gradient upper values. The data sets were created by considering the largest value of the equivalent stress levels, the smallest value of the largest value, the largest value of the smallest value, and the smallest value of the smallest value. In this study, training stages and performance values were examined and interpreted with 4 training algorithms in detail.

References

  • [1] Kakac S., Pramuanjaroenkij A., Zhou X.Y., “A review of numerical modeling of solid oxide fuel cells,” International Journal of Hydrogen Energy, 32(7).761-786, 2007, doi.org/10.1016/j.ijhydene.2006.11.028
  • [2] Ruys A., Popov E., Sun D., Russell J., Murray C., “Functionally graded electrical/thermal ceramic systems,” Journal of the European Ceramic Society, 21(10-11). 2025-2029, 2001, doi.org/10.1016/S0955-2219(01)00165-0
  • [3] Koizumi M., Niino M., “Overview of FGM research in Japan”, MRS Bulletin, 20(1).19-21, 1995.
  • [4] Cho J.R., Ha D.Y., “Optimal tailoring of 2D volume-fraction distributions for heat-resisting functionally graded materials using FDM.” Computer Methods in Applied Mechanics and Engineering. 191 (29-30). 3195-3211, 2002. doi: 10.1557/S0883769400048867
  • [5] Nemat-Alla M., “Reduction of thermal stresses by developing two-dimensional functionally graded materials,” International Journal of Solids and Structures, 40(26). 7339-7356, 2003. doi: 10.1016/j.ijsolstr.2003.08.017
  • [6] Ootao Y., Tanigawa Y., Nakamura T., “Optimization of material composition of FGM hollow circular cylinder under thermal loading a neural network approach.” Composites Part B: Engineering. 30(4). 415-422 1999. doi: 10.1016/S1359-8368(99)00003- 7
  • [7] Khoshnoodi H.,Yas M.H.,Samadinejad A., “Dynamic Analysis of Multi-Drectional Functionally Graded Panels and Comparetive Modeling by ANN.” Journal of Solid Mechabics, 8(3).482-494, 2016. Retrieved from http://jsm.iauarak.ac.ir/article_524265.html
  • [8] Xu.Y., You.T., “Minimizing thermal residual stresses in ceramic matrix composites by using Iterative MapReduce guided particle swarm optimization algorithm.” Composite Structures. 99. 388-396. 2013. doi: 10.1016/j.compstruct.2012.11.027
  • [9] Singh A.K., Siddhartha, Hussain S., “Wear peculiarity of TiO2 filled polyester-based Homogeneous composites and their Functionally Graded Materials using Taguchi methodology and ANN.” Materials Today: Proceedings. 2. 2718 – 2727, 2015. doi: 10.1016/j.matpr.2015.07.239
  • [10] Ghatage P.S.,Kar V.R., P., Sudhagara E., “On the numerical modelling and analysis of multi-directional functionally graded composite structures: A review’’,Composite Structures , .236 ,111837, 2020. doi.org/10.1016/j.compstruct.2019.111837
  • [11] Na K.S., Kim J.H., “Volume fraction optimization for step-formed functionally graded plates considering stress and critical temperature”, Composite Structures, 92(6):1283-129092:1283-1290, 2010. doi.org/10.1016/j.compstruct.2009.11.004
  • [12] Demirbaş M.D., Çakır D., “Thermal stress control in functionally graded plates with artificial neural network.” ISVOS Journal. 2(1). 39-55, 2018.
  • [13] Khayat M., Baghlani A., Najafgholipour M.A., “A hybrid algorithm for modeling and studying of the effect of material and mechanical uncertainties on stability of sandwich FGM materials under thermal shock,” Composite Structures 293, 115657, 2022. doi.org/10.1016/j.compstruct.2022.115657
  • [14] Demirbaş M.D., Çakır D., Ozturk C., Arslan S., “Stress Analysis of 2D-FG Rectangular Plates with Multi-Gene Genetic Programming,” applied sciences, 12,8198, 2022.
  • [15] Demirbaş M.D. “Düzlem içi ısı yüküne maruz iki yönlü işlevsel kademelendirilmiş dikdörtgen ve dairesel plakanın ısıl gerilme analizi.” Erciyes Üniversitesi Fen Bilimleri Enstitüsü, Makine Mühendisliği. 2012.
  • [16] Mori, T., Tanaka, K., ‘‘ Average stress in matrix and average elastic energy of materials with misfittings inclusions.’’ Acta Metallurgica, 21(5): 517-574. 1973. doi.org/10.1016/0001-6160(73)90064-3
  • [17] Apalak M.K. , Demirbaş M.D., “Thermal Residual Stresses İn İn-Plane Functionally Graded Clamped Hollow Circular Plates’’, Subjected To An Edge Heat Flux Proceedıngs Of The Instıtutıon Of Mechanıcal Engıneers Part L-Journal Of Materıals-Desıgn And Applıcatıons,229:.236-260. 2015. doi.org/10.1177/1464420713509
  • [18] Apalak M.K. , Demirbaş M.D., “Thermal residual stresses in adhesively bonded in-plane functionally graded clamped circular hollow Plates’’, Journal Of Adhesıon Scıence And Technology, 27:1590-1623, 2013. doı:10.1080/01694243.2012.747732
  • [19] Öztürk C. 2011. “Yapay Sinir Ağlarının Yapay Arı Kolonisi Algoritması ile eğitilmesi.” Erciyes Üniversitesi Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği. 2011.
  • [20] Hagan M.T., Menhaj M.,.‘‘Training feed-forward networks with the Marquardt algorithm’’, IEEE Transactions on Neural Networks, 5(6)989-993. 1994.
  • [21] Ümütlü R.C. Fault “Diagnosis of A Power Transmission System Using Artificial Neural Networks,” Dokuz Eylül Ege Üniversitesi Fen Bilimleri Enstitüsü. Yüksek Lisans, İzmir,104s. 2016.
  • [22] Metrotra K., Mohan C.K., Ranka S.,.“Elements of artificial neural networks.”1997.
  • [23] Haykin. S., “Neural networks”. Prentice Hall. New Jersey. 2005.
  • [24] MATLAB.Mathematical software, version 2009a, “TheMathWorks.” Retrieved from http://www.mathworks.com.
  • [25] Demirbaş M.D.,Çakır D., “Modeling of 2D Functionally Graded Circular Plates with Artificial Neural Network”, ISVOS Journal, 2020, 4(2): 97-110, 2020.
There are 25 citations in total.

Details

Primary Language Turkish
Subjects Mechanical Engineering
Journal Section Articles
Authors

Munise Didem Demirbaş 0000-0001-8043-6813

Didem Çakır (sofuoğlu) 0000-0001-7682-6923

Publication Date December 31, 2022
Acceptance Date December 26, 2022
Published in Issue Year 2022 Volume: 6 Issue: 2

Cite

APA Demirbaş, M. D., & Çakır (sofuoğlu), D. (2022). Evaluation of the Performance of ANN Algorithms with the Bidirectional Functionally Graded Circular Plate Problem. International Scientific and Vocational Studies Journal, 6(2), 103-115. https://doi.org/10.47897/bilmes.1207256
AMA Demirbaş MD, Çakır (sofuoğlu) D. Evaluation of the Performance of ANN Algorithms with the Bidirectional Functionally Graded Circular Plate Problem. ISVOS. December 2022;6(2):103-115. doi:10.47897/bilmes.1207256
Chicago Demirbaş, Munise Didem, and Didem Çakır (sofuoğlu). “Evaluation of the Performance of ANN Algorithms With the Bidirectional Functionally Graded Circular Plate Problem”. International Scientific and Vocational Studies Journal 6, no. 2 (December 2022): 103-15. https://doi.org/10.47897/bilmes.1207256.
EndNote Demirbaş MD, Çakır (sofuoğlu) D (December 1, 2022) Evaluation of the Performance of ANN Algorithms with the Bidirectional Functionally Graded Circular Plate Problem. International Scientific and Vocational Studies Journal 6 2 103–115.
IEEE M. D. Demirbaş and D. Çakır (sofuoğlu), “Evaluation of the Performance of ANN Algorithms with the Bidirectional Functionally Graded Circular Plate Problem”, ISVOS, vol. 6, no. 2, pp. 103–115, 2022, doi: 10.47897/bilmes.1207256.
ISNAD Demirbaş, Munise Didem - Çakır (sofuoğlu), Didem. “Evaluation of the Performance of ANN Algorithms With the Bidirectional Functionally Graded Circular Plate Problem”. International Scientific and Vocational Studies Journal 6/2 (December 2022), 103-115. https://doi.org/10.47897/bilmes.1207256.
JAMA Demirbaş MD, Çakır (sofuoğlu) D. Evaluation of the Performance of ANN Algorithms with the Bidirectional Functionally Graded Circular Plate Problem. ISVOS. 2022;6:103–115.
MLA Demirbaş, Munise Didem and Didem Çakır (sofuoğlu). “Evaluation of the Performance of ANN Algorithms With the Bidirectional Functionally Graded Circular Plate Problem”. International Scientific and Vocational Studies Journal, vol. 6, no. 2, 2022, pp. 103-15, doi:10.47897/bilmes.1207256.
Vancouver Demirbaş MD, Çakır (sofuoğlu) D. Evaluation of the Performance of ANN Algorithms with the Bidirectional Functionally Graded Circular Plate Problem. ISVOS. 2022;6(2):103-15.


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