Karafan Journal

Karafan Journal

Simulating the Behaviour of Nanobeams Using Adaptive Neural-fuzzy Inference System

Document Type : Original Article

Authors
Aslan Abbaslou 1
Ahmad Mashayekhi 1
Yasser Taghipour Lahijani 2
1 Department of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran.
2 Department of Mechanical Engineering, Vali-e-asr University of Rafsanjan, Rafsanjan, Iran.
10.48301/kssa.2024.410973.2657
Abstract
In the present research, the analysis of the behaviour of cantilever chromium nanobeams was investigated in the form of nanobeam calculations under static load. To simulate the behaviour and calculate the deflection of nanobeams, the adaptive neural-fuzzy inference system (ANFIS), which is a powerful combination of neural networks and fuzzy logic, was used. By using laboratory data, the mentioned system was trained and tested in three modes. In the first case, the system was trained with the laboratory results of two forces of 8 and 10.1 nanonewtons along the nanobeam with a thickness of 50 nm. Then, the system was tested by interpolation with 9.4 nanonewton forces. In the second case, the system was trained using the experimental 68 nm thick nanobeam for forces of 8 and 11 nanonewtons, and it was tested with 9.5 and 12.5 nanonewton forces in the form of interpolation and extrapolation. In the third case, with the laboratory results, the nanobeam with a thickness of 83 nm was used under a force of 8 nanonewtons at different points, one at a time, to train the system and to test the results of the system. In comparing the ANFIS results with the experimental results, the percentage error was found to be 2.88%. The results of this research showed that it is possible to accurately predict the nanobeam deflection with the adaptive neural-fuzzy inference system without the need to perform more experiments.
Keywords
Nanobeam Deflection Neural Network Fuzzy Logic Neural
fuzzy Inference
Subjects
[1] Jang, J. S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. Institute of Electrical and Electronics Engineers Transactions on Systems, Man, and Cybernetics, 23(3), 665-685. https://doi.org/10.1109/21.256541
[2] Di Carlo, S., Bonvicini, G., Althubiti, N. A., Ayad, R., De La Cruz-Burelo, E., Domínguez, I., El Bashir, B. O., Farhat, H., Flanagan, J., Gillard, R., Gamez, S. I., Kanazawa, K., Kumara, K., Liventsev, D., Podesta-Lerma, P. L. M., Ricalde-Herrmann, D., Perez, D. R., Tejeda-Muñoz, G., Tobiyama, M., & De La Cruz, I. H. (2022). A Neural Network approach to reconstructing SuperKEKB beam parameters from beamstrahlung. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1042(1), 167453. https://doi.org/10.1016/j.ni ma.2022.167453
[3] Karlik, B., Özkaya, E. A., Aydin, S., & Pakdemirli, M. (1998). Vibrations of a beam-mass systems using artificial neural networks. Computers & Structures, 69(3), 339-347. https://doi.org/10.1016/S0045-7949(98)00126-6
[4] Cheng, X., Al-Khafaji, S. H., Hashemian, M., Ahmed, M., Eftekhari, S. A., Alanssari, A. I., diaa, N. M., Karim, M. M., Toghraie, D., & Alawadi, A. H. (2023). Statistical analysis and Neural Network Modeling of functionally graded porous nanobeams vibration in an elastic medium by considering the surface effects. Engineering Applications of Artificial Intelligence, 123, 106313. https://doi.org/10.1016/j.engappai.2023.106313
[5] Zheng, Y., Chu, L., Dui, G., & Zhu, X. (2023). The deflection platform phenomenon of functionally graded flexoelectric simply supported nanobeam. Journal of Intelligent Material Systems and Structures, 34(12), 1406-1423. https://doi.org/10.1177/10453 89x221142088
[6] Vosoughi, A., & Nikoo, M. (2018). A new mixed method for nonlinear fuzzy free vibration analysis of nanobeams on nonlinear elastic foundation. Journal of Vibration and Control, 24(24), 5765-5773. https://doi.org/10.1177/1077546316648491
[7] Rajaei, A., Chizfahm, A., Vatankhah, R., & Montazeri, A. (2022). Adaptive self-organizing fuzzy sliding mode controller for a nonlocal strain gradient nanobeam. European Journal of Control, 65, 100626. https://doi.org/10.1016/j.ejcon.2022.100626
[8] Jiang, Y., Li, L., & Hu, Y. (2022). A nonlocal surface theory for surface–bulk interactions and its application to mechanics of nanobeams. International Journal of Engineering Science, 172, 103624. https://doi.org/10.1016/j.ijengsci.2022.103624
[9] Nuhu, A. A., & Safaei, B. (2022). State-of-the-Art of Vibration Analysis of Small-Sized Structures by using Nonclassical Continuum Theories of Elasticity. Archives of Computational Methods in Engineering, 29(7), 4959-5147. https://doi.org/10.1007/ s11831-022-09754-3
[10] Wong, E. W., Sheehan, P. E., & Lieber, C. M. (1997). Nanobeam Mechanics: Elasticity, Strength, and Toughness of Nanorods and Nanotubes. Science, 277(5334), 1971-1975. https://doi.org/10.1126/science.277.5334.1971
[11] Houshmand, F. (2023). Study of the Behavior of Graphdiyne Nanotubes in an Aqueous Environment: Car-Parrinello Molecular Dynamics Simulation. Quarterly Scientific Journal of Technical and Vocational University, 20(3), 683-699. https://doi.org/10. 48301/kssa.2023.382432.2433
[12] Suganthi, X. H., Natarajan, U., Sathiyamurthy, S., & Chidambaram, K. (2013). Prediction of quality responses in micro-EDM process using an adaptive neuro-fuzzy inference system (ANFIS) model. The International Journal of Advanced Manufacturing Technology, 68(1), 339-347. https://doi.org/10.1007/s00170-013-4731-5
[13] Aydın, M., Karakuzu, C., Uçar, M., Cengiz, A., & Çavuşlu, M. A. (2013). Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning. The International Journal of Advanced Manufacturing Technology, 67(1), 957-967. https://doi.org/10.1007/s00170-012-45 40-2
[14] Kumar, S., Dhanabalan, S., & Narayanan, C. S. (2019). Application of ANFIS and GRA for multi-objective optimization of optimal wire-EDM parameters while machining Ti–6Al–4V alloy. Springer Nature Applied Sciences, 1(4), 298. https://doi.org/10.1 007/s42452-019-0195-z
[15] Moayedi, H., Raftari, M., Sharifi, A., Jusoh, W. A. W., & Rashid, A. S. A. (2020). Optimization of ANFIS with GA and PSO estimating α ratio in driven piles. Engineering with Computers, 36(1), 227-238. https://doi.org/10.1007/s00366-018-00694-w
[16] Bhiradi, I., Raju, L., & Hiremath, S. S. (2020). Adaptive neuro-fuzzy inference system (ANFIS): modelling, analysis, and optimisation of process parameters in the micro-EDM process. Advances in Materials and Processing Technologies, 6(1), 133-145. https://doi.org/10.1080/2374068X.2019.1709309
[17] Xu, L., Huang, C., Li, C., Wang, J., Liu, H., & Wang, X. (2021). Estimation of tool wear and optimization of cutting parameters based on novel ANFIS-PSO method toward intelligent machining. Journal of Intelligent Manufacturing, 32(1), 77-90. https://do i.org/10.1007/s10845-020-01559-0
[18] Tahmasebi, M., & Gohari, M. (2023). Design and Simulation of an Adaptive Neuro_ Controller for a Wire Driven Flexible Arm Robot. Quarterly Scientific Journal of Technical and Vocational University, 20(1), 243-262. https://doi.org/10.48301/kssa .2023.361179.2280
[19] Ji, M., Muthuramalingam, T., Saravanakumar, D., Karmiris-Obratański, P., Karkalos, N. E., & Zhang, W. (2023). Predicting depth of cut in vibration-assisted EDM cutting on titanium alloy using adaptive neuro fuzzy inference system. Measurement, 219(5), 113245. https://doi.org/10.1016/j.measurement.2023.113245
[20] Mohamadi, M., & Aliasghary, M. (2023). Adaptive neuro-fuzzy inference system approach to predict dynamic thermo-mechanical responses of poly (vinylidene fluoride) blend-based nanocomposites. Polymer Bulletin, 80(6), 6989-7010. https://doi.org/10.1007 /s00289-022-04384-y
[21] Rao, T. B. (2023). Prediction of EDMed micro-hole quality characteristics using hybrid bio-inspired machine learning-based predictive approaches. International Journal on Interactive Design and Manufacturing 17(2), 747-764. https://doi.org/10.1007/s120 08-022-01117-3
[22] Nilsson, S. G., Borrisé, X., & Montelius, L. (2004). Size effect on Young’s modulus of thin chromium cantilevers. Applied Physics Letters, 85(16), 3555-3557. https://doi.org/1 0.1063/1.1807945
Karafan Journal
Volume 21, Issue 1 - Serial Number 66
Engineering & Technical
Spring 2024
Pages 355-368

  • Receive Date 04 October 2023
  • Revise Date 03 January 2024
  • Accept Date 30 January 2024