Abstract




 
   

IJE TRANSACTIONS C: Aspects Vol. 30, No. 12 (December 2017) 1457-1466    Article in Press

PDF URL: http://www.ije.ir/Vol30/No12/C/5.pdf  
downloaded Downloaded: 0   viewed Viewed: 70

  MODELING AND OPTIMIZATION OF ROLL-BONDING PARAMETERS FOR BOND STRENGTH OF TI/CU/TI CLAD COMPOSITES BY ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHM
 
M. Mahdavi, G. R. Khayati, M. Hosseini and H. Danesh-Manesh
 
( Received: April 02, 2017 – Accepted: September 08, 2017 )
 
 

Abstract    This paper deals with modeling and optimization of roll-bonding process of Ti/Cu/Ti composite for determination of the best roll-bonding parameters leading to the maximum Cu/Ti bond strength by combining neural network and genetic algorithm. An artificial neural networks (ANNs) program proposed to determine the effect of practical parameters, i.e., rolling temperature, reduction in thickness, post-annealing time, post-annealing temperature and rolling speed on the bond strength of Ti/Cu composite. The most suitable model with coefficient of determination (R2) 0.98 and mean absolute error (MAPE) 3.5 was determined and using genetic algorithm (GA) the optimum practice condition was proposed. Moreover, the sensitivity analysis results showed the post-annealing temperature with negative effects is the most influential parameter on the strength bonding.

 

Keywords    Ti/Cu/Ti clad composite; Roll-bonding; Bond strength; Genetic algorithm; Artificial neural network

 

چکیده    این مقاله به مدل‌سازی و بهنیه‌سازی فرایند اتصال نوردی کامپوزیت Ti/Cu/Ti با هدف تعیین مناسب‌ترین پارمترهای منجر به حداکثر استحکام پیوند با استفاده از ترکیب شبکه عصبی و الگوریتم ژنتیک می‌پردازد. یک برنامه از شبکه‌های عصبی مصنوعی (ANNs) برای تعیین اثر پارامتر‌های اثرگذار نظیر دمای نورد، کاهش در ضخامت، زمان تابکاری اولیه، دمای تابکاری اولیه و سرعت نورد بر استحکام پیوند کامپوزیت Ti/Cu اتخاذ شد. بهترین مدل با ضریب تعیین (R2) 98/0 و میانگین مطلق خطای (MAPE) 5/3 تعیین شد و با استفاده از الگوریتم ژنتیک (GA) شرایط بهینه عملیات پیشنهاد شد. علاوه بر این، نتایج آنالیز حساسیت نشان داد که دمای تابکاری اولیه با تاثیر منفی اثرگذارترین عامل بر استحکام پیوند است.

References    1. Hosseini, M., Danesh Manesh, H. and Eizadjou, M., “Development of high-strength, good-conductivity Cu/Ti bulk nano-layered composites by a combined roll-bonding process”, Journal of Alloys and Compounds, Vol. 701, (2017), 127-130. 2. Lee, J.S., Son, H.T., Oh, I.H., Kang, C.S., Yun, C.H., Lim, S.C. and Kwon, H.C., “Fabrication and characterization of Ti–Cu clad materials by indirect extrusion”, Journal of Materials Processing Technology, Vol. 187, (2007), 653-656. 3. Kahraman, N. and Gülenç, B., “Microstructural and mechanical properties of Cu–Ti plates bonded through explosive welding process”, Journal of Materials Processing Technology, Vol. 169, No. 1, (2005), 67-71. 4. Hosseini, M., Yazdani, A. and Danesh Manesh, H., “Al 5083/SiCp composites produced by continual annealing and roll-bonding”, Materials Science and Engineering: A, Vol. 585, (2013), 415-421. 5. Long, L., Kotobu, N. and Fuxing, Y., “Progress in cold roll bonding of metals”, Science and Technology of Advanced Materials, Vol. 9, No. 2, (2008), 023001. 6. Manesh, H.D. and Shahabi, H.S., “Effective parameters on bonding strength of roll bonded Al/St/Al multilayer strips”, Journal of Alloys and Compounds, Vol. 476, No. 1, (2009), 292-299. 7. Kalidass, S. and Ravikumar, T.M., “Cutting force prediction in end milling process of aisi 304 steel using solid carbide tools”, International Journal of Engineering-Transactions A: Basics, Vol. 28, No. 7, (2015), 1074-1081. 8. Jiang, Z., Gyurova, L., Zhang, Z., Friedrich, K. and Schlarb, A.K., “Neural network based prediction on mechanical and wear properties of short fibers reinforced polyamide composites”, Materials & Design, Vol. 29, No. 3, (2008), 628-637. 9. Mahdavi Jafari, M. and Khayati, G. R., “Artificial Neural Network Based Prediction Hardness of Al2024-Multiwall Carbon Nanotube Composite Prepared by Mechanical Alloying”, International Journal of Engineering (IJE), TRANSACTIONS C: Aspetcs, Vol. 29, No. 12, (2016), 1726-1733. 10. Kalantari, Z. and Razzaghi, M., “Predicting the buckling capacity of steel cylindrical shells with rectangular stringers under axial loading by using artificial neural networks”, International Journal of Engineering-Transactions B: Applications, Vol. 28, No. 8, (2015), 1154. 11. Ramana, K.V.S., Anita, T., Mandal, S., Kaliappan, S., Shaikh, H., Sivaprasad, P.V., Dayal, R.K. and Khatak, H.S., “Effect of different environmental parameters on pitting behavior of AISI type 316L stainless steel: Experimental studies and neural network modeling”, Materials & Design, Vol. 30, No. 9, (2009), 3770-3775. 12. Ates, H., “Prediction of gas metal arc welding parameters based on artificial neural networks”, Materials & Design, Vol. 28, No. 7, (2007), 2015-2023. 13. Babaei, H., “Prediction of deformation of circular plates subjected to impulsive loading using gmdh-type neural network”, International Journal of Engineering-Transactions A: Basics, Vol. 27, No. 10, (2014), 1635-1644. 14. Zhang, X.-J., Chen, K.-Z. and Feng, X.-A., “Material selection using an improved Genetic Algorithm for material design of components made of a multiphase material”, Materials & Design, Vol. 29, No. 5, (2008), 972-981. 15. Sousa, L.C., Castro, C.F. and António, C.A.C., “Optimal design of V and U bending processes using genetic algorithms”, Journal of Materials Processing Technology, Vol. 172, No. 1, (2006), 35-41. 16.  Tsoukalas, V.D., “Optimization of porosity formation in AlSi9Cu3 pressure die castings using genetic algorithm analysis”, Materials & Design, Vol. 29, No. 10, (2008), 2027-2033. 17. Zhang, Z. and Friedrich, K., “Artificial neural networks applied to polymer composites: a review”, Composites Science and Technology, Vol. 63, No. 14, (2003), 2029-2044. 18. Chun, M.S., Biglou, J., Lenard, J.G. and Kim, J.G., “Using neural networks to predict parameters in the hot working of aluminum alloys”, Journal of Materials Processing Technology, Vol. 86, No. 1-3, (1999), 245-251. 19. Mousavi Anijdan, S.H. and Bahrami, A., “A new method in prediction of TCP phases formation in superalloys”, Materials Science and Engineering: A, Vol. 396, No. 1-2, (2005), 138-142. 20. Esmaeili, R. and Dashtbayazi, M.R., “Modeling and optimization for microstructural properties of Al/SiC nanocomposite by artificial neural network and genetic algorithm”, Expert Systems with Applications, Vol. 41, No. 13, (2014), 5817-5831. 21. Murtagh, F, “Multilayer perceptrons for classification and regression”, Neurocomputing, Vol. 2, No. 5-6, (1991), 183-197. 22. Mousavi Anijdan, S.H., Madaah-Hosseini, H.R. and Bahrami, A., “Flow stress optimization for 304 stainless steel under cold and warm compression by artificial neural network and genetic algorithm”, Materials & Design, Vol. 28, No. 2, (2007), 609-615. 23. Blanco, A., Delgado, M. and Pegalajar, M.C., “A real-coded genetic algorithm for training recurrent neural networks”, Neural Networks, Vol. 14, No. 1, (2001), 93-105. 24. Hosseini, M. and Danesh Manesh, H., “Bond strength optimization of Ti/Cu/Ti clad composites produced by roll-bonding”, Materials & Design, Vol. 81, (2015), 122-132. 25. Hosseini, S.A., Hosseini, M. and Danesh Manesh, H., “Bond strength evaluation of roll bonded bi-layer copper alloy strips in different rolling conditions”, Materials & Design, Vol. 32, No. 1, (2011), 76-81.  


Download PDF 



International Journal of Engineering
E-mail: office@ije.ir
Web Site: http://www.ije.ir