Modeling and Optimization of Roll-bonding Parameters for Bond Strength of Ti/Cu/Ti Clad Composites by Artificial Neural Networks and Genetic Algorithm

Authors

1 Department of Materials Science and Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

2 Department of Mechanical Engineering, Faculty of Engineering, University of Hormozgan, Bandar Abbas, Iran

3 Department of Materials Science and Engineering, School of Engineering, Shiraz University, Shiraz, Iran

Abstract

This paper deals with modeling and optimization of the roll-bonding process of Ti/Cu/Ti composite for determination of the best roll-bonding parameters leading to the maximum Ti/Cu bond strength by combination of neural network and genetic algorithm. An artificial neural network (ANN) program has been 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 correlation coefficient (R2) of 0.98 and mean absolute error (MAPE) 3.5 was determined using genetic algorithm (GA) and the optimum practice condition are proposed. Moreover, the sensitivity analysis results showed the post-annealing temperature with the negative effects is the most influential parameter on the strength of bonding.

Keywords


1.     Hosseini, M., Manesh, H.D. 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, No., (2017), 127-130.

2.     Lee, J., Son, H., Oh, I., Kang, C., Yun, C., Lim, S. and Kwon, H., "Fabrication and characterization of Ti–Cu clad materials by indirect extrusion", Journal of Materials Processing Technology,  Vol. 187, No., (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 Manesh, H.D., "Al 5083/sic p composites produced by continual annealing and roll-bonding", Materials Science and Engineering: A,  Vol. 585, (2013), 415-421.

5.     Li, L., Nagai, K. and Yin, F., "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), 1154-1163.

11.   Ramana, K., Anita, T., Mandal, S., Kaliappan, S., Shaikh, H., Sivaprasad, P., Dayal, R. and Khatak, H., "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., Castro, C. and Antonio, 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., "Optimization of porosity formation in alsi 9 cu 3 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., Biglou, J., Lenard, J. and Kim, J., "Using neural networks to predict parameters in the hot working of aluminum alloys", Journal of Materials Processing Technology,  Vol. 86, No. 1, (1999), 245-251.

19.   Anijdan, S.M. and Bahrami, A., "A new method in prediction of tcp phases formation in superalloys", Materials Science and Engineering: A,  Vol. 396, No. 1, (2005), 138-142.

20.   Mahdavi Jafari, M., Soroushian, S. and Khayati, G.R., "Hardness optimization for al6061-mwcnt nanocomposite prepared by mechanical alloying using artificial neural networks and genetic algorithm", Journal of Ultrafine Grained and Nanostructured Materials,  Vol. 50, No. 1, (2017), 23-32.

21.   Murtagh, F., "Multilayer perceptrons for classification and regression", Neurocomputing,  Vol. 2, No. 5, (1991), 183-197.

22.   Anijdan, S.M., Madaah-Hosseini, H. 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 Manesh, H.D., "Bond strength optimization of ti/cu/ti clad composites produced by roll-bonding", Materials & Design,  Vol. 81, (2015), 122-132.

25.   Hosseini, S., Hosseini, M. and Manesh, H.D., "Bond strength evaluation of roll bonded bi-layer copper alloy strips in different rolling conditions", Materials & Design,  Vol. 32, No. 1, (2011), 76-81.