modeling and optimization of roll-bonding parameters for bond strength of ti/cu/ti clad composites by artificial neural networks and genetic algorithm

This paper deals with modeling and optimization of the rollbonding process of ti/cu/ti composite for determination of the best rollbonding 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, postannealing time, postannealing 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 postannealing temperature with the negative effects is the most influential parameter on the strength of bonding.