Optimization of mechanical property and shape recovery behavior of Ti-(∼49 at.%) Ni alloy using artificial neural network and genetic algorithm

Multi-objective genetic algorithm based searching is used for designing the process schedule of Ti-(∼49 at.%) Ni alloy, to achieve optimum mechanical property and shape recovery behavior. Artificial neural network technique based data driven models are developed to empirically describe the relationship between the processing conditions and the properties. The models are used as objective functions for the optimization process. The optimization search found to be helpful to design the decision space variables for the improvement in shape recovery behavior without sacrificing the mechanical properties of the alloy. The Pareto solutions have been used as the guideline to find the process schedules, which is validated by suitable experimentation.

► Maximizing the recoverable strain by optimizing process