Genetic algorithm based inverse analysis for the superplastic characterization of a Ti-6Al-4V biomedical grade

Abstract

The inverse analysis approach is recently spreading as a valuable methodology to calibrate constitutive models describing the superplastic behaviour of light alloys. In this work, the superplastic behaviour of a titanium grade for biomedical applications was characterized by means of a Genetic Algorithm based inverse analysis. Free inflation tests at 850 °C were carried out on the Ti-6Al-4V Extra Low Interstitial alloy at two different levels of constant pressure; in addition, a jump pressure test in which the gas pressure was changed during the same test between the same levels was performed. The dome height curve was acquired during each test and subsequently adopted as target data: a simple 2D Finite Element model, in which the material behaviour was implemented using the Backofen power law, was then created to calibrate the material constitutive equation by means of an inverse analysis approach. A set of constants, able to minimize the error between numerical and experimental data, was determined for each investigated load condition. Subsequent numerical simulations, run for validation purposes, demonstrated that the sets determined using as target the data from the free inflation tests under a constant level of pressure were capable of effectively describing the material behaviour only for load conditions close to the ones from which they had been determined. On the contrary, the set of material constants obtained using the same inverse approach but adopting as target data the acquisitions from the jump pressure test, revealed to describe the material behaviour over a wider span of operative conditions.