Micromechanisms of phase transformation in NiTi shape memory alloys

Resumen

A unified framework to design shape memory functional active devices with optimised performance is proposed. The framework combines micromechanics fundamentals of shape memory alloys (SMAs), phase transformation theory, continuum mechanics principles and topology optimisation, combined all together by a machine learning layer applied to the design of smart functional devices. First, the micromechanical principles of phase transformation in SMAs are studied and included in a thermodynamically consistent continuum framework. Second, this framework is custom implemented in finite element software in an implicit fashion. Third, the model is used to study the performance of shape memory aortic stent devices. The design requirements and materials de geometrical limits are extracted from the literature and technical specifications. The constitutive FE model is combined with machine learning tools to generate time-efficient subrogate models. These models are used to understand the influence of the stent design and the SMA on the device performance. Finally, an optimised STENT design and SMA material arise from the subrogate model with a 35% superior stretching performance and 60% less max. stress and fatigue strain, thus a reduced risk of fatigue and mechanical failure while maintaining the stretching performance of the aortic tissue.