An interpretable learning framework for exploring superelastic degradation of NiTi shape memory alloys using multimodal data.

Superelastic degradation (SED), a progressive loss of functionality in NiTi shape memory alloys under cyclic loading, remains challenging to characterize precisely, thereby constraining their engineering applications and broader adoption. In this study, an interpretable learning framework was proposed to predict the SED of NiTi alloys and uncover the degradation mechanisms using interpretability analysis methods. The framework incorporates multi-source microstructure and loading conditions through a multi-branch architecture that effectively decouples and integrates heterogeneous features, achieving an R2 of 0.981. The competition between slip and transformation was identified: at high amplitudes, SED is dominated by transformation regions with high Schmid factors, whereas at low amplitudes, dislocation slip on the {011}〈001〉 and {011}〈111〉 systems prevails. Subsequently, the influence of Ni4Ti3 precipitates was quantified, revealing a loading-dependent and non-uniformly beneficial role. The results highlight the potential of interpretable machine learning in exploring the cyclic deformation process and pave the way for AI-driven research on smart materials.