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

An interpretable learning framework for exploring superelastic degradation of NiTi shape memory alloys using multimodal data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article An interpretable learning framework for exploring superelastic degradation of NiTi shape memory alloys using multimodal data Chuanjie Wang, Yuejie Hu, Haiyang Wang, Ming Chen, Gang Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8353297/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract 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 application and broader adoption. In this study, an interpretable learning framework was proposed to predict the SED of NiTi alloys and unveil the degradation mechanisms using interpretative 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 0.9811. 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 by combining molecular dynamics simulations, 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. Physical sciences/Materials science/Theory and computation/Computational methods Physical sciences/Materials science/Structural materials/Metals and alloys Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Supplementaryinformation.pdf An interpretable learning framework for exploring superelastic degradation of NiTi shape memory alloys using multimodal data - Supplementary information Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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