Data-driven investigation of three-dimensional instabilities in magneto-active thin films heterogeneously patterned by design – InstaMATiF

We propose to investigate and exploit experimentally, theoretically, numerically and by data-integrated analysis, the instabilities inherently present in magnetorheological elastomer (MRE)-based materials and structures. MREs are smart materials comprising magnetizable particles embedded in a polymeric matrix that can be actuated by a magnetic field. Following earlier works, we plan to create and harness reversible on-demand remotely-controllable 3D surface patterns by imparting non-uniform magneto-mechanical properties on an MRE film resting on a passive or MRE substrate. We aim at achieving this by exploiting liquid additive manufacturing and, in parallel, by developing theoretical and computational tools to analyze the post-bifurcated MRE film patterns under coupled magneto-mechanical loading. The experimental, theoretical and computational information will be synthesized in a data-driven approach using machine learning in order to allow rapid forward predictions of the instabilities as well as for solving the inverse problem. Such developments are crucial for our ultimate goal of designing targeted patterns via machine learning, which could remarkably alleviate the computational burden of numerical design studies.