Damage mapping via electrical impedance tomography in complex AM shapes using mixed smoothness and Bayesian regularization

Abstract

Additive manufacturing (AM) holds immense potential for rapidly producing complexly-shaped composite components and structures. However, AM is also well known for producing composites of lesser quality than more traditional manufacturing methods. Hence, nondestructive evaluation (NDE) and/or embedded sensing (i.e. integrated NDE) is important for mitigating failures in these materials. Unfortunately, many prevailing NDE modalities are ill-suited for complex AM-produced shapes or are not amenable to embedded sensing. To overcome this limitation, we herein study electrical impedance tomography (EIT) as a means of detecting and localizing damage in a complexly-shaped carbon fiber composite truss structure produced by Impossible Objects. Unlike the current state of the art which is overly fixated on applying EIT to flat composite plates not representative of real components and structures, this work shows that EIT can indeed adeptly find damages in complex shapes. We also present a novel mixed smoothness + conditionally Gaussian regularization formulation for the EIT inverse problem that shows marked improvement over the traditionally used smoothness-only prior. Combined, these two contributions are an important step in translating EIT into practice for AM composites.