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18 April 2022 Machine learning based 3D electrical impedance tomography for detecting damage in lattice structures
Yening Shu, Said Quqa, Kenneth J. Loh
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Proceedings Volume PC12047, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVI; (2022) https://doi.org/10.1117/12.2612739
Event: SPIE Smart Structures + Nondestructive Evaluation, 2022, Long Beach, California, United States
Abstract
The objective of this study was to implement a supervised machine learning method that utilizes the radial basis function neural network for 3D electrical impedance tomography conductivity distribution reconstruction of complex cellular lattice structures. This data-driven algorithm, which was trained by a variety of damaged cases, is significantly faster than conventional EIT while enabling greater accuracy of 3D conductivity distribution reconstruction. Both numerical simulations and experimental results are presented in this work, and the machine learning based EIT results are compared with those obtained using conventional EIT.
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Yening Shu, Said Quqa, and Kenneth J. Loh "Machine learning based 3D electrical impedance tomography for detecting damage in lattice structures", Proc. SPIE PC12047, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVI, (18 April 2022); https://doi.org/10.1117/12.2612739
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KEYWORDS
Machine learning
Tomography
Nondestructive evaluation
3D acquisition
3D metrology
Additive manufacturing
Electrodes
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