Terahertz Nondestructive evaluation Techniques for Industrial Applications and Imaging
Author(s)
Shi, Haolian
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Abstract
This thesis is dedicated to advancing Terahertz (THz) technology for nondestructive evaluation and imaging, with specific applications in industrial manufacturing and cultural heritage conservation. The core of this work lies in the development and application of signal processing and machine learning techniques to overcome the limitations of conventional THz analysis.
In the industrial applications, the research first establishes a framework for comparing deconvolution methods to precisely measure mill scale thickness on steel, providing guidance for method selection under different conditions. Furthermore, a neural network model that accurately estimates thickness is trained, and a novel filtered deconvolution technique that improves signal clarity and simplifies analysis is proposed.
For electronics inspection, THz imaging demonstrates high sensitivity in mapping the thickness of conformal coatings on circuit boards and in identifying hidden defects within a complex multi-layer interposer. By combining deconvolution with polarization analysis and unsupervised machine learning, the study successfully locates and characterizes various defect types, validated by X-ray imaging.
Addressing a challenge in cultural heritage, the thesis reformulates the problem of detecting iron gall ink on multi-layer documents from a low-contrast imaging task into a classification problem. A convolutional neural network, trained with co-teaching which is a strategy to handle noisy labels, is developed to reliably identify ink patterns on single and multi-layer paper stacks, revealing features not discernible in traditional time or frequency domain images.
Collectively, this research underscores the significant potential of integrating advanced algorithmic approaches with THz technology to push the boundaries of precision and reliability in nondestructive testing across diverse fields.
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Date
2025-12
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Dissertation (PhD)