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1. Knife-edge interferogram analysis for corrosive wear propagation at sharp edges

   https://doi.org/10.1364/AO.417572  

   Wang, Zhikun; Lee, ChaBum (February 2021, Applied Optics)

   This paper presents a novel noncontact measurement and inspection method based on knife-edge diffraction theory for corrosive wear propagation monitoring at a sharp edge. The degree of corrosion on the sharp edge was quantitatively traced in process by knife-edge interferometry (KEI). The measurement system consists of a laser diode, an avalanche photodiode, and a linear stage for scanning. KEI utilizes the interferometric fringes projected on the measurement plane when the light is incident on a sharp edge. The corrosion propagation on sharp edges was characterized by analyzing the difference in the two interferometric fringes obtained from the control and measurement groups. By using the cross-correlation algorithm, the corrosion conditions on sharp edges were quantitatively quantified into two factors: lag and similarity for edge loss and edge roughness, respectively. The KEI sensor noise level was estimated at 0.03% in full scale. The computational approach to knife-edge diffraction was validated by experimental validation, and the computational error was evaluated at less than 1%. Two sets of razor blades for measurement and control groups were used. As a result, the lag will be increased at an edge loss ratio of 1.007/µm due to the corrosive wear, while the similarity will be decreased at a ratio of $5.4\times <\#comment/>{10}^{-<\#comment/>4}/\text{µ<\#comment/>}\mathrm{m}$with respect to edge roughness change. Experimental results showed a good agreement with computational results. 

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2. MOSS: AI Platform for Discovery of Corrosion-Resistant Materials

   Yin, B; Josselyn, N; Zhang, Z; Rundensteiner, E; Considine, T; Kelley, J; Rinderspacher, B; Jensen, R; Snyder, J (October 2023, Association for Computing Machinery)

   Amid corrosion degradation of metallic structures causing expenses nearing 3 trillion or 4% of the GDP annually along with major safety risks, the adoption of AI technologies for accelerating the materials science life-cycle for developing materials with better corrosive properties is paramount. While initial machine learning models for corrosion assessment are being proposed in the literature, their incorporation into end-to-end tools for field experimentation by corrosion scientists remains largely unexplored. To fill this void, our university data science team in collaboration with the materials science unit at the Army Research Lab have jointly developed MOSS, an innovative AI-based digital platform to support material science corrosion research. MOSS features user-friendly iPadOS app for in-field corrosion progression data collection, deep-learning corrosion assessor, robust data repository system for long-term experimental data modeling, and visual analytics web portal for material science research. In this demonstration, we showcase the key innovations of the MOSS platform via use cases supporting the corrosion exploration processes, with the promise of accelerating the discovery of new materials. We open a MOSS video demo at: https://www.youtube.com/watch?v=CzcxMMRsxkE 

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3. Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images.

   https://doi.org/10.1038/s41524-020-00363-x  

   Horwath, James P.; Zakharov, Dmitri N.; Mégret, Remi; Stach, Eric A. (July 2020, npj computational materials)

   null (Ed.)

   Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning meaningful image features that would enable extension to new datasets. In situ electron microscopy provides a clear platform for utilizing automated image analysis. In this work, we consider the case of studying coarsening dynamics in supported nanoparticles, which is important for understanding, for example, the degradation of industrial catalysts. By systematically studying dataset preparation, neural network architecture, and accuracy evaluation, we describe important considerations in applying deep learning to physical applications, where generalizable and convincing models are required. With a focus on unique challenges that arise in high-resolution images, we propose methods for optimizing performance of image segmentation using convolutional neural networks, critically examining the application of complex deep learning models in favor of motivating intentional process design. 

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4. Transferring Indoor Corrosion Image Assessment Models to Outdoor Images via Domain Adaptation

   https://doi.org/10.1109/ICMLA55696.2022.00219  

   Josselyn, Nicholas; Yin, Biao; Considine, Thomas; Kelley, John; Rinderspacher, Berend; Jensen, Robert; Snyder, James; Zhang, Ziming; Rundensteiner, Elke (December 2022, 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA))

   Corrosion of materials impacts critical economic sectors from infrastructure, transportation, defense, health, to the environment. The development of safe anti-corrosive materials is thus an important area of study in materials science. Corrosion science of preparing materials and then monitoring their corrosion under adverse conditions is labor intensive, time consuming, and extremely costly. While deep learning has become popular in automating various engineering tasks, the development of deep models for corrosion assessment is lacking. We are the first to study deep domain adaptation (DA) models for the automated assessment of the corrosion status of anti-corrosive materials. Corrosion data, i.e., photographic images of treated corroding materials, is abundant when produced in artificially controlled laboratory settings, while corrosion image data sets from rich natural outdoor environments are more challenging to produce and thus much smaller. We leverage the more readily available indoor corrosion data to train a classifier and then transfer it via deep domain adaptation to also perform well on the small yet more realistic outdoor corrosion image data set – without requiring target labels. We empirically compare 5 popular domain adaptation models on real-world corrosion image data sets. Our study finds that DA achieves 27% improvement in test accuracy compared to the performance of the no-DA baseline for classifying real-world outdoor corrosion data. 

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5. A deep learned nanowire segmentation model using synthetic data augmentation

   https://doi.org/10.1038/s41524-022-00767-x  

   Lin, Binbin; Emami, Nima; Santos, David A.; Luo, Yuting; Banerjee, Sarbajit; Xu, Bai-Xiang (April 2022, npj Computational Materials)

   Abstract Automated particle segmentation and feature analysis of experimental image data are indispensable for data-driven material science. Deep learning-based image segmentation algorithms are promising techniques to achieve this goal but are challenging to use due to the acquisition of a large number of training images. In the present work, synthetic images are applied, resembling the experimental images in terms of geometrical and visual features, to train the state-of-art Mask region-based convolutional neural networks to segment vanadium pentoxide nanowires, a cathode material within optical density-based images acquired using spectromicroscopy. The results demonstrate the instance segmentation power in real optical intensity-based spectromicroscopy images of complex nanowires in overlapped networks and provide reliable statistical information. The model can further be used to segment nanowires in scanning electron microscopy images, which are fundamentally different from the training dataset known to the model. The proposed methodology can be extended to any optical intensity-based images of variable particle morphology, material class, and beyond. 

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