|About this Abstract
||7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
||Automated Analysis Pipeline to Investigate Bond-wire Corrosion Under Salt-water Exposure
||Jayvic Cristian Jimenez, Liangyi Huang, Kristen J. Hernandez, Harsha Madiraju, Pawan K. Tripathi, Alp Sehirlioglu, Roger H. French
|On-Site Speaker (Planned)
||Roger H. French
X-ray computed tomography (XCT) is a powerful tool for studying corrosion of commercial bond-wires. This investigation amasses large amounts of image data. Analysis of a 3D-rendered object can be computationally costly and time-consuming, while performing the task manually is impractical. The cylindrical geometry of commercial bond-wires is challenging to characterize as 3D renders, adding to the computational complexity of the analysis. Developments in computer vision tools, which leverage convolutional neural networks (CNNs) are computationally efficient and fast, making them desirable tools for automated feature extraction. In our work, we demonstrate an automated workflow for transforming a cylindrical object into a 2D representation and performing background denoising that allows for full surface view for further characterization. We integrated semantic segmentation algorithms such as DeepLab into our workflow pipeline allowing for further characterization of the surface features of the bond-wires. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.