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Meeting Materials Science & Technology 2020
Symposium Materials Informatics for Images and Multi-dimensional Datasets
Presentation Title Automated Optical Microscopy for Rapid Defect Screening
Author(s) Andrew R. Kitahara, Elizabeth A. Holm
On-Site Speaker (Planned) Andrew R. Kitahara
Abstract Scope Quality control processes in manufacturing settings may utilize optical microscopy as a primary product screening tool for quickly identifying obvious defects in surface microstructure. We present the outcomes of a project to automate this process in a manufacturing setting for high-throughput screening by utilizing a pre-trained convolutional neural network that was fine-tuned for defect classification. The classifier works in real-time using the microscope camera video output, and the motorized stage is used to manipulate the sample such that entire specimens can be analyzed with minimal human interaction. The primary goal is to reduce the human expert’s time expense on an easily automated task to allow more time to address more significant challenges. But more than this, we propose that this platform can be applied in academic research settings as a rapid data acquisition tool to complement the growing interest in materials informatics research disciplines.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Accelerate TEM and Tomography Imaging by Deep-learning Enabled Compressive Sensing and Information Inpainting in High-dimensional Manifold
Assessment of the Ability of Laboratory Accelerated Corrosion Tests to Accurately Predict On-road Corrosion of 6xxx Al Alloys
Automated Optical Microscopy for Rapid Defect Screening
Computer Vision and Machine Learning for Microstructural Image Data
Developing Granular Dielectrics Based on Reconstructed Micro-CT Images
FAIR Digital Object Framework and High Throughput Experiment
Feature Characterization of Electron Backscatter Patterns from Rotating Lattice Single Crystals Using Machine Learning
Identifying Crack Initiation Sites with CNNs
Incorporating Materials Physics into Imaging Algorithms for Microscope Image Interpretation
Introductory Comments: Materials Informatics for Images and Multi-dimensional Datasets
Keyhole Porosity Threshold in Laser Melting Revealed by High-Speed X-ray Imaging
Microstructure Representation for Physically Meaningful Descriptors
Neural Networks and Community Driven Software for Scanning Transmission Electron Microscopy
Towards Smart Categorization of Growth Morphology by Machine Learning

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