About this Abstract |
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. |