On the use of physics in machine learning for manufacturing process inspection
Name
126220Y.pdf
Description
Published version
Size
2.15 MB
Format
Adobe PDF
Checksum (MD5)
6ae9005c8c52c58cbf375c74998c4061
Author(s)
Barbastathis, George
Zhang, Qihang
Pandit, Ajinkya
Tang, Wenlong
Papageorgiou, Charles
Braatz, Richard
Myerson, Allan S
Tan, Bingyao
Schmetterer, Leopold
Date Issued
August 11, 2023
Journal
Optical Methods for Inspection, Characterization, and Imaging of Biomaterials VI
Publisher
SPIE
Citation
Proceedings Volume 12622, Optical Methods for Inspection, Characterization, and Imaging of Biomaterials VI; 126220Y (2023).
Version
Final published version
Abstract
We discuss the use of machine learning in computational imaging for manufacturing process inspection and control. In a recent article we described a physics-enhanced auto-correlation based estimator (Peace) for quantitative speckle. We derived an explicit forward relationship between the Particle Size Distribution (PSD) and the speckle autocorrelation for particle sizes significantly larger than the wavelength (x100 to approximately x1,000). We subsequently trained a machine learning kernel to invert the autocorrelation and obtain the PSD, using the explicit forward model to reduce the number of experimentally acquired examples. In this talk, we present an expanded discussion of Peace and its properties, including spatial and temporal sampling and accuracy, and more general applications.
Description
SPIE Optical Metrology, 2023, Munich, Germany
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Department of Chemical Engineering
Singapore-MIT Alliance in Research and Technology (SMART)
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1117/12.2678259
