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SQuID™ aims to provide an exploratory data analysis and Auto ML framework for surface classification

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SQuID™

Surface Quality and Inspection Descriptions


SQuID™ aims to provide an exploratory data analysis and Auto ML framework for surface classification.

Try out the beta version at the link below.

https://jesse-redford-squid-main-otrfh7.streamlit.app/

Need Help?


Shoot me an email at tredford@charlotte.edu

Paper & Citation


Construction of a multi-class discrimination matrix and systematic selection of areal texture parameters for quantitative surface and defect classification

https://authors.elsevier.com/sd/article/S0278-6125(23)00152-8

@article{REDFORD2023131,
title = {Construction of a multi-class discrimination matrix and systematic selection of areal texture parameters for quantitative surface and defect classification},
journal = {Journal of Manufacturing Systems},
volume = {71},
pages = {131-143},
year = {2023},
issn = {0278-6125},
doi = {https://doi.org/10.1016/j.jmsy.2023.08.002},
url = {https://www.sciencedirect.com/science/article/pii/S0278612523001528},
author = {Jesse Redford and Brigid Mullany},
keywords = {Surface characterization, Feature selection, Defect classification},
}

To cite this repository in publications:

@misc{SQuID2022,
author = {Jesse Redford},
title = {Surface Quality and Inspection Descriptions},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository}
howpublished = {\url{https://github.com/Jesse-Redford/SQuID.git}}}

Copyright (c) 2022, Jesse Redford.

Resources


ISO 25178-2

Zygo MX Parameters

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