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Title: A Labelled training and testing dataset for autonomous non-destructive evaluation for Resistance SPOT welding

Abstract

This dataset contains all the training and testing data generated from 90 videos (over seven sets of welding material stack-ups). A new method is developed to assemble sufficient datasets from these videos for neural network training. These data also contains the ground truth on the weld nuggets, derived from the post-weld measurement and video conversion ratios. More specific technical details can be found in the manuscript: Jian Zhou, Dali Wang, Jian Chen, Zhili Feng. (2019), Autonomous non-destructive evaluation of resistance Spot Welded Joints.

Authors:
; ; ;
Publication Date:
DOE Contract Number:  
34715860
Product Type:
Dataset
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
Subject:
42 ENGINEERING
Keywords:
Non-destructive evaluation, SPOT welding, Deep neural network, Autonomous detection
OSTI Identifier:
1559947
DOI:
https://doi.org/10.13139/OLCF/1559947

Citation Formats

wang, dali, Zhou, Jian, Chen, Jian, and Feng, Zhili. A Labelled training and testing dataset for autonomous non-destructive evaluation for Resistance SPOT welding. United States: N. p., 2019. Web. doi:10.13139/OLCF/1559947.
wang, dali, Zhou, Jian, Chen, Jian, & Feng, Zhili. A Labelled training and testing dataset for autonomous non-destructive evaluation for Resistance SPOT welding. United States. doi:https://doi.org/10.13139/OLCF/1559947
wang, dali, Zhou, Jian, Chen, Jian, and Feng, Zhili. 2019. "A Labelled training and testing dataset for autonomous non-destructive evaluation for Resistance SPOT welding". United States. doi:https://doi.org/10.13139/OLCF/1559947. https://www.osti.gov/servlets/purl/1559947. Pub date:Wed Sep 04 00:00:00 EDT 2019
@article{osti_1559947,
title = {A Labelled training and testing dataset for autonomous non-destructive evaluation for Resistance SPOT welding},
author = {wang, dali and Zhou, Jian and Chen, Jian and Feng, Zhili},
abstractNote = {This dataset contains all the training and testing data generated from 90 videos (over seven sets of welding material stack-ups). A new method is developed to assemble sufficient datasets from these videos for neural network training. These data also contains the ground truth on the weld nuggets, derived from the post-weld measurement and video conversion ratios. More specific technical details can be found in the manuscript: Jian Zhou, Dali Wang, Jian Chen, Zhili Feng. (2019), Autonomous non-destructive evaluation of resistance Spot Welded Joints.},
doi = {10.13139/OLCF/1559947},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}