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

  • wang, dali | Oak Ridge National Laboratory
  • Zhou, Jian | Oak Ridge National Laboratory
  • Chen, Jian | Oak Ridge National Laboratory
  • Feng, Zhili | Oak Ridge National Laboratory
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Overview

Description

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.

Funding Resources

DOE Contract Number

34715860

Originating Research Organization

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization

Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)

Project Identifier

311L0207

Details

DOI

10.13139/OLCF/1559947

Release Date

September 4, 2019

Dataset

Dataset Type

IP Still Images or Photos

Acknowledgements

Users should acknowledge the OLCF in all publications and presentations that speak to work performed on OLCF resources:

This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

Category

  • 42 ENGINEERING

Keywords

  • Non-destructive evaluation,
  • SPOT welding,
  • Deep neural network,
  • Autonomous detection