Artificial intelligence and machine learning
Published December 30, 2020 | Version 1.0
Dataset Open

Point Cloud Object Shape Error Datasets for Root Cause Analysis of Multi-Station Assembly Systems

Authors/Creators

  • 1. WMG, University of Warwick

Description

The dataset consists of supervised shape error datasets (point clouds) and corresponding process parameters. It is genrated using the Variation Response Method (VRM) kernel. The dataset can be used for training deep learning frameworks to test performance for Root Cause Analysis (RCA) of Multi-Station Assembly Systems. The python library for implementation of the work can be found at this link: https://github.com/sumitsinha/Deep_Learning_for_Manufacturing 

Files

CAE_AI_Assembly_datasets.zip

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Additional details

Related works

Is derived from
Journal article: 10.1109/TII.2020.3043226 (DOI)
Journal article: 10.1016/j.finel.2019.103319 (DOI)

References

  • S. Sinha, P. Franciosa, and D. Ceglarek, "Object Shape Error Response using Bayesian 3D Convolutional Neural Networks for Assembly Systems with Compliant Parts," IEEE Trans. Ind. Informatics, 2020, doi: 10.1109/TII.2020.3043226.
  • S. Sinha, E. Glorieux, P. Franciosa, and D. Ceglarek, "3D convolutional Neural networks to estimate assembly process parameters using 3D point-clouds," in Proceedings of SPIE, 2019, vol. 11059, doi: 10.1117/12.2526062.
  • P. Franciosa, M. Sokolov, S. Sinha, T. Sun, and D. Ceglarek, "Deep learning enhanced digital twin for Closed-Loop In-Process quality improvement," CIRP Ann., vol. 69, no. 1, pp. 369–372, Jan. 2020, doi: 10.1016/j.cirp.2020.04.110.
  • P. Franciosa et al., "A novel hybrid shell element formulation (QUAD+ and TRIA+):A benchmarking and comparative study," Finite Elem. Anal. Des., vol. 166, 2019, Art. no. 103319.
  • S. Sinha, P. Franciosa, and D. Ceglarek, "Bayesian deep learning for manufacturing," 2020. [Online]: Available. https://github.com/sumitsinha/Deep_Learning_for_Manufacturing

Subjects

Object Shape Error Response (OSER)
10.1109/TII.2020.3043226
Variation Response Method (VRM)
10.1016/j.finel.2019.103319