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Scanned Image Data from 3D-Printed Specimens Using Fused Deposition Modeling

Author

Listed:
  • Felix W. Baumann

    (Institute of Computer-aided Product Development Systems, University of Stuttgart, 70569 Stuttgart, Germany)

  • Julian R. Eichhoff

    (Institute of Computer-aided Product Development Systems, University of Stuttgart, 70569 Stuttgart, Germany)

  • Dieter Roller

    (Institute of Computer-aided Product Development Systems, University of Stuttgart, 70569 Stuttgart, Germany)

Abstract

This dataset provides high-resolution 2D scans of 3D printed test objects (dog-bone), derived from EN ISO 527-2:2012. The specimens are scanned in resolutions from 600 dpi to 4800 dpi utilising a Konica-Minolta bizHub 42 and Canon LiDE 210 scanner. The specimens are created to research the influence of the infill-pattern orientation; The print orientation on the geometrical fidelity and the structural strength. The specimens are printed on a MakerBot Replicator 2X 3D-printer using yellow (ABS 1.75 mm Yellow, REC, Moscow, Russia) and purple ABS plastic (ABS 1.75 mm Pink Lion&Fox, Hamburg, Germany). The dataset consists of at least one scan per specimen with the measured dimensional characteristics. For this, software is created and described within this work. Specimens from this dataset are either scanned on blank white paper or on white paper with blue millimetre marking. The printing experiment contains a number of failed prints. Specimens that did not fulfil the expected geometry are scanned separately and are of lower quality due to the inability to scan objects with a non-flat surface. For a number of specimens printed sensor data is acquired during the printing process. This dataset consists of 193 specimen scans in PNG format of 127 objects with unadjusted raw graphical data and a corresponding, annotated post-processed image. Annotated data includes the detected object, its geometrical characteristics and file information. Computer extracted geometrical information is supplied for the images where automated geometrical feature extraction is possible.

Suggested Citation

  • Felix W. Baumann & Julian R. Eichhoff & Dieter Roller, 2017. "Scanned Image Data from 3D-Printed Specimens Using Fused Deposition Modeling," Data, MDPI, vol. 2(1), pages 1-24, January.
    • Handle: RePEc:gam:jdataj:v:2:y:2017:i:1:p:3-:d:86711
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    References listed on IDEAS

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    1. Asif Equbal & Anoop Kumar Sood & S.S. Mahapatra, 2011. "Prediction of dimensional accuracy in fused deposition modelling: a fuzzy logic approach," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 7(1), pages 22-43.
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    Cited by:

    1. Chun Fai Lui & Ahmed Maged & Min Xie, 2024. "A novel image feature based self-supervised learning model for effective quality inspection in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3543-3558, October.

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