Skip to main content
SpringerLink
Log in

Defect Identification and Mitigation Via Visual Inspection in Large-Scale Additive Manufacturing

  • Solid Freeform Fabrication
  • Published:
JOM Aims and scope Submit manuscript

Abstract

Defect identification and mitigation is an important avenue of research to improve the overall quality of objects created using additive manufacturing (AM) technologies. Identifying and mitigating defects takes on additional importance in large-scale, industrial AM. In large-scale AM, defects that result in failed prints are extremely costly in terms of time spent and material used. To address these issues, researchers at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility investigated the use of a laser profilometer and thermal camera to collect data concerning an object as it was constructed. These data provided feedback for an in situ control system to adjust object construction. Adjustments were made in the form of automated height control. This paper presents results for both a polymer- and metal-based system. Object construction for both systems was improved significantly, and the resulting objects were more geometrically identical to the ideal 3D representation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. S.K. Everton, M. Hirsh, P. Stravroulakis, R.K. Leach, and A.T. Clare, Mater. Des. 95, 431 (2016).

    Article  Google Scholar 

  2. F. Calignano, D. Manfredi, E. Ambrosio, S. Biamino, M. Lombardi, E. Atzeni, A. Salmi, P. Minetola, L. Iuliano, and P. Fino, Proc. IEEE 105, 593 (2017).

    Article  Google Scholar 

  3. Z. Zhu, N. Anwer, and L. Mathieu, Procedia CIRP 60, 211 (2017).

    Article  Google Scholar 

  4. T.A. Book and M.D. Sangid, JOM 68, 1780 (2016).

    Article  Google Scholar 

  5. F. Baumann and D. Roller, MATEC Web Conference. (2016). https://doi.org/10.1051/matecconf/20165906003.

  6. J. Straub, Machines 3, 55 (2015).

    Article  Google Scholar 

  7. M. Faes, F. Vogeler, K. Coppens, H. Valkenaers, E. Ferraris, W. Abbeloos and T. Goedeme, arXiv preprint. (2016) arXiv:1612.02219.

  8. S. Kleszczynski, J. zur Jacobsmuhlen, G. Witt, and D. Merhof, Improving Process Stability of Laser Beam. Fraunhofer Direct Digital Manufacturing Conference (2014)

  9. J. zur Jacobsmuhlen., S. Kleszczynski, D. Schneider, and G. Witt. IEEE Instrumentation and Measurement Technology Conference. (2013) https://doi.org/10.1109/i2mtc.2013.6555507.

  10. S. Hurd, C. Camp, and J. White, International Conference on Mobile Computing, Applications, and Services. (2015). https://doi.org/10.1007/978-3-319-29003-4_12.

  11. J. Ferguson, “Additive Manufacturing Defect Detection using Neural Networks” (SemanticScholar), https://www.semanticscholar.org/paper/Additive-Manufacturing-Defect-Detection-using-Ferguson/655df04cb172fb2c5e7c49261dcd0840fd8f674e. Accessed 9 Sept 2018.

  12. G. Bradski, “The OpenCV Library” (Dr. Dobb’s Journal of Software Tools, 2000). http://www.drdobbs.com/open-source/the-opencv-library/184404319. Accessed 9 Sept 2018.

  13. Y. M. Amir and B. Thornberg, Int. J. Opt. (2017). https://doi.org/10.1155/2017/4134205.

Download references

Acknowledgements

This material is based upon work supported by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Office of Advanced Manufacturing, under Contract Number DE-AC05-00OR22725. The authors would also like to acknowledge the contributions of Christy White in the preparation of this work.

Author information

Authors and Affiliations

  1. Manufacturing Demonstration Facility, Oak Ridge National Laboratory, Knoxville, TN, 37932, USA

    Michael Borish, Brian K. Post, Alex Roschli, Phillip C. Chesser, Lonnie J. Love & Katherine T. Gaul

Authors
  1. Michael Borish
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brian K. Post
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alex Roschli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Phillip C. Chesser
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lonnie J. Love
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Katherine T. Gaul
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael Borish.

Additional information

This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Borish, M., Post, B.K., Roschli, A. et al. Defect Identification and Mitigation Via Visual Inspection in Large-Scale Additive Manufacturing. JOM 71, 893–899 (2019). https://doi.org/10.1007/s11837-018-3220-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11837-018-3220-6

Search

Navigation