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Profile monitoring based quality control method for fused deposition modeling process

Author

Listed:
  • Ketai He

    (University of Science and Technology Beijing)

  • Qian Zhang

    (University of Science and Technology Beijing)

  • Yili Hong

    (Virginia Tech)

Abstract

In order to monitor the quality of parts in printing, the methodology to monitor the geometric quality of the printed parts in fused deposition modeling process is researched. A non-contact measurement method based on machine vision technology is adopted to obtain the precise complete geometric information. An image acquisition system is established to capture the image of each layer of the part in building and image processing technology is used to obtain the geometric profile information. With the above information, statistical process control method is applied to monitor the geometric quality of the parts during the printing process. Firstly, a border signature method is applied to transform complex geometry into a simple distance-angle function to get the profile deviation data. Secondly, monitoring of the profile deviation data based on profile monitoring method is studied and applied to achieve the goal of layer-to-layer monitoring. In the research, quantile-quantile plot method is used to transform the profile deviation point cloud data monitoring problem into a linear profile relationship monitoring problem and EWMA control charts are established to monitor the parameters of the linear relationship to detect shifts occurred in the Fused Deposition Modeling process. Finally, laboratory experiments are conducted to demonstrate the effectiveness of the proposed approach.

Suggested Citation

  • Ketai He & Qian Zhang & Yili Hong, 2019. "Profile monitoring based quality control method for fused deposition modeling process," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 947-958, February.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:2:d:10.1007_s10845-018-1424-9
    DOI: 10.1007/s10845-018-1424-9
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    References listed on IDEAS

    as
    1. Ketai He & Min Zhang & Ling Zuo & Theyab Alhwiti & Fadel M. Megahed, 2017. "Enhancing the monitoring of 3D scanned manufactured parts through projections and spatiotemporal control charts," Journal of Intelligent Manufacturing, Springer, vol. 28(4), pages 899-911, April.
    2. Qiang Huang & Jizhe Zhang & Arman Sabbaghi & Tirthankar Dasgupta, 2015. "Optimal offline compensation of shape shrinkage for three-dimensional printing processes," IISE Transactions, Taylor & Francis Journals, vol. 47(5), pages 431-441, May.
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    Cited by:

    1. Douglas A. J. Brion & Sebastian W. Pattinson, 2022. "Generalisable 3D printing error detection and correction via multi-head neural networks," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Lee J. Wells & Romina Dastoorian & Jaime A. Camelio, 2021. "A novel NURBS surface approach to statistically monitor manufacturing processes with point cloud data," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 329-345, February.
    3. Antonio Caputi & Davide Russo, 2021. "The optimization of the control logic of a redundant six axis milling machine," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1441-1453, June.
    4. Tingting Huang & Shanggang Wang & Shunkun Yang & Wei Dai, 2021. "Statistical process monitoring in a specified period for the image data of fused deposition modeling parts with consistent layers," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2181-2196, December.

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    1. Lee J. Wells & Romina Dastoorian & Jaime A. Camelio, 2021. "A novel NURBS surface approach to statistically monitor manufacturing processes with point cloud data," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 329-345, February.

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