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A review on measurement science needs for real-time control of additive manufacturing metal powder bed fusion processes

Author

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  • Mahesh Mani
  • Brandon M. Lane
  • M. Alkan Donmez
  • Shaw C. Feng
  • Shawn P. Moylan

Abstract

Additive manufacturing technologies are increasingly used in the development of new products. However, variations in part quality in terms of material properties, dimensional tolerances, surface roughness and defects limit its broader acceptance. Process control today based on heuristics and experimental data yields limited improvement in part quality. In an effort to identify the needed measurement science for real-time closed-loop control of additive manufacturing (AM) processes, this paper presents a literature review on the current AM control schemes, process measurements and modelling and simulation methods as it applies to the powder bed fusion process, though results from other processes are reviewed where applicable. We present our research findings to identify the correlations between process parameters, process signatures and product quality. We also present research recommendations on the key control issues to serve as a technical basis for standards development in this area. Complimentary details to this paper with summary tables, range of values, preliminary correlations and correlation figures can be accessed from a National Institute of Standards and Technology Report (http://nvlpubs.nist.gov/nistpubs/ir/2015/NIST.IR.8036.pdf). This paper is developed based on the report.

Suggested Citation

  • Mahesh Mani & Brandon M. Lane & M. Alkan Donmez & Shaw C. Feng & Shawn P. Moylan, 2017. "A review on measurement science needs for real-time control of additive manufacturing metal powder bed fusion processes," International Journal of Production Research, Taylor & Francis Journals, vol. 55(5), pages 1400-1418, March.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:5:p:1400-1418
    DOI: 10.1080/00207543.2016.1223378
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    Cited by:

    1. Matteo Bugatti & Bianca Maria Colosimo, 2022. "Towards real-time in-situ monitoring of hot-spot defects in L-PBF: a new classification-based method for fast video-imaging data analysis," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 293-309, January.
    2. Vivek Mahato & Muhannad Ahmed Obeidi & Dermot Brabazon & Pádraig Cunningham, 2022. "Detecting voids in 3D printing using melt pool time series data," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 845-852, March.
    3. Yuval Cohen & Gonen Singer, 2021. "A smart process controller framework for Industry 4.0 settings," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1975-1995, October.
    4. Marić, Josip & Opazo-Basáez, Marco & Vlačić, Božidar & Dabić, Marina, 2023. "Innovation management of three-dimensional printing (3DP) technology: Disclosing insights from existing literature and determining future research streams," Technological Forecasting and Social Change, Elsevier, vol. 193(C).

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