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Environmentally Sustainable Management of 3D Printing Network: Decision Support for 3D Printing Work Allocation

  • Jungmok MaEmail author
Regular Paper
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Abstract

The purpose of this study is to provide a model for environmentally sustainable management of 3D printing network systems. The proposed model provides not only a flexible structure to describe 3D printing processes but also a computational structure to find the optimal work allocation plan for minimizing environmental impact. A mathematical model is formulated to assist the optimal part-to-printer allocation decision in 3D printing network systems even under uncertainty. Numerical examples show that the proposed model can determine the operation of shared 3D printers in order to have minimum environmental impact. The proposed model can also deal with data uncertainty and provide robust solutions.

Keywords

3D printing network Environmental sustainability Optimal allocation Robust optimization 

Notes

References

  1. 1.
    Ransikarbum, K., Ha, S., Ma, J., & Kim, N. (2017). Multi-objective optimization analysis for part-to-printer assignment in a network of 3D fused deposition modeling. Journal of Manufacturing Systems,43(1), 35–46.CrossRefGoogle Scholar
  2. 2.
    Telenko, C., & Seepersad, C. C. (2012). A comparison of the energy efficiency of selective laser sintering and injection molding of nylon parts. Rapid Prototyping Journal,18(6), 472–481.CrossRefGoogle Scholar
  3. 3.
    Kerbrat, O., Le Bourhis, F., Mognol, P., & Hascoët, J.-Y. (2015). Environmental performance modelling for additive manufacturing processes. International Journal of Rapid Manufacturing,5(3–4), 339–348.CrossRefGoogle Scholar
  4. 4.
    Faludi, J., Hu, Z., Alrashed, S., Braunholz, C., Kaul, S., & Kassaye, L. (2015). Does material choice drive sustainability of 3D printing? International Journal of Mechanical and Mechatronics Engineering,9(2), 216–223.Google Scholar
  5. 5.
    McAlister, C., & Wood, J. (2014). The potential of 3D printing to reduce the environmental impacts of production. In Proceedings of ECEEE 2014 industrial summer study.Google Scholar
  6. 6.
    Mognol, P., Lepicart, D., & Perry, N. (2006). Rapid prototyping: energy and environment in the spotlight. Rapid Prototyping Journal,12(1), 26–34.CrossRefGoogle Scholar
  7. 7.
    Baumers, M., Tuck, C., Wildman, R., Ashcroft, I., & Hague, R. (2011). Energy inputs to additive manufacturing: Does capacity utilization matter? In Proceedings of solid freeform fabrication symposium (pp 30–40).Google Scholar
  8. 8.
    Dotchev, K., & Yusoff, W. (2009). Recycling of polyamide 12 based powders in the laser sintering process. Rapid Prototyping Journal,15(3), 192–203.CrossRefGoogle Scholar
  9. 9.
    Hur, S. M., Choi, K. H., Lee, S. H., & Chang, P. K. (2001). Determination of fabricating orientation and packing in SLS process. Journal of Materials Processing Technology,112(2–3), 236–243.CrossRefGoogle Scholar
  10. 10.
    Zhang, Y., Bernard, A., Harik, R., & Karunakaran, K. P. (2017). Build orientation optimization for multi-part production in additive manufacturing. Journal of Intelligent Manufacturing,28(6), 1393–1407.CrossRefGoogle Scholar
  11. 11.
    Gebisa, A. W., & Lemu, H. G. (2017). Design for manufacturing to design for additive manufacturing: Analysis of implications for design optimality and product sustainability. Procedia Manufacturing,13, 724–731.CrossRefGoogle Scholar
  12. 12.
    Wang, R., & Work, D. (2014). Application of robust optimization in matrix-based LCI for decision making under uncertainty. The International Journal of Life Cycle Assessment,19(5), 1110–1118.CrossRefGoogle Scholar
  13. 13.
    Ma, J. (2019). Robust optimal usage modeling of product systems for environmental sustainability. Journal of Computational Design and Engineering,6(3), 429–435.CrossRefGoogle Scholar
  14. 14.
    Hirsch, M., Patel, R., Li, W., Guan, G., Leach, R. K., Sharples, S. D., et al. (2017). Assessing the capability of in situ nondestructive analysis during layer based additive manufacture. Additive Manufacturing,13, 135–142.CrossRefGoogle Scholar
  15. 15.
    Wilson, J. W., & Tian, G. Y. (2007). Pulsed electromagnetic methods for defect detection and characterisation. NDT & E International,40(4), 275–283.CrossRefGoogle Scholar
  16. 16.
    Ma, J., & Kim, N. (2016). Optimal product design for life cycle assessment (LCA) with the case study of universal motors. International Journal of Precision Engineering and Manufacturing,17(9), 1229–1235.CrossRefGoogle Scholar
  17. 17.
    ISO 14040. (2006). Environmental management—Life cycle assessment: Principles and framework. Geneva: International Organization for Standardization.Google Scholar
  18. 18.
    Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research,52(1), 35–53.MathSciNetCrossRefGoogle Scholar

Copyright information

© Korean Society for Precision Engineering 2019

Authors and Affiliations

  1. 1.Department of Defense ScienceKorea National Defense UniversityNonsanRepublic of Korea

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