Log in

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

  • Regular Paper
  • Published:
International Journal of Precision Engineering and Manufacturing Aims and scope Submit manuscript

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.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  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.

    Article  Google Scholar 

  2. Telenko, C., & Seepersad, C. C. (2012). A comparison of the energy efficiency of selective laser sintering and injection molding of nylon parts. Rapid Prototy** Journal,18(6), 472–481.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

  6. Mognol, P., Lepicart, D., & Perry, N. (2006). Rapid prototy**: energy and environment in the spotlight. Rapid Prototy** Journal,12(1), 26–34.

    Article  Google Scholar 

  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).

  8. Dotchev, K., & Yusoff, W. (2009). Recycling of polyamide 12 based powders in the laser sintering process. Rapid Prototy** Journal,15(3), 192–203.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  13. Ma, J. (2019). Robust optimal usage modeling of product systems for environmental sustainability. Journal of Computational Design and Engineering,6(3), 429–435.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  15. Wilson, J. W., & Tian, G. Y. (2007). Pulsed electromagnetic methods for defect detection and characterisation. NDT & E International,40(4), 275–283.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  17. ISO 14040. (2006). Environmental management—Life cycle assessment: Principles and framework. Geneva: International Organization for Standardization.

    Google Scholar 

  18. Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research,52(1), 35–53.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jungmok Ma.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, J. Environmentally Sustainable Management of 3D Printing Network: Decision Support for 3D Printing Work Allocation. Int. J. Precis. Eng. Manuf. 21, 537–544 (2020). https://doi.org/10.1007/s12541-019-00280-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12541-019-00280-0

Keywords

Navigation