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Benchmarking of rapid prototyping systems using grey relational analysis

Author

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  • Siba Sankar Mahapatra
  • Biranchi Narayan Panda

Abstract

In this paper, grey relational analysis and fuzzy technique of order preference by a similarity to ideal solution (fuzzy TOPSIS) methods are proposed for selection of rapid prototyping systems considering both benefit and non-benefit criteria. The criteria may be qualitative or quantitative in nature. The results of grey relational analysis are compared with well-known fuzzy TOPSIS method. The advantage of grey theory over fuzzy theory is that grey theory considers the condition of the fuzziness, i.e., grey theory can deal flexibly with the fuzziness situation. Six RP systems are chosen and compared addressing to various criteria such as dimensional accuracy and surface quality, part cost, build time and material properties. Finally, it has been concluded that selective laser sintering (SLS 2500) is the most appropriate RP system for better dimensional accuracy and surface quality whereas 3-D printing (Z 402) is an appropriate RP system for better build time.

Suggested Citation

  • Siba Sankar Mahapatra & Biranchi Narayan Panda, 2013. "Benchmarking of rapid prototyping systems using grey relational analysis," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 16(4), pages 460-477.
    • Handle: RePEc:ids:ijsoma:v:16:y:2013:i:4:p:460-477
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    Citations

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    Cited by:

    1. Biranchi Panda & K. Shankhwar & Akhil Garg & M. M. Savalani, 2019. "Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 809-820, February.
    2. David M. Goldberg & Jason K. Deane & Terry R. Rakes & Loren Paul Rees, 2022. "3D Printing Technology and the Market Value of the Firm," Information Systems Frontiers, Springer, vol. 24(4), pages 1379-1392, August.
    3. Jose M. Framinan & Paz Perez-Gonzalez & Victor Fernandez-Viagas, 2023. "An overview on the use of operations research in additive manufacturing," Annals of Operations Research, Springer, vol. 322(1), pages 5-40, March.

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