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Determinants of blockchain-machine learning adoption in additive manufacturing

Author

Listed:
  • Swati V. Narwane
  • Irfan Siddavatam
  • Mahesh S. Kavre

Abstract

This work concentrates on determining, inspecting, as well as ranking the critical barriers and alternatives to help the adoption of BC-ML practices in additive manufacturing (ADM). AHP-VIKOR methodology was applied to examine 20 identified barriers within the BC-ML adoption in ADM. The findings of the study reveal the rankings of the significant barriers as well as alternatives aimed at the trouble-free adoption of BC-ML practices in the ADM industry. 'Higher build time' and 'complicated design process of blockchain-based platforms' emerge as the most critical barriers, with a higher value of weights by using the AHP approach. The outcome of the alternative evaluation shows that the 'vat polymerisation' process ranks at the topmost position. The findings of this study can be useful to practitioners and policymakers to develop proper understanding, alleviation approaches, and make well-informed decisions.

Suggested Citation

  • Swati V. Narwane & Irfan Siddavatam & Mahesh S. Kavre, 2024. "Determinants of blockchain-machine learning adoption in additive manufacturing," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 48(2), pages 198-227.
    • Handle: RePEc:ids:ijisen:v:48:y:2024:i:2:p:198-227
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