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Automated manufacturability analysis in smart manufacturing systems: a signature mapping method for product-centered digital twins

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Listed:
  • Kaishu Xia

    (University of South Carolina)

  • Thorsten Wuest

    (West Virginia University)

  • Ramy Harik

    (University of South Carolina)

Abstract

Developing affordable and customizable cyber-physical production system and Digital Twin (DT) implementations infuses new vitality for current Industry 4.0 and Smart Manufacturing initiatives. The ability to precisely address material handling processes for manufacturability analysis further connects the physical and cyber components of today’s smart manufacturing systems. In this work, we propose a product-centered signature mapping approach to automated digital twinning featuring a hybrid implementation of smart sensing, signature-based feature extractor, and knowledge taxonomy. First, we integrate 3D scanning and surface reconstruction at to implement shape retrieval from both the virtual environment (from Computer-Aided Engineering data) and the real-world production environment (from scanned point cloud frames). Second, Shape Terra, an algorithm for intrinsic curvatures, simulates Persistent Heat Values for fast signature extraction from retrieved shape files. Finally, a systematic integration of the proposed shape analysis based on knowledge taxonomy is prototypically implemented. The objective of this testbed is to illustrate a proof-of-concept DT-aided process autonomy fed by rapid 3D surface signatures. As a result, by hybridizing smart sensing and simulative approaches, we exploit shape signatures as manufacturing knowledge by integrating domain knowledge and data-driven decision-makings. Moreover, human–machine interoperability enabling system-level intelligent controls becomes feasible in complex material handling, shape forming, measuring, and inspection processes.

Suggested Citation

  • Kaishu Xia & Thorsten Wuest & Ramy Harik, 2023. "Automated manufacturability analysis in smart manufacturing systems: a signature mapping method for product-centered digital twins," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3069-3090, October.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:7:d:10.1007_s10845-022-01991-4
    DOI: 10.1007/s10845-022-01991-4
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    References listed on IDEAS

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    1. Lee J. Wells & Romina Dastoorian & Jaime A. Camelio, 2021. "A novel NURBS surface approach to statistically monitor manufacturing processes with point cloud data," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 329-345, February.
    2. A. J. H. Redelinghuys & A. H. Basson & K. Kruger, 2020. "A six-layer architecture for the digital twin: a manufacturing case study implementation," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1383-1402, August.
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