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Optimisation-driven design to explore and exploit the process–structure–property–performance linkages in digital manufacturing

Author

Listed:
  • Iñigo Flores Ituarte

    (Tampere University)

  • Suraj Panicker

    (Tampere University)

  • Hari P. N. Nagarajan

    (Tampere University)

  • Eric Coatanea

    (Tampere University)

  • David W. Rosen

    (Georgia Institute of Technology
    Singapore University of Technology and Design)

Abstract

An intelligent manufacturing paradigm requires material systems, manufacturing systems, and design engineering to be better connected. Surrogate models are used to couple product-design choices with manufacturing process variables and material systems, hence, to connect and capture knowledge and embed intelligence in the system. Later, optimisation-driven design provides the ability to enhance the human cognitive abilities in decision-making in complex systems. This research proposes a multidisciplinary design optimisation problem to explore and exploit the interactions between different engineering disciplines using a socket prosthetic device as a case study. The originality of this research is in the conceptualisation of a computer-aided expert system capable of exploring process–structure–property–performance linkages in digital manufacturing. Thus, trade-off exploration and optimisation are enabled of competing objectives, including prosthetic socket mass, manufacturing time, and performance-tailored socket stiffness for patient comfort. The material system is modelled by experimental characterisation—the manufacturing time by computer simulations, and the product-design subsystem is simulated using a finite element analysis (FEA) surrogate model. We used polynomial surface response-based surrogate models and a Bayesian Network for design space exploration at the embodiment design stage. Next, at detail design, a gradient descent algorithm-based optimisation exploits the results using desirability functions to isolate Pareto non-dominated solutions. This work demonstrates how advanced engineering design synthesis methods can enhance designers’ cognitive ability to explore and exploit multiple disciplines concurrently and improve overall system performance, thus paving the way for the next generation of computer systems with highly intertwined material, digital design and manufacturing workflows. Graphical abstract

Suggested Citation

  • Iñigo Flores Ituarte & Suraj Panicker & Hari P. N. Nagarajan & Eric Coatanea & David W. Rosen, 2023. "Optimisation-driven design to explore and exploit the process–structure–property–performance linkages in digital manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 219-241, January.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:1:d:10.1007_s10845-022-02010-2
    DOI: 10.1007/s10845-022-02010-2
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    References listed on IDEAS

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    1. Ercan Oztemel & Samet Gursev, 2020. "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 127-182, January.
    2. Isaac Kofi Nti & Adebayo Felix Adekoya & Benjamin Asubam Weyori & Owusu Nyarko-Boateng, 2022. "Applications of artificial intelligence in engineering and manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1581-1601, August.
    3. Liang Hou & Roger J. Jiao, 2020. "Data-informed inverse design by product usage information: a review, framework and outlook," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 529-552, March.
    4. Jingchao Jiang & Yi Xiong & Zhiyuan Zhang & David W. Rosen, 2022. "Machine learning integrated design for additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1073-1086, April.
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