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

Iñigo Flores Ituarte, Suraj Panicker, Hari P.N. Nagarajan, Eric Coatanea, David W. Rosen

Research output: Contribution to journalArticleScientificpeer-review

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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: [Figure not available: see fulltext.].

Original languageEnglish
Pages (from-to)219-241
JournalJournal of Intelligent Manufacturing
Volume34
Issue number1
Early online date17 Sept 2022
DOIs
Publication statusPublished - 2023
Publication typeA1 Journal article-refereed

Keywords

  • Bayesian networks
  • Digital manufacturing
  • Intelligent manufacturing
  • Multidisciplinary optimisation
  • Optimisation driven design
  • Process–structure–property–performance linkages

Publication forum classification

  • Publication forum level 2

ASJC Scopus subject areas

  • Software
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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