Hierarchical multi-response Gaussian processes for uncertainty analysis with multi-scale composite manufacturing simulation

Zhou, Kai; Enos, Ryan; Xu, Dong; Zhang, Dianyun; Tang, Jiong

doi:10.1016/j.commatsci.2022.111257

Cited by 4 publications

(1 citation statement)

References 35 publications

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“…The virtual simulation is a key aspect of DT that requires continuous iterations between physical and virtual entities 80,81 . These simulations include physics based computational approaches (FEM/CFD) [82][83][84][85][86][87] as well as data driven stochastics simulations [88][89][90] . The advantages of digital simulations over experimental procedures is evident in material consumption, labor hours and overall cost reduction.…”

Section: The Meta-verse Of Composites Manufacturingmentioning

confidence: 99%

Graphene nanoparticles as data generating digital materials in industry 4.0

Ali

Irfan

Khan

et al. 2023

Sci Rep

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One of the potential applications of 2D materials is to enhance multi-functionality of structures and components used in aerospace, automotive, civil and defense industries. These multi-functional attributes include sensing, energy storage, EMI shielding and property enhancement. In this article, we have explored the potential of using graphene and its variants as data generating sensory elements in Industry 4.0. We have presented a complete roadmap to cover three emerging technologies i.e. advance materials, artificial intelligence and block-chain technology. The utility of 2D materials such as graphene nanoparticles is yet to be explored as an interface for digitalization of a modern smart factory i.e. “factory-of-the-future”. In this article, we have explored how 2D material enhanced composites can act as an interface between physical and cyber spaces. An overview of employing graphene-based smart embedded sensors at various stages of composites manufacturing processes and their application in real-time structural health monitoring is presented. The technical challenges associated with interfacing graphene-based sensing networks with digital space are discussed. Additionally, an overview of the integration of associated tools such as artificial intelligence, machine learning and block-chain technology with graphene-based devices and structures is also presented.

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