An “AI” Pathway Towards Sustainable Manufacturing | Discovery

A decade after the conceptualization of “digital twin” and the revival of artificial intelligence (AI) led by deep neural networks, the manufacturing industry is witnessing their accelerated convergence. Supported by the exponential growth of data arising from widespread deployment of sensors and the continued advancement of computational infrastructure, the advancement of AI technologies over the past decade have not only changed the way how a digital replica of a physical system in manufacturing can be created and updated that looks and behaves like its physical counterpart, but also new possibilities for the digital twin to guide the physical system towards optimized design, operation, and control. The result is improved functionality, productivity, and reliability of a new generation of products that directly contribute to sustainability in manufacturing. This presentation traces this “AI” pathway towards digital manufacturing and presents essential building blocks of digital twins. Examples of AI-enabled design, assembly, maintenance, process control, etc., which complement existing physical knowledge, are illustrated. This presentation demonstrates the potential of AI technologies as a key enabler in integrating physical science with data science through a digital platform, to facilitate the advancement of sustainable manufacturing.

Speakers:
Robert Gao
Professor of Engineering and Department Chair of Mechanical and Aerospace Engineering
Case Western Reserve University

Moderators:
Lihui Wang
Professor and Chair of Sustainable Manufacturing
KTH Royal Institute of Technology, Sweden

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The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.

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