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1st International Workshop on Smart Manufacturing Modeling and Analysis [SM2N]

Today's manufacturing paradigm is in the midst of a transformation towards smart manufacturing, driven by the generation and analysis of high-volume data coming from interconnected cyber-physical components. This has necessitated an advancement in a number of the tenets of smart manufacturing such as Industrial Internet of Things (IIoT), Artificial Intelligence (AI), anomaly detection, security of industrial plants, novel communication infrastructures, etc. Among the many Smart Manufacturing tenets, a "digital twin" (DT) represents an opportunity to leverage existing and emerging technologies in modeling, simulation and emulation - to improve quality, productivity, and the ability to customize, and reduce energy consumption and waste. While DTs might help address many key performance and effectiveness metrics in manufacturing, however the science needs to be better understood in terms of definitions, capabilities, metrics, technical challenges and potential solutions. There exist some academic and industry efforts that aim to tackle this problem, but more is required. In addition, digital twins are just one way to model theses type of systems; to improve the overall design, efficiency and even security of future manufacturing systems, there is a need for new science that can capture/explain their behavior and new tools for modeling them.
In this new workshop, we intend to bring together multidisciplinary researchers and engineers (from academia, industry, as well as standards organizations) from a broad range of fields (manufacturing, control, cyber-security, networking) to provide an overview of the latest advances in the modeling and analysis of smart manufacturing systems. This area (smart manufacturing and especially modeling/analysis) has not received much focus but is an important area, not just from the research perspective but also from societal impact.

The areas of focus for the workshop are (broadly defined but not limited to):

  • What is a digital twin? What are the minimum requirements of any DT? Other there other modeling techniques/technologies that can be used?
  • What is the role of such modeling frameworks in cyber-physical manufacturing systems?
  • What are the metrics, if any, to evaluate the quality and the results produced from DTs and other modeling frameworks?
  • Can the DTs and/or other modeling frameworks be integrated with each other? Is there a need for more than one such solution in a system? Can we create frameworks where more than one can co-exist?
  • What are example case studies and success of the use of DTs and other such technologies?
  • How can security-specific applications such as anomaly detection be enabled by such modeling and analysis efforts? What other security problems exist?
  • Do areas such as machine learning better enable the development/use of DTs and other modeling/analysis frameworks?

NOTE: The workshop will also feature a roundtable discussion that involves multiple NSF program managers, experts from NIST, academia and industry. The session will also be moderated in a way to allow for audience participation with questions and answers. There will be a report out session after the roundtable discussion, and a written report in the months to follow. Details will be posted on the website shortly.


Kira Barton, University of Michigan, Ann-Arbor
Sibin Mohan, University of Illinois at Urbana-Champaign


Abhijit Chakraborty, UTRC
Dawn Tilbury, University of Michigan Ann Arbor
Miguel Saez, General Motors
James Moyne, University of Michigan Ann Arbor and Applied Materials
Mu Zhang, Cornell University
Arvind Easwaran, Nanyang Technological University
Saman Zonouz, Rutgers University
Prahlada Rao, University of Nebraska-Lincoln
Ye Sun, Michigan Technologies University
Binil Starly, North Carolina State University
Raheem Beyah, Georgia Tech
Qiang Huang, University of Southern California
Saumay Puchala, Ford Motor Company


Bin-Chou Kao, University of Illinois at Urbana-Champaign