The System Integration & Design Informatics Laboratory (SiDi Lab) is a research lab in the Walker Department of Mechanical Engineering at the University of Texas at Austin. The research of SiDi Lab is focused on system science and design science as well as the intersection between these two areas. The lab aims at building the decision-centric foundation for scientific research on engineering design and large-scale complex systems. Such a foundation is expected to promote convergence research and amplify the data-driven power in design and systems science. Particularly, our research projects encompass the following areas:

• Complex systems engineering and design, such as predicting system structural evolution using complex networks, estimating local decision-making behaviors in complex socio-technical systems.
• Decision-making in design, such as modeling and analysis of strategic decision-making behaviors in design competition and/or collaboration using game theory and behavioral experimentation.
• Artificial intelligence in engineering design (e.g., generative design) and its application in human-computer interaction and human-machine symbiosis.
• Decision-based enterprise-driven design, such as customer preference modeling, choice modeling, and spatiotemporal analysis of customer behaviors in product design and development.
• Swarm manufacturing, such cooperative 3D printing (C3DP) and cooperative hybrid manufacturing for general purpose factory.
• Social design and manufacturing, such as open-source product development, crowdsourcing, and community-based manufacturing.

For more information, please see SiDi Research and SiDi Projects. To access the publications, please go to Publication or Zhenghui Sha’s Google Scholar.

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Video Highlights of SiDi Research and Technologies

Demonstration of the Chunk-based Printing and Manufacturing Scheduling Strategy in Cooperative 3D Printing Project (C3DP).

Swarm Manufacturing of a Mini-Electrical Vehicle as a Demo for Future General Purpose Factories.

AI-Assisted Design. We train a deep neural network using human design decisions in a solar energy system design project (left) and generate artificial design sequences to guide novice designers to learn design tools and the design process (right).

Dr. Sha recently shared his vision toward Human-Machine Design Symbiosis: Transforming Engineering Systems Design with Augmented Intelligence in the workshop on Accelerating Design with Human-Machine Teaming at the Ninth International Conference On Design Computing And Cognition.

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