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 to build a 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:
- AI for design, including generative design, cross-modal synthesis, large language models for computer-aided design (LLM4CAD), and their application in human-AI interaction and human-machine symbiosis.
- Design decision-making, including modeling and analysis of strategic decision-making behaviors in design competitions and/or collaborations, using game theory and behavioral experimentation.
- Swarm manufacturing, such as cooperative 3D printing (C3DP) and multi-robot hybrid manufacturing, for general-purpose factories.
- Complex systems engineering and design, such as predicting system structural evolution using complex networks, and estimating local decision-making behaviors in complex socio-technical systems.
- Decision-based enterprise-driven design, such as customer preference modeling, choice modeling, and spatiotemporal analysis of customer behaviors in product design and development.
- 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.
Video Highlights of SiDi Research and Technologies
Highlights of Layerwise Cooperative 3D Printing (C3DP)
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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