The Intelligent Manufacturing Lab is a research group at the Industrial and Manufacturing Systems Engineering Department at the University of Michigan-Dearborn. We focus on the AI-driven design and optimization of next-generation manufacturing and energy systems. Our research integrates physics-based modeling, physics-informed machine learning, and generative design to enhance efficiency, performance, and reliability.
Energy Storage Research - Battery systems and thermal management
Energy Storage
Manufacturing Research - Additive manufacturing and advanced manufacturing processes
Manufacturing
AI for Engineering - Machine learning and artificial intelligence applications
Data Center HVAC - Cooling systems and thermal management
Data Center HVAC
More Engineering Applications - Diverse engineering solutions and collaborations
More Engineering Applications

Zheng Liu
Zheng Liu

Assistant Professor

University of Michigan-Dearborn
Dr. Zheng Liu is the Director of the Intelligent Manufacturing Lab. His research focuses on developing computational framework and methods for complex systems design, integration, and optimization, enabling a systems-level approach to quantify cost, performance, and reliability trade-offs in future manufacturing and energy applications. Learn more about Zheng →
Recent News

IML secured new funding (also Zheng as single PI)

IML is happy to share that Zheng secured his second funding (also as single PI): Physics-Informed Machine Learning for Data Center Cooling Systems Control Co-Design Optimization. IML will be diving deep into the world of data center cooling to push the boundaries of next generation AI infrastructure.

IML secured the first funding (also Zheng as a PI)

IML is happy to share that Zheng secured his first funding (also as a PI): Digital Light Processing of Passive Visible Light Tags. IML will be diving deep into the world of 3D printed smart tags alongside Professor Xiao Zhang and Professor Jing Tang to push the boundaries of smart manufacturing.

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Featured Publications
Physics-Informed Machine Learning for Battery Pack Thermal Management

Jan 1, 2025

Uncertainty Quantification of Additively Manufactured Architected Cellular Materials for Energy Absorption Applications

Nov 1, 2024

Machine learning enhanced control co-design optimization of an immersion cooled battery thermal management system

Jul 1, 2024

Reliability-Based Design Optimization of Additive Manufacturing for Lithium Battery Silicon Anode

Jun 1, 2024

Life cycle assessment of hydrometallurgical recycling for cathode active materials

Oct 1, 2023

Generative Design and Optimization of Battery Packs with Active Immersion Cooling

Jun 1, 2023

Acoustophoretic Liquefaction for 3D Printing Ultrahigh-Viscosity Nanoparticle Suspensions

Feb 1, 2022