Welcome to the Connected and Intelligent Manufacturing Systems (CIMS) Lab!
We are an interdisciplinary research group dedicated to advancing the intelligence, quality, and efficiency of manufacturing. Our work integrates methodologies from diverse fields, with a strong focus on machine learning and statistics, to drive innovation and improve manufacturing processes and systems.
Our research spans a wide range of topics, including:
Smart manufacturing | Machine learning | Statistics | Big data analytics | In-situ process monitoring and real-time control | Materials joining | Manufacturing systems control and automation | Quality control | Robotic additive manufacturing | 3D metrology | Cyber-physical infrastructure | Human-robot collaboration
To learn more about our research, please visit the research and publications pages.
Our alumni have gone on to successful careers in academia, including at City University of Hong Kong, and in leading companies such as Meta, KLA, Intel, Apple, C3 AI, Solventum, Lucid, Seagate, and MathWorks. Meanwhile, our current members regularly intern at top manufacturing and IT companies (e.g., General Motors, Meta, 3M, Intel, Fanuc, TSMC, Milwaukee Tool, Wayfair), gaining valuable industry experience along the way.
We invite you to explore our work and connect with us!
Recent News
- CIMS Lab: 2024 Year in Review
Looking back on 2024, we reflect on an eventful and rewarding year for our group at the University of Michigan—a year of growth, achievements, and exciting new directions. Here are some highlights from the past year: 🌟 Student Success Our […] - Paper on uncertainty-aware constrained optimization for air convective drying of thin apple slices using machine-learning-based response surface methodology
Our paper, “Uncertainty-aware constrained optimization for air convective drying of thin apple slices using machine-learning-based response surface methodology,” has been published in the Journal of Food Engineering. This study introduces a Monte Carlo simulation approach to quantify inherent uncertainties in […] - Paper on WeldMon: A Cost-effective Ultrasonic Welding Machine Condition Monitoring System Published in IEEE UEMCON (Best Paper Award)
Our paper titled “WeldMon: A Cost-effective Ultrasonic Welding Machine Condition Monitoring System” has been published in the IEEE Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). The paper was co-authored by Beitong Tian, Ahmadreza Eslaminia, Kuan-Chieh Lu, Yaohui Wang, Prof. […] - Exciting Summer 2025 Internships
Li-Wei will begin a summer internship in Research and Development for Advanced Joining Processes. He will utilize his advanced expertise in deep learning and computer vision methods for non-destructive quality monitoring in joining systems. His Ph.D. research focuses on leveraging […] - Paper Published at SME NAMRC 52 Conference
Our PhD student Alice published a paper and presented her research at NAMRC 52 at the Knoxville Convention Center (Knoxville, TN) this summer. She also visited Oak Ridge National Laboratory, one of the world’s premier research institutions, for advanced manufacturing […] - Yuquan has joined City University of Hong Kong as a Assistant Professor
Yuquan Meng has joined the Department of System Engineering in City University of Hong Kong as an Assistant Professor. His primary research interests lie in data-efficient learning, physics-informed machine learning, in-situ process monitoring and smart decision-making, manufacturing systems control and […]