3255 Mechanical Engineering Building
In this laboratory, we focus on interdisciplinary research on new methodologies of data analysis, knowledge discovery, and control of manufacturing processes for quality and productivity improvement. The research is based on the fusion of the diverse information sources, such as the in-process sensing information of the machine conditions, and the final product quality information, and the discrete event signals from the logic controller of the process. The research utilizes theories of engineering field knowledge, signal processing, advanced statistical analysis, and system and control. The research of this lab is sponsored by National Science Foundation (NSF), Department of Energy (DOE), National Institute of Standard and Technology-Advanced Technology Program (NIST-ATP), Society of Manufacturing Engineering (SME) Education Foundation, the State of Wisconsin - Industrial and Economic Development Research Program (I&EDR), Graduate School of the University of Wisconsin-Madison, OG Technologies, etc.
The representative ongoing research projects are:
Variation Analysis and Reduction for Complex Manufacturing Processes
In this research, both analytical methods based on product/process design and engineering knowledge and data-driven methods are used to link process variation sources and product quality characteristics. The quantitative model allows system theory and advanced statistical techniques (e.g., variance component analysis of linear mixed models) to be adopted in quality and productivity improvement. Specific industrial processes such as machining and assembly are used as testbed.
Design, Fabrication and Application of Distributed Micro Sensors Embedded in Metal Tooling
The objective is to develop a sensing methodology that enables highly reliable and accurate monitoring and diagnosis for manufacturing processes. This research is to use a system approach to study the design, fabrication, optimization, assessment, and applications of distributed micro sensors embedded in metal tooling. The challenging issues involve the embedding process, the control and improvement of the embedding process, and huge dataset processing for both process and sensor failure diagnosis.
On-Line Surface Defects Reduction for Hot-Rolling Processes
The surface integrity is an extremely important quality characteristic of the hot rolled products. This research focuses on the monitoring and diagnosis of surface defects in hot rolling process. The basic approach is to apply advanced image processing and statistical analysis to the hot surface images collected in real-time by using an innovative imaging system. Unlike other quality assurance techniques, the developed methodology can quickly detect the process abnormal change.