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. This research is funded by
NSF and NIST-Advanced Technology Program and industries.
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. This
research is funded by NSF 2003 sensor and sensor networks solicitation.
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. This
research is funded by Society of Manufacturing Engineering (SME) foundation [P1],
Industrial and Economic Development Research Fund at the University of Wisconsin - Madison,
local industries and Department of Energy (DOE).
