Seminar on Industrial Mathematics and Statistics
Friday, March 31, 2000
8:00 A.M. - 12:00 Noon
Department of Mathematics and Statistics
College of Arts and Sciences
Oakland Room at Oakland Center
Presentation I: Mathematical and Computational Questions in Body
Manufacturing
Abstract: The goals of reducing vehicle mass and shortening vehicle
development time are forcing significant changes in materials and
process
for body manufacturing. The ability of manufacturing designers and
engineers to efficiently implement these technology changes is enhanced
by
the availability of mathematical and computational tools to synthesize,
model and analyze manufacturing operations prior to hardware
installation.
In this presentation we review, for the area of body manufacturing,
mathematical and computational issues which emerge as virtual body
manufacturing capability is sought. Several specific examples will
illustrate progress as well as gaps. Our efforts to develop solutions
to
some of these problems through a recent academic partnership with
University of Michigan will also be briefly reviewed.
Speaker: Dr. Samuel P. Marin, General Motors Research and Development
Center.
Sam received his Ph.D. in Mathematics from Carnegie Mellon University in
1978. He is the Laboratory Group Manager of the Body Assembly Group in
the GM R&D Center's Enterprise Systems Lab. In this role, he is
responsible for research focused on the development of mathematical
tools
to improve joining and assembly operations in body manufacturing. His
research interests are in geometric design and approximation, and in the
numerical solutions of PDEs
Presentation II: Assembly Modeling for Quality and Productivity
Abstract: Assembly is critical to successful product realization. In
this
talk, I will review various researches that are currently available in
the
general area of mechanical assembly. Research at the University of
Michigan in modeling assembly quality and productivity will be presented
with emphasis on automotive body assembly. New techniques have been
developed for predicting the variation of compliant, non-rigid part
assembly by combining engineering structure analysis with statistical
techniques. In addition, analytical models have also been developed for
evaluating the productivity of assembly systems with various
configurations.
Speaker: Dr. S. Jack Hu, The University of Michigan.
Dr. Hu received his Ph.D. from the University of Michigan in 1990.
Currently he is an associate professor in the Department of Mechanical
Engineering and Applied Mechanics. His research interest is in assembly
and joining, and engineering statistics. He is currently the Director
of
the National Science Foundation Industry/University Cooperative Research
Center at UM, and is also co-directing the Advanced Body Design and
Manufacturing Division of the General Motors Satellite Research Lab at
UM.
Presentation III: Variation Drivers Analysis - A Variance Decomposition
Method for Multi-Stage Data
Abstract: When an item is assembled in a series of distinct steps, each
step can contribute variation to the final product. If the final
variation
bute variation to the final product. If the final variation
is unacceptable or if process improvement is a goal, how does one
determine
the stage that make the biggest contribution to the final variation? The
variation drivers methodology provides an empirical model-based approach
to
answer that question by partitioning the variance at each stage into
added
variation and transmitted variation. This talk will discuss the model
and
the computations and give simple graphical techniques for analyzing the
data and presenting results. Complications, such as missing data and
measurement error, that frequently arise in practice will also be
discussed.
Speaker: Dr. Michael Wincek, General Motors, Research and Development
Center.
Mike is a staff research scientist at the GM R&D center. He received
his
PhD in statistics from the University of Wisconsin, Madison. His recent
work involved visualization techniques for and analysis of dimensional
data. His interests include linear models, time series analysis, and
wavelets.