|
Research
Interests
This group conducts fundamental research in geometric and physical
modeling, and develops tools and method that enable the automation of
machineries, processes and systems.
Our work is computational
in that we develop theories and accompanying algorithms
that have provable properties. We focus on fundamental research that can
result in new concepts that can be demonstrated on real machineries,
processes and systems.
Our current research focuses on
o Automated nano-manipulation through
atomic force microscopy. Ongoing projects include: Automated nano-manipulation, and resolving tip-convolution effect in
scanning probe microscopy.
o Cyber-enabled geometric computing. Ongoing
projects include: geometric
processing of massive point-cloud data, and dynamic sensing-and-modeling.
Automated nano-manipulation
|
o This research
aims to develop methods and tools enabling automated manipulation of
nanoscale particles, rods and wires with atomic force microscopy.
|
Snapshot of an AFM
instrument (Agilent 5500) in our lab
|
AFM manipulation of
latex particles (50nm) to form "IIT NANO CAD"
|
Recent
publications:
1. Zhang, D. and
Qian, X., "Adaptive Scanning in Atomic Force Microscopy" Proceedings of 2009 IEEE Conference on
Robotics and Automation (ICRA), Kobe,
Japan, May 2009.
|
Resolving
tip-convolution effect in SPM
(supported by NSF,
NIST, SME)
|
- The research objective is to develop theories and
algorithms for tip-specimen shape interaction modeling for an emerging
class of scanning probe microscopy (SPM) instruments that is capable of
imaging general 3D structures
with vertical sidewalls and undercut features at the nanometer or
even atomic scale.
- If successful, the results of this research will provide a means to understand and
correct potential dimensional bias in SPM imaging of general 3D
nanostructures. It
will lead to high accuracy and high throughput nano-imaging of general
3D nano-structures. It can benefit a host of industries that use SPM
such as semiconductor, data storage, MEMS, and molecular imaging
industries.
|
Sample surface
|
Resulting AFM image through morphological dilation
|
|
Spatial relationship
among AFM tip, undercut surface, image, and reconstructed surface
|
|
Recent
publications:
1. Tian, F.,
Qian, X., and Villarrubia, J. S., "Blind
estimation of general tip shape in AFM imaging," Ultramicroscopy, Vol. 109,
No. 1, pp. 44 - 53, Dec 2008.
2. Qian, X. and Villarrubia, J. S., "General
Three-Dimensional Image Simulation and Surface Reconstruction in Scanning
Probe Microscopy using a Dexel Representation," Ultramicroscopy,
Vol. 107, No. 13, pp. 29 - 42, Dec 2007.
Collaborators:
John Villarrubia
(NIST)
Greg Dahlen (Veeco)
|
Processing
massive point-cloud data
(supported by NSF)
|
o The goal is to develop computational
tools enabling a new way of developing products,
direct design and manufacturing from
3D sensing of pre-existing objects,
one that can bypass the
painstaking CAD model reconstruction involved in current product development
process.
o If
successful, this research would
change the way a large number of 3D parts are developed, enable custom
product development at mass production efficiency by circumventing laborious
and error-prone CAD model reconstruction and lead to quantum-leap progress
in bringing physical objects into digital space for direct engineering
processing
|
|
|
|
|
Acquired head
model and designed base template
|
Hybrid digital
model
|
Rapid prototyped head-mask
|
Development of a
customer-specific head mask (Pinghai Yang, Tim Schmidt, Xiaoping Qian)
Representative publications:
1.
Yang, P. and Qian, X., "Adaptive Slicing of Moving Least Squares
Surfaces: Toward Direct Manufacturing from Point Cloud Data," ASME Transactions Journal of Computing and
Information Science in Engineering. Accepted.
2. Yang, P. and
Qian, X., "Direct computing of surface curvatures for point-set
surfaces," Proceedings of 2007 IEEE/Eurographics
Symposium on Point-based Graphics(PBG), Prague, Czech Republic, Sep. 2007.
|
Dynamic
sensing and modeling
(supported by
AFOSR)
|
- The ability to obtain a digital
geometric model directly from physical objects marks a revolutionary step
in the information era. Three-dimensional (3D) digitization is a
process to create three-dimensional computer models of a physical
object. Such reconstructed 3D shape models have become indispensable
in a variety of applications such as product design and manufacturing
in aerospace, automotive, and die and mold industry, patient-specific
medical device design and analysis, as well as target recognition and
scene understanding in homeland security and military applications
- The
objective of this research is to develop foundational shape
digitization theories and algorithms for a multimodal digitization
system that can couple 3D sensing and post-sensing shape
reconstruction in a dynamic manner in order to fundamentally improve
digitization automation level, speed, efficiency and the resulting
surface quality. (video)
|
|
|
|
|
Multi-stage multi-sensor digitization system
(Minolta Vivid 9i area sensor, Optimet
line/point sensor, touch probe)
|
Acquired point cloud data from the system
|
Low-discrepancy based dynamic
sensing-and-modeling
|
Representative
publications:
1.
Huang, Y. and Qian, X., "Dynamic B-spline Surface
Reconstruction: Closing the Sensing-and-Modeling Loop in 3D Digitization," Computer-Aided
Design, Vol. 39, No. 11, pp. 987-1002, Nov 2007.
2.
Huang, Y. and Qian, X., "A dynamic
sensing-and-modeling approach to 3D point- and area-sensor
integration," ASME Transactions
Journal of Manufacturing Science and Engineering, Vol. 129, pp. 623-
635, June 2007.
|
|
