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Research
Interests
The goal of our research is to develop
computer methods for design and manufacturing automation, with technical
emphasis on geometric computing, and shape/topology optimization.
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 Shape and topology design. We are
developing new techniques and applications in shape and topology
optimization. Our research includes B-spline based shape and topology
optimization.
o Geometry processing of dense scan data.
Ongoing projects include: geometric
processing of massive point-cloud data, and dynamic sensing-and-modeling.
o Automated nano-manipulation through atomic force
microscopy. Ongoing projects include: Automated nano-manipulation, and resolving tip-convolution effect in
scanning probe microscopy.
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Automated nano-manipulation
o This
research aims to develop methods and tools enabling automated manipulation
of nanoscale particles, tubes, and wires via atomic force microscopes with
the eventual goal of nanoscale device prototyping.
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Snapshot of
an AFM instrument (Agilent 5500) in our lab
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AFM
manipulation of latex particles (50nm) to form "IIT NANO CAD"
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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.
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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.
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Sample surface
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Resulting AFM image through morphological dilation
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Spatial
relationship among AFM tip, undercut surface, image, and reconstructed
surface
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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)
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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
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Acquired
head model and designed base template
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Hybrid
digital model
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Rapid prototyped head-mask
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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.
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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)
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Multi-stage multi-sensor digitization
system (Minolta Vivid 9i area sensor, Optimet line/point sensor, touch
probe)
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Acquired point cloud data from the
system
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Low-discrepancy based dynamic
sensing-and-modeling
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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.
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