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Qiang Huang, Ph.D. Associate Professor and
Gordon S. Marshall Early Career Chair in Engineering Epstein Department of
Industrial and Systems Engineering University of Southern
California Associate Editor, IEEE
Transactions on Automation Science and Engineering Contributing Editor,
InterNano (www.internano.org) |
Contact Information:
Phone: 213-740-2433
Fax: 213-740-1120
Email: qiang.huang@usc.edu
Research
Assistant Positions Available.
Contact me if you have strong
engineering and analytical background
Nanomanufacturing
Quality Control Laboratory (Nano-QCLab)
My lovely daughter and son, My daughter’s Cello recital
Ph.D.
Graduates & Current Students
Associate Professor, Daniel J. Epstein
Department of Industrial and Systems Engineering,
University of Southern
California, Los Angeles, CA, March 2012 ~ Present.
Assistant Professor, Daniel J. Epstein
Department of Industrial and Systems Engineering,
University of Southern
California, Los Angeles, CA, August 2009 ~ March 2012.
Associate Professor, Department of Industrial
and Management Systems Engineering,
University of South Florida,
Tampa, FL, June 2008 ~ August 2009.
Assistant Professor, Department of
Industrial and Management Systems Engineering,
University of South Florida,
Tampa, FL, August 2003 ~ June 2008.
Ph.D. Industrial
& Operations Engineering, University of Michigan, 2003
M.A. Statistics,
University of Michigan, 2002
Ph.D., M.S. B.S. Mechanical
Engineering, Shanghai Jiao Tong University, 1998, 1996, 1993
ISE 599: Foundation of Applied Mathematics for Engineered System Analytics (New)
Synopsis: As technologies evolve to a high degree of complexity, e.g., nanotechnology and its scale-up in nanomanufacturing, applying the principles and concepts of applied mathematics to these emerging areas faces the dilemma of whether or not to connect the generic modeling methodologies with the underlying physical mechanisms. Particularly, when we attempt to use modeling methods developed in applied mathematics to investigate complicated engineering phenomena and mechanisms, reconnecting applied mathematics to its engineering roots is essential for not only the resurgence of the classical techniques of applied mathematics, but also the development of new principles, concepts, and methods not existing in the current applied mathematics. For example, modeling of nanomanufacturing for scale-up faces the issue of insufficient measurement data and physical understandings. The uncertainties in data and physical knowledge result in a large set of candidate models and their corresponding underlying physical mechanisms equally explain the data well. Since some physical mechanisms compete with each other it makes applied mathematics hard to proceed to interpretation and predication. Reconnecting applied mathematics with complex engineering phenomena provides exciting opportunities for improved understanding of engineering problem and methodological development for applied mathematics. The improved physical understandings also lead to advanced engineered systems for varieties of application in scalable nanomanufacturing, additive manufacturing, healthcare systems, and social network.
Course objective: The objective of this course is therefore to introduce principles, concepts, and methods of applied mathematics from the perspective of modeling of engineering phenomena. We will also introduce our on-going research and results of reconnecting applied mathematics with nanomanufacturing for engineering scalable systems. The course is not intended to provide a standard procedure or method for the reconnection. Rather, it points out a viable path to discover exciting solutions for engineering complex systems.
ISE 525: Design of Experiments
ISE 426: Statistical Quality Control
Epstein Institute Seminar Series
My general area of interest is modeling and analysis
of complex systems for quality and productivity improvement, in particular,
-
Integrated
Nanomanufacturing & Nanoinformatics (INN)
-
Predictive Model-Based Control for Additive Manufacturing
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Modeling, control & diagnosis of variations in complex systems
-
Quality and Applied Statistics
1.
PI, "CAREER: Nanomanufacturing Process Modeling and Control -- A
Foundation for Large-Scale Production," NSF CMM-1055394, $400,001,
2011-2016.
2.
Co-PI (50% effort), "Cyber-Enabled Manufacturing Systems (CeMS): Real-Time Shape Compensation for Accurate Direct
Digital Manufacturing," Office of Naval Research, ONR Grant#
N000141110671, $439,645, 2011-2014 (PI: Y. Chen at USC).
3.
Leading PI, “Collaborative
Research: Nanostructure Growth Process Modeling and Optimal Experimental
Strategies for Repeatable Fabrication of Nanostructures for Application in Photovoltaics,” $300K, NSF CMMI-1000972, 2010-2013
(Co-PI: C. Zhou at USC, PI: T. Dasgupta at Harvard).
4.
Co-PI, “Bayesian process control for nanomanufacturing
with mixed resolution information,” Hong Kong RGC (Research Grant Council),
2009-2011 (PI: F. Tsung, Co-PI: J. Shi).
5.
PI, “In Situ
Nanomanufacturing Process Control Through Multiscale
Nanostructure Growth Modeling,” $350K, NSF CMMI-0728100, 2007-2010 (Co-PI:
A. Kumar).
6.
PI, “Analysis
of Correlated Functional Variables for Manufacturing Process Diagnosis,”
$280K, NSF CMMI-0600066, 2006-2009, (Co-PI, A. Kumar) (Including the supplement
request of $15K for Cyberinfrastructure Experiences
for Graduate Students (CIEG)).
7. Co-PI, “Nanoengineered, Manufacturable, Ion-Implantation Seeded Silica Nanowires for Sensitive BioScreening, ” NSF CMMI-0700659, $289,980, 2007~2010 (PI: S. Bhansali).
8.
PI, “Multiscale Nanostructure Growth Modeling
for Control of Nanomanufacturing,” Functional Multiscale Materials by Design Initiative, University of
South Florida, Summer Support, 2007.
9.
PI, “Measurement Strategy for Serial-Parallel Reconfigurable
Manufacturing Systems with Real-Time RF-Tag Information,” sponsored by NSF
ERC-RMS at University of Michigan (Co-PI: Dr. R. Katz, Chief Engineer at
ERC-RMS), 2004.
10.
PI, “Process Control based on Multivariate Functional Data,” USF
Internal Award, 2004.
· Patent and Provisional Patent
1.
US Provisional Patent Application No. 61/712,723: Algorithm of compensating shape shrinkage for 3D printing processes, filed on October 11, 2012.
Refereed Journals and
Transactions
1. Zhu, L., Dasgupta, T., and Huang, Q.,2012, “A Locally D-Optimal Design for Estimation of Parameters of an Exponential-Linear Growth Curve of Nanostructures,” Technometrics, Accepted.
2.
Wang, L., and Huang, Q., 2012, “Cross-Domain
Model Building and Validation (CDMV): A New Modeling Strategy to Reinforce
Understanding of Nanomanufacturing Processes,”IEEE Trans on Automation Science and Engineering,
Conditionally accepted.
(Finalist of 2012 QSR Best Student Paper
Competition)
3. Xu, L., and Huang, Q., 2012, “EM Estimation of Nanostructure Interactions with Incomplete Feature Measurement and Its Tailored Space Filling Design,” IEEE Trans on Automation Science and Engineering, Conditionally accepted.
4.
Xu,
L., and Huang, Q., 2012, “ Modeling the Interactions among Neighboring
Nanostructures for Local Feature Characterization and Defects Detection, "IEEE Trans on Automation Science and
Engineering, Vol. 9, pp.745-754.
5.
Chang, C.J., Xu, L.,
Huang, Q., and Shi, J., 2011, “Quantitative Characterization and Modeling
Strategy of Nanoparticle Dispersion in Polymer Composites, "IIE Transactions, Special Issue on Quality, Sensing
and Prognostics Issues in Nanomanufacturing, Volume 44, Issue 7, pp.
523-533.
6.
Huang, Q., Wang, L., Dasgupta,
T., Zhu, L., Sekhar, P.K., and, Bhansali,
S., An, Y., 2011, “Statistical
Weight Kinetics Modeling for Silica Nanowires Growth Catalyzed by Pd Thin Film,” IEEE
Trans on Automation Science and Engineering, Vol. 8, pp.303-310.
7.
Huang, Q., 2011, “Physics-Driven Bayesian
Hierarchical Modeling of Nanowire Growth Process at Each Scale," IIE
Transactions, IIE Transactions on Quality
and Reliability, Vol. 43, pp. 1-11.
8.
Zhang, X., Huang, Q., 2010, “Analysis of
Interaction Structure Among Multiple Functional Process Variables for Process
Monitoring in Semiconductor Manufacturing,” IEEE Transactions on Semiconductor Manufacturing, DOI:
10.1109/TSM.2010.2041580.
9.
Chen, S., Wang, H., and Huang, Q., 2010, “Diagnosis of Multiple Error
Sources Under Variation Equivalence," Accepted
to NAMRI/SME Transactions, Vol. 38.
10.
Zhang, X., Wang, H., Huang, Q., Kumar, A., and Zhai,
J., 2009,“Statistical and Experimental Analysis of
Correlated Time-varying Process Variables for Condition Diagnosis in
Chemical-Mechanical Planarization,” IEEE
Transactions on Semiconductor Manufacturing, Vol.22 (3), pp. 512-521.
11.
Wang, H., Zhang, X., Kumar, A., Huang, Q., 2009, “Nonlinear Dynamics Modeling of Correlated Functional
Process Variables for Condition Monitoring in Chemical-Mechanical Planarization”,
IEEE Transactions on Semiconductor
Manufacturing, Vol. 22, pp.188-195.
12.
Wang, H., Kababji, H., and Huang, Q., 2009,
“Monitoring Global and Local Variations in Multichannel Functional Data for
Manufacturing Processes,” SME
Transactions, Journal of Manufacturing Systems,
doi:10.1016/j.jmsy.2009.03.001.
13.
Wang, H., and Huang, Q., 2007, “Using Error
Equivalence Concept to Automatically Adjust Discrete Manufacturing Processes
for Dimensional Variation Reduction,” ASME
Transactions, Journal of Manufacturing Science and Engineering, 129, pp.
644—652.
14.
Kim, J., Huang, Q., Shi, J., 2007, “Latent Variable-based Key Process
Variable Identification and Process Monitoring for Forging,” SME Transactions Journal of Manufacturing
Systems, Vol. 26, pp. 53-61.
15.
Wang, H., and Huang, Q., 2006, “Error
Cancellation Modeling and Its Application in Machining Process Control,” IIE Transactions on Quality and Reliability,
38, pp.379-388.
16.
Wang, H., and Huang, Q., Yang, H., 2006, “In-Line Statistical
Monitoring of Machine Tool Thermal Error Through Latent Variable Modeling,” SME Transactions Journal of Manufacturing
Systems, Vol. 25, No.4, pp. 279-292.
17.
Kim, J., Huang, Q., Shi, J., and Chang, T.-S., 2006, “Online Multi-Channel Forging Tonnage Monitoring and Fault
Pattern Discrimination Using Principal Curve,” ASME Transactions, Journal of Manufacturing Science and Engineering,
128, pp. 944--950.
18.
Wang, H., Huang, Q., Katz, R., 2005, “Multi-Operational
Machining Processes Modeling for Sequential Root Cause Identification and
Measurement Reduction,” ASME
Transactions, Journal of Manufacturing Science and Engineering, 127, pp.
512-521.
19.
Huang, Q., and Shi, J., 2004, “ Stream of
Variation Modeling of Serial-Parallel Multistage Manufacturing Systems with
Coupled Process Routes,” ASME
Transactions, Journal of Manufacturing Science and Engineering, 126, pp.611-618.
20.
Huang, Q., and Shi, J., 2004, “Variation
Transmission Analysis and Diagnosis of Multi-Operational Machining Processes,”
IIE Transactions on Quality and
Reliability, 36, pp. 807-815.
21.
Zhou, S., Huang, Q., and Shi, J., 2003,"State
Space Modeling for Dimensional Monitoring of Multistage Machining Process Using
Differential Motion Vector," IEEE
Transactions on Robotics and Automation, 19, 296-309.
22.
Huang, Q., Shi, J., 2003, “Simultaneous
Tolerance Synthesis through Variation Propagation Modeling of Multistage
Manufacturing Processes,” NAMRI/SME
Transactions, 31, pp. 515-522.
23.
Huang, Q., Shi, J., and Yuan, J., 2003, “Part
Dimensional Error and Its Propagation Modeling in Multi-Operational Machining
Processes,” ASME Transactions,
Journal of Manufacturing Science and Engineering, 125, 255-262.
24.
Huang, Q., Zhou, S., and Shi, J., 2002, “Diagnosis
of Multi-Operational Machining Processes through Variation Propagation Analysis,”
Robotics and Computer-Integrated
Manufacturing, 18, 233-239.
Referred Conference Proceedings
1.
Huang, Q., Zhou, N., and Shi, J., 2000, “Stream of Variation Modeling and Diagnosis of
Multi-Station Machining Processes,” Proc.
2000 ASME Int. Mech. Eng. Congress & Exposition, MED-Vol. 11, pp.81-88,
November 5-10, Orlando, FL., 2000.
2.
Huang, Q., Zhou, S., and Shi, J., 2001, “Diagnosis of Multi-Operational
Machining Processes By Using Virtual Machining,” Int. Conf. on Flexible Automation & Intelligent Manufacturing,
pp. 804-813, July 16th - 18th, Dublin, IRELAND.
3.
Huang, Q., and Shi, J., 2001, “Stream of Variation Analysis and Root
Cause Diagnosis for Multi-Operational Machining Processes,” 2002 Japan-USA Symposium on Flexible
Automation, July 15-17, 2002, Hiroshima, Japan.
4.
Kim, J., Huang, Q., Shi, J., and Chang, T.-S., 2004, “Online
Multi-Channel Forging Tonnage Monitoring and Fault Pattern Discrimination Using
Principal Curve,” ASME IMECE 04.
5.
Wang, H., Huang, Q., Katz, R., 2004, “Multi-Operational Machining
Processes Modeling for Sequential Root Cause Identification and Measurement
Reduction,” ASME IMECE 2004.
6.
Wang, H., Huang, Q., 2005, “Automatic Process Adjustment for Reducing
Dimensional Variation in Discrete Part Machining Processes,” ASME IMECE 2005.
7.
Wang, H., Chen, S., and Huang, Q., 2009, “Multistage Machining Process
Design and Optimization Using Error Equivalence Method ", 2009 ASME International Manufacturing
Science and Engineering Conference (MSEC), October 4-7, 2009, West
Lafayette, IN.
8.
Huang, Q., 2011,
“Integrated Nanomanufacturing and Nanoinformatics for
Quality Improvement ”, 44th CIRP International Conference on Manufacturing
Systems, June 1-3, 2011, Madison, Wisconsin (Invited).
· Nanomanufacturing Quality Control Laboratory (Nano-QCLab)
Nano-QCLab is established
to enhance research and teaching infrastructures on nanomanufacturing
process control for process yield and repeatability improvement.
1. NSF Faculty Early
Career Development (CAREER) Award, 2011.
2. Featured article IIE
Transactions on Quality and Reliability
Huang, Q., 2011, “Physics-Driven Bayesian Hierarchical Modeling of Nanowire
Growth Process at Each Scale," IIE
Transactions on Quality and Reliability, Vol. 43, pp. 1-11.
3. Featured article in IIE
Transactions on Quality and Reliability
Wang, H., and Huang, Q.,
2006, “Error Cancellation Modeling and Its Application
in Machining Process Control," IIE
Transactions on Quality and Reliability, Vol.38, pp.379-388.
4. 2002-2003 Student of the
Year at NSF Engineering Research Center for Reconfigurable Machining Systems
(ERC-RMS) at University of Michigan, May 2003.
5. Graduate Student Research
Assistantship at ERC-RMS of University of Michigan, 1999-2003.
· Ph.D. Graduates & Current Students
1. Hui Wang, “Error Equivalence
Theory for Manufacturing Process Control,” Graduated in 2007.
Current position: Assistant Research
Scientist at University of Michigan, Ann Arbor, 01/08 ~ present.
2. Xi Zhang, “Statistical and
Physical Analysis of Functional Process Variables for Process Control in
Semiconductor Manufacturing,” December, 2009.
Current position: Assistant Professor,
Industrial Engineering & Management Department, Peking University, China,
09/2010 ~ present.
Current Ph.D.
Students
|
Industrial and Systems Engineering, Ph.D.
student, BS (09): Industrial Engineering,
Tsinghua University, Beijing, China Email: lijuanxu at usc.edu |
Industrial and Systems Engineering, Ph.D.
student, BS (09): Industrial Engineering,
Tsinghua University, Beijing, China Email: wang40 at usc.edu |
|
Jian Wu Industrial and Systems Engineering, Ph.D.
student, BS (11): Automation Control, Tsinghua
University, Beijing, China |
Jizhe Zhang Industrial and Systems Engineering, Ph.D.
student, BS (11): Automation Control, Tsinghua
University, Beijing, China |
1. Member of scientific committee (Editorial Board) for
the North American Manufacturing Research Institution (NAMRI) of SME, 2013-2015.
2.
Associate Editor, IEEE Transactions on Automation Science and Engineering,
01/2012-12/2014.
3. Contributing Editor, InterNano (www.internano.org), online resource of the
National Nanomanufacturing Network (NNN),
04/2012-04/2013.
4. Associate Editor (Quality, Micro and Nano Manufacturing Systems), SME Journal of
Manufacturing Systems, 2008-2011.
5. Special Issue Editor, “Quality, Sensing and Prognostics Issues in Nanomanufacturing”, Special Issue of the IIE Transactions
on Quality and Reliability Engineering/Manufacturing and Design.
6. Member of scientific committee (Editorial Board) for
the North American Manufacturing Research Institution (NAMRI) of SME, 2009-2011
7. IEEE CASE 2010 Program Committee for Track Automation
in Meso, Micro and Nano-Scale.
8.
Council member of QSR (Quality, Statistics, and Reliability) section at
INFORMS, 2010-2012.
9. Associate Editor (Automation in Meso, Micro and
Nano-Scale), 2009 IEEE Conference on Automation Science and Engineering (CASE
2009).
10. Member of INFORMS, SME, IIE, ASME, IEEE.
Last updated on 11/2012
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