Qiang Huang, Professor, FIISE & FASME & SMNAI
Epstein Department of Industrial and Systems Engineering
Mork Family Department of Chemical Engineering & Materials Science
University of Southern California
Email: qiang.huang@usc.edu
Phone: 213-740-2433
Office: GER 216 C
Research Interests:
- Quality control theory and methods for personalized manufacturing
- Engineering-informed data analytics and machine learning methods
- Finite Manufacturing Primitive (FMP) method for Small-Sample Machine Learning in Additive Manufacturing
- Control-Theoretic Machine Learning for Engineering Systems
- Foundations of Accuracy Control for Additive Manufacturing (FACAM): FACAM Blog
- Nanomanufacturing and Nanoinformatics
2025 IEEE International Conference on Automation Science and Engineering (CASE) will be hosted by USC. Hope to see you in LA!
General Chair: Prof. Qiang Huang (USC)
General Co-chair: Prof. SK Gupta (USC)
Program Chair: Prof. Yu Ding (Texas A&M)
Smart 3D Printing Quality Control Service Portal: PrintFixer 1.0
Selected users can be provided free dimensional quality control service. Join us to build a quality control service platform for 3D printing user community!
Position Opening:
- Research Assistant Positions for PhD students
Updates and Media Reports:
March 2024: PhD candidate Weizhi Lin’s work, “Finite manufacturing primitives: a representation scheme for additive manufacturing quality assurance,” has been accepted by CIRP Annals – Manufacturing Technology. Congratulations!
February 2024: National Academy of Inventors (NAI) senior member
November 2023: Ph.D. student Weizhi Li’s paper on landmark selection for automated registration of 3D printed products is accepted to IISE Transactions Data Science, Quality and Reliability:
Weizhi Lin and Q. Huang, 2023, “Automated Deviation-Aware Landmark Selection for Freeform Product Accuracy Qualification in 3D Printing, ” IISE Transactions, Data Science, Quality and Reliability, DOI: 10.1080/24725854.2023.2280606, in press.
August 2023: Prof. Cesar Ruiz, a former post-doc research fellow in our group, is one of the three finalists for the first Peter Luh Memorial Best Paper Award for Young Researcher in 2023 IEEE CASE. His paper is:
Ruiz, Cesar; Bhatt, Prahar; Gupta, Satyandra K.; Huang, Qiang, 2023, “Process-Informed Segmentation of Dense Point Clouds for Layer Quality Assessment in Large-Scale Metal Additive Manufacturing”, 2023 IEEE International Conference on Automation Science and Engineering (CASE 2023), August 26-30, 2023, Auckland, New Zealand.
Spring 2023: On sabbatical leave.
November 2022
Prof. Huang will deliver a plenary talk at 2022 19th International Conference on Electrical Engineering, Computing Science and Automation Control (CCE 2022), Mexico City, Mexico, November 9-11, 2022
October 2022
Congratulation to Weizhi Lin for winning the Best Poster Award of the “QSR Student Poster Competition” at the 2022 Annual INFORMS Conference out of a total of 23 posters. The title of the poster was “Patch-Based Functional Deviation Characterization and Prediction for Complex Freeform Manifolds in Additive Manufacturing”.
July 2022
A process-informed, control-theoretic machine learning approach for small-sample learning in additive manufacturing:
- Huang, Q., 2022,“An Impulse Response Formulation for Small-Sample Learning and Control of Additive Manufacturing Quality,” IISE Transactions on Design and Manufacturing, Special issue of AI and Machine Learning for Manufacturing, DOI:10.1080/24725854.2022.2113186, in press.
June 2022
Dr. Yuanxiang Wang will join the School of Mechanical Engineering at Tongji University, Shanghai, as a faculty member! Congratulations, Prof. Wang!
Share two new journal papers: one extending the fabrication-aware convolution learning framework to a broader class of 3D geometries for 3D printing accuracy control, and the other providing accuracy control for Wire and Arc Additive Manufacturing:
- Yuanxiang Wang*, Cesar Ruiz*, and Q. Huang, 2022, “Learning and Predicting Shape Deviations of Smooth and Non-Smooth 3D Geometries through Mathematical Decomposition of Additive Manufacturing, ” IEEE Transactions on Automation Science and Engineering, DOI: 10.1109/TASE.2022.3174228, in press.
- Cesar Ruiz*, Davoud Jafari, Vignesh V. Subramanian, Tom H.J. Vaneker, Wei Ya, and Qiang Huang, 2022, “Prediction and Control of Product Shape Quality in Wire and Arc Additive Manufacturing Using Generalized Additive Models,” ASME Transactions, Journal of Manufacturing Science and Engineering, DOI: 10.1115/1.4054721, in press.
May 2022
PhD student Weizhi Lin’s paper “Functional Characterization and Correction of Biofouling in Multi-Receptor Biosensors” (co-authored with Post-doc Cesar Ruiz) won the 2022 IISE/QCRE Track Best Paper Award during the 2022 Institute of Industrial and Systems Engineers (IISE) Conference in Seattle.
Yuanxiang Wang passed his dissertation defense! His dissertation title is “Fabrication-Aware Machine Learning for Accuracy Control in Additive Manufacturing.” Congratulations, Dr. Wang!
The following two journal papers are accepted for publication:
- Cesar Ruiz*, Davoud Jafari, Vignesh V. Subramanian, Tom H.J. Vaneker, Wei Ya, and Qiang Huang, 2021,“ Prediction and Control of Product Shape Quality in Wire and Arc Additive Manufacturing Using Generalized Additive Models,” ASME Transactions, Journal of Manufacturing Science and Engineering.
- Yuanxiang Wang*, Cesar Ruiz*, and Q. Huang, 2021, “Learning and Predicting Shape De- viations of Smooth and Non-Smooth 3D Geometries through Mathematical Decomposition of Additive Manufacturing, ” IEEE Transactions on Automation Science and Engineering, DOI: 10.1109/TASE.2022.3174228.
April 2022
Post-doc Dr. Cesar Ruiz accepted an offer of a tenure-track Assistant Professor position from the School of Industrial and Systems Engineering at the University of Oklahoma. He will join OU in fall 2022. Congratulations, Prof. Ruiz!
March 2022
PhD student Weizhi Lin’s paper “Functional Characterization and Correction of Biofouling in Multi-Receptor Biosensors” (co-authored with Post-doc Cesar Ruiz) is among the finalists of 2022 IISE/QCRE Best Track Paper Competition!
Nathan Decker passed his dissertation defense! His dissertation title is “Machine Learning-Driven Deformation Prediction and Compensation for Additive Manufacturing.” Congratulations, Dr. Decker!
June 2021
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The IEEE CASE2021 Best Conference Paper Award: Yuanxiang Wang, Cesar Ruiz, and Q. Huang, 2021, “Extended Fabrication-Aware Convolution Learning Framework for Predicting 3D Shape Deformation in Additive Manufacturing,” 2021 IEEE International Conference on Automation Science and Engineering (CASE 2021), August 23-27, 2021, Lyon, France.
- Chris and Nathan received the Honorable Mention of NAMRC Student Research Presentation Award. Christopher Henson, Nathan Decker and Qiang Huang, 2021, “A Digital Twin Strategy for Major Failure Detection in Fused Deposition Modeling Processes,” SME North American Manufacturing Research Conference (NAMRC) 49, June 21-25, 2021, University of Cincinnati, Cincinnati, OH.
May 2021
- Nathan Decker received USC Viterbi School’s Jenny Wang Excellence in Teaching Award!
- Nathan Decker received ISE Department Teaching Assistant of the Year Award!
- NAMRI/SME Scientific Committee selected Chris Henson’s and Nathan Decker’s paper titled “A digital twin strategy for major failure detection in fused deposition modeling processes” as a finalist for the Best Student Research Presentation Competition.
- Nathan Decker and Yuanxiang Wang passed their qualifying exams!
March 25, 2021
“Control-Theoretic Machine Learning for Intelligent 3D Printing Accuracy Control,” Panel of Artificial Intelligence in Healthcare 3D Printing
June 6 2020
Speaker at Panel on Artificial Intelligence and Healthcare 3D Printing at 3DHEALS2020
March 2020
- AITopics.org
- DesignNews
- The Fabricator
- 3DPrints
- Dophin CMC Global Internal News
- 3D Printing Zoom
- 3D Print Pulse
- innovations report
- Saintifia (Indonesia)
February 2020
Making 3-D printing smarter with machine learning featured in:
February 2019
- Industry Week: Universities’ New Machine Learning Tool Improves Additive Manufacturing
- 3D Printing Industry: Purdue and University of Southern California Enhance 3D Printing Quality Control with Machine Learning
- Campus Technology: Machine Learning Augments 3D Design for Greater Precision
October 2019
- Machine Learning for Additive Manufacturing: Software Demo of 3D Shape Learning and Accuracy Control
Group Updates posted here