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Publications & Patents

Refereed Journals and Transactions

  1. Huang, Q.; Wang*, Y.; Lyu*, M.; Lin*, W; 2019, “Shape Deviation Generator (SDG) — A Convolution Framework for Learning and Predicting 3D Printing Shape Accuracy,” IEEE Transactions on Automation Science and Engineering, in press, DOI: 10.1109/TASE.2019.2959211.
  2. Jin*, Y., Qin, S., and Huang, Q., 2019, “Modeling Inter-layer Interactions for Out-of-Plane Shape Deviation Reduction in Additive Manufacturing ”IISE Transactions on Design and Manufacturing, in press, https://doi.org/10.1080/24725854.2019.1676936.
  3. Ferreira, R., Sabbaghi, A., Huang, Q., 2019, “Automated Geometric Shape Deviation Modeling for Additive Manufacturing Systems via Bayesian Neural Networks,”IEEE Transactions on Automation Science and Engineering, in press, DOI: 10.1109/TASE.2019.2936821.
  4. Jin*, Y., Qin, S., Huang, Q., Saucedo, V., Li, Z., Meier, A., Kunda, S., Lehr, B., and Charaniya, S., 2019, “Classification and Diagnosis of Bioprocess Cell Growth Productions Using Early- Stage Data ”Industrial & Engineering Chemistry Research, Vol. 58 (30), pp. 13469-13480.
  5. Luan*, H, Grasso, M., Colosimo, B., Huang, Q., 2019, “Prescriptive Data-Analytical Modeling of Selective Laser Melting Processes for Accuracy Improvement ”ASME Transactions, Journal of Manufacturing Science and Engineering, Vol. 141(1), 011008 (13 pages).
  6. Colosimo, B., Huang, Q., Dasgupta, T., Tsung, F., 2018, “Opportunities and Challenges of Quality Engineering for Additive Manufacturing ”Invited position paper, Journal of Quality Technology, Vol. 50(3), pp. 233-252.
  7.  Sabbaghi, A. and Huang, Q., 2018, “Model Transfer Across Additive Manufacturing Processes via Mean Effect Equivalence of Lurking Variables,”Annals of Applied Statistics, in press (available online).
  8. Duanmu*, Y., Riche, C., Gupta, M., Malmsdat, N., and Huang, Q., 2018, “Scale-up Modeling for Droplet Formation in Coated Microfluidic T-junction Across Multiple Domains,”IISE Transactions on Quality and Reliability, Vol. 50(10), DOI: 10.1080/24725854.2018.1443529, in press (available online).
  9. Sabbaghi, A., Huang, Q., and Dasgupta, T., 2017, “Bayesian Model Building From Small Sam- ples of Disparate Data for Capturing In-Plane Deviation in Additive Manufacturing,” Technometrics, DOI: 10.1080/00401706.2017.1391715, in press (available online).
  10. Zhang*, X., Wang*, H., Chen*, S., and Huang, Q., 2018, “A Novel Two-stage Optimization Approach to Machining Process Selection Using Error Equivalence Method ”Journal of Man- ufacturing Systems, Vol. 49, pp. 36-45.
  11. Zhu, Z., Anwer, N., Huang, Q., and Mathieu, L., 2018, “Machine Learning in Tolerancing for Additive Manufacturing,”CIRP Annals – Manufacturing Technology, Vol. 67(1), pp.157-160.
  12. Luan, H., and Huang, Q., 2017, “Prescriptive Modeling and Compensation of In-plane Geometric Deviations for 3D Printed Freeform Products,” IEEE Transactions on Automation Science and Engineering, in press, Vol. 14(1), pp. 73–82.
  13. Song, S., Wang, A., Huang, Q., Tsung, F., 2017, “In-Plane Shape-Deviation Modeling and Compensation for Fused Deposition Modeling Processes,” IEEE Transactions on Automation Science and Engineering, Vol. 14(2), pp. 968-976.
  14. Aghdam, F.F, Liao, H., and Huang, Q., 2017, “Modeling Interaction in Nanowire Growth Pro- cesses Toward Improved Yield,”IEEE Transactions on Automation Science and Engineering, Vol. 14(2), pp. 1139-1149
  15. Jin, Y., Qin, S., and Huang, Q., 2016, “Offline Predictive Control of Out-of-Plane Geomet- ric Errors for Additive Manufacturing, ”ASME Transactions on Manufacturing Science and Engineering, Vol. 138(12), pp.121005 (7 pages).
  16. Sosina, S., Dasgupta, T., and Huang, Q., 2016, “A Stochastic Graphene Growth Kinetics Model, ”Journal of the Royal Statistical Society, Series C, Vol. 65(5),  pp. 705–729.
  17. Bao, L., Huang, Q., and Wang, K., 2016 “Robust Parameter Design for Profile Quality Con- trol,”Quality and Reliability Engineering International, Vol. 32(3), pp.1059-1070.
  18. Wang, L. and Huang, Q., 2016, “A Strategy to Characterize Nanofabrication Processes with Large RPM (Experimental Run, Physics, and Measurement) Uncertainties,” IEEE Transactions on Semiconductor Manufacturing, Vol. 29(1), pp. 50-56.
  19. Huang, Q., 2016, “An Analytical Foundation for Optimal Compensation of Three-Dimensional Shape Deviations in Additive Manufacturing,” ASME Transactions, Journal of Manufacturing Science and Engineering, Vol. 138, No. 6, 061010.
  20. Duanmu, Y. and Huang, Q., 2015 “Analysis and Optimization of Skirt-Area Effect for III-V Nanowire Synthesis via Selective Area Metal-Organic Chemical Vapor Deposition,” IIE Transactions on Design and Manufacturing, Vol. 47, Issue 12, pp.1424–1431.
  21. Huang, Q., Zhang, J., Sabbaghi, A., and Dasgupta, T., 2015, “Optimal Offline Compensation of Shape Shrinkage for 3D Printing Processes,” IIE Transactions on Quality and Reliability, Vol. 47, No. 5, pp. 431-441.  (INFORMS 2014, IIE Transactions Invited Session paper and IIE Magazine Featured Article).
  22. Huang, Q., Nouri, H., Xu, K., Chen, Y., Sosina, S., and Dasgupta, T., 2014, “Statistical Predictive Modeling and Compensation of Geometric Deviations of 3D Printed Products, ” ASME Transactions, Journal of Manufacturing Science and Engineering, Special Issue on Additive Manufacturing (AM) and 3D Printing, Vol. 136, pp. 061008 – 061018.
  23. Sabbaghi, A., Dasgupta, T., Zhang, J., and Huang, Q., 2014, “Inference with Interference and Interference for Inference Modeling Potential Outcomes and the Structure of Interference in a 3D Printing Experiment, ” Annals of Applied Statistics, Annals of Applied Statistics, Vol. 8, No. 3, pp. 1395-1415.
  24. Xu, L. and Huang, Q., 2014, “Growth Process Modeling of III-V Nanowire Synthesis via Selective Area Metal Organic Chemical Vapor Deposition, ” IEEE Transactions on Nanotechnology,  Vol. 13, No. 6, pp. 1093 – 1101.
  25. Xu, L., Wang, L., and Huang, Q., 2014, “Semiconductor Nanowires Growth Process Modeling for Scale-up Nanomanufacturing: A Review, ” IIE Transactions on Quality and Reliability, Vol. 47, Issue 3, pp. 274-284.
  26. Wu, J., and Huang, Q., 2014 “Graphene Growth Process Modeling: A Physical-Statistical Approach, ” Applied Physics A, Materials Science & Processing, Vol. 116, No. 4, pp. 1747- 1756.
  27. Zhu, L., Dasgupta, T., and Huang, Q., 2014, “A Locally D-Optimal Design for Estimation of Parameters of an Exponential-Linear Growth Curve of Nanostructures,” Technometrics, Volume 56, Issue 4, pp. 432-442.
  28. Wang, L., and Huang, Q., 2013, “Cross-Domain Model Building and Validation (CDMV): A New Modeling Strategy to Reinforce Understanding of Nanomanufacturing Processes,” IEEE Trans on Automation Science and Engineering, Vol. 10(3), pp. 571–578.   (Finalist of 2012 QSR Best Student Paper Competition)
  29. Xu, L., and Huang, Q., 2013, “EM Estimation of Nanostructure Interactions with Incomplete Feature Measurement and Its Tailored Space Filling Design,” IEEE Trans on Automation Science and Engineering, Vol. 10(3), pp. 579–587.  IEEE Transactions on Automation Science and Engineering Best Paper Award
  30. 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.
  31. 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.
  32. 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.
  33. 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.
  34. Alvi, F., Joshi, R., Huang, Q., and Kumar, A., 2011, “Coarse-grained Kinetic Scheme-based Simulation Framework for Solution Growth of ZnO Nanowires,” Journal of Nanoparticle Research,  Vol.13(6), pp. 2451-2459.
  35. 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, Vol. 23, pp. 263 – 272.
  36. Chen, S., Wang, H., and Huang, Q., 2010, “Diagnosis of Multiple Error Sources Under Variation Equivalence,” NAMRI/SME Transactions, Vol. 38.
  37. 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.
  38. 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.
  39. 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, Vol. 28, pp. 11 – 16.
  40. 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.
  41. 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.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. Huang, Q., and Shi, J., 2004, “Stream of Variation Modeling of Serial-Parallel Multistage Manufacturing Systems,” ASME Transactions, Journal of Manufacturing Science and Engineering, 126, pp.611-618.
  47. 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.
  48. 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.
  49. Huang, Q., Shi, J., 2003, “Simultaneous Tolerance Synthesis through Variation Propagation Modeling of Multistage Manufacturing Processes,” NAMRI/SME Transactions, 31, pp. 515-522.
  50. 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.
  51. 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. Decker*, N. and Huang, Q., 2019, “Geometric Accuracy Prediction for Additive Manufac- turing Through Machine Learning of Triangular Mesh Data, ”2019 ASME 14th International Manufacturing Science & Engineering (MSEC) Conference, Erie, PA, USA.
  2. Luan, H., Post, B., and Huang, Q., 2017, “Statistical Process Control of In-Plane Shape Deformation for Additive Manufacturing, ” 2017 13th IEEE International Conference on Automation Science and Engineering (CASE 2017), Special Session on Predictive Modeling and Control of Additive Manufacturing, pp. 1274-1279, August 20-23, 2017, Xi’an, China.
  3. Jin, Y., Qin, S., and Huang, Q., 2016, “Prescriptive Analytics for Understanding of Out-of- Plane Deformation in Additive Manufacturing, ”2016 12th IEEE International Conference on Automation Science and Engineering (CASE 2016, ISAM 2016), August 21-24, 2015. Dallas, TX, USA
  4. Wang, J., Duanmu, Y., and Huang, Q., 2016, “Grade-Efficiency Modeling for Gas-Solids Cyclone Separators, ”2016 12th IEEE International Conference on Automation Science and Engineering (CASE 2016, ISAM 2016), August 21-24, 2015. Dallas, TX, USA.
  5. Sabbaghi, A., and Huang, Q., 2016, “Predictive Model Building Across Different Process Con- ditions and Shapes in 3D Printing, ” 2016 12th IEEE International Conference on Automation Science and Engineering (CASE 2016, ISAM 2016), August 21-24, 2015. Dallas, TX, USA.
  6. Luan, H. and Huang, Q., 2015, “Predictive Modeling of In-plane Geometric Deviation for 3D Printed Freeform Products, ” 2015 IEEE International Conference on Automation Science and Engineering (CASE 2015), Special Session on Predictive Modeling and Control of Additive Manufacturing, August 24-28, 2015, Gothenberg, Sweden.
  7. Jin, Y., Qin, S., and Huang, Q., 2015, “Out-of-Plane Geometric Error Prediction for Additive Manufacturing, ” 2015 IEEE International Conference on Automation Science and Engineering (CASE 2015), Special Session on Predictive Modeling and Control of Additive Manufacturing, August 24-28, 2015, Gothenberg, Sweden.
  8. Sabbaghi, A., Huang, Q., and Dasgupta, T., 2015, “Bayesian Additive Modeling for Quality Control of 3D Printed Products, “2015 IEEE International Conference on Automation Science and Engineering (CASE 2015), Special Session on Predictive Modeling and Control of Additive Manufacturing, August 24-28, 2015, Gothenberg, Sweden.
  9. Huang, Q., Nouri, H., Xu, K., Chen, Y., Sosina, S., and Dasgupta, T., 2014, “Predictive Modeling of Geometric Deviations of 3D Printed Products – A Unified Modeling Approach for Cylindrical and Polygon Shapes, ”the tenth IEEE International Conference on Automation Science and Engineering (CASE 2014) , Special Session on Predictive Modeling and Control of Additive Manufacturing, August 18-22, 2014, Taipei, Taiwan.  Best Application Paper Award Finalist
  10. Song, S., Wang, A., Huang, Q., Tsung, F., 2014, “Shape Deviation Modeling for Fused Deposition Modeling Processes, ”the tenth IEEE International Conference on Automation Science and Engineering (CASE 2014) , Special Session on Predictive Modeling and Control of Additive Manufacturing, August 18-22, 2014, Taipei, Taiwan.
  11. Sabbaghi A., Dasgupta T., Huang Q., and Zhang J., 2013, “Posterior Predictive Checks for Interference in a 3D Printing Experiment, ”Conference on Statistical Practice 2014.
  12. Wang, L. and Huang, Q., 2014, “Characterizing and Identifying Variations Among Nano Experimental Runs,”  ISCIE/ASME 2014 International Symposium on Flexible Automation (ISFA2014), July 14-16, 2014, Awaji-Island, Hyogo, Japan.
  13. Xu, L., Huang, Q., Sabbaghi, A., and Dasgupta, T., 2013 “Shape Deviation Modeling for Dimensional Quality Control in Additive Manufacturing, ”Proceedings of the ASME 2013 International Mechanical Engineering Congress & Exposition, November 15-21, 2013, San Diego,  USA.
  14. Sabbaghi A., Dasgupta T., Zhang J., Huang Q. “Inference with Interference and Interference for Inference: Modeling Potential Outcomes and the Structure of Interference in a 3D Printing Experiment, ”2013 Joint Statistical Meetings, August 2013.
  15. Wang, L., Huang, Q., Krishanan, S., Huey, E, and Bhansali, S., 2012,  “Physical knowledge integration in nano-manufacturing using approximate Bayesian computation,”  22nd International Conference on Flexible Automation and Intelligent Manufacturing (FAIM 2012).
  16. Huang, Q., 2011, “Integrated Nanomanufacturing and Nanoinformatics for Quality Improvement ”, 44th CIRP International Conference on Manufacturing Systems, June 1-3, 2011, Madison, Wisconsin (Invited).
  17. 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.
  18. Huang}, Q., Wang, H., 2008, “Error Equivalence Methodology for Dimensional Variation Control in Manufacturing,” 2008 IEEE International Conferences on Robotics, Automation & Mechatronics (RAM), RAM2008-1013, June 3-6, Chengdu, China.
  19. Wang, H., Huang, Q., 2005, “Automatic Process Adjustment for Reducing Dimensional Variation in Discrete Part Machining Processes,” ASME IMECE 2005.
  20. Wang, H., Huang, Q., Katz, R., 2004, “Multi-Operational Machining Processes Modeling for Sequential Root Cause Identification and Measurement Reduction,” ASME IMECE 2004.
  21. 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.
  22. 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.
  23. 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.
  24. 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.

Patents and Provisional Patents 


  1. U.S. Patent No. 9,886,526 3D Printing Shrinkage Compensation Using Radial and Angular Layer Perimeter Point Information, Published in February 2018.
  2. U.S. Patent No. 9,827,717 B2 Statistical Predictive Modeling and Compensation of Geometric Deviations of 3D Printed Products, Published in November 2017.
  3. U.S. Patent Application No. 15/143,358 Systems and Methods for Predicting and Improving Scanning Geometric Accuracy for 3D Scanners, filed on April 29, 2015.