Sunday, May 27, 2018

New Article: Feature-Based Methodology for Design of Geometric Benchmark Test Artifacts for Additive Manufacturing Processes My recent paper ''Feature-Based Methodology for Design of Geometric Benchmark Test Artifacts for Additive Manufacturing Processes'' is online.  The paper is about following a systematic methodology to design a geometric benchmark test artifact for characterizing and quantifying the GD&T characteristics for an additive manufacturing system.  Comments and discussions are most welcome!

Tuesday, April 17, 2018

Marie Skłodowska Curie Action (MSCA) training program - Individual Fellowship call 2018 at Politecnico di Milano

The aim of the call is to attract and train young and talented researchers to successfully applying for MSCA European Fellowship with the Politecnico di Milano as host institution. Candidates will be pre-selected based on their expression of interest, CV and motivation. Promising candidates will be invited to the Politecnico di Milano for 3 days (11-13 June 2018) to meet with their supervisors, to visit laboratories and facilities, to attend an in depth training course on the proposal writing and to make use of full support in the application writing process by POLIMI advisors.
The call involves several topics.

One topic at the Department of Mechanical Engineering of Polimi: Metal Additive Manufacturing for Industry 4.0. The focus is on bridging data analytics and statistical methods to additive manufacturing for novel intelligent systems and zero-defect manufacturing.

Details can be found here:

The due date for submission is April 30th, 2018.

Thursday, March 1, 2018

Model Transfer Across Additive Manufacturing Processes via Mean Effect Equivalence of Lurking Variables (accepted by the Annals of Applied Statistics)

Shape deviation models constitute an important component in quality control for additive manufacturing (AM) systems. However, specified models have a limited scope of application across the vast spectrum of processes in a system that are characterized by different settings of process variables, including lurking variables. In this paper, which was recently accepted by the Annals of Applied Statistics, Dr. Arman Sabbaghi and Dr. Qiang Huang present a new effect equivalence framework and Bayesian method that enables deviation model transfer across processes in an AM system with limited experimental runs. Model transfer is performed via inference on the equivalent effects of lurking variables in terms of an observed factor whose effect has been modeled under a previously learned process. Studies on stereolithography illustrate the ability of their framework to broaden both the scope of deviation models and the comprehensive understanding of AM systems. More details about this paper can be found at the following link:

Friday, January 19, 2018

Tentative Agenda of FACAM 2018 Workshop

FACAM 2018
Location: GER 206 at University of Southern California
Feb 8
1:30 PMProf. Q. Huang (USC)
Welcome and Introduction
2:00 PMProf. Charyar SOUZANI (ENS Paris-Saclay)
Prediction of geometrical and dimensional deviations of Additive Manufacturing parts
2:45 PM Dr. Jun Zeng (HP), He Luan (HP, USC)
Thoughts on applying machine learning to help to uncover causal effects in
HP’s multi-jet fusion printing process physics
3:30 PMProf. Prahalad Rao (UNL)
The Unusual Effectiveness of Spectral Graph Theory for Quality Assurance in Additive Manufacturing
4:30 PMJie Ren (FSU)
Joint learning of process variation models for process co-monitoring and control using cloud data
5:30 PMWelcome Dinner at USC Faculty Club
Feb 9
9:00 AMRefreshment
9:15 AMProf. Marco Grasso (Politecnico di Milano)
Statistical process monitoring of Powder Bed Fusion processes via in-situ video imaging
10:00 AM
Prof. Arman Sabbaghi (Purdue)
Deviation Modeling Across Different Process Conditions and Shapes in Additive Manufacturing Systems
10:45 AMProf. Matthew Plumlee (Northwestern U)
Calibration with coverage and consistency
11:30 AMYuanxiang Wang (USC)
Statistical Inter-Layer Bonding Effects Modeling and Estimation with Convolution Formulation
12:15 PMBox lunch at GER 206
1:30 PMLinkan Bian (MSU)
Predicting Deformation in Laser-Based Additive Manufacturing Processes
2:15 PMRaquel de Souza Borges Ferreira (Purdue)
Automated Geometric Shape Deviation Modeling
for Additive Manufacturing Systems via Bayesian Neural Networks
3:00 PMProf. Matthew Plumlee (Northwestern U)
3D Printing Product Matching with Approximate Models
4:00 PMAdjourn
4:30 PMLA Live

Wednesday, January 3, 2018

"Revitalizing manufacturing through AI" by Andrew Ng


"AI is already transforming the IT industry." "It is now time to build not just an AI-powered IT industry, but an AI-powered society. One in which our physical needs, health care, transportation, food, and lodging are more accessible through AI, and where every person is freed from repetitive mental drudgery. For the whole world to experience the benefits of AI, it must pervade many industries, not just the IT industry."

The IT industry has primarily shaped our digital environment. Manufacturing touches nearly every part of our society by shaping our physical environment. It is through manufacturing that human creativity goes beyond pixels on a display to become physical objects. By bringing AI to manufacturing, we will deliver a digital transformation to the physical world.

AI technology is well suited to addressing the challenges facing manufacturing, such as variable quality and yield, inflexible production line design, inability to manage capacity, and rising production costs. AI can help address these issues, and improve quality control, shorten design cycles, remove supply-chain bottlenecks, reduce materials and energy waste, and improve production yields."

"Many companies are figuring out how to use AI, but this is not easy. The technology is still complex, and few teams understand AI well enough to implement it effectively. Outside the IT industry, almost no companies have enough access to AI talent.

Further, just as using IT to transform a traditional company requires more than building a website, using AI to transform a company requires much more than training a few machine learning models. The strategy of integrating AI — everything from data acquisition, to organizational structure design, to figuring out how to prioritize AI projects — is as complex as the technology, and good AI strategists are even rarer than good AI technologists."

Tuesday, December 12, 2017

Reading List for Student Participants of FACAM 2018 Workshop

For PhD students joining the FACAM 2018 workshop, you may read the following references for better communication with each others:
  1. 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(5), pp. 431–441. 
  2. Luan, H., and Huang, Q., 2017, “Prescriptive Modeling and Compensation of In-Plane Shape Deformation for 3-D Printed Freeform Products, ”IEEE Transactions on Automation Science and Engineering, Vol. 14(1), pp. 73–82.
  3. Huang, Q., 2016, “An Analytical Foundation for Optimal Compensation of Three-Dimensional Shape Deformation in Additive Manufacturing, ”ASME Transactions, Journal of Manufacturing Science and Engineering, Vol. 138(6), 061010.
  4. Sabbaghi, A., Huang, Q., and Dasgupta, T., 2017, “Bayesian Model Building From Small Sam- ples of Disparate Data for 3D Printing ,” Technometrics, in press.
  5. Sabbaghi, A. and Huang, Q., 2017, “Model Transfer via Equivalent Effects of Lurking Variables in 3D Printing,” Annals of Applied Statistics.
  6. Shireen, T., Shao, C., Wang, H., Li, J., Zhang, X., and Li, M., (2017), Iterative Multi-Task Learning for Time-Series Modeling of Solar Panel PV Outputs, Applied Energy. (Appear Soon).
  7. Shao, C., Ren, J., Wang, H., Jin, J., & Hu, S. J. (2017). Improving Machined Surface Shape Prediction by Integrating Multi-Task Learning with Cutting Force Variation Modeling. ASME Journal of Manufacturing Science and Engineering, 139 (1), 011014.
  8. Plumlee, M., 2017, "Bayesian calibration of inexact computer models,"  Journal of the American Statistical Association 112 (519), 1274-1285
  9. M Plumlee, VR Joseph, H Yang, 2015, "Calibrating functional parameters in the ion channel models of cardiac cells," Journal of the American Statistical Association.
  10. Plumlee, M., and Apley, D., 2017,  "Lifted Brownian kriging models," Technometrics 59 (2), 165-177.
  11. Colosimo, B., 2017, "Modeling and monitoring methods for spatial and image data, " Quality Engineering,"  30:1, 94-111, DOI: 10.1080/08982112.2017.1366512.
  12. M. Grasso, A.G. Demir, B. Previtali, B.M. Colosimo, 2017, "In situ monitoring of selective laser melting of zinc powder via infrared imaging of the process plume," Robotics and Computer–Integrated Manufacturing 49 (2018) 229–239.
  13. M. Grasso and B.M. Colosimo, "Process defects and in situ monitoring methods in metal powder bed fusion: a review, " Meas. Sci. Technol. 28 (2017) 044005 (25pp).
  14. Schleich, B., Anwer, N., Mathieu, L., Wartzack, S., 2014, Skin Model Shapes: A new paradigm shift for geometric variations modelling in mechanical engineering, Computer-Aided Design, 50:1-15.
  15. Schleich, B., Wartzack, S., Anwer, N., Mathieu, L., 2015, Skin Model Shapes: Offering now potentials for modelling product shape variability, ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Volume 1A: 35th Computers and Information in Engineering Conference, ISBN: 978-0-7918-5704-5, doi:10.1115/DETC2015-46701, 2–5 August, 2015, Boston, Massachusetts, USA. 
  16. Schleich, B., Anwer, N., Mathieu, L., Wartzack, S., 2016, Status and Prospects of Skin Model Shapes for Geometric Variations Management, Procedia CIRP, 43:154-159, Proceedings of the 14th CIRP Conference on Computer Aided Tolerancing (CAT 2016), 18-20 May 2016, Gothenburg, Sweden.

The access link to copies of these manuscripts might be provided upon request. (Email:

Wednesday, November 22, 2017

Updated on the 2nd FACAM Workshop at USC

The 2nd FACAM (Foundation of Accuracy Control for Additive Manufacturing) Workshop will be held at USC on February 8th and 9th, 2018.  Currently we have participants from
  • Purdue University
  • Florida State University
  • Northwestern University
  • École Normale Supérieure (ENS) Paris-Scalay
  • Politecnico di Milano (Polimi)
  • HP Labs at Palo Alto
  • University of Nebraska-Lincoln
  • Mississippi State University

Saturday, October 21, 2017

Bayesian Model Building From Small Samples of Disparate Data for Capturing In-Plane Deviation in Additive Manufacturing (accepted by Technometrics)

Quality control of geometric shape deviation in additive manufacturing relies on statistical deviation models. However, resource constraints limit the manufacture of test shapes, and consequently impede the specification of deviation models for new shape varieties. In this paper, which was recently accepted by Technometrics, Dr. Arman Sabbaghi, Dr. Qiang Huang, and Dr. Tirthankar Dasgupta present an adaptive Bayesian methodology that effectively combines in-plane deviation data and models for a small sample of previously manufactured, disparate shapes to aid in the model specification of in-plane deviation for a broad class of new shapes. The power and simplicity of this general methodology is demonstrated with illustrative case studies on in-plane deviation modeling for polygons and straight edges in free-form shapes using only data and models for cylinders and a single regular pentagon. Their new Bayesian approach facilitates deviation modeling in general, and thereby can help advance additive manufacturing as a high-quality technology. More details about this paper can be found at the following link: