Tentative Agenda of FACAM 2018 Workshop

	FACAM 2018
	Location: GER 206 at University of Southern California
Feb 8
1:30 PM	Prof. Q. Huang (USC) Welcome and Introduction
2:00 PM	Prof. 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 PM	Prof. Prahalad Rao (UNL) The Unusual Effectiveness of Spectral Graph Theory for Quality Assurance in Additive Manufacturing
4:30 PM	Jie Ren (FSU) Joint learning of process variation models for process co-monitoring and control using cloud data
5:30 PM	Welcome Dinner at USC Faculty Club
Feb 9
9:00 AM	Refreshment
9:15 AM	Prof. 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 AM	Prof. Matthew Plumlee (Northwestern U) Calibration with coverage and consistency
11:30 AM	Yuanxiang Wang (USC) Statistical Inter-Layer Bonding Effects Modeling and Estimation with Convolution Formulation
12:15 PM	Box lunch at GER 206
1:30 PM	Linkan Bian (MSU) Predicting Deformation in Laser-Based Additive Manufacturing Processes
2:15 PM	Raquel de Souza Borges Ferreira (Purdue) Automated Geometric Shape Deviation Modeling for Additive Manufacturing Systems via Bayesian Neural Networks
3:00 PM	Prof. Matthew Plumlee (Northwestern U) 3D Printing Product Matching with Approximate Models
4:00 PM	Adjourn
4:30 PM	LA Live

"Revitalizing manufacturing through AI" by Andrew Ng

Source: https://medium.com/@andrewng/revitalizing-manufacturing-through-ai-a9ad32e07814

"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."

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: qiang.huang@usc.edu)

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

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:

http://amstat.tandfonline.com/doi/full/10.1080/00401706.2017.1391715

New Article on Shrinkage Compensation

I just published an article on shrinkage compensation in FDM process. I used shrinkage as a tool to compensate for shrinkage. You may have a look at the paper from the following link.

The 15th CIRP Conference on Computer Aided Tolerancing – June 11-13, 2018, Milano, Italy

http://www.cirpcat2018.polimi.it

Research Assistant Positions Available

RA positions are available for dedicated and motivated students working on Advanced Manufacturing (Additive Manufacturing or Nanomanufacturing) at Prof. Huang's Lab. Students with engineering background and analytical training are encouraged to apply. (Email: qiang.huang@usc.edu)

The 2nd FACAM Workshop Will be Held at USC on February 8th & 9th, 2018

With the generous support from Epstein Institution at USC's Viterbi School of Engineering, the 2nd FACAM workshop will be held at USC on February 8th & 9th, 2018.

Limited seats might be open to public for sessions on February 8th, 2018.  Please contact Prof. Huang at qiang.huang@usc.edu for more information.

PhD student He Luan works at summer intern at HP Labs on 3D Printing

https://newsblog.ext.hp.com/t5/HP-newsroom-blog/Summer-2017-interns-at-HP-Labs-He-Luan/ba-p/981

As an undergraduate majoring in robotics and intelligent systems at the University of Science and Technology of China, He Luan was introduced to a new but fast growing engineering field: 3D printing. “It gave me an idea of how revolutionary 3D printing could become in terms of how we make things and also what the challenges were to improving it,” she recalls. The insight spurred Luan to enroll in the Ph.D. program in industrial and systems engineering at the University of Southern California, where she specializes in applying data and statistical learning to 3D printing research and applies knowledge gained from her studies in USC’s computer science master’s degree program.

HP: What’s your HP Labs research project this summer?

A major initiative in the Print Adjacencies and 3D Lab explores ways to analyze job data and data from heat sensors in HP’s 3D printers to predict how well the printing process is working and then asks how we can use those predictions to optimize the process. As part of that effort, I’m applying deep learning to these data sets and exploring different neutral network architectures to predict the thermal behavior of each layer of material as it is printed. If we are successful, our goal is to use this model to make the machines print even more precisely than they can at present.

HP: How has it been going so far? 

I’m in my sixth week of fourteen and I have an outline for what I hope to achieve and a plan for getting there. The data source is very rich and there’s deep science inside our printing physics, so it’s a challenge. We are actually designing our own neutral network architectures.  We’ve done some preliminary tests. So far so good.  

HP: What’s the value of using deep learning here? 

We are trying to predict thermal behavior using models derived from physical sciences. The challenge is that thermal behavior in a 3D printer is incredibly complex. Other HP research teams are running a lot of experiments to help us to uncover the underlying interactions, and thanks to our collaborators in HP’s 3D printing business unit, we have great access to that data. Deep learning allows us to examine all the different data sources and automate the discovery process for the underlying inter-connectivity between different factors. By applying deep learning to the data, we’re hoping to reveal information patterns that allow us to predict thermal behavior and help us to build even more accurate physical models.

HP: What is the biggest challenge you face in doing this work?

Our problem is a bit different from a typical deep learning problem like video prediction, for example. In our case the predictions we come up with have to be very high resolution – at the scale of a prediction per voxel (the 3D equivalent of a pixel). That’s not always something you need in a typical deep learning application - when it can be enough just to be pointed in a better direction. Like most recurrent neutral network problems, we have both tempo and spatial components. Our system is different in that we have external spatial excitations in the form of agent amount data flowing to the printheads. To attack that, our “Big Idea” is to generate a new, scalable network structure. We may write an invention disclosure by the end of summer if we have good results.

HP: How does your HP work relate to your academic research?

My Ph.D. advisor has been collaborating with HP. In fact, he is visiting HP’s 3D printing group in Barcelona next month. My dissertation is looking at ways to improve geometric accuracy, or what we call geometrical fidelity control, in 3D printing. This is one of the leading problems that HP is interested in. I am definitely hoping to continue working with HP Labs after my internship.  I’d love to help some of the ideas we cook up over the summer find their way into products.