Thursday, September 14, 2017

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.

Thursday, August 10, 2017

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.

Thursday, June 29, 2017

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.


Friday, March 3, 2017

Deadline Extended - Call for Papers: Special Issue on Additive Manufacturing for IISE Transactions

CALL FOR PAPERS
Special Issue on Additive Manufacturing
IISE Transactions Focus Issue on Design and Manufacturing


Deadline Extended 

Additive manufacturing (AM), or known as three-dimensional (3D) printing, refers to a new class of technologies associated with the direct fabrication of physical products from Computer-Aided Design (CAD) models by a layered manufacturing process. It is widely recognized as a disruptive technology, having the potential to fundamentally change the nature of future manufacturing. The changes involve each stage of the product life-cycle: design, modeling and simulation, manufacturing, quality control, metrology, and logistics, etc. This special issue intends to collect cutting-edge research works illustrating the impact of AM on design and manufacturing. Topics of interest for this special issue include, but are not limited to the following:

  •        Design for AM
  •        Process planning for additive manufacturing
  •        New AM processes and new material processing using AM techniques
  •        Functionally graded and multimaterial AM
  •        Design, analysis and optimization of AM parts
  •        Modeling and simulation of AM processes
  •        In-situ and in-line sensing, monitoring, and control of AM processes
  •        Statistical process control and capability analysis for AM processes
  •        Inspection, validation, verification and qualification of AM parts
  •        Data analytics and learning for AM, including cyber-enabled AM
  •        Shape analysis for AM 
  •      Logistics and supply chain for AM
  •      Uncertainty quantification for AM
  •       AM applications in biomedical, energy, and transportation fields


Important Dates
         Manuscript submission deadline: September 1st, 2017  December 1st, 2017
       Notification of disposition of the manuscript: December 1st, 2017  March 1st, 2018
       Final revisions due:  February 1st, 2018  May 1st, 2018
       Final paper acceptance decision, April 1st, 2018    July 1st, 2018

       Publication date: Fall/Winter 2018

Submission Guidelines:

Authors should submit their paper through Scholar One's Manuscript Central online manuscript submission system at http://mc.manuscriptcentral.com/iietransactions. Please follow the instructions carefully.  When queried, indicate the paper is being submitted to a Special Issue, identify the special issue as Additive Manufacturing Special Issue and select Qiang Huang as the preferred Editor.

Special Issue Editors:
Prof. Qiang Huang, qiang.huang@usc.edu, University of Southern California
Prof. Zhengyu (James) Kong, zkong@vt.edu, Virginia Tech
Prof. Xiaoping Qian, xiaoping.qian@wisc.edu, University of Wisconsin-Madison.