SiDi Lab Received Fund to Accelerate Technology Transfer and Commercialization

SiDi Lab directed by Dr. Sha and AM3 Lab directed by Dr. Wenchao Zhou recently received Commercialization Fund from University of Arkansas Office of Economic Development to accelerate technology transfer and commercialization of our research on Cooperative 3D Printing (C3DP). This project will collaborate with AMBOTS Inc., a startup company aims to automate manufacturing with a swarm of smart and autonomous mobile robots, to commercialize key technologies span off from our research, such as mobile print robots, chunk-based printing and C3DP scheduling. The Commercialization Fund is generously funded by Walton Family Charitable Support Foundation and administered by UA’s Office of Economic Development to provide short-term funding for actionable projects that move technologies toward the marketplace.


Three Papers Accepted by IDETC 2019

This year we have three papers accepted by ASME 2019 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference. Congratulations to the PhD students Molla Rahman and Laxmi Poudel. They will present their work in August at the conference that will be held at Anaheim in California this year.

  • M. Rahman, C. Xie, Z. Sha, “A Deep Learning Based Approach to Predicting Sequential Design Decisions”.
  • L. Poudel, W. Zhou, Z. Sha, “Computational Design of Scheduling Strategies for Multi-Robot Cooperative 3D Printing“.
  • J. Xie, Y. Bi, Z. Sha, M. Wang, Y. Fu, N. Contractor, W. Chen, “A Dynamic Network Analysis Approach to Understanding the Impact of Engineering Design on Evolutions of Market Competition”.

First Cooperatively Printed Part

On March 20th 2019, after more than two year’s collaborative efforts with Dr. Wenchao Zhou, an Assistant Professor from the Department of Mechanical Engineering at the UA and the startup company AMBOTS, we made the world-first cooperatively 3D-printed part. It is the first time that multiple independent 3D printers work together to print a part with a size way bigger than the printer itself. This achievement is realized through our Swarm Printing concept and the cooperative 3D printing (C3DP) platform. The workpiece (a beehive structural beam) shown in the picture is approximately 840×70×20 mm in L×W×H. The workpiece in this size would be impossibly printed by any existing commercial desktop 3D printers. The C3DP platform is backed by several research methods and technologies developed by the team, including 3D printing robotics, chunk-based slicer, chunk bond strength testing, and automatic scheduling and path planning for chunk-based C3DP. Our vision is to establish an Autonomous Digital Additive Manufacturing (ADAM) system of the future. On March 21st, we demonstrated our research and technologies to the R&D group of ArcBest. See media coverage on our project here.img_6927-e1553292874446.jpg

SiDi Research on Cooperative 3D Printing is Highlighted by Media

Our research on cooperative 3D printing, especially our recent efforts on developing a new computational framework for automatically generating scheduling strategies of coordinating a swarm of printing robots was recently highlighted by Research Frontiers at the University of Arkansas. This project is collaborated with Dr. Wenchao Zhou from the Department of Mechanical Engineering at the UA and the startup company AMBOTS. Please see SiDi Publication and our previous post for details. The work has been also highlighted in several mainstream media of 3D printing industry, such as,, and all3dp.

SiDi Received Seed Grant for Initiating Industrial Collaboration

SiDi Lab recently received a seed grant for initiating an industrial collaboration with Ford Motor Company. This grant is awarded by the Office of Research & Innovation at the University of Arkansas. We will collaborate with Ford Global Data, Insight & Analytics (GDIA) to establish a novel approach to modeling customer choice behaviors in electric vehicle (EV) market based on network theory. Particularly, we will investigate how well the network model performs in predicting customers’ choices in EV market as compared to existing choice modeling techniques, such as the Discrete Choice Model.