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Researchers from 91³Ô¹ÏÍø and two other universities have received funding from the NSF to refine and streamline coordination between robotic 3D printers – a project aimed at improving the automated manufacturing of large industrial parts.

, an associate professor at the University of Houston's Cullen College of Engineering in the Industrial Engineering Department, is the lead researcher for an NSF-funded project “Integrated Framework for Cooperative 3D Printing: Uncertainty Quantification, Decision Models, and Algorithms.” The $505,789 award will cover research through 2026.

Harsha Gangammanavar

Co-principal investigators for the research are , an associate professor in the Mechanical Engineering Department at the University of Arkansas; and Harsha Gangammanavar, associate professor in Operations Research and Engineering Management at 91³Ô¹ÏÍø.

“There has been a significant push towards enhancing domestic manufacturing in recent years, and the present project aligns with these initiatives,” Gangammanavar said. “Particularly, additive manufacturing (AM) or 3D printing, is gaining momentum in various industries, including automotive, aerospace, energy, defense, and construction.

“Mathematical optimization, the focus of our research at 91³Ô¹ÏÍø, plays a vital role in realizing the full potential of AM,” he said.

The team’s research is focused on advancing data analytics and decision-making methods for the efficiency of the novel cooperative 3D printing (C3DP) technology. According to the project's abstract, a critical barrier to the widespread adoption of additive manufacturing (AM) technologies has been slow printing speeds, leading to excessive printing times for large parts.

C3DP utilizes a fleet of printhead-carrying mobile robots to perform printing jobs cooperatively, significantly improving scalability and reducing print time. Operational control of these systems needs to address the decline in accuracy of mobile printers. This decline can have cascading effects on product quality and production efficiency. Additionally, uncertainties in the printing process make scheduling for C3DP very challenging.

The research team identified three goals:

1. Develop advanced statistical machine learning models to precisely forecast the location of robot printers and infer hidden conditions. This will help ensure timely maintenance of robot printers.
2. Develop a suite of stochastic optimization models using dynamic chance constraints for maintenance planning, production scheduling, and collision-free routing.
3. Validate and demonstrate the research methods through proof-of-concept experiments at their research labs, computational simulations, and collaborations with industrial partners.

Successful development of these models and algorithms will potentially transform AM into a new, ultra-efficient era of automated 3D printing. 

“The models and algorithms we will develop will not only aid in improving additive manufacturing operations, but the methods and theory developed will also be applicable in other settings where preventive maintenance is beneficial,” Gangammanavar said.

This material is based upon work supported by the National Science Foundation under Award No. 2329739.

Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. – University of Houston