Integrated Framework for Cooperative 3D Printing: Uncertainty Quantification, Decision Models, and Algorithms

University Of Houston, Houston TX

Investigators

Abstract

This award will fund research that contributes to national prosperity and economic welfare by advancing data analytics and decision-making methods for enhancing the operational efficiency of the novel cooperative 3D printing (C3DP) technology. 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. Effective methods for operational control of these systems must consider the accuracy degradation of mobile printers, which can lead to cascading effects in product quality and production efficiency, as well as uncertain factors in the production process and are unsuitable for C3DP. This research will address these issues by providing innovative, integrated models and algorithms to improve the operational efficiency of C3DP. This project will also prepare the next generation of scientists and engineers by providing multidisciplinary research and training opportunities for K-12, undergraduate, and graduate students. The multidisciplinary team will incorporate AM, optimization, and stochastic models to achieve three specific research objectives: (1) Create an advanced mixed higher-order hidden Markov model for positional accuracy prediction of robot printers and inference of hidden conditions, facilitating 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. A simulator and a C3DP platform will be developed to validate and demonstrate the methods. Successful development of these models and algorithms will potentially transform AM into a new, ultra-efficient era of automated 3D printing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.