A Universal Edge-Cloud Architecture for Autonomous 3D Printing

As 3D printing moves toward full autonomy, the need for a universal, scalable, and intelligent architecture becomes increasingly critical. Modern additive manufacturing faces persistent barriers: fragmented device interfaces, lack of remote operability, ad hoc parameter tuning, and insufficient integration between physical processes and data-driven control. As additive manufacturing evolves toward more complex applications such as structural color fabrication, there is a growing demand for a universal, intelligent control framework capable of integrating diverse printer hardware, automating parameter optimization, and managing experimental data at scale. Our work presents a novel universal edge-cloud architecture that combines edge-side hardware adaptors with a cloud-based backend to support real-time control, intelligent optimization, and robust data management in the domain of autonomous 3D print. A centralized, user-friendly web interface allows users to configure and launch print jobs (campaigns), define experimental parameters, and review historical results. The cloud backend is built with scalable microservices, including a high-performance RabbitMQ-based messaging system for edge-cloud communication, MongoDB for structured storage of experimental metadata, print records and Clowder for managing archival files and analysis reports. Distributed image analysis and machine learning-based prediction services run in parallel to optimize print parameters. This supports autonomous closed-loop experimentation across large-scale campaigns. To ensure flexibility and hardware independence, print jobs are abstracted using a custom PCP (Parameterized Control Protocol) file format. PCP files encapsulate device-specific instructions along with structured metadata describing print sequences, geometric layout, and adjustable parameters. This abstraction enables batch-based execution and seamless orchestration across printers running different configurations. The system exchanges messages with 3D printers via a custom-developed edge adaptor deployed to the near printers. We validate the system through an application in structural color 3D printing, focusing on the relationship between color properties—represented in the HSV color space—and key printing parameters such as pressure, speed, and bed temperature. The system was deployed on the UIUC Radiant cloud cluster and tested across multiple print shapes defined by PCP files on the 3D printers using Marlin firmware. To efficiently explore the parameter space and enhance predictive performance, we incorporate a Bayesian optimizer into the machine learning workflow. The system’s adaptability and modular design enable scalable, reproducible experimentation across a wide range of hardware, demonstrating its potential as a universal platform for autonomous 3D printing.