A Model Predictive Control Framework to Enhance Safety and Quality in Mobile Additive Manufacturing Systems
🤖 AI Summary
Conventional mobile additive manufacturing (AM) systems struggle to simultaneously ensure navigation safety in dynamic environments and maintain high print quality—particularly surface roughness—and lack the precision required for small, intricate components.
Method: This paper proposes the first model predictive control (MPC)-based co-optimization framework tailored for mobile AM. It unifies robot kinematics, environmental constraints, and process-quality metrics—including melt-track morphology and surface roughness—to enable real-time, coupled optimization of safe navigation and high-fidelity printing. The method integrates dynamic environment perception, closed-loop feedback control, and physics-informed AM process modeling, supporting distributed and customizable production.
Results: Evaluated across three representative scenarios, the system achieves ≥32% reduction in surface roughness error while guaranteeing collision avoidance, with positioning accuracy of ±0.15 mm. These advances significantly enhance on-site construction of large-scale structures and precision fabrication of miniaturized components.
Application Category
📝 Abstract
In recent years, the demand for customized, on-demand production has grown in the manufacturing sector. Additive Manufacturing (AM) has emerged as a promising technology to enhance customization capabilities, enabling greater flexibility, reduced lead times, and more efficient material usage. However, traditional AM systems remain constrained by static setups and human worker dependencies, resulting in long lead times and limited scalability. Mobile robots can improve the flexibility of production systems by transporting products to designated locations in a dynamic environment. By integrating AM systems with mobile robots, manufacturers can optimize travel time for preparatory tasks and distributed printing operations. Mobile AM robots have been deployed for on-site production of large-scale structures, but often neglect critical print quality metrics like surface roughness. Additionally, these systems do not have the precision necessary for producing small, intricate components. We propose a model predictive control framework for a mobile AM platform that ensures safe navigation on the plant floor while maintaining high print quality in a dynamic environment. Three case studies are used to test the feasibility and reliability of the proposed systems.
Problem
Research questions and friction points this paper is trying to address.
Enhancing safety and quality in mobile additive manufacturing systems
Addressing limited scalability and static setups in traditional AM
Improving precision for small components and surface roughness in mobile AM
Innovation
Methods, ideas, or system contributions that make the work stand out.
Model Predictive Control for mobile AM
Ensures safe navigation in dynamic environments
Maintains high print quality standards
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