Towards scalable intelligence at the edge: a multi-agent framework for quality assurance in additive manufacturing
Year: 2026
Type: article
Abstract: Real-time quality assurance in Additive Manufacturing remains challenging, as most monitoring frameworks do not directly account for process dynamics on the printer. This work presents the Additive Edge Multi-Agent (AEMA) framework, an acoustic monitoring system that runs entirely on an embedded single-board computer connected directly to a fused deposition modeling printer. AEMA separates the task into three parts: Specialist Agents that analyze short, phase-specific windows of spectral audio features, a Transformer-based Historian that models the full print history, and an Orchestrator that combines their outputs using an attention mechanism to predict the current print condition. Experiments on six print classes (normal print, over-extrusion, under-extrusion, no extrusion, poor layer adhesion, and toolhead failure) show an improvement in accuracy from 87.76% for the baseline multi-agent model to 98.37% for the final AEMA architecture, with an average inference time of about 105 ms per 1-s segment on an ODROID-XU4Q board. Findings indicate that a multi-agent, edge-deployed design can support practical real-time defect monitoring in AM.
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,290
Awards: Excellence in Research: Convergent Physics-based Data-driven Bioprinting of Regenerative Tissues for Future Biomanufacturing, G3208942897, CAP: Expanding AI at North Carolina A&T State University Through Interdisciplinary Research, Education and Workforce Development, Excellence in Research: Cyber Threats Early Warning Framework for Operational Technology Systems, Excellence in Research: A Cyber-Physical System Framework for In-process Quality Assurance of Inkjet-based Additive Manufacturing, G6290493839, G79896949, G813708806
OpenAlex ID: W7140528231