MUMBAI, India, Aug. 24 -- Intellectual Property India has published a patent application (202641097781 A) filed by Sri Eshwar College Of Engineering on August 12, 2026, for A Multi-Agent Artificial Intelligence Architecture For Real-Time Edge Inference And Closed-Loop Decision-Making In Additive Manufacturing Process Monitoring.
Inventors include Tamil Selvan M; Nivashini M; and Keshav K.
The application for the patent was published on August 21, 2026, under issue no. 34/2026.
Abstract: The present invention relates to a multi-agent artificial intelligence architecture (100) for real-time monitoring and intelligent control of additive manufacturing processes. The architecture comprises a data acquisition module (101) configured to collect real-time process data from an RGB camera, thermal camera, vibration sensor, and printer parameters during 3D printing. The acquired data is transmitted to an edge computing module (200), where preprocessing and real-time AI inference are performed with minimal latency. The processed data is forwarded to a multi-agent artificial intelligence engine (300) comprising a vision agent, thermal agent, vibration agent, and process monitoring agent, each configured to independently analyze specific process parameters and detect manufacturing defects. The outputs of the multiple agents are integrated by a decision agent (400), which evaluates the overall process condition and determines appropriate corrective actions. The decision agent communicates with a closed-loop controller (500), configured to automatically optimize printing parameters including nozzle temperature, print speed, material flow rate, and bed temperature. The optimized parameters are transmitted to a printer controller (600), which updates the operating conditions of the additive manufacturing system in real time. The architecture continuously repeats the monitoring, analysis, decision-making, and parameter optimization cycle through a feedback mechanism, thereby enabling early defect detection, reducing material waste, improving print quality, minimizing manual intervention, and enhancing the overall reliability and efficiency of additive manufacturing operations.
Disclaimer: Curated by HT Syndication.
