MUMBAI, India, Aug. 31 -- Intellectual Property India has published a patent application (202641101035 A) filed by Sri Eshwar College Of Engineering on August 20, 2026, for Layer-By-Layer Digital Twin-Based Real-Time Defect Detection And Adaptive Process Optimization For Additive Manufacturing.
Inventors include Muthu Samy M; Navasree R S; P Ganeshan; and Tamil Selvan M.
The application for the patent was published on August 28, 2026, under issue no. 35/2026.
Abstract: The present invention relates to an intelligent Digital Twin-based Additive Manufacturing system for real-time layer-by-layer quality monitoring, defect prediction, and adaptive process optimization. The system establishes a synchronized digital twin of a physical additive manufacturing process and continuously updates the virtual model after the completion of each printed layer. Process data collected from multiple sensors, including a vision camera, thermal sensor, vibration sensor, extrusion monitoring sensor, and temperature sensor, are transmitted to the digital twin for real-time analysis. An Artificial Intelligence (AI)-based defect detection module compares the printed layer with the corresponding digital layer to identify manufacturing defects such as layer shifting, warping, under-extrusion, over-extrusion, porosity, and dimensional deviations. A Layer Health Score (LHS) is computed for each completed layer to evaluate print quality. When the Layer Health Score falls below a predefined threshold, a defect propagation prediction module estimates the impact of the detected defect on subsequent layers. Based on the prediction, an adaptive process controller automatically modifies one or more printing parameters, including nozzle temperature, print speed, extrusion rate, cooling fan speed, and material flow, before deposition of the next layer. The layer-by-layer monitoring and adaptive optimization continue throughout the printing process, thereby improving print quality, reducing material waste, minimizing manufacturing failures, and enhancing the reliability of additive manufacturing systems.
Disclaimer: Curated by HT Syndication.
