MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641079538 A) filed by Madhankumar C; Mr. R. Ranjith Kumar; Mrs. M. Kowsalya; Nelluri Laxmi Narayana; A. M. Hemalatha; Dr. P. Thangaselvi; and Lamya Sainambu S. on June 28, 2026, for Ai-Driven Electro-Mechanical Self-Healing Smart Composite Structural System Using Embedded Wireless Sensor Networks, Edge Intelligence, And Autonomous Microvascular Repair Technology.
Inventors include Mr. R. Ranjith Kumar; Mrs. M. Kowsalya; Nelluri Laxmi Narayana; A. M. Hemalatha; Dr. P. Thangaselvi; and Lamya Sainambu S..
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: AI-Driven Electro-Mechanical Self-Healing Smart Composite Structural System Using Embedded Wireless Sensor Networks, Edge Intelligence, and Autonomous Microvascular Repair Technology Abstract The AI-Driven Electro-Mechanical Self-Healing Smart Composite Structural System Using Embedded Wireless Sensor Networks, Edge Intelligence, and Autonomous Microvascular Repair Technology presents an advanced interdisciplinary framework that integrates Mechanical Engineering, Electronics and Communication Engineering (ECE), Artificial Intelligence (AI), and smart materials to enhance the reliability, safety, and lifespan of critical engineering structures. The proposed system employs an embedded network of wireless sensors, including strain, vibration, temperature, acoustic emission, and pressure sensors, continuously monitoring the structural health of composite components in real time. Sensor data are processed through an edge intelligence module equipped with AI based predictive analytics to detect anomalies, classify damage types, estimate crack propagation, and predict structural failure before catastrophic breakdown occurs. By performing local processing at the edge, the system minimizes communication latency, reduces cloud dependency, and enables rapid autonomous decision-making. When damage is identified, the intelligent controller activates an embedded microvascular repair network that delivers self healing agents directly to the damaged region. These healing agents polymerize within the crack, restoring mechanical integrity without requiring manual intervention. The system further incorporates wireless communication technologies for remote monitoring, diagnostics, and maintenance through IoT-enabled platforms. Adaptive machine learning algorithms continuously improve prediction accuracy using historical and real-time operational data, enabling proactive maintenance scheduling and reducing unexpected failures. The integration of autonomous sensing, AI-driven diagnostics, embedded electronics, and self-healing composite materials significantly enhances structural durability while lowering maintenance costs and operational downtime. The proposed invention is suitable for aerospace structures, automotive components, bridges, wind turbine blades, railway infrastructure, marine systems, industrial machinery, and smart civil infrastructure. By combining predictive intelligence with autonomous repair capabilities, the system provides a next-generation solution for intelligent structural health monitoring and resilient electro-mechanical composite systems, contributing to safer, more sustainable, and cost-effective engineering applications.
Disclaimer: Curated by HT Syndication.
