MUMBAI, India, Sept. 22 -- Intellectual Property India has published a patent application (202641097780 A) filed by Sri Eshwar College Of Engineering on August 12, 2026, for Ai-Driven Real-Time Analytics Dashboard For Predictive Defect Detection And Process Quality Visualization In 3d Printing.
Inventors include Tamil Selvan M; Keshav K; and Nivashini.
The application for the patent was published on September 18, 2026, under issue no. 38/2026.
Abstract: The present invention relates to an AI-driven real-time analytics dashboard for predictive defect detection and process quality visualization in additive manufacturing. The dashboard comprises a live inspection interface including synchronized RGB and thermal image visualization modules, process quality indicators, and real-time machine status monitoring. Which is configured to continuously acquire, process, and display multi-modal inspection data generated during layer-wise fabrication. The acquired information is analyzed using an artificial intelligence prediction engine comprising defect probability estimation, quality score evaluation, machine health assessment, and predictive risk analysis. Which continuously evaluates printing conditions and generates real-time recommendations for defect prevention and process optimization. The dashboard further comprises a historical analytics module configured to store production records, inspection results, process telemetry, and machine performance trends within a local edge database for traceability and statistical evaluation. A statistical process control module continuously monitors process stability using control charts, process capability analysis, and quality variance monitoring to identify abnormal manufacturing conditions and support corrective decision- making. The integrated dashboard provides synchronized visualization, predictive analytics, explainable artificial intelligence insights, historical trend analysis, and statistical quality monitoring in a unified interface, thereby enabling early defect detection, reducing manufacturing failures, improving process consistency, minimizing operator intervention, and enhancing the overall reliability and quality of additive manufacturing processes.
Disclaimer: Curated by HT Syndication.
