MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202621066209 A) filed by Meghana Pritam Lokhande on May 26, 2026, for Ai-Powered Cnc Quotation And Production Optimization Web Platform.
Inventors include Dr. Namrata Gawande; Meghana Pritam Lokhande; Prof. Ashwini Matange; Omkar Shivaji Deshmukh; Atharv Vijay Dhanade; Sahil Bandu Dalvi; and Mahesh Bandopant Dhule.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: The present invention relates to an AI-powered CNC quotation and production optimization web platform configured to automate generation of machining quotations and manufacturing optimization insights from two-dimensional CAD files. The system comprises a file ingestion module configured to receive CAD files containing manufacturing geometry and a geometry processing engine configured to extract geometric primitives including lines, arcs, circles, splines, ellipses, and polylines. The geometry processing engine further computes manufacturing parameters including total cutting length, bounding dimensions, centroid coordinates, geometric density metrics, and a normalized manufacturing complexity index representative of machining difficulty and geometric intricacy. A deterministic quotation engine utilizes the extracted geometric and operational parameters to generate repeatable machining quotations based on machining time, material utilization, setup cost, machine operating cost, taxation parameters, and manufacturing complexity. The platform further includes a traversal optimization engine configured to optimize machining traversal paths between geometric entities using heuristic and iterative refinement techniques to reduce tool travel distance and machining time. A nesting estimation engine simulates component placement on raw material sheets to estimate projected material utilization efficiency and wastage reduction. The system additionally incorporates an advisory generation engine configured to generate manufacturability recommendations through machine-learning or large- language-model inference using geometric, financial, and optimization-derived manufacturing context. An asynchronous execution controller enables quotation generation independently of advisory processing to reduce response latency and improve computational efficiency. The platform outputs a consolidated quotation document containing machining cost estimates, manufacturing complexity information, traversal optimization metrics, material utilization estimates, and AI- generated manufacturability recommendations, thereby improving quotation accuracy, production planning efficiency, and manufacturing decision support in CNC machining environments.
Disclaimer: Curated by HT Syndication.
