MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641096740 A) filed by Dr. Sangupandy; Dr. P. Prabhuraj; Dr. I. Karthic Subramaniyan; Dr. V. S.; V. Nagaraj; Dr. P. Vijayakumar; and Dr. A. Arun Negemiya on August 11, 2026, for Ai Driven Adaptive Surface Reconstruction For Reverse Engineering And Rapid Prototyping.
Inventors include Dr. Sangupandy; Dr. P. Prabhuraj; Dr. I. Karthic Subramaniyan; Dr. V. S. Ajith; Dr. P. Vijayakumar; V. Nagaraj; and Dr. A. Arun Negemiya.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: Reverse engineering is one of the most widely adopted techniques for the reproduction and modification of existing products. Compared with conventional manufacturing approaches, it provides an efficient method for reconstructing complex components with improved dimensional accuracy. In this study, two different categories of components were selected for reverse engineering to evaluate the influence of geometric characteristics on surface reconstruction. To minimize reconstruction errors, a Coordinate Measuring Machine (CMM) was employed to capture accurate point-cloud data from the selected components. The investigation considered two distinct curve categories, namely analytical curves and synthetic (free-form) curves. Since both components possessed regular geometries and were suitable for contact-based measurement, CMM scanning was selected as the preferred data acquisition method. The acquired scan data were imported into CATIA V5 for surface reconstruction. Two surface modelling techniques, namely Lofted Surface and Coons Surface, were applied to generate the corresponding CAD models. The reconstructed models were subsequently converted into solid models for geometric and volumetric analysis. The performance of the selected surface modelling techniques was evaluated by comparing reconstructed analytical and free-form components. Guide curve lengths and material volumes were determined for each reconstructed model and compared with the corresponding measurements of the original physical components. The percentage deviations in guide curve length and material volume between the reconstructed CAD models and the actual components were calculated to assess the reconstruction accuracy and determine the most suitable surface modelling approach for reverse engineering applications.
Disclaimer: Curated by HT Syndication.
