AI-Driven Feature Recognition in CAD Models Using Topological Optimization

Abstract

Recognizing features in CAD models is vital for manufacturing and automated planning, yet conventional rule and shape-based techniques often fail with complicated and overlapping geometries. This paper presents an AI-driven approach for feature recognition in CAD models that leverages machine learning and deep learning techniques to automatically identify and classify geometric and manufacturing features. By learning patterns directly from CAD data, the proposed method improves recognition accuracy, reduces manual intervention, and adapts effectively to complex and free-form designs. Experimental results demonstrate that AI-based feature recognition outperforms conventional techniques in terms of robustness, efficiency, and scalability, making it a promising solution for intelligent CAD/CAM integration and smart manufacturing systems.

Cite as:

Gadhave A. R. (2026). AI-Driven Feature Recognition in CAD Models Using Topological Optimization. Recent Trends in Automation and Automobile Engineering, 9(1), 56–61. https://doi.org/10.5281/zenodo.19348063