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All Journal Asian Journal of Science, Technology, Engineering, and Art
Das, Rajnandani
Unknown Affiliation
Arts Computer Science & IT Engineering Social Sciences

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Geometric Foundations of Engineering Design: The Role of Conic Sections Enhanced by Artificial Intelligence Das, Rajnandani; Shah, Neha; Sahani, Suresh Kumar
Asian Journal of Science, Technology, Engineering, and Art Vol 4 No 1 (2026): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v4i1.8700

Abstract

Many branches of engineering rely on four fundamental geometric shapes: circle, ellipse, parabola, and hyperbola, whose intrinsic properties enable engineers to develop more accurate mathematical models, optimize trajectories, and enhance structural integrity in complex design contexts. This study examines how these classical conic sections are applied in real-world engineering problems and explores the utilization of geometric principles in robotics, signal processing, and automated systems to support efficient problem-solving. By relating the properties of conic sections to engineering requirements in areas such as bridge design, trajectory optimization, and structural analysis, the study elucidates how these forms underpin both analytical modelling and practical implementation in contemporary engineering practice. The analysis shows that the relevance of conic sections to practical engineering applications is clearly demonstrated across multiple domains, highlighting their role in improving modelling accuracy, guiding system optimization, and informing robust design strategies. The study concludes that classical geometry, particularly the theory of conic sections, continues to play a vital role in shaping modern engineering practices and carries important implications for advancing engineering education, promoting interdisciplinary integration, and sustaining innovation in technology and infrastructure development.
Deep Learning - Based Shape Recognition and Classifications of Conic Geometries in Engineering Drawing Das, Rajnandani; Shah, Neha; Sah, Dilip Kumar; Sahani, Kameshwar; Sahani, Suresh Kumar
Asian Journal of Science, Technology, Engineering, and Art Vol 4 No 2 (2026): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v4i2.9335

Abstract

Engineering drawings frequently contain conic geometries such as circles, ellipses, parabolas, and hyperbolas, which are fundamental to mechanical design and industrial applications. Accurate identification and classification of these shapes are therefore essential for computer-aided design (CAD) systems, automated inspection, and intelligent design analysis. However, conventional geometry-based or rule-based approaches often perform poorly when drawings are noisy, complex, or partially incomplete. This study proposes a deep learning-based approach using convolutional neural networks (CNNs) to automatically extract features and classify conic shapes in engineering drawings. By learning discriminative visual representations directly from input data, the proposed method enhances classification accuracy, improves robustness, and reduces the need for manual intervention. The study concludes that CNN-based conic shape recognition offers a reliable and efficient solution for engineering and industrial contexts, with practical implications for improving automation and intelligent analysis in design-related applications.
Co-Authors Sah, Dilip Kumar Sahani, Kameshwar Sahani, Suresh Kumar Shah, Neha