AI-DRIVEN COMPUTER-AIDED DESIGN (CAD) SYSTEMS: LEVERAGING NEURAL NETWORKS FOR OPTIMIZED ENGINEERING PRODUCT DEVELOPMENT

Computer-Aided Design (CAD) systems are foundational to modern engineering, enabling the creation of precise digital models. However, traditional CAD often relies heavily on manual input and iterative testing, leading to prolonged development cycles. The integration of Artificial Intelligence (AI), specifically Neural Networks, presents a transformative opportunity to overcome these limitations. This paradigm shift towards AI-driven CAD leverages machine learning to automate and enhance the design process. By learning from vast datasets of existing designs and performance metrics, these systems can predict optimal geometries, suggest design improvements, and perform real-time simulation and optimization. The key benefits include significantly reduced design time, lower costs, and the generation of superior, high-performance products that may surpass conventional human-centric design paradigms.

Authors

Publication Details

Journal
International Journal of Apllied Mathematics
Published
2025-10-08
DOI
https://doi.org/10.12732/ijam.v38i5s.341
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
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article

AI-DRIVEN COMPUTER-AIDED DESIGN (CAD) SYSTEMS: LEVERAGING NEURAL NETWORKS FOR OPTIMIZED ENGINEERING PRODUCT DEVELOPMENT

S. Menaka
International Journal of Apllied Mathematics
Manufacturing Process and Optimization
article

AI-DRIVEN COMPUTER-AIDED DESIGN (CAD) SYSTEMS: LEVERAGING NEURAL NETWORKS FOR OPTIMIZED ENGINEERING PRODUCT DEVELOPMENT

S. Menaka
article en

Abstract

Computer-Aided Design (CAD) systems are foundational to modern engineering, enabling the creation of precise digital models. However, traditional CAD often relies heavily on manual input and iterative testing, leading to prolonged development cycles. The integration of Artificial Intelligence (AI), specifically Neural Networks, presents a transformative opportunity to overcome these limitations. This paradigm shift towards AI-driven CAD leverages machine learning to automate and enhance the design process. By learning from vast datasets of existing designs and performance metrics, these systems can predict optimal geometries, suggest design improvements, and perform real-time simulation and optimization. The key benefits include significantly reduced design time, lower costs, and the generation of superior, high-performance products that may surpass conventional human-centric design paradigms.

International Journal of Apllied MathematicsVol. 38(5s)
Openalex Percentile: Top 60%
Manufacturing Process and Optimization
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