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

Abstract

Artificial Intelligence (AI) has revolutionized engineering design workflows, particularly through its integration into Computer-Aided Design (CAD) systems. Traditional CAD tools rely heavily on manual input and deterministic modeling, which limits flexibility, adaptability, and optimization potential. This study explores the development of AI-driven CAD systems that leverage neural networks specifically Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) to automate, optimize, and refine engineering product designs. The proposed framework introduces an intelligent CAD architecture that learns from existing model datasets and applies pattern recognition to generate optimized design configurations with minimal human intervention. Simulation-based evaluations demonstrate that AI-driven CAD can enhance design efficiency by up to 35%, reduce prototype iteration time by 28%, and improve design accuracy by 22% compared to conventional systems. The neural network’s predictive capability enables rapid identification of design flaws and adaptive modifications, establishing a feedback-driven development cycle. This approach signifies a transformative shift from static modeling to dynamic, data-driven design ecosystems, aligning with Industry 4.0 principles and sustainable manufacturing goals. The study concludes that neural-network-assisted CAD platforms are a critical step toward achieving fully autonomous, intelligent design environments in engineering innovation.