Published April 9, 2025 | Version v1
Journal article Open

ARTIFICIAL INTELLIGENCE IN THE CAD PROCESS: MACHINE LEARNING MODELS, GENERATIVE OPTIMISATION, AND THEIR IMPACT ON DESIGN

Creators

  • 1. ROR icon Polytechnic University of Timişoara

Contributors

  • 1. 1 PhD Student at Technical University of Cluj-Napoca, Department of Manufacturing Engineering, B-dul Muncii, no. 103-105, Cluj-Napoca, Romania, E-mail: alexandrina.buga.phd@gmail.com 2 Technical University of Cluj-Napoca, Department of Manufacturing Engineering, B-dul Muncii, no. 103-105, Cluj-Napoca, Romania, E-mail: marian.borzan@tcm.utcluj.ro 3 Technical University of Cluj-Napoca, Department of Manufacturing Engineering, B-dul Muncii, no. 103-105, Cluj-Napoca, Romania, E-mail: adrian.trif@tcm.utcluj.ro

Description

: The integration of Artificial Intelligence (AI) into Computer-Aided Design (CAD) is transforming the product development process by enhancing efficiency, accuracy, and innovation. AI-driven approaches, including Machine Learning Models (MLMs) and optimisation algorithms, automate design processes, improve decision-making, and enable the exploration of optimised solutions that transcend traditional design limitations. Despite these advancements, the adoption of AI in CAD presents significant technological, ethical, and professional challenges. This study aims to analyse the impact of AI on CAD workflows, focusing on its role in automating repetitive tasks, optimising manufacturability, enhancing design validation through AI-assisted simulation, and facilitating collaborative workflows in distributed teams. Additionally, it explores the challenges associated with AI implementation and the future prospects of AI-driven CAD systems. The research is based on a systematic review of AI applications in CAD, examining Machine Learning (ML) techniques such as generative design, reinforcement learning, and genetic algorithms. Furthermore, the study also evaluates AI's influence on Product Lifecycle Management (PLM) and team collaboration, while addressing industry adoption barriers. Findings indicate that AI significantly reduces design time, enhances creativity through generative models, and improves design validation via automated simulations. AI-powered tools provide real-time feedback, streamline collaboration, and enable continuous optimisation of product performance and sustainability. Ultimately, AI-assisted CAD marks a paradigm shift in engineering and design.

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References

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