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This is a Literature Review study.

September 10, 2025

Challenges and opportunities in the integration of generative AI with computer-aided design

Key Points

Generative AI has not fully transformed computer-aided design, remaining underutilized despite its success in other fields.
Key challenges include small datasets and difficulty with mixed data types, alongside proprietary file formats hindering integration.
Case studies and a literature review highlight the need for high-quality datasets and novel neural network architectures for progress.
Future developments should focus on creating vendor-neutral formats and expanding generative methods to improve CAD applications.

Abstract

ABSTRACT: Computer-aided design (CAD) has become essential for hardware product development in our industrial age. However, increasing complexity, shorter lead times, and cost pressures present new challenges. While generative AI has gained significant attention and transformed various business functions, its application in engineering design with CAD remains underdeveloped. Our research aims to explore why generative AI has not yet reached its potential in CAD, despite its prominence in other fields, by identifying key challenges through case studies and a literature review. These challenges include small datasets, difficulty representing mixed data types, proprietary file formats, and lack of advanced CAD modeling commands. We propose future developments such as high-quality datasets, a vendor-neutral format, novel neural network architectures, and expanded generative methods.

Challenges and opportunities in the integration of generative AI with computer-aided design

Key Points

Abstract

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