Generative AI and CAD automation for diverse and novel mechanical component designs under data constraints

• Published in: Discover applied sciences Vol. 7; no. 4; pp. 315 - 21
• Main Authors: Li, Kun-Ying, Huang, Cheng-Kai, Chen, Qing-Wei, Zhang, Hsuan-Cheng, Tang, Tsann-Tay
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 09.04.2025; Springer Nature B.V; Springer
• Subjects: Aesthetic design; Applied and Technical Physics; Automation; CAD; CAD automation; Chemistry/Food Science; Computer aided design; Constraints; Costs; Data-constrained environments; Datasets; Deep learning; Design optimization; Designers; Diffusion models; Earth Sciences; Efficiency; Engineering; Engineering design; Environment; Generative artificial intelligence; Image processing; Machine learning; Manufacturing; Materials Science; Mechanical components; Methods; Performance evaluation; Product development; Prototyping; Engineering design; Data-constrained environments; CAD automation; Generative artificial intelligence

The efficient design of complex engineering components in data-constrained environments presents significant challenges to traditional methodologies. Existing deep learning-based generative design approaches often depend on large datasets, which limits their applicability in data-scarce contexts. Ad...