A generative adversarial network-based methodology in systematic machine design at concept phase for solutions

Artificial intelligence (AI) has made a significant impact in engineering through its data analysis and interpretation capabilities. In systematic machine design, AI tools can enhance the design process, from improving quality analysis in computer-aided design (CAD) drawings to generating three-dimensional models and inverse design structures. This research proposes a methodology for using generative AI in the conceptual design phase, based on the StyleGAN2ADA architecture, an image generation model designed for high-quality synthesis with limited training data. The method aims to preserve trademark or otherwise recognizable features in the design while exploring new combinations of desired design attributes. A case study demonstrates the approach by blending visual features from cars and shoes using a combined dataset of 3667 images, with StyleGAN2-ADA trained to an optimal point of ∼4800 kimg. Across the generated samples, about 2% of generated images showed clear car–shoe feature mixing, while fewer than 2% were classified as blurred, and the full training run required on the order of 6–10 GPU hours depending on hardware configuration. These results indicate that StyleGAN2-ADA-based methods have practical potential for supporting conceptual design while also revealing limitations in mixing rate, controllability, and output quality that warrant further work.