Search for: All records

We introduce a novel actor-critic framework that utilizes vision-language models (VLMs) and large language models (LLMs) for design concept generation, particularly for producing a diverse array of innovative solutions to a given design problem. By leveraging the extensive data repositories and pattern recognition capabilities of these models, our framework achieves this goal through enabling iterative interactions between two VLM agents: an actor (i.e., concept generator) and a critic. The actor, a custom VLM (e.g., GPT-4) created using few-shot learning and fine-tuning techniques, generates initial design concepts that are improved iteratively based on guided feedback from the critic—a prompt-engineered LLM or a set of design-specific quantitative metrics. This process aims to optimize the generated concepts with respect to four metrics: novelty, feasibility, problem–solution relevancy, and variety. The framework incorporates both long-term and short-term memory models to examine how incorporating the history of interactions impacts decision-making and concept generation outcomes. We explored the efficacy of incorporating images alongside text in conveying design ideas within our actor–critic framework by experimenting with two mediums for the agents: vision language and language only. We extensively evaluated the framework through a case study using the AskNature dataset, comparing its performance against benchmarks such as GPT-4 and real-world biomimetic designs across various industrial examples. Our findings underscore the framework’s capability to iteratively refine and enhance the initial design concepts, achieving significant improvements across all metrics. We conclude by discussing the implications of the proposed framework for various design domains, along with its limitations and several directions for future research in this domain. 

Generative adversarial networks (GANs) have recently been proposed as a potentially disruptive approach to generative design due to their remarkable ability to generate visually appealing and realistic samples. Yet, we show that the current generator-discriminator architecture inherently limits the ability of GANs as a design concept generation (DCG) tool. Specifically, we conduct a DCG study on a large-scale dataset based on a GAN architecture to advance the understanding of the performance of these generative models in generating novel and diverse samples. Our findings, derived from a series of comprehensive and objective assessments, reveal that while the traditional GAN architecture can generate realistic samples, the generated and style-mixed samples closely resemble the training dataset, exhibiting significantly low creativity. We propose a new generic architecture for DCG with GANs (DCG-GAN) that enables GAN-based generative processes to be guided by geometric conditions and criteria such as novelty, diversity and desirability. We validate the performance of the DCG-GAN model through a rigorous quantitative assessment procedure and an extensive qualitative assessment involving 89 participants. We conclude by providing several future research directions and insights for the engineering design community to realize the untapped potential of GANs for DCG. 

Generative Adversarial Networks (GANs) have shown stupendous power in generating realistic images to an extend that human eyes are not capable of recognizing them as synthesized. State-of-the-art GAN models are capable of generating realistic and high-quality images, which promise unprecedented opportunities for generating design concepts. Yet, the preliminary experiments reported in this paper shed light on a fundamental limitation of GANs for generative design: lack of novelty and diversity in generated samples. This article conducts a generative design study on a large-scale sneaker dataset based on StyleGAN, a state-of-the-art GAN architecture, to advance the understanding of the performance of these generative models in generating novel and diverse samples (i.e., sneaker images). The findings reveal that although StyleGAN can generate samples with quality and realism, the generated and style-mixed samples highly resemble the training dataset (i.e., existing sneakers). This article aims to provide future research directions and insights for the engineering design community to further realize the untapped potentials of GANs for generative design.