Research on concept generation design of automobile seats based on human-machine co-creation.

This study aims to develop a human-machine co-creation framework for automobile seat conceptual design, leveraging an improved Deep Convolutional Generative Adversarial Network (ResNet-DCGAN) to lower design barriers for non-professionals and enhance cross-disciplinary innovation. By constructing a dataset of automobile seat images and implementing generative design strategies across three key stages, this research seeks to demonstrate the feasibility of AI-driven creativity augmentation in product design. The cooperation of human-machine co-creation can stimulate the innovative thinking of participants from different industries, reduce the design difficulty, arouse participants' enthusiasm, and provide abundant creativity in designing automobile seats. At each stage of the automobile seat design, methodological strategies are proposed to promote the successful implementation of the design. Specifically, by creating a data set of automobile seats and using the deep convolutional generative adversarial network improved by the ResNet residual module (ResNet-DCGAN) to stimulate creative inspiration, we let the computer take the preliminary sketches of the participants as input to generate design schemes that are both innovative and meet the aesthetic requirements of the public, providing a theoretical basis for human-machine collaborative innovation research.