Download pdf
Application of human-machine collaborative creative generation process in industrial design
- 1 East China University of Science and Technology
- 2 East China University of Science and Technology
* Author to whom correspondence should be addressed.
Abstract
This paper proposes a closed-loop human-machine co-creation process suitable for the early stages of industrial design. By integrating the Stable Diffusion model with the LoRA fine-tuning strategy, and constructing an image quality evaluation mechanism based on the dual metrics of CLIP and CMMD, the system guides designers in filtering and providing feedback on generated outputs to iteratively optimize prompts. The system integrates automatic scoring, manual filtering, and keyword clustering recommendation to form a collaborative closed loop of “generation—selection—optimization.” In a desk lamp design task, experiments demonstrate that this process significantly enhances the consistency of image styles and the quality of creative expression. The study verifies the feasibility of the human-machine collaboration mechanism in complex design tasks and offers a new paradigm for the application of generative AI in industrial product design.
Keywords
human-machine collaborative design, generative artificial intelligence, prompt optimization, product appearance design
[1]. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-Resolution Image Synthesis With Latent Diffusion Models. 10684–10695. https://openaccess.thecvf.com/content/CVPR2022/html/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper
[2]. Lawson, B. (2010). How designers think: The design process demystified (Reprint). Elsevier Architectural Press.
[3]. Cross, N. (2001). Designerly Ways of Knowing: Design Discipline Versus Design Science. Design Issues, 17(3), 49–55. https://doi.org/10.1162/074793601750357196
[4]. Song, B., Zhu, Q., & Luo, J. (2024). Human-AI collaboration by design. Proceedings of the Design Society, 4, 2247–2256. https://doi.org/10.1017/pds.2024.227
[5]. Wang, D., Churchill, E., Maes, P., Fan, X., Shneiderman, B., Shi, Y., & Wang, Q. (2020). From Human-Human Collaboration to Human-AI Collaboration: Designing AI Systems That Can Work Together with People. Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 1–6. https://doi.org/10.1145/3334480.3381069
[6]. Jiang, T., Sun, Z., Fu, S., & Lv, Y. (2024). Human-AI interaction research agenda: A user-centered perspective. Data and Information Management, 8(4), 100078. https://doi.org/10.1016/j.dim.2024.100078
[7]. Puranam, P. (2021). Human–AI collaborative decision-making as an organization design problem. Journal of Organization Design, 10(2), 75–80. https://doi.org/10.1007/s41469-021-00095-2
[8]. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning, 8748–8763. https://proceedings.mlr.press/v139/radford21a.html
[9]. Jayasumana, S., Ramalingam, S., Veit, A., Glasner, D., Chakrabarti, A., & Kumar, S. (2024). Supplementary Material for Rethinking FID: Towards a Better Evaluation Metric for Image Generation. arXiv:2401.09603. https://doi.org/10.48550/arXiv.2401.09603
Cite this article
Yao,H.;Yu,W. (2025). Application of human-machine collaborative creative generation process in industrial design. Advances in Engineering Innovation,16(5),54-62.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Journal：Advances in Engineering Innovation
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).