Innovation Direction of Intelligent Products - Research on Personalized Product Design System Based on Generative AI

In the context of consumption upgrade and intelligent manufacturing, personalized customization has become a core trend in product innovation. Generative AI (AIGC) is gradually a new tool, for the automation product design. This paper puts forward an AI-driven personalized product design system, that integrates engineering constraints and business dimensions. This is built on a diffusion generation model combined with a dual-cycle generative-evaluation architecture, and we use the smart lunch box as an empirical case to verify its feasibility. Through our research, workflow reengineering and quantitative evaluation, our study implecates that the system achieves an engineering feasibility for generated solutions, compared to unconstrained AIGC generation. The design cycle is reduced greatly, representing an efficiency gain. This research not only provides new tools and for product design but also offers a feasible path to achieve mass customization.

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Publication Details

Journal
Applied and Computational Engineering
Published
2026-11-03
DOI
https://doi.org/10.54254/2755-2721/2026.ld38648
Primary Topic
Product Development and Customization
Type
article
Field-Weighted Citation Impact
0.00
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article

Innovation Direction of Intelligent Products - Research on Personalized Product Design System Based on Generative AI

Yiran Wang
Applied and Computational Engineering
Product Development and Customization
article

Innovation Direction of Intelligent Products - Research on Personalized Product Design System Based on Generative AI

Yiran Wang
article en

Abstract

In the context of consumption upgrade and intelligent manufacturing, personalized customization has become a core trend in product innovation. Generative AI (AIGC) is gradually a new tool, for the automation product design. This paper puts forward an AI-driven personalized product design system, that integrates engineering constraints and business dimensions. This is built on a diffusion generation model combined with a dual-cycle generative-evaluation architecture, and we use the smart lunch box as an empirical case to verify its feasibility. Through our research, workflow reengineering and quantitative evaluation, our study implecates that the system achieves an engineering feasibility for generated solutions, compared to unconstrained AIGC generation. The design cycle is reduced greatly, representing an efficiency gain. This research not only provides new tools and for product design but also offers a feasible path to achieve mass customization.

Applied and Computational EngineeringVol. 279(1)
China Academy of Art (CN)
Openalex Percentile: Top 11%
Product Development and Customization
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