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Abstract

To solve the issue of the digital transformation of Chinese manufacturing in terms of the bottleneck between industrial interfaces not being able to adapt to heterogeneous operators and the high cognitive load imposed on them, the authors propose the SkeuoUI-Gen framework based on the adaptation of skeuomorphic design principles and the use of conditional diffusion models to produce personalized industrial interfaces in the context of Chinese manufacturing. In this regard, the experiment used a within-subjects design involving 250 manufacturing industry operators (diverse in age, experience, and industry sectors) to evaluate three interface types: traditional flat interfaces, fixed skeuomorphic interfaces, and personalized adaptation interfaces. The experiment used objective evaluations (FID and PSNR) and subjective evaluations (SUS score and cognitive load), and trained the model on multiple sources: 50,000 interaction logs from operators and 50,000 screenshots of industrial user interfaces. The experiment found that the personalized adaptation interface resulted in a 78.6% SUS score (an increase of 15.4% compared to the traditional baseline), improved efficiency by 24.7%, and reduced serious safety-related errors by 52% and 67%. The network achieved a lower FID (21.5) than GAN-based approaches and required only 2.3 seconds per generation. In addition, the network presented robustness through multi-dimensional validation. This framework expands the cognitive load theory and the technology acceptance model.

Keywords

SkeuoUI-Gen framework Conditional diffusion models Skeuomorphic design, Personalized industrial UI Chinese manufacturing context

Article Details

Author Biographies

Fanglei Liu, College of Creative Arts, Universiti Teknologi MARA, 40150 Shah Alam, Selangor, Malaysia

FANGLEI LIU is currently pursuing the doctor'sdegree with College of Creative Arts, Universiti Teknologi MARA. Her research interests Interaction Design and Artificial Intelligence Art. She is the Associate Professor of the Digital Media Art Department at the College of Art and Design of Qilu University of Technology, specializes in cultural Semiotics, media art, human computer interaction.(HCI).

Mohamed Razeef Abd Razak, College of Creative Arts, Universiti Teknologi MARA, 40150 Shah Alam, Selangor, Malaysia

Dr MOHAMED RAZEEF BIN ABDUL RAZAK is a Senior Lecturer at Kolej Pengajian Seni Kreatif, Universiti Anggkat Malaysia. He is an expert in Visual Communication and Creative Arts, and his research focuses on society, creativity and innovation.

Mohd Hafnidzam Bin Adzmi, College of Creative Arts, Universiti Teknologi MARA, 40150 Shah Alam, Selangor, Malaysia

Dr Mohd Hafnidzam Bin Adzmi is a senior lecturer at the Faculty of Art and Design, University of Technology MARA, Malaysia. He has taught Graphic Design, Interactive Media and Game Design. His research focuses on the creativity and design education.

Xuelin Li, College of Arts & Design, Qilu University of Technology, Jinan 250353, China

Associate Professor XUELIN LI is the vice dean of the College of Arts & Design at Qilu University of Technology and a member of the Expert Committee of the Jinan Industrial Design Industry Alliance. His research fields include product design, human-computer interaction, emotional design and industrial design.

Fang Liu, College of Arts & Design, Qilu University of Technology, Jinan 250353, China

Professor FANG LIU is the director of the Decorative Art Studio at the College of Art and Design of Qilu University of Technology. Her research fields include decorative art, semiotics, and emotional interaction.

Lei Feng, College of Arts & Design, Qilu University of Technology, Jinan 250353, China

LEI FENG is the Associate Professor of the Fashion Art Department at the College of Art and Design of Qilu University of Technology, specializes in cultural semiotics, fashion design, media technology and parameterization.

How to Cite
Liu, F., Razeef Abd Razak, M., Hafnidzam Bin Adzmi, M., Li, X., Liu, F., & Feng, L. (2025). Personalized skeuomorphic UI generation for industrial interfaces using diffusion models: a user-centric approach in Chinese manufacturing context. Future Technology, 5(1), 290–302. Retrieved from https://fupubco.com/futech/article/view/634
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