In a groundbreaking advancement at the intersection of cultural heritage and artificial intelligence, researchers have unveiled a pioneering framework that revolutionizes the digital design of intangible cultural heritage (ICH), specifically focusing on wood-carved window grilles in historical architecture. This innovative study introduces a human-machine collaborative design paradigm that transcends traditional generative AI techniques by embedding a sophisticated balance of cultural value, aesthetic expression, and technical feasibility into the design process. By integrating fuzzy multi-criteria decision-making (MCDM) methods with advanced AI models, this research establishes a comprehensive and closed technical loop for ICH digital innovation, promising to transform how traditional crafts are digitally revitalized and preserved.
Traditional generative AI frameworks have often been critiqued for their emphasis on form generation without adequate incorporation of deeper cultural context and interpretative value. Addressing this gap, the new study leverages the intuitionistic fuzzy VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method, a refined fuzzy MCDM approach, enabling more nuanced, multidimensional evaluations that consider not only aesthetics but also cultural significance and practical manufacturability. This human-machine intelligent fusion design model thus nurtures a holistic creative transformation process that stays true to heritage while embracing modern technological possibilities.
One of the standout achievements of the research is its versatile and replicable digital design framework. Crafted with scalability in mind, the framework is adaptable to a myriad of traditional crafts beyond wood carving, serving as a blueprint for the digital transformation journey of various cultural artifacts. By systematizing the balance between cultural authenticity and design innovation, the framework paves a new pathway for craftsmen and designers to enhance efficiency and creativity in digital heritage preservation efforts.
The digital design process itself marks a significant leap in production efficiency, drastically shortening the research and development cycles for wood carving products. Through the seamless fusion of AI-driven design tools and expert human input, craftsmen can now translate intricate ideas into tangible products at an accelerated pace. This timely responsiveness is critical in today’s market, enabling artisans to capitalize on fleeting opportunities and expand the reach of traditional crafts within contemporary design markets.
To underpin these design innovations, the study employs TracePro software, a sophisticated ray-tracing engine used to simulate the transmittance efficiency and visual comfort index of three distinct wood-carved window pattern lamps. This detailed simulation work not only provides invaluable parameter guidance for manufacturers but also delivers evidence-based recommendations for optimizing product development. These insights are essential for bridging the gap between artistic heritage designs and practical lighting performance in architectural applications.
Despite these advancements, the researchers candidly acknowledge several limitations inherent in their approach. For instance, the scope of data collection was constrained by time and resource limitations, particularly regarding field samples of ancient wood-carved window grilles. The dataset, while valuable, is not exhaustive, and future studies are encouraged to broaden the empirical base to enhance the generalizability and robustness of derived conclusions.
Another technical challenge lies in the parameter setting within the intuitionistic fuzzy VIKOR method, which to some extent is influenced by subjective judgment during esthetic ranking. The authors propose that subsequent research could benefit from more rigorous experimental designs and objective assessment techniques to refine and standardize the evaluation of aesthetic qualities in wood carvings.
While TracePro is recognized for its swift and precise computation, the study highlights the software’s limitations when addressing more intricate real-world scenarios. The simulation’s accuracy can be compromised by complex variables such as diverse light source characteristics, material heterogeneity, and nuanced surface properties. In their present model, the researchers focused primarily on window carving patterns and two material types, leaving out other potentially influential factors — a gap future investigations are poised to address through the integration of complementary tools or more comprehensive modeling strategies.
This intersection of cultural heritage preservation and intelligent design models signals a new era in how historical crafts can be innovatively conserved and rejuvenated. By rooting AI systems within a cultural interpretative framework, the study challenges the conventional boundaries of form-focused automatic design, inviting a more textured collaboration between human expertise and machine intelligence. This shift holds meaningful implications not only for wood-carved window grilles but also for a wide spectrum of intangible cultural expressions facing the risk of obsolescence in a modernizing world.
By designing this intelligent fusion model, the team has also highlighted the potential for broader applications in heritage industries where digitization efforts frequently wrestle with the tension between technological advancement and fidelity to original craft techniques. The experience gained from this research sets a precedent for integrating multi-criteria optimization within generative AI workflows, enabling a leap beyond surface aesthetics to encompass cultural semantics and practical feasibility into core digital design protocols.
Furthermore, the acceleration of manufacturing cycles directly impacts both the economic sustainability and cultural vitality of craftspeople communities. Empowering artisans with AI-enhanced design tools democratizes access to innovation, allowing for rapid iteration and adjustment in response to consumer feedback and market trends. This agility, combined with a culturally sensitive design lens, fosters an ecosystem where traditional craftsmanship thrives rather than stagnates.
The TracePro simulation’s contribution cannot be understated, as it translates cultural design elements into measurable performance metrics. This technical lens provides manufacturers with the clarity needed to innovate functionally without sacrificing the intricate nuances of heritage aesthetics. Consequently, the digital design framework becomes an enabling conduit between artistic intent and engineering precision, encouraging cross-disciplinary collaboration among designers, material scientists, and engineers.
In envisioning future directions, expanding the data acquisition to include a more diverse range of samples will facilitate a deeper understanding of regional and temporal variations in wood carving traditions. Such comprehensive datasets would enhance the adaptability and cultural fidelity of AI-generated designs, ensuring that the essence of localized craft identities is preserved while exploring new creative possibilities.
Moreover, refining the fuzzy MCDM techniques with less subjective parameter tuning and incorporating machine learning to dynamically learn ranking criteria may lead to more objective and replicable design assessments. This evolution could standardize how aesthetic and cultural parameters are integrated into AI workflows across diverse heritage domains.
Addressing the complexity inherent in simulation environments by incorporating additional parameters, such as varying material textures, complex lighting setups, and environmental conditions, will make the virtual prototypes more realistic and directly translatable to physical production scenarios. Experimental coupling of TracePro with other simulation or imaging tools could provide more holistic analytic frameworks for these intricate design challenges.
Ultimately, this study’s innovative amalgamation of AI technologies and human expert knowledge has charted a promising new trajectory for cultural heritage preservation in the digital age. By emphasizing a multidimensional approach that respects tradition while embracing modern computational capabilities, it offers a proof-of-concept that can inspire further research and application in the dynamic field of intelligent design for cultural revitalization.
As the global community increasingly recognizes the importance of protecting intangible cultural heritage, such human-machine collaborative frameworks represent a meaningful advance toward sustainable, accessible, and creative cultural innovation. The integration of cutting-edge AI with fuzzy decision-making models not only enriches the design process but also fosters a culturally informed technological evolution that honors the past while innovating for the future.
Subject of Research: Design optimization and digital transformation of wood-carved window grilles in historical architectures through human-machine collaborative models integrating stable diffusion and intuitionistic fuzzy VIKOR algorithms.
Article Title: Design optimization of wood-carved window grilles in historical architectures using stable diffusion model and intuitionistic Fuzzy VIKOR.
Article References:
Kang, X., Wang, J. Design optimization of wood-carved window grilles in historical architectures using stable diffusion model and intuitionistic Fuzzy VIKOR.
Humanit Soc Sci Commun 12, 972 (2025). https://doi.org/10.1057/s41599-025-05388-5
Image Credits: AI Generated
