https://orcid.org/0009-0005-7035-5696
Figures
Abstract
This study establishes a novel Bio-Cultural Heritage Design Framework that integrates grounded theory and biomechanics to address the sustainable development of traditional crafts in the digital era. Using Chaozhou woodcarving as a case study, we constructed a hierarchical demand system through a three-stage coding analysis of 1,250 online reviews, 381 survey responses, and expert interviews, identifying biomechanical utility (A1, 40% weight) as the dominant factor. Key sub-criteria—ergonomic compatibility (C12, 0.21 weight), material durability (e.g., nanmu wood with elastic modulus ≥8 GPa), and structural stability (C22, 0.15 weight)—were translated into AI design constraints via Analytic Hierarchy Process (AHP). AI-generated prototypes (e.g., Sample 15, score 4.53/5) demonstrated superior biomechanical performance, validated by finite element analysis (stress distribution <10 MPa) and user evaluations, outperforming traditional designs by 40–47%. This framework bridges cultural authenticity with scientific rigor, offering actionable strategies to optimize functional reliability while preserving heritage aesthetics, thereby addressing the critical challenge of balancing craft preservation with modern market demands.
Citation: Li L, Wu F, Zang W, Fan L (2026) Research on the development of Chaozhou woodcarving from the perspectives of grounded theory and biomechanics. PLoS One 21(2): e0341450. https://doi.org/10.1371/journal.pone.0341450
Editor: Peng Liu, Macau University of Science and Technology, MACAO
Received: May 16, 2025; Accepted: January 7, 2026; Published: February 18, 2026
Copyright: © 2026 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are in the paper and its Supporting information files.
Funding: This study was supported by the Ministry of Education of China Humanities and Social Sciences Research Project in the form of a grant awarded to L.L. (23YJA760052), the Guangdong Provincial Department of Education Higher Education Institutions Research Platform Project in the form of a grant awarded to L.L. (2023WTSCX143), and the Teaching Quality Management Committee for Private Higher Education Institutions of Guangdong Association of Higher Education in the form of a grant awarded to W.Z. (GDZLGL25059). The funders supported this study through research grants that facilitated study design, data collection, and manuscript preparation.
Competing interests: No authors have competing interests.
1. Introduction
In the digital era, traditional craftsmanship, a cornerstone of Intangible Cultural Heritage (ICH), faces an existential crisis. As modern manufacturing technologies and shifting consumer lifestyles reshape the global market, many heritage crafts struggle to maintain their relevance and functionality in daily life [1–3]. The tension between preserving cultural authenticity and adapting to modern utility has created a bottleneck for sustainable development. While government policies often focus on preservation through archival documentation (e.g., National Intangible Cultural Heritage lists) and financial subsidies for representative inheritors, there is an urgent need for innovative strategies that can reintegrate these crafts into the contemporary consumer ecosystem without diluting their artistic essence.
This challenge is particularly evident in Chaozhou Woodcarving, a premier school of folk art from Guangdong, China. Renowned for its distinct multi-layered “hollow carving” (Tongdiao) technique and rich gold lacquer finish, Chaozhou Woodcarving dates back to the Tang Dynasty and reached its zenith during the Qing Dynasty [4]. Historically, it served as intricate architectural decoration for temples and ancestral halls, as well as functional religious artifacts. However, in the modern context, these elaborate forms often clash with the minimalist aesthetics and ergonomic requirements of contemporary living spaces. This incompatibility has led to a shrinking market share and reduced economic viability, which discourages young talent from entering the profession. Consequently, to mitigate market risks, producers often retreat to repetitive reproduction of classic patterns, resulting in severe product homogenization and a further disconnect from mass-market utility [5,6].
The existing body of literature has extensively documented the artistic characteristics and cultural symbolism of Chaozhou Woodcarving [7–9], and recent studies have identified the market challenges it faces [10–13]. Furthermore, emerging research has begun to explore the application of digital technologies, such as AI-Generated Content (AIGC), in art and design [14,15]. However, a critical gap remains: existing studies predominantly focus on aesthetic digitization or visual style transfer, often neglecting user-centered design considerations such as ergonomic constraints and functional reliability. Currently, few frameworks effectively translate empirical consumer insights—specifically latent needs for comfort and durability—into actionable, scientifically grounded design constraints for AI.
To bridge this gap, this study establishes and validates a novel Bio-Cultural Heritage Design Framework. Unlike traditional approaches, this framework integrates Grounded Theory, Biomechanics, and Artificial Intelligence. Its core logic is to transform qualitative user feedback into quantitative biomechanical parameters (e.g., ergonomic curvature, stress thresholds), which then serve as objective constraints for AIGC tools. By doing so, we aim to create a replicable pathway that not only preserves the visual identity of Chaozhou Woodcarving but also optimizes its functional reliability for the modern market.
2. Materials and methods
The following two methods were mainly used in this study.
2.1. Research methods and process description
Grounded theory, developed by Glaser and Strauss in 1967 [16], is a qualitative research method that constructs theory from empirical data. It involves generating concepts from data, comparing data and concepts, developing theoretical concepts, establishing connections between concepts, and systematically coding data hierarchically [17]. The research process does not start with preconceived theoretical assumptions but emphasizes the generative construction of theory through continuous concept development [18,19]. In this study, grounded theory was used to analyze the characteristics of Chaozhou Woodcarving Art and construct a system of sustainable development demand elements. The process included data collection from online reviews, interviews, and questionnaires, followed by open, axial, and selective coding to identify key concepts and themes. Theoretical sampling was conducted until theoretical saturation was achieved, ensuring the theory was derived directly from the data.
AIGC (Artificial Intelligence Generated Content) refers to the generation of text, images, or audio/video content based on prompt information. It leverages technologies such as Natural Language Processing (NLP), Generative Adversarial Networks (GANs), and diffusion models to create new content [20,21]. AIGC has brought significant transformation to content creation and production, offering new opportunities and challenges to the creative industry. In the design field, AIGC tools such as large language models like ChatGPT and image generation tools like Midjourney and Stable Diffusion are widely used. They are applied to improve repetitive tasks and generate large-scale solutions at low cost in a short time, mainly serving as a source of inspiration. For example, Zhu applied Generative Adversarial Networks (GANs) to achieve high-quality replication and restoration of calligraphy art. This method not only preserved the original style of the artworks but also improved efficiency and reduced costs, offering a novel solution for cultural heritage preservation [22].
In this study, AIGC was used to generate design solutions for Chaozhou Woodcarving. Specifically, AI tools such as Midjourney and Stable Diffusion were employed to create prototypes based on the weighted criteria derived from the Analytic Hierarchy Process (AHP) [23]. These AI-generated designs were then evaluated through public voting, demonstrating their potential to meet consumer demands while preserving the cultural heritage of Chaozhou Woodcarving. The research process can be seen in Fig 1 Research Roadmap.
2.2. Data collection and organization
2.2.1. Participant demographics.
The survey yielded 381 valid responses with a balanced gender distribution (Males: 182, 47.8%; Females: 199, 52.2%). The age structure was predominantly young to middle-aged, with 86.4% of participants aged between 18 and 44 (18–24: 128; 25–34: 120; 35–44: 81), while those under 18 (5) and over 45 (37) constituted smaller portions. In terms of occupation, white-collar and office workers formed the majority (253, 66.4%), followed by homemakers (55), freelancers (38), students (17), and others (18). Monthly income distribution showed that middle-to-high income groups (≥5,000 CNY) accounted for 55.4% of the sample (5,000–10,000 CNY: 122; > 10,000 CNY: 89), indicating a strong potential purchasing power for cultural products. Informed consent was obtained from all participants prior to the survey and interviews (S2 File).
2.2.2. Data collection channels and procedures.
To address the current development challenges of Chaozhou Woodcarving, this study collected and organized data from the objective market perspective, obtaining research data from three main channels. One of the channels was online reviews from third-party platform users. Using Python, a web crawler program was developed to collect reviews related to Chaozhou Woodcarving products from major e-commerce platforms such as Taobao, JD.com, and Pinduoduo. The content obtained included product types, images, and review texts. As of November 2024, a total of 2,247 reviews related to “Chaozhou Woodcarving” were collected, with 1,250 valid reviews remaining after removing those deemed to be distracting (S1 File).
Product evaluations were sourced from official platforms, primarily the Guangdong Provincial Museum’s Tmall flagship store and the National Museum of China’s JD.com flagship store. The Guangdong Provincial Museum’s store offered 14 Chaozhou Woodcarving products, including creative bookmarks, paper-cut lamps, stickers, and DIY ornaments, with 37 valid reviews collected. The National Museum of China’s JD.com store, using “wooden” as the search tag, yielded 216 data entries, mainly featuring cultural and creative decorations and tabletop ornaments.
In addition, interviews and questionnaires were conducted with potential users. After organizing the online review data, a survey questionnaire was created based on the product types sold through the main official channels. These were conducted both offline in the Guangdong-Hong Kong-Macao Greater Bay Area and online, targeting young and middle-aged consumers to gather data on the desired forms of Chaozhou Woodcarving products. See Table 1 for details.
To ensure the quality and validity of the dataset, a rigorous data cleaning process was implemented following the initial collection. This process involved: (1) removing duplicate entries and automated spam; (2) filtering out reviews that were irrelevant to the product’s attributes, such as comments solely concerning logistics, packaging, or customer service; and (3) excluding non-descriptive, low-information content (e.g., “good,” “received”). This procedure refined the initial dataset of 2,247 reviews to 1,250 valid entries for analysis. Furthermore, sourcing data from multiple platforms (Taobao, JD.com, Pinduoduo) served to mitigate potential sampling bias from any single user population.
Data Compliance Statement: The collection and analysis of online reviews from e-commerce platforms (Taobao, JD.com, and Pinduoduo) were conducted in compliance with the respective platforms’ terms and conditions. The data collected were publicly available user-generated content, and no private or personally identifiable information was accessed or used. The study focused solely on the textual content of the reviews for academic research purposes. For the survey and interview data, informed consent was obtained from all participants prior to their involvement, as stated in the ‘Informed Consent Statement’ within the manuscript.
2.3. Coding and analysis process
2.3.1. Grounded theory methodology.
This study adopted the systematic procedures of Grounded Theory, specifically the Strauss and Corbin approach [24], rather than simple thematic analysis. The goal was not merely to categorize themes, but to construct a substantive theory that explains the hierarchical and causal relationships between user demands and design elements. The process involved three rigorous stages:
- (1). Open Coding: Deconstructing raw data into initial concepts (e.g., labeling user feedback on “grip comfort” as specific ergonomic indicators).
- (2). Axial Coding: Identifying relationships between categories to form main categories (e.g., establishing how Biomechanical Utility functionally supports Consumption Value).
- (3). Selective Coding: Integrating all categories around the core category to build the final demand system.
This theoretical construction process ensures that the resulting framework is not just a list of requirements, but a structured system of interacting variables, validated manually by researchers to ensure integrity.
2.3.2 Implementation and coding results.
Given the volume of raw data, the coding process was primarily managed using NVivo and Excel software. To enhance rigor and mitigate potential researcher bias, ChatGPT was employed as a supplementary tool for cross-validation. Initially, the research team performed manual open coding to generate 42 initial concepts (S3 File). Subsequently, the raw data and these initial concepts were presented to ChatGPT to generate thematic classifications. The AI-generated themes were then systematically compared against the manually derived concepts by the researchers. This comparative process helped to refine category definitions and identify nuanced relationships, leading to the final set of 33 initial concepts. Crucially, all final coding decisions, the categorization into nine basic categories, and the construction of the theoretical framework were performed and validated manually by the researchers to ensure integrity (S4 File).
This leads to the clear identification of 33 initial concepts, such as a1 Ergonomic Woodcarving Ornament, a2 Ergonomic Woodcarving Handle Piece, a3 Ergonomic Woodcarving Jewelry,..., a31 Stress Relief Effect, a32 Emotional At-traction, and a33 Willingness to collect. Immediately following this, nine basic categories are derived: C1 Form and Quality Fit, C2 Material Mechanics and Craftsmanship, C3 Sensory Feedback during Use,..., C7 Value Judgment, C8 Educational Function, and C9 Psychological Needs. To avoid subjective interference, the original sentences are listed alongside their corresponding concepts and categories, as detailed in Table 2.
Based on open coding, the main categories were summarized and the relationships between them were clarified [25]. A1 Biomechanical Utility Value, A2 Cultural Aesthetic Value, A3 Consumption and Social Value, A4 Educational and Heritage Value, and A5 Emotional Fulfillment Value. Specifically, A1 involves form and quality compatibility, material and craftsmanship, and Sensory Feedback during Use, which together determine a product’s practical usability and user experience. A2 is reflected in cultural symbolism and aesthetic style, showcasing the cultural heritage of Chaozhou woodcarving through creative designs. A3 focuses on shopping attributes and value assessment, such as gift-giving purposes and packaging quality, which influence purchasing decisions. A4 is primarily about educational functions and heritage transmission, with woodcarvings serving as carriers for cultural education and skill inheritance. A5 includes stress relief, emotional appeal, and collecting intentions, with cute or unique designs attracting consumers and evoking a sense of affection. These categories are interwoven, forming consumers’ comprehensive understanding of Chaozhou woodcarving and providing a basis for heritage strategies. For specific classification, see Table 3.
Selective coding involves establishing connections between main categories and other categories, and deeply analyzing their relationships. This process constructs a systematic and complete framework reflecting the original data categories [26]. Through grounded theory, the study identified the interrelatedness of the five main categories: A1 Biomechanical Utility Value, A2 Cultural Aesthetic Value, A3 Consumption and Social Value, A4 Educational and Heritage Value, and A5 Emotional Fulfillment Value. These categories influence and interact with each other.
A1 serves as the foundation, enhancing A2 through high-quality materials and exquisite craftsmanship, which better present cultural symbolism and aesthetic styles. It also supports A3, as products with excellent utilitarian experience have advantages in shopping attributes and value assessment, making them suitable for social purposes. A1 promotes A4, allowing consumers to experience the charm of woodcarving in practice, facilitating its inheritance. A2 adds unique charm to the product, enriching A5 by stimulating consumers’ affection and desire to collect. A3 is influenced by the other categories and affects the product’s market positioning. A4 enhances consumers’ understanding of cultural aesthetics and strengthens their emotional identification with woodcarving. A5 permeates the entire consumption process, driving consumers’ choices and evaluations of products and influencing the realization of the other categories. For details, see Table 4: Selective Coding.
2.4. Establishment of demand element system model and parameter calculation
Following the progressive coding process from open coding to axial coding and then selective coding, a questionnaire survey was conducted among potential users regarding the modern inheritance and development factors of Chaozhou woodcarving, with a total of 381 responses collected to test the theoretical saturation. The results showed that the close relationships among the five main categories, as well as the interconnections among the factors within each category, were also reflected in these data. No new categories emerged, and there was no impact on the core category concepts that had been identified. This is consistent with the principle of theoretical saturation.
Following the hierarchical progression of category coding, we have established the current needs system for Chaozhou woodcarving. The main categories serve as the criterion layer, while the initial categories form the sub-criterion layer. We have removed identical or synonymous initial concepts to avoid redundancy and integrated them into representative semantic phrases, which are matched one-to-one to build the current needs system for Chaozhou woodcarving, as shown in Table 5. The five criterion layers cover all the core value areas involved in the development of Chaozhou woodcarving in today’s society. These layers don’t exist in isolation; they’re closely connected. This system can provide practical guidance parameters for the development of Chaozhou woodcarving.
To assess the criteria, sub-criteria, and representative semantic phrases within the system, we employed a Likert scale using a comparative rating method. We invited nine experts, including practitioners of Chaozhou woodcarving, as well as masters and doctoral students in design and art, along with relevant researchers, to form a rating panel. Selection criteria for these experts included a minimum of 5 ~ 10 years of professional experience in their respective fields and holding professional titles (S5 File). Using a scale of 1–9 and their reciprocals for judgment [27], we first collected pairwise comparison ratings from the nine experts for the criteria, sub-criteria, and semantic phrases. We then calculated the mean values of these ratings to construct matrices. The product of each row was calculated to find the geometric mean, and the results were normalised to obtain the weights (see Table 6 for an example of the criteria weights). Finally, the weights were integrated and ranked, as shown in Table 7, which details the weights of each level of needs elements.
To ensure the reliability of the expert judgments, the consistency ratio (CR) was calculated for each judgment matrix. For the primary criterion layer matrix (Table 6), the calculated CR was 0.0591. As this value is below the standard threshold of 0.10, it confirms that the pairwise comparisons were logically consistent and the resulting weights are valid for subsequent analysis.
3. Integrating AI into design practice and strategy analysis
While grounded theory is effective in identifying what consumers demand, it does not quantify the relative importance of these, often conflicting, needs (e.g., intricate aesthetics vs. structural durability). The Analytic Hierarchy Process (AHP) serves as the critical bridge in our framework to address this challenge. It provides a mathematically rigorous method to prioritize these competing demands based on expert judgment, resolving potential design trade-offs. This weighted hierarchy is not merely an academic exercise; it is essential for systematically translating user priorities into objective functions and constraints for AI. Without AHP, the process of generating AI prompts would be subjective and arbitrary, failing to create designs that are genuinely optimized according to a user-centric model.
3.1. Biomechanics-driven AI design framework
The integration of biomechanical principles into AI-generated design workflows hinges on systematically translating the weighted demand elements (Table 5) into actionable AI constraints through Analytic Hierarchy Process (AHP) prioritization. By leveraging the AHP-derived weights (Table 7), the framework quantifies user priorities and directly maps them to biomechanical design parameters. For instance, the dominant biomechanical utility value (A1, 40% weight) mandates stringent thresholds for ergonomic compatibility and material mechanics. Sub-criteria such as C12 (Ergonomically Comfortable, weight 0.21) are translated into explicit geometric constraints (e.g., curvature radius ≥15 mm, weight <200 g), while C22 (Exquisite Carving, weight 0.15) dictates structural stability rules (e.g., minimum wall thickness ≥5 mm).
To operationalize this, the AHP weights were embedded into AI prompt engineering as follows:
- (1). Weight-to-Parameter Mapping: High-weight sub-criteria (e.g., C12) were prioritized in AI algorithms through weighted objective functions. For example, see Equation 1.
Where represents the AHP weight of criterion i, and
quantifies the compliance of parameter
(e.g., curvature radius) with biomechanical thresholds.
- (2). Conflict Resolution: When sub-criteria conflicted (e.g., material strength vs. lightweight design), the weighted sum model ensured higher-weight parameters dominated. For instance, C12 (weight 0.21) overrode C21 (weight 0.11) in prioritizing ergonomic curvature over material density. The specific AHP weight prompt weight situation is shown in Table 8.
This weighted translation ensures that designs generated by artificial intelligence adhere to biomechanical principles while preserving cultural themes, and has been validated through user evaluation.
3.2. Case Study: Biomechanics-informed AI design solutions
To validate the biomechanical AIGC integrated framework, three representative Chaozhou wood carving products were generated and evaluated: home decoration and lighting, educational puzzles, and personalized collectible gifts. Each case demonstrates how biomechanical constraints (see methodology section) guide artificial intelligence to balance cultural aesthetics and functional reliability. The constraint parameters A2, A3, A4, A5, etc. involved in other requirement systems will be adjusted according to their proportions in specific situations. Please refer to Fig 2 Case Library.
3.3. User feedback and evaluation
To evaluate the effectiveness of the AI-generated designs, we presented the 15 design samples to a target audience, including professionals in the Chaozhou woodcarving industry, regular consumers, and experts in design studies. Feedback on the samples was collected through both online surveys and in person interviews. Using the Chaozhou woodcarving needs system as the evaluation framework, we scored each sample across the five main criteria using a 5-point Likert scale. A total of 17 survey responses were received [30]. After averaging the scores, we ranked the samples to identify those that required further optimization. The detailed scoring results are shown in Table 9.
The results demonstrate a clear consumer preference for biomechanically optimized designs, as evidenced by the superior performance of AI-generated prototypes. Sample 15, the highest-rated iteration (4.53/5), exemplifies this trend through its meticulous integration of ergonomic and structural enhancements. Featuring an 18 mm curvature radius (C12, 0.21 weight) for palm-friendly grip comfort and 5.5 mm wall thickness (C22, 0.15 weight) in nanmu wood (elastic modulus ≥8 GPa), this design achieved exceptional durability while preserving traditional craftsmanship. Computational validation via SolidWorks Simulation 2023 confirmed its biomechanical utility: under simulated 50 N grip forces and gravitational loading, maximum von Mises stresses remained at 7.8 MPa (below the 10 MPa safety threshold) with minimal deformation (<0.2 mm). The simulation workflow included tetrahedral mesh refinement (average element size: 1.2 mm) and linear static analysis, ensuring compatibility with standard product design practices. This contrasts starkly with traditional carvings (scores 2.24–3.53), where stress concentrations exceeding 15 MPa at sharp edges correlated with user reports of structural fragility and discomfort. The success of Sample 15 – praised by 82% of users for its “natural handling” and “robust construction” – underscores how AHP-weighted AI constraints effectively reconcile cultural authenticity with engineering precision, offering a replicable model for heritage craft modernization through parametric biomechanical thresholds.
4. Results and discussion
This study successfully constructed and quantified a biomechanics-driven demand framework for Chaozhou woodcarving by synthesizing qualitative user data with a quantitative prioritization model. The three-stage grounded theory process, analyzing 1,250 online reviews and 381 survey responses, culminated in a hierarchical demand system comprising five core value dimensions. The most significant finding, confirmed by the Analytic Hierarchy Process (AHP), was the primacy of Biomechanical Utility (A1), which commanded a dominant weight of 40%. This empirically substantiates that modern consumers prioritize functional attributes—such as ergonomic comfort (C12, 0.21 weight), material durability, and structural stability (C22, 0.15 weight)—over purely aesthetic or cultural values when engaging with traditional crafts.
4.1. Theoretical implications and dialogue with literature
Our findings offer a significant theoretical shift from traditional art history perspectives. While previous scholarship [31–33] has predominantly focused on the aesthetic evolution and cultural symbolism of woodcarving, our results demonstrate a critical pivot in consumer perception: from viewing heritage crafts as static “artifacts” to functional “products”.
Specifically, our study validates the necessity of integrating scientific rigor into heritage preservation. This resonates with Guo & Zu’s application of structural mechanics to sculpture, but our framework advances this by quantifying specific biomechanical thresholds (e.g., elastic modulus ≥8 GPa) as design constraints. Furthermore, while Xia & Xing explored bioengineering in ink-making, our work extends this interdisciplinary approach to AIGC workflows. We proved that when AI is guided by AHP-weighted biomechanical constraints rather than just style prompts, the resulting prototypes (e.g., Sample 15) achieve significantly higher user satisfaction. This provides a verified theoretical model for the “functional modernization” of intangible cultural heritage.
4.2. Practical implications
The validated Bio-Cultural Heritage Design Framework serves as a practical roadmap for the sustainable revitalization of Chaozhou woodcarving and other traditional crafts. Based on our findings, we propose the following strategies:
For Artisans and the Craft Industry: A critical shift from a purely aesthetic focus to a user-centric, biomechanics-informed approach is recommended. Artisans can leverage the AHP-weighted demand system (Table 7) to guide their creative process, ensuring that ergonomic comfort and structural durability—dominant consumer demands—are prioritized alongside traditional aesthetics.
For Designers and Commercial Innovators: The framework provides a quantitative methodology for market-oriented product development. The high weights assigned to “Educational Function” (A4, 15%) and “DIY Feasibility” (C23, 0.06 weight) signal strong market potential for innovative products like educational woodcarving kits. Designers are encouraged to use the AHP-derived parameters as a creative brief for AIGC tools, ensuring generated designs are grounded in empirical user data.
For Cultural Heritage Policymakers and Educators: We recommend that governmental and cultural institutions foster interdisciplinary research that merges intangible cultural heritage with scientific disciplines. In education, design and art curricula should incorporate integrated methodologies like the one proposed here to equip the next generation of practitioners with the skills to innovate within tradition.
4.3. Limitations and future research
Despite the robustness of the framework, this study has limitations. The user evaluation cohort (n = 17) was moderate in size, and the data collection was primarily focused on consumers in the Chaoshan region, which may limit the generalizability of the specific AHP weights. Future research should aim to validate the framework with a larger, more diverse participant pool and conduct long-term durability testing on the manufactured prototypes. Exploring additional biomechanical factors, such as the effects of humidity on wood stability, would also represent a valuable extension of this work.
5. Conclusion
This study proposes and validates the Bio-Cultural Heritage Design Framework, an interdisciplinary approach that synergizes Grounded Theory, Biomechanics, and AIGC. By systematically translating qualitative user demands—specifically ergonomic comfort and structural stability—into quantitative design constraints, the framework successfully guided the generation of prototypes that significantly outperformed traditional designs in both mechanical reliability and user satisfaction. This process confirms that integrating scientific parameters into the creative workflow effectively harmonizes cultural authenticity with engineering precision.
The primary contribution of this research lies in bridging the methodological gap between subjective cultural insights and objective design specifications. It offers a replicable, scientifically grounded model for modernizing intangible cultural heritage, resolving the long-standing conflict between preservation and market utility. Ultimately, this framework provides a tangible roadmap for artisans and policymakers, demonstrating that the sustainable revitalization of traditional crafts in the digital era depends on the seamless fusion of artistic heritage and biomechanical science.
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