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About

Xingzhi Wang is a researcher specializing in smart manufacturing, data-driven engineering design, and product customization. His work sits at the intersection of knowledge engineering, digital twins, and user-centered design, addressing how emerging technologies can transform the way products are developed and tailored to individual customers. Wang's most impactful contribution is his research on constructing Product Usage Context Knowledge Graphs from user-generated content, which has garnered 20 citations since 2022. This work advances user-driven customization by enabling customers to configure products more intelligently and compatibly with their real-world environments — a significant step forward in co-design methodology. Complementing this, his ontology-based modeling approach for smart customization provides a structured framework for interpreting complex customer preferences at scale. His investigations into digital twin technology further demonstrate his breadth, exploring how virtual replicas of products and systems can support constraint analysis and data-driven design decision-making. Wang has also contributed to broader discussions on managing design constraints, complexities, and contradictions in the data era, reflecting his interest in both theoretical foundations and practical applications. Overall, Wang's research shapes a vision of smarter, more responsive product development ecosystems, making him a notable emerging voice in intelligent manufacturing and design informatics.