A Multi-Criteria Decision-Making Approach for Sustainable Product Texture Design in Smart Manufacturing

In the context of advancing manufacturing, production systems are shifting toward human-centric and personalized production. However, accurately quantifying subjective user needs into precise product specifications remains a challenge. Taking child companion robots as an example, this paper proposed a novel product innovation design framework based on Extenics and Kansei engineering to optimize the texture design of smart products. By systematically integrating synergistic relationships among colour, material, and surface processing technology, the framework aimed to enhance the sustainable value and social sustainability of products by more precisely meeting users’ perceptual and emotional needs. The research methodology employed the semantic differential method to quantify user perception and utilized the K-means clustering algorithm to construct a chromatic colour sample library for smart products. Subsequently, by combining the multi-criteria decision-making tool grey relational analysis with statistical verification, the optimal design scheme was selected from the generated alternatives. Experimental results demonstrated that this method significantly reduced design subjectivity and ambiguity. By bridging the gap between user expectations and engineering solutions, the framework provides a systematic solution for mass customization and process optimization that promotes resource efficient and sustainable production, while also reducing the resource waste associated with traditional trial and error design processes.