Abstract :
An experimental study was developed to investigate how users perceived product material texture. The semantic differential (SD) method, cluster analysis and protocol analysis were employed to examine users´ material texture cognition of plastic samples. Completed in computer by g.eneral statistics software, the cluster analysis results revealed that the samples can be described with neutral, substantial, laconic, glazed, comely, elegant and luxury. Level analysis was also performed to analyze the sample features and obtained the weight factors of reflection, granularity, transparency and roughness, and then a computer-aided texture image design system was built to validate this experiment so as to improve the system for further intelligent texture design and cyber-design.
Keywords :
CAD; pattern clustering; production engineering computing; statistical analysis; cluster analysis; computer-aided texture image design system; general statistics software; intelligent texture design; level analysis; plastic samples; product material texture cognition; protocol analysis; semantic differential method; user requirement; Art; Cognition; Educational institutions; Image analysis; Image texture analysis; Performance analysis; Plastics; Product design; Product development; Psychology;
