Identification of the Most Significant Properties Influencing Tactile Fabric Comfort Using Regression Analysis

摘要

Engineered fabrics are being used increasingly in commercial and domain-specific systems. Such fabrics with specified consumer-desired characteristics can be computationally designed. Through the use of an extensive database that correlates sensory and mechanical properties with tactile comfort assessments, desired comfort can be predicted by measuring a limited number of properties. In this paper we are focusing on the most significant sensory and mechanical properties influencing tactile fabric comfort. Output systems can be optimized to exhibit the highest level of comfort by engineering a fabric with specific sensory and mechanical properties. This paper examines stepwise regression analysis and identifies the most significant properties influencing tactile fabric comfort. The reported Beta coefficients are the standardized regression coefficients. Their absolute magnitudes reflect their relative importance in predicting comfort values. A universe of 48 fabrics is examined to analyze and map the relations. The initial 17 mechanical and 17 sensory parameter sets are reduced to sets of 1 to 4 and 1 to 5 properties, respectively. Adjusted R~2 values were 0.360 to 0.657 for mechanical and 0.713 to 0.863 for sensory parameters, reflecting sound goodness-of-fit measures, and providing reasonable ways for identifying the mechanical and sensory properties that are most significant influences on tactile fabric comfort. Elongation and hysteresis of shear force were found to be the most influential mechanical properties, while compression resilience rate and graininess were found to be the sensory properties that most impacted comfort.