Intelligent advisory systems for fabric selection

In general, the selection of fabric relies on the user's preferences. Such preferences are expressed in terms of psychological perceptions of fabric hand. Fabric hand is commonly adopted for the assessment of fabric quality and prospective performance in a particular end use. The fabric hand is primarily assessed by subjective judgments from sensory experts. Subjective judgment treats fabric hand as the outcome of the psychological chain reactions initiated from the sense of touch and combined with the sensitivity and the experience of judges.; Conventional fabric hand evaluation system utilizes an old-fashioned Total Hand Value (THV) as a metric to model the subjective fabric hands based on fabric properties. The Kawabata's evaluation system (KES) developed by Kato Tech Company is an example that has a systematic approach on modeling fabric hand. This method has become a standard evaluation system in the industries nowadays. However, these specialized systems could not advise the users which type of fabric is most likely matched his/her psychological perceptions of fabric hand.; In this thesis, we have built and characterized an intelligent advisory system for individuals in fabric selection based on his/her psychological perceptions of fabric hand. We modeled the relationship between the sensory perceptions of fabric hand and fabric properties using a neural networks approach for generating the rules for the selection making functionality. The selection advisory system was constructed using a fuzzy rule-based expert system and was validated by individuals. It was proved that the proposed advisory system is able to advise on the desired fabrics for individuals based on inputs of his/her preferred sensory ratings of 14 fabric hand descriptors. The results verified the robustness of the proposed hybrid fuzzy-neural advisory system that could provide satisfactory performance in the new idea.