Determination of Difficulty Level for Garment Model with Fuzzy Logic Method

Abstract

The purpose of this study is a rule-based fuzzy logic approach is proposed for determining model difficulty in manufacturing top clothing for ladies. A decision framework concerned with different scenarios (main pattern types and material types) is proposed for determining the model difficulty. Each scenario modeled as a Mamdani type fuzzy inference system which is known as one of the best approximator fuzzy logic models. The fuzzified input variables are unit operation time, second quality rate and fabric weight. Moreover, two different defuzzification methods which are centroid and middle of maxima are compared for finding best fuzzy logic structure over the six different test instances. According to the results, both deffuzzification methods find similar model difficulty determinations. A graphical user interface of the proposed decision framework is designed in order to apply this to real-life applications. Finally, six different clothing models are identified to be simple, medium-hard, hard and very hard. The results of this study showed that defuzzification methods is not significantly effected the model difficulty decisions off is systems regarding different test instances. The model difficulty values range between 0-10. In order to find a useful difficulty assignment (linguistic), the model difficulty is determined by using the closeness to center value (a2) of membership functions. This research offers a solution to determine the difficulty levels of the garment models.