| Fabric sewing ability is one of important factors that influence the quality of clothing appearance. Controlling and evaluation of seam pucker is a lasting problem of fashion industry during the course of clothing manufacture.AATCC (American Association of Textile Chemists and Colorists) Method 88B has been commonly used for the subjective evaluation of seam pucker. According to this method, the appearance of seams are compared with photographic standards and the severity of seam pucker is graded into five classes, Class 5 being little or no pucker, and Class 1 severe pucker. Normally, Class 5 and 4 are acceptable, Class 3 is critical or borderline, and Class 2 and 1 are unacceptable. The merit of this method is directness, simplicity, low investment and easy to master, but it is influenced by uncertain factors from the evaluator and loses its objectivity easily. So, the research for objective evaluation of seam pucker is on going, and designing an effective evaluation algorithm continues to be hotspot and difficult problem of the fashion design and manufacture industry.In this paper, a supervised fuzzy clustering RBF neural network (SFCM-RBFNN) based on kernel principal component analysis is introduced for constructing the garment seam evaluation system. Experimental results demonstrate that the proposed system could efficiently evaluate the fabric sewing ability.Firstly, this paper use FAST(Fabric Assurance by Simple Testing) apparatuses to test the mechanic indexes of fabric, and analyze the correlation between the indexes and fabric sewing ability of warp action and weft action, such as fabric structure mechanical properties, fabric extension mechanical properties, fabric formality ability, dimensional stability. Then figures are plotted to observe the trend of seam pucker grade with these FAST mechanical properties data. According to the correlation analysis, a few low related indexes with seam pucker are eliminated.With the fabric inclined mechanical properties theory, research was done on the relations between fabric anisotropy and sewing ability, such as extension properties, bendability properties, formality ability. This paper also plots figures to observe the trend of seam pucker grade as the fabric diagonal cutting angle.Secondly, according to these high dimension and nonlinear FAST mechanical properties data (in input space), The kernel principal component analysis (KPCA) method was applied to project these data in a whole new linear feature space, then linear clustering method was used to analyze it effectively in the new space. This paper extracts the principal component of fabric FAST mechanical properties by KPCA method with gaussion function and reduces the dimension of the data.Thirdly, to improve the accuracy of fuzzy clustering based on fuzzy C mean (FCM) algorithm, this paper proposes a novel supervised fuzzy C mean clustering algorithm including input space sample x (principal component of fabric FAST mechanical properties) and output space expectation y (seam pucker grade) at the same time. Using local linear regression model to represent each fuzzy cluster, the actual output y can be calculated by sum of these local linear model. And the clustering effect of the fabric FAST mechanical properties PCA data by SFCM algorithm is quite good. The SFCM algorithm proposed by this paper not only shows the clustering action in input space, but also shows the approximating action in output space.This paper modifies the radius base function of hidden nodes of RBF neural network by importing the fuzzy partition matrix U and clustering center v of SFCM algorithm to construct a SFCM-RBF neural network. The modification makes the width (receipt field) and center of hidden nodes of RBF neural network become more effectively optimizing and controlling. Experimental results demonstrate that the proposed system could efficiently be used as an objective garment seam evaluation system with high accuracy and is robust for various structures fabric.
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