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The performance of spunbonded nonwoven is closely related to its pore structure. However, pore shape has been less studied than other measures of nonwoven pore structure such as the pore size and its distribution. In order to study the influence of processing parameters on pore shape in spunbonded nonwoven, thirty spunbonded nonwovens were produced using different combinations of metering pump and mesh belt frequencies. Using digital image processing technology, eight parameters were used to characterize the pore shape. Rough set theory, principal component analysis, and cluster analysis were used to reduce the attributes of these parameters. The results prove that rough set theory is better for attribute reduction than principal component or cluster analysis. The most representative pore shape parameters, i.e., compactness and eccentricity, were identified based on rough set theory. The results show that as the mesh belt frequency is increased, the value of eccentricity tends to increase and the value of compactness declines. In contrast, when the metering pump frequency increases, the value of compactness increases. (Received September 13, 2015 ; Accepted June 14, 2016) # corresponding author Determining the Influence of Processing Parameters on Spunbonded Non-Woven Pore Shape Using Rough Set Theory Guanxiu JIN, Qizheng Li, and Chengyan ZHU # *1College of Materials and Textiles, Zhejiang Sci-Tech University, 928 Second Avenue, Xiasha Higher Education Zone, Hangzhou, Zhejiang 310018, China *2Zhejiang Industry Polytechnic College, 151 Qutun Road, Shaoxing, Zhejiang 312000, China *3Modern Textile Processing Technology National Engineering Research Center, 928 Second Avenue, Xiasha Higher Education Zone, Hangzhou 310018, China J. Fiber Sci. Technol., 72(9), 184-194 (2016) doi 10.2115/fiberst.2016-0028 ©2016 The Society of Fiber Science and Technology, Japan 184 Journal of Fiber Science and Technology (JFST), Vol.72, No. 9 (2016) useful mathematical tool for discovering data dependencies [13]. Compared with conventional data mining techniques including linear models (such as multiple regression analyses), principal component analysis (PCA), and cluster analysis, the advantage of rough set theory is its strong ability to solve inconsistency and uncertainty problems. It can discover and obtain the knowledge and rules hidden in known but incomplete information. Its core function is attribute reduction, that is, the reduction of the number of attributes without requiring any additional information other than the data. For a given dataset with discretized attribute values, it is possible to find the subsets of the original attributes that are the most informative [14]. Let X, Y∈U , where U is a nonempty set of finite objects (the universe). Assuming R is the equivalence relation defined on U , we define the lower approximation of X about R as R = U {Y∈U/R : Y⊆X }. Moreover, the upper approximation of X about R is defined as R _ (X ) = U {Y∈U/R : Y∩X≠φ}, where φ denotes an empty set. The boundary region of X is defined as Bnd(X ) = R _ (X )-R_(X ). We can now give the definition of rough sets. If Bnd(X ) is empty, set X is crisp. If Bnd(X ) is nonempty, set X is rough. Assume that an information system S is expressed as S =〈U, A, V, f〉. Here, A is a nonempty finite set of attributes and V is a set of attribute values. Moreover f : U×A→V is an information function, that is, for any u∈U , a∈A, there is a function f (u, a) = V . Assuming B⊆A, the indiscernibility relation IND (B) is defined as : IND B x y x y U b B b x b y ( ) ( , ) | ( , ) , ( ( ) ( )) = ∈ ∀ ∈ = { } 2 (1) If IND(B) = IND(B–{b}), b is considered to be redundant in B. In this investigation, this attribute reduction was used to decrease the dimensions of the pore shape parameters of spunbonded nonwoven, and the result was compared with the results of attribute reduction using principal component analysis and cluster analysis.