IEEE International Conference on Fuzzy Systems

The aim of this paper is to highlight the role of the Decision Support System within the field of multi-criteria decision aid (MCDA). The MCDA tools have been incorporated into systems to create Multi-Criteria Decision Support Systems (MCDSSs). In our literature review, we noticed that more than 100 papers have been written over a 20-year period in which MCDSS was used as a decision-making tool. The present paper describes some real applications of MCDSS in different fields, harmoniously combined with decision-making methods such as analytic hierarchy process, Utility Additive, and Goal Programming. The present study proposes an integrative MCDSS evaluation through guidance on the tools most useful for a specific user with a particular decision problem. Copyright © 2014 John Wiley & Sons, Ltd.

Decision Support System and Multi-Criteria Decision Aid: A State of the Art and Perspectives

As the need for quickly exploring a textile manufacturing process is increasingly costly along with the complexity in the process. The development of manufacturing process modeling has attracted growing attention from the textile industry. More and more researchers shift their attention from classic methods to the intelligent techniques for process modeling as the traditional ones can hardly depict the intricate relationships of numerous process factors and performances. In this study, the literature investigating the process modeling of textile manufacturing is systematically reviewed. The structure of this paper is in line with the procedure of textile processes from yarn to fabrics, and then to garments. The analysis and discussion of the previous studies are conducted on different applications in different processes. The factors and performance properties considered in process modeling are collected in comparison. In terms of inputs’ relative importance, feature selection, modeling techniques, data distribution, and performance estimations, the considerations of the previous studies are analyzed and summarized. It is also concluded the limitations, challenges, and future perspectives in this issue on the basis of the summaries of more than 130 related articles from the point of views of textile engineering and artificial intelligence.

Modeling of textile manufacturing processes using intelligent techniques: a review

The strength and elongation at break of a seam are very important characteristics of comfort clothing. Optimum seam strength must be durable enough to do our daily activities easily. Some parameters such as the type and count of the sewing yarn, the seam density, the size of the sewing needle, and type of stitch affecting the strength and elongation at break of the seam. In this study two kinds of fabrics (gabardine and poplin) were chosen for experiments. As sewing parameters, two different types of stitches (plain and chain stitch), five seam densities (3, 4, 5, 6 and 7 seams/cm), two kinds of sewing needles (SPI and SES), and three kinds of sewing yarns (cotton, core-spun, and PBT yarns) were used in experiments. With these materials 120 different seam variations were developed. Each sampless seam strength was tested according to the ISO 13935-1[1] standard using an Instron 4411 instrument. After the testing process, an artificial neural network model was developed to predict the seam strength and elongation at break values. The test results were applied to multi layer perceptron and radial basis function neural network modeling. These two neural network types were compared in terms of the accuracy of the modeling system. The results show that the artificial neural network model produces reliable estimates of seam strength and elongation at break (R=1, MSE=3.33E-05).

/pdf/use-of-artificial-neural-networks-for-modelling-of-seam-4lcl2m7fth.pdf

Use of Artificial Neural Networks for Modelling of Seam Strength and Elongation at Break

Historically the main use of the textile fabrics has been limited mainly to clothing and domestic applications. The technical uses were of minor importance. However in the last decades the use of the textile structures has started to spread over other sectors like construction, medicine, vehicles, aeronautics, etc. The increased interest in technical applications have improved the fabric design and engineeringprocedures, given that the final products must be characterized by certain mechanical, electrical etc. properties. The performance of the fabrics should be predictable right from the design phase. The design of a fabric is focusing on the materials selection as well as on the definition of its structural parameters, so that the requirements of the end use be fulfilled. These changes in the application field of the textile structures caused a move from the esthetic design to the total technical design, where the fabric appearance and the particular properties affecting its final performance are taken in account. However, the textile structures are highly complex. A textile fabric consists of yarns; yarns in turn consist of fibres. Thus the mechanical performance of the fabrics is characterized by the structural geometrical complexity and non-linearity, as well as from the non-linearities of the materials themselves. This double non-linear bahaviour of the textile fabrics increases the difficulty in the fabric design and engineering processes. The complex structure and the difficulties introduced by the raw materials do not allow the use of precise analytical models for the technical design of the fabrics. Fabric engineering activities are increasingly based on computational models that aim at the prediction of the properties and the performance of the fabrics under consideration. Various computational tools have been used in order to represent the fabrics in a computational environment and to predict their final properties. Among others, Finite Element Method (FEM) analysis has supported mainly the prediction of the behaviour of the complex textile structures under mechanical loads. In the case of classification problems Artificial Neural Networks (ANNs) have proved a very efficient tool for the fast and precise solution. ANNs have found an increasing application in the textile field in the classification as well as in the

/pdf/artificial-neural-networks-and-their-applications-in-the-4ktes4pzyy.pdf

Artificial Neural Networks and Their Applications in the Engineering of Fabrics

Product prototype evaluation is an important phase in new product development (NPD). Such evaluation often requires multiple criteria that are within a hierarchy and a group of evaluators. The evaluation process and these evaluation criteria often involve uncertain and fuzzy data in the weights of these criteria and the judgments of these evaluators. To evaluate nonwoven cosmetic product prototypes, this study first develops a NPD evaluation model, which has evaluation criteria within three levels, based on the features of nonwoven products. It then proposes a fuzzy (multi-level) multi-criteria group decision-making (FMCGDM) method for supporting the evaluation task. A fuzzy multi-criteria group decision support system (FMCGDSS) is developed to implement the proposed method and applied in nonwoven cosmetic product development evaluation.

A fuzzy multi-criteria group decision support system for nonwoven based cosmetic product development evaluation

A soft computing approach to model the structure–property relations of nonwoven fabrics for filtration use is developed. Because the number of samples is very limited, the artificial neural network model to be established must be a small-scale one. Consequently, this soft computing approach includes two stages. In the first stage, the structural parameters are selected by using a ranking method, to find the most relevant parameters as the input variables to fit the small-scale artificial neural network model. The first part of this method takes the human knowledge on the nonwoven products into account. The second part uses a data sensitivity criterion based on a distance method that analyzes the measured data of nonwoven properties. In the second stage, the artificial neural network model of the structure–property relations of nonwoven fabrics is established. The results show that the artificial neural network model yields accurate prediction and a reasonably good artificial neural network model can be achieved with relatively few data points by integrated with the input variable selecting method developed in this research. The results also show that there is great potential for this research in the field of computer-assisted design in nonwoven technology. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 442–450, 2007

A soft computing approach to model the structure–property relations of nonwoven fabrics

Inthis paper, wepresent anewfuzzy logic based selection criterion whichtakesintoaccountboththe conformity ofhumanknowledge on nonwovenprocess technology andthesensitivity ofmeasureddatatothe functional properties. Thisselection criterion permits to rankallprocess parameters (input variables) andselect the mostrelevant parameters asinputvariables ofthemodel fordesigning nonwovensproducts. Comparedwiththe otherrelevant parameter selection criteria, theproposed methodismorerobustandlesssensitive tomeasuring noises. Thismethodhasbeenvalidated using mathematical simulation andrealdataofatextile nonwovenprocess. Nonwovenproducts arefibrous materials characterized byalarge range ofinteresting properties. Duetovarious application fields andgoodratio ofperformance/cost price, thenumberofend-uses designed withnonwoven materials hassignificantly growninthelast decades. Facetotheinternational concurrence ofthetextile market, nonwovenmaterials should bedeveloped to satisfy more andmore demanding andcomplex specifications and increasing requirements for international standards indifferent application fields. In thesametime, nonwovenproduct designers arestrongly involved incostreduction projects andapply thebasics ofvalue analysis during thedesign andthedevelopment ofthese manufacturing products. Consequently, great attention hasbeenpaid tomodelthe relationship betweenprocess parameters (machine parameters, rawmaterial parameters, andenvironment parameters) andquality features (functional properties, structural parameters). Newnonwovenproducts meeting specific consumer's requirements canbedesigned and produced byadjusting process parameters according to therelated model. Inthis paper, we suppose that the environment ofthequality features isconstant and acceptable forproduction. Underthis condition, machine andmaterial parameters aretaken asinput variables and structural parameters andfunctional properties asoutput variables respectively. Thecorresponding modelis illustrated inFig. 1.

Afuzzy logic basedcriterion forselecting relevant process parameters fordesign ofnonwovenproducts

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A fuzzy multi-criteria group decision support system for nonwoven based cosmetic product development evaluation

A soft computing approach to model the structure–property relations of nonwoven fabrics

Afuzzy logic basedcriterion forselecting relevant process parameters fordesign ofnonwovenproducts