www.tlist-journal.org Textiles and Light Industrial Science and Technology (TLIST) Volume 2 Issue 1, January 2013 6 Yarn Parameterization and Fabrics Prediction Using Image Processing Vitor Carvalho *1,6 , Nuno Gonçalves 2 , Filomena Soares 3 , Rosa Vasconcelos 4 , Michael Belsley 5 1,2,3 Dept. Industrial Electronics, 4 Dept. Textile Engineering, 5 Dept. Physics, University of Minho 1, 2 ,3 ,4 Campus de Azurém, 4800-058 Guimarães, Portugal 5 Campus de Gualtar, 4700-010 Braga, Portugal 6 IPCA-EST, 4750-810 Barcelos, Portugal (* 1,6 vcarvalho, 2 ngoncalves, 3 fsoares)@dei.uminho.pt, 4 rosa@det.uminho.pt, 5 belsey@fisica.uminho.pt Abstract This paper presents the main characteristics and functionalities of a system based on image processing techniques applied to quality assessment of yarns. In Textile Industry we used image processing to determine yarn mass parameters as well as yarn production characteristics. A low cost solution based on a web-pc camera plus the optics of a low cost analogue microscope and a software tool based on IMAQ Vision from LabVIEW was designed. Several tests were performed and compared with other methodologies of yarn parameterization validating the proposed solution. With the results one can support that this can be an alternative solution to the traditional yarn testers, with several advantages (among others, low cost, weight, volume, easy maintenance and reduced hardware). Moreover, this yarn parameterization can be used to assess the quality of the fabrics resultant. Keywords Yarn; Mass; Diameter; Hairiness; Image Processing; Faults; Fabrics Introduction The yarn mass parameters are essential for the quality of fabrics. These include the yarn diameter, mass and hairiness. The most used commercial equipment for measuring these parameters is developed by Uster [1- 3]. Although, they present several drawbacks associated with cost, portability, accuracy, resolution, and complexity, among others. To overcome these problems, we are developing a technological solution using image processing capable of determining the yarn mass parameters and the yarn production characteristics allowing a new level of yarn parameterization. These characteristics can be used to predict the quality of the fabrics resultant from the yarn parameterized using artificial intelligence. The image acquisition is performed using a web-pc camera plus the optics of a low cost analogue microscope with a maximum amplification of 40X (Fig. 1). A custom software tool developed with the IMAQ Vision form LabVIEW is then used [4]. Analogue microscope USB - Web Camera PC NI IMAQ VISION FIG. 1 DESIGNED SYSTEM FLOWCHART This paper is organized as follows: Section 2 presents the module of determination of mass parameters, Section 3 presents the module of yarn production characteristics, Section 4 shows theoretical concepts of fancy yarns and prediction models and Section 5, the conclusion and future work developments. Mass Parameters Determination with Image Processing This section presents the description of the yarn mass parameters determination (hairiness, diameter and faults) as well as the algorithms developed with LAbVIEW, from National Instruments to characterize them. Some results are also presented. Hairiness Image Procssing (IP) based applications have been used in the textile industry since 1964 [5], although they have not been converted to viable quality control methods [6]. Several algorithms are currently under development to characterize the yarn hairiness (Fig. 2) with IP, in particular to detect and characterize the protruding fibres length [3, 7-9]. FIG. 2 IDENTIFICATION OF YARN CORE AND YARN HAIRINESS (LOOPED FIBRES AND PROTRUDING FIBRES) [7]