Prof. Dr. Hemdan A. Abou-Taleb
Eng. Aya Tallah M. Sallam
Textile Engineering Department ., Faculty of Engineering,
Mansoura University, Mansoura, Egypt.
Email :- Haboutaleb_mm@yahoo.com
ABSTRACT : This study has shown that image analysis has great potential to provide reliable measurements for defect detection of knitted fabrics. Using the principles of image analysis, an automatic fabric evaluation system, which enables automatic computerized defect detection ï¿½ analysis of knitted fabrics, was developed. Onï¿½line fabric defect detection was tested automatically by analyzing fabric images captured by digital camera. The results of the automatic fabric defect detection correspond well with the experimental values. Therefore, it is shown that the developed image capturing and analysis system is capable of on ï¿½ line fabric defect detection and full control in knitting machine (i.e., stopping the circular knitting machine as soon as defect acquisition at once by the digital camera.
Any variation to the knitting process needs to be investigated and corrected. Defects fall into this category. Since when they appear, repair is needed, which is time consuming and sometimes results in fabric rejection.Fabric defect detection has been a long ï¿½ felt need in the textile and apparel industry. Surveys carried out as early as 1975  show that inadequate or inaccurate inspection of fabrics has led to fabric defects being missed out, which has in turn had great effects on the quality and subsequent costs of the fabric finishing and garment manufacturing processes.
Circular knitting is one of the easiest and fastest ways (20 million stitches per minute) of producing cloth and textile pieces such as garments, socks and gloves. Fabric faults, or defects, are responsible for nearly 85% of the defects found by the garment industry. An automated defect detection and identification system enhances the product quality and results in improved productivity to meet both customer demands and to reduce the costs associated with off-quality. Higher production speeds make the timely detection of fabric defects more important than ever. Presently, inspection is done manually when a significant amount of fabric is produced, the fabric roll is removed from circular knitting machine and then sent to an inspection frame. An optimal solution would be to automatically inspect fabric as it is being produced and to encourage maintenance personnel to prevent production of defects or to change process parameters automatically and consequently improve product quality .
The study of this problem has led to the identification of two main categories of defects in knitted fabrics: horizontal and vertical variations [3,4]. While the first category is mainly due to the yarn (quality and management), the second category is related with the knitting elements: needles, sinkers, feeders, and so on. The solutions encountered for solving these problems are, for the first category, a careful selection and management of the yarn, and for the second, the correction or substitution of the defective elements. In order to deal with these problems, various studies have been conducted and some specialised systems were developed, which can detect abnormalities in the yarn being fed, defects in the knitted fabric and defects in the knitting elements [4,5].
In the previous work , the neural network methods applied on images of simple circular knitted fabrics for classification of faults. The result showed the successfulness of the methods but this approach can not be useful for industries because the process is time consuming and there are no ways for determination of fault location and area. To prevent the problem, in current research , the wavelet transform was applied for processing of image of circular knitted fabrics.
Depending on knitted structure, the defects can be categorized in three types of vertical, horizontal and regional defects [8, 9].