Fabric faults are responsible for major defects found by the garment industry, say Saber Ben Abdessalem, Meriam Azeiz, and Sofiene Mokhtari, who also propose a new inspection method permitting to appreciate objectively fabric quality.
Due to the increasing demand for quality knitted fabrics, high quality requirements are today greater since customer has become more aware of “Non-quality” problems. In order to avoid fabric rejection, knitting mills have to produce fabrics of high quality, constantly. Detection of faults during production of knitted fabric with circular knitting machine (CKM) is crucial for improved quality and productivity. Any variation to the knitting process needs to be investigated and corrected. The high quality standard can be guaranteed by incorporating appropriate quality assurance. Industrial analysis indicate that product quality can be improved, and defect cost minimised, by monitoring of the circular knitting process (Jearranai and Tiluk, 1999).
Fine gauge knitted fabric faults are very different in nature and appearance and are often superimposed. They can be attributed not only to the knitting, but also to the quality of yarns, dyeing and finishing (Iyer et al, 1995). Some faults can be easily avoided by respecting some fundamental pre-requisites on the circular knitting machine such as the use of positive yarn feeders and the respect of rigorous machine maintenance and cleaning schedule. Other faults are much more difficult to expect because they are not related to just one cause.
New generation circular knitting machines are conceived with auxiliary equipment that ensure less fabric faults during knitting such as filter creel, lint removal, thread survey, precise oiling and fabric faults detector devices. Nevertheless, some fabric faults are not detectable with these equipment and fabric has to be inspected after knitting. Fabric fault detector is able to detect holes and dropped stitches, but it is sometimes not enough reliable and have to be disconnected especially when a structured fabric is knitted because special fabric structures could be confused with faults by the sensor. Other faults cannot be detected during knitting but only after fabric relaxing or finishing such as fabric spirality and colour mismatching.
Many researchers have applied computer vision to improve inspection method of human vision in textile products. In most of them, the image of a knitted garment had been considered to specify the fault features (Shady et al, 2006, Kuo and Su, 2003, Palmer and Wang, Celik et al, 2005, Hemdan and Ayatallah, 2008). Other works aimed to classify defects in knitted fabric by using image analysis and neural network algorithm or Fuzzy logic (Shady et al, 2006, Slah et al). All theses methods are not completely reliable because image analysis of knitted fabrics involves difficulties due to the loop structures and yarn hairiness, compared to woven fabrics consisting of neat warp and weft yarns. Knitted fabric faults can also be detected by inspecting yarn input tension and loop length (De Araujo and Catarino, 1999, Semnani and Sheikhzadeh, 2007) but only few types of faults are concerned by theses methods.
Human inspection by using knitted fabric inspection machines remains today the most used way to classify faults after knitting and after finishing. Generally, faults are classified by type and by frequency in the inspected knitted roll. The inspection assessment permits to appreciate fabric quality. The judgement of fabric quality depend on faults tolerance levels fixed by each knitter and could be in some cases subjective because it is often based only on the number of faults and not on fault size and gravity.
The purpose of this study was to classify fine gauge knitted fabric faults types according to their origin and propose solutions for their avoidance by analysing their causes. A new inspection method was proposed in order to classify faults and help knitter to appreciate more objectively fabric quality.
Material and methods
Fine gauge knitted fabric faults were collected from an integrated circular knitting and finishing factory during a period of six months. Produced fabrics were classical structures such as single jersey and rib knitted fabrics. Knitted fabrics rolls were visually inspected by using a knitted fabric inspection machine (CHEVALLERIN – France) that enables fabric rolling and simultaneous fabric visualisation in the front and back sides. Fabric faults associated to yarn, to knitting and to finishing were classified and causes were analysed. Solutions aiming to avoid theses faults were tested and then clearly formulated.
In order to evaluate objectively, the knitted fabric roll quality, a fault index (FI) for each fault type was defined. Classically, during inspection only the number and the fault type are taken into account to judge fabric quality, but fault gravity and fault size are generally not considered. Fault length and fault frequency inside the fabric roll were measured and introduced in the expression of fault index:
F1 = FpFqG
Fp(%) = ---------------------------------
Fp is fault proportion defined as:
Fsub>q(frequency) is the number of fault in the inspected roll. When the same fault is repeated inside 0.5 m fabric, only one fault is recorded.
G is gravity grade attributed to fabric faults according to their importance. These grades were defined according to an inquiry made on clothes industry requirements.
Results and discussion
The study of fine gauge knitted fabric defects during the six months showed that fabric “Non-quality” is linked to three main sources: Yarn, knitting and finishing. Some defects such as those linked to yarn can be detected before knitting and avoided when an adequate control is applied to yarn. Other faults linked to knitting or finishing are detectable during or after theses steps and only some of them can be avoided or corrected. Faults attributed to yarn, knitting and finishing are presented respectively in Tables 1, 2 and 3. Tables show description of each kind of fault, possible causes and solutions permitting its avoidance or correction.
The inquiry made was in collaboration with several apparel garment factories during the six months permitted to evaluate the gravity grades attributed to fabric faults according to their importance. Special attention was accorded to the requirements of cutting and quality services. Table 4 and table 5 show the gravity grade of each fault kind. Faults were classified in four groups according to the following considerations:
* Fault proportion: Fp
* Fault frequency: Fq
* Detectability during knitting or finishing
* Possibility of preparation
The authors distinguished between gravity grades of faults detected after knitting and after finishing because fabric roll quality can be judged after one of these two manufacturing steps. The calculated fault indexes corresponding to each kind fault allowed a more objective evaluation of fabric quality since it takes into account fabric fault size, frequency and gravity. Classic evaluation of fine gauge knitted fabric quality after inspection is generally based only on faults frequencies. This only criteria is not enough to classify reliably rolls because some faults are reparable, others faults are local and pieces containing faults are simply eliminated after cutting operation.
In order to determine the most frequent faults that have to be eliminated in priority for a given knitting situation, the authors used Pareto diagram analysis technique. Pareto analysis is a statistical technique in decision making that is used for selection of a limited number of tasks that produce significant overall effect. In terms of quality improvement, a large majority of problems are produced by a few key causes. A Pareto diagram is a special type of bar diagram where the values being plotted are arranged in descending order. The graph is accompanied by a line graph which shows the cumulative totals of each category, left to right.
Figure 1 shows an example of Pareto diagram of fabric faults frequencies in the case of cotton plain knitted fabric. For this purpose, 76 knitted fabric rolls of 80 metres length each were inspected and faults frequencies recorded.
According to this figure, 50% of the fault causes represents 80% of the recorded faults. This means that faults linked unsettled fabric, holes and dropped stitches have to be repaired in priority to improve significantly fabric quality. Consequently, urgent measures have to be taken such as systematic yarn quality control, knitting organs setting and use of fabric fault detector. Major fabric faults origins are specific to the knitted structure, yarn quality, machines state and workers experience. The Pareto diagram test has to be performed for each situation in order to act on faults origins accordingly.
The results presented in the present work constitute a technical data base that could be useful for knitters to identify fabric faults linked to fine gauge fabrics produced with CKM. This data base helps also to determine possible faults origins and proposes specific solutions for theses faults causes. The calculation of fault index based on fault size, frequency and gravity permitted to classify more objectively knitted fabric. The Pareto diagram analysis is a practical tool that permitted to determine most important faults origins that have to be corrected in priority to improve fabric quality.
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Note: For detailed version of this article please refer the print version of The Indian Textile Journal November 2009 issue.
Saber Ben Abdessalem
Technology High School of Ksar Hellal
Textile Research Unit, Tunisia.
Technology High School of Ksar Hellal
Textile Research Unit, Tunisia.
National Engineering School of Monastir
Textile Department, Tunisia.