In this paper, we evaluate the efficiency and accuracy of a method of detecting fabric defects that have been classified into different categories by a neural network. Four kinds of fabric defects most likely to be found during weaving were learned by the network. Based on the principle of the back-propagation algorithm of learning rule, fabric defects could be detected and classified exactly. The method used for processing image feature extraction is a co-occurrence-based method, by which six feature parameters are obtained. All of them consist of contrast measurements, which involve three spatial displacements (i.e., 1, 12, 16) and four directions (0, 45, 90, 135 degrees) of fabric defects' images used for classification. The results show that fabric defects inspected by means of image recognition in accordance with the artificial neural network agree approximately with initial expectations.It has become more and more important for textile engineers to use automatic techniques in production processes and management procedures. At present, fabric inspection still depends on human sight, and inspection results are greatly influenced by the mental and physical condition of an inspector. To economize on personnel and to increase the competitive ability of products it is necessary to automate the inspection of fabric defects during weaving.We have used an artificial neural network to construct a pattern recognition system for inspecting fabric defects in this study. Because of the learning capacity and fault tolerant nature of an artificial neural network, it can be used for this purpose.In this study, we use a neural network topology known as a &dquo;multi-layer perceptron,&dquo; which has not been used in the past because of the lack of effective training algorithms for it. Recently this has changed due to the development of an iterative gradient procedure known as the back-propagation algorithm [ 7 ] . Through this supervised learning algorithm, the neural network can become a classifier of fabric defects. Because of the highly parallel operation and quick response nature of a neural network, it is easy to apply this method to on-line monitoring of fabric defects in weaving.

This paper evaluates the efficiency and accuracy of a way to detect a fabric's weaving density using a co-occurrence-based method. Three basic fabric weave structures—plain, twill, and satin—are evaluated by this method. Based on the co-occurrence matrix algorithm, the weaving density of a plain weave structure can be computed exactly. The method used to process image feature extraction, the co-occurrence-based method, is one in which a feature parameter (the contrast parameter or CON) is obtained. It consists of contrast measurements involving sixty-four spatial displacements ( i.e., 1-64) and two directions (0 and 90 degrees) of fabric images used for calculation. The results show that the calculation precision for the plain weave is far better than that for the twill and satin weaves.

In this study, an intelligent R&D design system is used to obtain the best combination of weaving parameters for woven fabric designs. The searching mechanism, developed in the Turbo C programming environment, and the theory, based on a genetic algorithm, can find several desired solutions of weaving parameters to produce woven fabrics within controlled costs. In addition, the system can simultaneously calculate the fractional cover of the fabric for each set of surveyed solutions in order to provide the designer with options for the functionality of these fabrics. With this system, the weaving mill can integrate the resources of different divisions (e.g., design, production, and financial divisions) to achieve perfect designs for woven fabrics and enhance the enterprise's competitive power.

In this study, an intelligent diagnosis system capable of tracing possible breakdown causes of fabric defects is applied to fabric inspection processing. Apart from the basic structure ( e.g., user interface, inference engine, and knowledge base) of the expert system, there is also a diagnosis system developed using fuzzy set theory. This system, which uses the Turbo C program, can act as an expert consultant for operators tracing the causes of breakdowns at any time, and the expert system, developed in an artificial intelligence language PROLOG, can provide the operator with a knowledge base for further consultation in fabric inspection. The accuracy and efficiency of system implementation are also discussed. Implementing automation techniques for production processes and management procedures has become increasingly important in textile engineering. At present, however, fabric inspection processing and tracing breakdowns that occur during weaving still heavily depend on the experience of the technical operator. Results obtained from inspection and diagnosis are exclusively influenced by the mental and physical condition of the inspector.In addition to the time needed to train a new operator to become expert in the specific technical knowledge of weaving engineering, there is the problem of retraining once the trained operator leaves the job. This approach is not only time-consuming but also economically infeasible.To solve these problems, we have applied an intelligent diagnosis system developed using an artificial intelligence programming language PROLOG and Turbo C to fabric inspection processing and diagnosis of breakdowns during weaving.Expert systems are computer-based and use a large body of heuristic and logical rules for solving a specific class of problems [11. These systems have been evaluated for many years by many companies to guide plant operations [ 12,13,14 ] . They work in ways similar to those of very experienced operating and technical personnel and make evaluations to enhance operations or diagnose problems. These systems are potentially important for early detection and diagnosis of mechanical or process problems in the plant.Apart from the basic structure [ 1 ] ( e.g., user interface, inference engine, and knowledge base) of a conventional expert system, the intelligent diagnosis system also has a diagnosis component based on fuzzy set theory. By combining these two components, we can construct an intelligent diagnosis system that has the ability to function as an expert consultant to help a new operator trace breakdowns, to provide the operator with a knowledge base and a data base for various weaving problems, and to apply these bases to fabric inspection. . Theory . AUTOMATIC DIAGNOSIS USING FUZZY SET THEORY ' ,The automatic diagnosis procedure can be demonstrated by an electric circuit, as shown in Figure 1 a, in which switches xi -xm represent m kinds of breakdown causes and lamps yi -yn represent n kinds of symptoms. respectively. Assuming that the breakdown is caused by a switching e...

In this study, the shrinkages of warp and weft yarn in the finished woven fabrics can be estimated by neural net. The shrinkages of warp and weft yarn in woven fabrics are affected by various factors such as loom setting, fabric type, and the properties of warp and weft yarns, which are hard to be defined and estimated. The neural nets are used to find the relationships between the shrinkage of yarns and the cover factors of yarns and fabrics. The prediction of yarn shrinkage in the off-loomed fabrics can thus be fulfilled through a prediction model constructed with neural net.