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

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Artificial Neural Networks and Their Applications in the Engineering of Fabrics

In this research, image analysis was used to optimize the visual output of a traditional Closed Circuit Television (CCTV) magnifying system and a head-mounted display (HMD) for people with low vision. There were two purposes: (1) To determine the benefit of using an image analysis system to customize image quality for a person with low vision, and (2) to have people with low vision evaluate a traditional CCTV magnifier and an HMD, each customized to the user’s needs and preferences. A CCTV system can electronically alter images by increasing the contrast, brightness, and magnification for the visually disabled when they are reading texts and pictures. The test methods was developed to evaluate and customize a magnification system for persons with low vision. The head-mounted display with CCTV was used to obtain better depth of field and a higher modulation transfer function from the video camera. By sensing the parameters of the environment (e.g., ambient light level, etc.) and collecting the user’s speci...

Evaluating the image quality of Closed Circuit Television magnification systems versus a head-mounted display for people with low vision. .

Purpose – The purpose of this paper is to apply an on‐line automatic inspection and measurement of surface defect of thin‐film transistor liquid‐crystal display (TFT‐LCD) panels in the polyimide coating process with a modified template matching method and back propagation neural network classification method.Design/methodology/approach – By using the technique of searching, analyzing, and recognizing image processing methods, the target pattern image of TFT‐LCD cell defects can be obtained.Findings – With template match and neural network classification in the database of the system, the program judges the kinds of the target defects characteristics, finds out the central position of cell defect, and analyzes cell defects.Research limitations/implications – The recognition speed becomes faster and the system becomes more flexible in comparison to the previous system. The proposed method and strategy, using unsophisticated and economical equipment, is also verified. The proposed method provides highly accu...

Automatic inspection and strategy for surface defects in the PI coating process of TFT‐LCD panels

Purpose – The purpose of this paper is to present an automatic inspection method of colors and textures classification of paper and cloth objects.Design/methodology/approach – In this system, the color image is transformed from RGB model to other suitable color model with one of the components being chosen as the gray‐level image for extracting textures. The gray‐level image is decomposed into four child images using wavelet transformation. Two child images capable of detecting variations along columns and rows are used to generate 0° and 90° co‐occurrence matrices, respectively. Some of the distinguishable texture features are derived from the two co‐occurrence matrixes. Finally, the test image is classified using neural networks. Nine color papers and eight color cloths are used to test the developed classification method.Findings – The results show that recognition rate higher than 97.86 percent can be achieved if color and texture features are both used as the inputs to the networks.Originality/value ...

Guan‐Zi Chen

Papers

Automatic texture inspection in the classification of papers and cloths with neural networks method