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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 10 Issue: 07 | July 2023

p-ISSN: 2395-0072

www.irjet.net

Detection Of Faults In Fabric Using Image Processing In MATLAB Dr. K. R. Desai1, Mr. Shree S. Kesarkar2 1Professor, Department of Electronics & Telecommunication, Bharati Vidyapeeth’s College of Engineering,

Kolhapur, Maharashtra, India.

2Student, Department of Electronics & Telecommunication, Bharati Vidyapeeth’s College of Engineering, Kolhapur,

Maharashtra, India. ---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - In the textile industry, quality control is essential

some companies who prioritize just-in-time delivery over creating high-quality products.

to competing in the fiercely competitive global market. In these textile industries, faults in the fabric are discovered through tedious, inaccurate manual testing. The profits of the textile business have decreased as a result of fabric faults, which cause unfavorable losses. To solve these issues and speed up and simplify defect diagnosis, an image processing system has been implemented. When the patterns and textures of a fabric are changed, the fabric's appearance and physical properties are affected, which is known as a defect. Either "minor" or "major" problems could exist. The faulty area must be located in order to cut the fabric correctly. In order to easily locate Fabric Texture defects, this project aims to create a tool. The approach outlined in this proposal for labor lessens the physical effort needed from the employees performing the manual examination. To process images in this study, "MATLAB" is employed. Along with centimeters, pixels are used to measure the fault's size. The fault's data representation and its size are displayed using a histogram and the GUI, respectively.

The main goal of today's textile business is to produce First Quality, a high-quality fabric. This premium fabric is devoid of any serious faults as well as any surface or minor structural imperfections. After the first quality, the remaining fabric is recognized as second-class fabric. A few serious defects as well as countless structural or surface flaws in this kind of fabric could cost the manufacturer money. When selling second-quality fabric, just 45% to 65% of the price of first-quality cloth is charged. Manufacturing moves quickly, therefore the manufacturer needs to be able to spot problems, figure out what caused them, and fix them very away. The load on the manufacturer's inspection teams may increase as a result, and the output of second-quality fabric may decline while first-quality fabric production rises. Compared to hiring numerous employees, investing in automated fabric fault identification is less expensive. Realtime fabric inspection is a challenging undertaking because there are many various types of problems based on the weaving process employed and post-production issues like holes and oil stains. While some defects can be detected by sophisticated loom machines on their own, a large number of flaws still require scrutiny after the weaving stage. Therefore, enhancing the quality of materials requires automated fabric inspection. The automated method for spotting fabric flaws will take care of imperfections in the fabric including holes, scratches, stretch, fly yarn, unclean patches, cracked points, misprints, and color bleeding, among others. If these defects are not discovered, the fabric businesses run the danger of losing money.

Key Words: Fabric Faults, Flaw Measurement, Image Processing, MATLAB, Textile Industry

1. INTRODUCTION In every industry, the quality factor is essential. In a similar vein, a textile manufacturer must maintain production quality. Hand testing is still performed today to assess the fabric's quality. The manual inspection result, however, falls short of expectations due to complacency and disinterest. With the help of highly qualified inspectors, only 70% of fabric faults can be identified. A basic cloth's defects can be manually detected in about 90% of cases, but complicated materials are more difficult. Consequently, automated fabric fault identification can lead to quick and high-quality product manufacture. Recently, automated fabric inspection has attracted a lot of attention.

Automated fabric fault identification is feasible by utilizing MATLAB's image processing method. With minimal work and expenditure, you might get superb precision using this method. The faults in a captured image are discovered by using this MATLAB-based image processing technique. After applying the noise filtering, histogram, and thresholding processes to the image, the output is created. The fabric image can be compared to other common textured images. It is vital to identify the precise position and severity of the faults when deciding that the cloth is faulty. Patterns can be detected anywhere in the visual area, regardless of their size, brightness, or direction. This automation strategy can be used to address these problems

The textile business, like other sectors, strives for high quality and mass manufacturing in order to meet consumer demands and reduce losses resulting from poor fabric quality and flaws. Rapid product flaw detection is necessary due to the rapid pace of manufacturing. The larger roll sizes used to weave the cloth may result in lower production quality before any inspection. In this industry, there are

Impact Factor value: 8.226

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