International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022
p-ISSN: 2395-0072
www.irjet.net
Defect Detection and Classification of Optical Components : A Review Jancy J S1, Kanjana G2 1PG
Student, Dept. of Electronics & Communication Engineering, LBSITW, Kerala, India Professor, Dept. of Electronics & Communication Engineering, LBSITW, Kerala, India ---------------------------------------------------------------------***--------------------------------------------------------------------2Assistant
Abstract - Optics engineering is a field that is rapidly
Surface quality checking is essential in the manufacture of optical components. Several sorts of errors develop as a result of imperfections in the manufacturing process. The traditional dark field imaging approach is unsuitable for this purpose because optical components have radically different reflection and scattering properties than regular clear glass. Throughout the production process, errors in precision optical components always arise, posing a hazard to the entire optical system. Images with minor faults have low contrast and a skewed grayscale distribution, making scrutiny difficult.
changing, inventive, and developing. To address the growing demand for optical components, product quality requirements must be maintained. Automatic optical inspection (AOI) is a non-destructive technology used in product quality inspection. This technology is regarded reliable and may be used to replace human inspectors who are tired and bored while doing inspection chores. Hardware and software configurations make comprise a fully automated optical inspection system. The hardware setup is responsible for acquiring the digital picture, while the software portion implements an inspection algorithm to extract the attributes of the collected images and categories them as defected or non-defective depending on the user requirements. To distinguish between faulty and excellent items, a sorting system might be utilized. This study examines the numerous AOI systems utilized in the electronics, microelectronics, and optoelectronics sectors. The imaging, pre-processing, feature extraction, and classification technologies used to detect flaws in optical components are reviewed.
Optical elements are employed in a variety of devices that are significant in a variety of sectors, including semiconductor manufacturing, defense, space, astronomy, and medicine. Optical surfaces should be accurately constructed, polished, handled, and maintained dust-free for imaging applications. Even the minute scratch, crack, irregularity in period, or other form of imperfection in the optics may scatter the incident light, generating noise signal and thereby affecting the findings. The dispersed light can cause severe ghost interference patterns, which can lead to inaccurate findings. A scratch or dust particle might also influence the desired outcomes in non-imaging areas by generating noise signal. For accurate readings, it is critical to keep the optics clear of scratches and dust. To identify these flaws, several approaches such as spatial filtering, diffraction-based methods, interferometric, holographic, and digital holographic methods are used. The majority of these approaches are only useful for detecting defects in periodic items. Time is running out to create a system that can analyses both periodic structures like semiconductor wafers and gratings, as well as non-periodic components like mirrors and glass plates. The detection and categorization of defects in periodic as well as non-periodic patterns will be discussed in the next session.
Key Words: Automatic optical inspection (AOI), Defect detection and Classification, Machine Learning, Deep Learning, Digital Holography (DH).
1. INTRODUCTION Defect detection and classification is an important task in manufacturing industries, to ensure the quality of products. The past few years, have been characterised by growth in emerging markets and invention of new products, which leading more people to buy new the new one. Therefore, ensure that the manufacturing process is under control and prevent the defect formation. Based on the type of materials defect detection and classification methods are different. Early defect detection helps to replace the defected parts of the system. Moreover, it helps to reduce the material wastage. There are two classifications in quality check, destructive and non-destructive testing. Most of the testing are non-destructive type of testing. They are visual-based method, dye penetrant inspection, radiography, ultrasonic testing, Eddy current approach, thermography, X-ray, circuit probe testing and optical inspection[1][2].
2. DEFECT DETECTION AND CLASSIFICATION METHODS Tao[3] proposed a binarization method by combining the LSD (line segment detection) method and Hough transform. Using a coarse-to-fine detection strategy in dark field imaging for weak scratch detection. First a bionic vision used to detect all possible scratches. Then connect all the scratches using LSD and a priori knowledge. For classification GIST (global image descriptor) method is used. In this method the defects and interference are classified
In an optical system, optical components are the most important components. Transparency and reflection are the main properties of optical components. The system's dependability is impacted by optical component defects.
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