Technical Library | 2009-11-18 23:37:52.0
Accurate component placement is a basic requirement for any pick and place machine. The first step towards accurate placement is accurate centering, or measurement of the component’s position on the placement head. One of the most widely used centering methods for ICs, connectors, and odd‐shaped components are a camera based system that measures the component position relative to a known point. Camera based centering systems include three main elements: lighting, camera, and software. Each of these elements are critical to obtaining an accurate measurement of the component and ultimately for accurate component placement on the PCB. As the old adage goes, the system is only as strong as its weakest link.
Technical Library | 2022-10-31 09:08:50.0
Product Name: ALLIED Vision Technologies GmbH M60S Processing Customization: No Quality: 100% Tested Automatic Manual: Automatic Brand: Siemens AG Custom Processing: Yes High Light: 00344065-03, 00344065-03 Siemens PCB Camera, KST-75-UP Siemens PCB Camera
Technical Library | 2022-10-31 09:08:51.0
Product Name: ALLIED Vision Technologies GmbH M60S Processing Customization: No Quality: 100% Tested Automatic Manual: Automatic Brand: Siemens AG Custom Processing: Yes High Light: 00344065-03, 00344065-03 Siemens PCB Camera, KST-75-UP Siemens PCB Camera
Technical Library | 2022-06-27 16:50:26.0
Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.
Technical Library | 2020-08-27 01:15:10.0
Automobile surface defects like scratches or dents occur during the process of manufacturing and cross-border transportation. This will affect consumers' first impression and the service life of the car itself. In most worldwide automobile industries, the inspection process is mainly performed by human vision, which is unstable and insufficient. The combination of artificial intelligence and the automobile industry shows promise nowadays. However, it is a challenge to inspect such defects in a computer system because of imbalanced illumination, specular highlight reflection, various reflection modes and limited defect features. This paper presents the design and implementation of a novel automatic inspection system (AIS) for automobile surface defects which are the located in or close to style lines, edges and handles. The system consists of image acquisition and image processing devices, operating in a closed environment and noncontact way with four LED light sources. Specifically, we use five plane-array Charge Coupled Device (CCD) cameras to collect images of the five sides of the automobile synchronously. Then the AIS extracts candidate defect regions from the vehicle body image by a multi-scale Hessian matrix fusion method. Finally, candidate defect regions are classified into pseudo-defects, dents and scratches by feature extraction (shape, size, statistics and divergence features) and a support vector machine algorithm. Experimental results demonstrate that automatic inspection system can effectively reduce false detection of pseudo-defects produced by image noise and achieve accuracies of 95.6% in dent defects and 97.1% in scratch defects, which is suitable for customs inspection of imported vehicles.
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