Technical Library | 2023-11-20 17:30:11.0
Summary for today 1. Electronic component inspection and failure analysis. 2. Component counting and material management. 3. Reverse engineering. 4. Counterfeit detection. 5. Real-time defect verification. 6. Computed tomography (CT) techniques and how to differentiate between 2D, 2.5D, and 3D x-ray inspection. 7. Design for manufacturing (DFM) and design for x-ray inspection (DFXI). 8. Voids, bridging, and head-in-pillow failures in bottom terminated components (BTC). 9. Artificial Intelligence and x-ray inspection
Technical Library | 2019-11-20 14:36:27.0
5G: The Future of IoT takes a look at the market drivers, trends and cellular technology solutions that will create our connected future. Market drivers provide added value through connectivity of all “things” ranging from street lighting to home appliances to industrial robotics. Providing connectivity to things has become easier with improvements in the economics of end devices, large investments in IoT systems, adoption of global standards and availability of spectrum. Overall trends in information technology such as cloud computing and edge cloud, artificial intelligence and security assurance have accelerated the IoT ecosystem. The whitepaper discusses some of the market segments in more details, including industrial IoT, smart cities, enterprise IoT and consumer IoT.
Technical Library | 2021-11-22 20:39:44.0
Quality control is a key activity performed by manufacturing companies to verify product conformance to the requirements and specifications. Standardized quality control ensures that all the products are evaluated under the same criteria. The decreased cost of sensors and connectivity enabled an increasing digitalization of manufacturing and provided greater data availability. Such data availability has spurred the development of artificial intelligence models, which allow higher degrees of automation and reduced bias when inspecting the products. Furthermore, the increased speed of inspection reduces overall costs and time required for defect inspection. In this research, we compare five streaming machine learning algorithms applied to visual defect inspection with real world data provided by Philips Consumer Lifestyle BV. Furthermore, we compare them in a streaming active learning context, which reduces the data labeling effort in a real-world context. Our results show that active learning reduces the data labeling effort by almost 15% on average for the worst case, while keeping an acceptable classification performance. The use of machine learning models for automated visual inspection are expected to speed up the quality inspection up to 40%.
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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