Technical Library | 2023-09-15 10:03:54.0
Achieve thorough quality control with our DIP On-line Dual Side PCBA AOI system. Detect defects on both sides of PCBAs in real-time, ensuring impeccable quality and production efficiency. Elevate your electronic manufacturing process today.
Technical Library | 2023-09-15 10:00:02.0
Experience top-tier quality inspection with our DIP Inverted Camera Online PCBA AOI solution. Ensure flawless PCB assemblies and detect defects with precision using advanced technology. Improve efficiency and minimize production issues with this cutting-edge automated optical inspection system.
Technical Library | 2023-08-04 15:38:36.0
The MicroLeadFrame® (MLF®)/Quad Flat No-Lead (QFN) packaging solution is extremely popular in the semiconductor industry. It is used in applications ranging from consumer electronics and communications to those requiring high reliability performance, such as the automotive industry. The wide acceptance of this packaging design is primarily due to its flexible form factors, size, scalability and thermal dissipation capabilities. The adaptation and acceptance of MLF/QFN packages in automotive high reliability applications has led to the development of materials and processes that have extended its capabilities to meet the performance and quality requirements. One of process developments that is enabling the success of the MLF/QFN within the automotive industry has been the innovation of side wettable flanks that provide the capability to inspect the package lead to printed circuit board (PCB) interfaces for reliable solder joints. Traditionally, through-board X-ray was the accepted method for detecting reliable solder joints for leadless packages. However, as PBC layer counts and routing complexities have increased, this method to detect well-formed solder fillets has proven ineffective and incapable of meeting the inspection requirements. To support increased reliability and more accurate inspection of the leadless package solder joints, processes to form side-wettable flanks have been developed. These processes enable the formation of solder fillets that are detectable using state-of-the-art automated optical inspection (AOI) equipment, providing increased throughput for the surface mount technology (SMT) processes and improved quality as well.
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 | 2024-04-29 21:39:52.0
In this paper, we develop and put into practice an Automatic Optical Inspection (AOI) system based on machine vision to check the holes on a printed circuit board (PCB). We incorporate the hardware and software. For the hardware part, we combine a PC, the three-axis positioning system, a lighting device and CCD cameras. For the software part, we utilize image registration, image segmentation, drill numbering, drill contrast, and defect displays to achieve this system. Results indicated that an accuracy of 5µm could be achieved in errors of the PCB holes allowing comparisons to be made. This is significant in inspecting the missing, the multi-hole and the incorrect location of the holes. However, previous work only focusses on one or other feature of the holes. Our research is able to assess multiple features: missing holes, incorrectly located holes and excessive holes. Equally, our results could be displayed as a bar chart and target plot. This has not been achieved before. These displays help users analyze the causes of errors and immediately correct the problems. Additionally, this AOI system is valuable for checking a large number of holes and finding out the defective ones on a PCB. Meanwhile, we apply a 0.1mm image resolution which is better than others used in industry. We set a detecting standard based on 2mm diameter of circles to diagnose the quality of the holes within 10 seconds.
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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