Technical Library | 2023-08-14 09:06:53.0
In the operation of SMT mounter, the process and integrity of the nozzle of the mounter will have a significant impact on the performance of the machine. As one of the important components of the SMT machine, it is necessary for us to carry out daily maintenance and upkeep of the SMT nozzle. To ensure that the suction nozzle of the SMT machine is intact before operation, how should we do a good job of maintaining the suction nozzle of the SMT machine during normal use? KINGSUN technical team analysis operation has the following main points: 1.Wipe the surface of the SMT nozzle with a dust-free cloth. 2.The small aperture nozzle can be passed through with a thin steel wire and then blown with an air gun. 3.The surface of the nozzle should not be soaked with corrosive solution such as alcohol, as this may cause the surface to fall off. 4.HOLDER should use a cotton swab to wipe the cavity and not damage the filter screen. 5.Regular addition of special grease to HOLDER claws. 6.According to production, it is best to regularly maintain and do other maintenance regularly. (* Suitable for Yamaha SMT machine nozzles , JUKI SMT machine nozzles, Samsung SMT machine nozzles, Panasonic SMT machine nozzles, Fuji SMT machine nozzles, Siemens SMT machine nozzles etc.) Regarding the SMT machine nozzle daily maintenance operation instructions, KINGSUN share with you here , hoping to be helpful to you. More information about Products please Contact US at jenny@ksunsmt.com or visit www.ksunsmt.com , thanks.
Technical Library | 2023-11-20 18:10:20.0
The electronics production is prone to a multitude of possible failures along the production process. Therefore, the manufacturing process of surface-mounted electronics devices (SMD) includes visual quality inspection processes for defect detection. The detection of certain error patterns like solder voids and head in pillow defects require radioscopic inspection. These high-end inspection machines, like the X-ray inspection, rely on static checking routines, programmed manually by the expert user of the machine, to verify the quality. The utilization of the implicit knowledge of domain expert(s), based on soldering guidelines, allows the evaluation of the quality. The distinctive dependence on the individual qualification significantly influences false call rates of the inbuilt computer vision routines. In this contribution, we present a novel framework for the automatic solder joint classification based on Convolutional Neural Networks (CNN), flexibly reclassifying insufficient X-ray inspection results. We utilize existing deep learning network architectures for a region of interest detection on 2D grayscale images. The comparison with product-related meta-data ensures the presence of relevant areas and results in a subsequent classification based on a CNN. Subsequent data augmentation ensures sufficient input features. The results indicate a significant reduction of the false call rate compared to commercial X-ray machines, combined with reduced product-related optimization iterations.
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