Technical Library | 2023-09-15 10:04:55.0
Discover our SMT Offline AOI Machine for accurate and efficient quality control in electronics manufacturing. Ensure flawless PCB assemblies, detect defects, and enhance production quality with this advanced offline automated optical inspection solution. Elevate your manufacturing process today.
Technical Library | 2024-02-02 07:48:31.0
Maximizing Efficiency: The High-Speed SMT Line With Laser Depanelizer In today's rapidly evolving electronics manufacturing landscape, optimizing efficiency, cost-effectiveness, and precision remains paramount. Businesses engaged in producing industrial control boards, computer motherboards, mobile phone motherboards, and mining machine boards face ongoing challenges in streamlining production processes. The integration of expensive equipment strains budgets, making the creation of an efficient, cost-effective high-speed SMT line a daunting task. However, a solution exists that seamlessly combines these elements into a singular, high-performance, and cost-effective SMT line. Let's delve into the specifics. A Comprehensive High-Speed SMT Line Our innovative solution amalgamates two pivotal components: a cutting-edge SMT (Surface Mount Technology) production line and a laser cutting line equipped with a depanelizer. The SMT Production Line The high-speed SMT line comprises several essential components, each fulfilling a unique role in the manufacturing process: 1. PCB Loader: This initial stage involves loading boards onto the production line with utmost care. Our Board Loader prioritizes safety, incorporating various safety light curtains and sensors to promptly halt operations and issue alerts in case of any anomalies. 2. Laser Marking Machine: Every PCB receives a unique two-dimensional code or barcode, facilitating comprehensive traceability. Despite the high-temperature laser process potentially leading to dust accumulation on PCB surfaces, our dedicated PCB Surface Cleaner swiftly addresses this issue. 3. SMT Solder Paste Printer: This stage involves applying solder paste to the boards, a fundamental step in the manufacturing process. 4. SPI (Solder Paste Inspection): Meticulous inspections are conducted at this stage. Boards passing inspection proceed through the NG (No Good) Buffer Conveyor to the module mounters. Conversely, "No Good" results prompt storage of PCBs in the NG Buffer Conveyor, capable of accommodating up to 25 PCBs. Operators can retrieve these NG boards for rework after utilizing our specialized PCB Mis Cleaner to remove solder paste. 5. Module Mounters: These machines excel in attaching small and delicate components, necessitating precision and expertise in the module mounting process. 6. Standard Pick And Place Machines: The selection of these machines is contingent upon your specific BOM (Bill of Materials) list. 7. Pre-Reflow AOI (Automated Optical Inspection): Boards undergo examination for component quality at this stage. Detected issues prompt the Sorting Conveyor to segregate boards for rework. 8. Reflow Oven: Boards undergo reflow soldering, with our Lyra series reflow ovens recommended for their outstanding features, including nitrogen capability, flux recycling, and water cooling function, ensuring impeccable soldering results. 9. Post-Reflow AOI: This stage focuses on examining soldering quality. Detected defects prompt the Sorting Conveyor to segregate boards for further inspection or rework. Any identified defects are efficiently addressed with the BGA rework station, maintaining the highest quality standards. 10. Laser Depanelizer: Boards advance to the laser depanelizer, where precision laser cutting, often employing green light for optimal results, ensures smoke-free, highly accurate separation of boards. 11. PCB Placement Machine: Cut boards are subsequently managed by the PCB Placement Machine, arranging them as required. With this, all high-speed SMT line processes are concluded. Efficiency And Output This production line demonstrates exceptional productivity when manufacturing motherboards with approximately 3000 electronic components, boasting the potential to assemble up to 180 boards within a single hour. Such efficiency not only enhances output but also ensures cost-effectiveness and precision in your manufacturing processes. At I.C.T, we specialize in crafting customized SMT production line solutions tailored to your product and specific requirements. Our equipment complies with European safety standards and holds CE certificates. For inquiries or to explore our exemplary post-sales support, do not hesitate to contact us. The I.C.T team is here to elevate your electronics manufacturing to new heights of efficiency and cost-effectiveness.
Technical Library | 2021-11-22 20:44:44.0
Many automated optical inspection (AOI) companies use supervised object detection networks to inspect items, a technique which expends tremendous time and energy to mark defectives. Therefore, we propose an AOI system which uses an unsupervised learning network as the base algorithm to simultaneously generate anomaly alerts and reduce labeling costs. This AOI system works by deploying the GANomaly neural network and the supervised network to the manufacturing system. To improve the ability to distinguish anomaly items from normal items in industry and enhance the overall performance of the manufacturing process, the system uses the structural similarity index (SSIM) as part of the loss function as well as the scoring parameters. Thus, the proposed system will achieve the requirements of smart factories in the future (Industry 4.0).
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 | 2017-08-31 13:43:48.0
Wire bonded packages using conventional copper leadframe have been used in industry for quite some time. The growth of portable and wireless products is driving the miniaturization of packages resulting in the development of many types of thin form factor packages and cost effective assembly processes. Proper optimization of wire bond parameters and machine settings are essential for good yields. Wire bond process can generate a variety of defects such as lifted bond, cracked metallization, poor intermetallic etc. NSOP – non-stick on pad is a defect in wire bonding which can affect front end assembly yields. In this condition, the imprint of the bond is left on the bond pad without the wire being attached. NSOP failures are costly as the entire device is rejected if there is one such failure on any bond pad. The paper presents some of the failure modes observed and the efforts to address NSOP reduction
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%.
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