Technical Library | 2023-09-15 10:05:59.0
Elevate your electronics manufacturing with SMT Online AOI. Achieve real-time quality control, defect detection, and production efficiency optimization with our advanced automated optical inspection system. Improve your production process and ensure top-notch quality with SMT Online AOI technology.
Technical Library | 2023-09-15 09:58:06.0
Elevate your electronics production with our PCBA Coating Online SMT AOI solution. Achieve precision coating and comprehensive quality assurance in one integrated system. Boost efficiency and reduce defects with cutting-edge automated optical inspection technology.
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 | 2023-09-15 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 | 2019-01-23 21:33:32.0
Automated Optical Inspection (AOI) is advantageous in that it enables defects to be detected early in the manufacturing process, reducing the Cost of Repair as the AOI systems identify the specific components that are failing removing the need for any additional test troubleshooting1-3. Because of this, more Electronic Contract Manufacturing Services (EMS) companies are implementing AOI into their SMT lines to minimize repair costs and maintain good process and product quality, especially for new component types. This project focuses on the testing of component package 03015 which is challenging for AOI.
Technical Library | 2015-06-30 22:02:41.0
This paper describes the losses from defects at the placement process in the SMT line. Two case studies of European and Taiwanese SMT manufacturers illustrate the actual losses from their defects. An evaluation method to select a pre-reflow AOI system maximizing the return on investment (ROI) is introduced. In the end, ROIs of three commercial pre-reflow AOI systems are compared to demonstrate the importance of selecting an appropriate AOI system. This paper will increase the probability that anyone installing an AOI system during the pre-reflow process will obtain a successful gain with short payback period.
Technical Library | 2010-07-22 18:10:17.0
The utilisation of Automated Optical Inspection systems has become an integral part in quality assurance of electronic assemblies. Depending on batch size and product mixture, AOI systems as Inline integration or as a stand-alone solution benefit efficien
Technical Library | 2013-07-02 16:44:31.0
AOI and AXI systems can address multiple tasks in various locations of the manufacturing process and have become the leading technologies in the quest to identify defects and improve process yields.
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 | 2015-04-29 03:29:56.0
Statistical Appearance Modelling technology enables an AOI system to “learn real world variation” based on operator interaction with inspection task results. This provides an accurate statistical description of normal variation in a product. With modelling technology, the user does not have to anticipate potential defects as the system will “flag” anything outside the “normal production range”. And, since the system is programmed with real production variation, it is sensitive to small subtle changes enabling reliable defect detection. Autonomous prediction of process variation enables an AOI system to be set up from a single PCB with production-ready performance. Setup time can be
