Technical Library | 2009-08-26 19:32:32.0
Automated optical inspection (AOI) and automated X-ray inspection (AXI) have been around for some time in various configurations and both have played a role in improving the quality of circuit boards. While some companies opt for one technology over the other, each form of inspection contributes its own unique benefit to the manufacturing process.
Technical Library | 2012-02-23 21:16:28.0
Installed for the first time 20 years ago, Automated Optical Inspection (AOI) more recently has become an essential part of our SMT environment. Today, most process engineers are turning to machines as an inspection strategy for addressing quality and pro
Technical Library | 2017-08-28 17:14:41.0
PCB suppliers in the automotive space are vastly accelerating their time to market by using automated optical inspection (AOI) systems during PCB assembly. However, this next-generation technique is not limited in scope to the automotive industry – it has powerful implications for the entire PCB industry.
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 | 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 | 2021-11-22 20:32:10.0
The aim of this work is to define a procedure to develop diagnostic systems for Printed Circuit Boards, based on Automated Optical Inspection with low cost and easy adaptability to different features. A complete system to detect mounting defects in the circuits is presented in this paper. A low cost image acquisition system with high accuracy has been designed to fit this application. Afterward, the resulting images are processed using the Wavelet Transform and Neural Networks, for low computational cost and acceptable precision. The wavelet space represents a compact support for efficient feature extraction with the localization property. The proposed solution is demonstrated on several defects in different kind of circuits.
Technical Library | 2013-08-07 21:52:15.0
PCB architectures have continued their steep trend toward greater complexities and higher component densities. For quality control managers and test technicians, the consequence is significant. Their ability to electrically test these products is compounded with each new generation. Probe access to high density boards loaded with micro BGAs using a conventional in-circuit (bed-of-nails) test system is greatly reduced. The challenges and complexity of creating a comprehensive functional test program have all but assured that functional test will not fill the widening gap. This explains why sales of automated-optical and automated X-ray inspection (AOI and AXI) equipment have dramatically risen...
Technical Library | 2023-10-19 22:03:14.0
Koh Young Technology, the industry leader in True 3D measurement-based inspection solutions, proudly releases another customer success story with Matric Group. This case study shows how Matric Group has leveraged their partnership with Koh Young to be one of the first in the industry to use pre-reflow AOI as a game-changer for line efficiency and improved yield. All while creating a central inspection war room to allow just one person to manage all inline inspection, increasing automation, and control and mitigating talent shortages.
Technical Library | 2013-07-25 14:02:15.0
Bottom-termination components (BTC), such as QFNs, are becoming more common in PCB assemblies. These components are characterized by hidden solder joints. How are defects on hidden DFN joints detected? Certainly, insufficient solder joints on BTCs cannot be detected by manual visual inspection. Nor can this type of defect be detected by automated optical inspection; the joint is hidden by the component body. Defects such as insufficients are often referred to as "marginal" defects because there is likely enough solder present to make contact between the termination on the bottom-side of the component and the board pad for the component to pass in-circuit and functional test. Should the board be subjected to shock or vibration, however, there is a good chance this solder connection will fracture, leading to an open connection.
Technical Library | 2021-05-06 13:41:55.0
Quality control is of vital importance during electronics production. As the methods of producing electronic circuits improve, there is an increasing chance of solder defects during assembling the printed circuit board (PCB). Many technologies have been incorporated for inspecting failed soldering, such as X-ray imaging, optical imaging, and thermal imaging. With some advanced algorithms, the new technologies are expected to control the production quality based on the digital images. However, current algorithms sometimes are not accurate enough to meet the quality control. Specialists are needed to do a follow-up checking. For automated X-ray inspection, joint of interest on the X-ray image is located by region of interest (ROI) and inspected by some algorithms. Some incorrect ROIs deteriorate the inspection algorithm.
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