IC leadframe inspection system using multiple thread on the Windows NT

This paper describes a SIMM (Single Inline Memory Module) inspection system called SIMMI. The system inspects the front 'C-Gap' of the SIMMS and the back 'True Position' of the solder tails at 1.07 seconds/connector using 3 synchronized shuttered cameras with Telecentric Optics. This system is a very high speed (75 frames/second) inspection system designed from off-the-shelf hardware for inspecting SIMMs to a high degree of accuracy (< 4 micron repeatability). The system used standard linear and area techniques to process 700 measurements/second on an EISA based framestore hosted in 486 based PC. The paper will describe the system, and the techniques which were used to test and debug the system, which is not a trivial problem when the system is processing 75 frames/second. In particular, the paper will describe the techniques used to synchronize the camera and SIMM driver mechanics and the evolution of the lighting techniques.

PurposeHeritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals. Many social and natural factors seriously threaten heritage structures by deteriorating and damaging the original. Therefore, regular visual inspection of heritage structures is necessary for their conservation and preservation. Conventional inspection practice relies on manual inspection, which takes more time and human resources. The inspection system seeks an innovative approach that should be cheaper, faster, safer and less prone to human error than manual inspection. Therefore, this study aims to develop an automatic system of visual inspection for the built heritage.Design/methodology/approachThe artificial intelligence-based automatic defect detection system is developed using the faster R-CNN (faster region-based convolutional neural network) model of object detection to build an automatic visual inspection system. From the English and Dutch cemeteries of Surat (India), images of heritage structures were captured by digital camera to prepare the image data set. This image data set was used for training, validation and testing to develop the automatic defect detection model. While validating this model, its optimum detection accuracy is recorded as 91.58% to detect three types of defects: “spalling,” “exposed bricks” and “cracks.”FindingsThis study develops the model of automatic web-based visual inspection systems for the heritage structures using the faster R-CNN. Then it demonstrates detection of defects of spalling, exposed bricks and cracks existing in the heritage structures. Comparison of conventional (manual) and developed automatic inspection systems reveals that the developed automatic system requires less time and staff. Therefore, the routine inspection can be faster, cheaper, safer and more accurate than the conventional inspection method.Practical implicationsThe study presented here can improve inspecting the built heritages by reducing inspection time and cost, eliminating chances of human errors and accidents and having accurate and consistent information. This study attempts to ensure the sustainability of the built heritage.Originality/valueFor ensuring the sustainability of built heritage, this study presents the artificial intelligence-based methodology for the development of an automatic visual inspection system. The automatic web-based visual inspection system for the built heritage has not been reported in previous studies so far.

컴퓨터용 Lead Screw는 검사방식이 수동검사로 이루어져 작업자의 주관적 판단으로 인한 미검출과 과검출이 발생하고, 생산성이 저하되는 단점이 있다. 따라서 본 연구에서는 이러한 단점을 보완하고자 Lead Screw 자동검사 시스템을 개발했다. 검사시스템은 Lead Screw 자동공급 및 이송을 위한 공급 이송 장치와, 표면검사를 위한 <TEX>$360^{\circ}$</TEX> 회전장치, 치수검사를 위한 머신비젼 부분으로 구성된다. Lead Screw 자동검사 시스템으로 표면과 치수 측정결과, 오검률 및 미검률이 수동검사 결과보다 우수함을 보였다. In manual inspection of Lead Screw of computers many workers are needed to inspect samples, and its main disadvantage is that such types of inspection system not only gives low production, but also gives low perfection. Besides, in manual inspection system, the inspection cost of samples is higher than that of the automatic inspection system. Therefore, in this study to compensate these shortcomings, an automatic inspection system is developed. For the inspection of the surfaces and different dimensional parameters of computer Lead screw, a <TEX>$360^{\circ}$</TEX> rotating machine vision system is developed. From the detailed analysis of the inspection results using the present developed inspection system, it is observed that the developed Lead Screw automatic inspection system is superior to those of manually inspection system.

A more result-driven, holistic view on the performance of an In-line inspection (ILI) system is desired by pipeline operators, namely an integrated view on the Probabilities of Detection, Identification and Sizing, i.e. POD, POI and POS respectively. It is self-evident to combine the product of the individual probabilities to a single value POX as proposed also by API 1163. The changes in material properties of existing pipelines related to the repurposing to Hydrogen service yields smaller acceptable anomaly dimension. Consequently the dependency of the POX vs. anomaly dimensions need to be considered as function. The current practice to document the performance of an ILI system is typically based only on the specific dimensions of minimum detectable crack-like anomaly. In the case of crack-like anomalies, the contribution of the dimensions (e.g. length, depth, orientation) and interactions of crack-like anomalies can be used to turn POX into a meaningful function/dependency depending on the anomaly dimensions instead of a single value. To establish a POX to anomaly dimension relation successfully for a specific ILI crack detection service, the relevance of field verifications needs to be taken into account as well. Previous publications as well as physics foundations of NDE demonstrate that an increase in anomaly dimensions typically yields a higher sensitivity of the underlying inspection system. This can be immediately linked to the POX function as product of POD, POI and POS. This paper provides access to an in-depth assessment of POX based on the results of recent field verification data. The paper focuses on the interaction between POX and different anomaly dimensions. This is supported by the most commonly applied standard for in-line inspection, API 1163, which implies the validation of inspection performance with use of field verifications based on the reported results. In addition, a performance specification can be derived exclusively based on a field verification campaign, in the absence of other comparable performance information. As an example, as part of a conversion of natural gas transmission pipelines to hydrogen service the sensitivity of inspection systems is frequently discussed. A potentially increased crack growth rate da/dN and a reduction in pipe steel toughness seem to require a reduction of minimum acceptable anomaly dimensions, in the absence of better statistical evidence of the actual ILI performance. The proposed POX assessment will be a credible means to establish optimal re-inspection intervals for existing in-line crack inspection technologies.

Electronic Toll Collection (ETC) system is an important part of modern intelligent transport system, which plays an important role in intelligent car parks and congestion charging. Electronic Toll Collection (ETC) system includes automatic vehicle identification system and electronic billing system. The automatic vehicle identification system includes three parts: automatic vehicle identification (AVI), automatic vehicle model identification (AVC) and inspection system (ES).The application of ETC in intelligent car parks and congestion charging also includes automatic vehicle identification system (automatic vehicle identification, automatic vehicle model identification and inspection system) and electronic billing system. In intelligent car parks, ETC technology achieves fast and accurate automatic toll payment by automatically identifying the ETC tags carried by vehicles, which greatly improves the efficiency of car parks and reduces the queuing and waiting time due to manual toll collection. In terms of congestion charging, ETC technology provides an effective means to implement congestion charge collection. By setting up ETC toll lanes in core urban areas or busy road sections, the system can automatically record the information of the vehicles passing through and carry out toll settlement, thus realising dynamic toll collection according to the degree of congestion and the frequency of vehicle use. This congestion charging strategy aims to regulate urban traffic flow through economic means, alleviate traffic congestion and promote the sustainable development of urban transport. Although China's theoretical research on congestion charging, has been relatively mature, but affected by a variety of factors, no city has yet to start the implementation of congestion charging, the lack of experience in practice. Singapore will not stop the car charging system (ETC) technology successfully applied to urban congestion charging and intelligent car parks, ETC system in intelligent car parks and congestion charging application, help to promote the development of urban transport to a more intelligent, green direction. China can learn from the successful experience of foreign countries and apply it to its own intelligent car parks and congestion charging. This paper introduces the basic principle of ETC, discusses the research progress of ETC in terms of technical characteristics, and analyses the application scenarios of ETC in intelligent car parks and congestion charging. By examining the opportunities offered by Electronic Toll Collection (ETC) systems in smart car parks and congestion pricing, this study aims to delve into the transformative impacts of Electronic Toll Collection (ETC) systems on intelligent mobility and contribute to the sustainable development of intelligent mobility.

This paper deals with the integrity of sealing process based on Allen Bradley PLC. The whole process is changed to facilitate the need of demand. This method currently meets industries requirement for very high speed, accurate and quality inspection with very low associated production cost. The existing pneumatic system was used to wait for all three LPG cylinders to enter the unit. This PLC based seal packing process will be very stable and reliable, along with it there will be distant monitoring, and operator convenience. This new system won't wait for all three LPG cylinders if there is delay of more than three seconds between them. The whole sealing process should take place only if the temperature of hot air is above 250°C. This hot air will be passed onto the blue color plastic seal so that it seals it. SCADA will be used to monitor sealing unit from distance.

The perfect Printed Circuit Board (PCB) plays a very important role in every electronic device as well as in automation systems. So, it is very important to find defects in the PCB before installing it to any system or any device. However, PCB Manufacturers use various inspection systems in the process of manufacturing PCBs for detecting various types of defects in the PCB. In this article, we present the Automated assembled PCB Inspection System. This system finds defects such as missing components and improper position of its components by using the Pattern matching Technique where a good known score of template image is matched with the score of the test image. This system gives results at each inspection within 10 Seconds and the result given by this system are passed or fail in the form of an array sheet. This automated inspection system is created by using NI Vision Builder AI and NI LabVIEW technology. Ni Vision Builder AI has been used to create the algorithm. And NI LabVIEW has been used to create the application.

Manual visual inspection is the standard method to evaluate cleanliness, scratches, and digs on optical components according to ISO 10110-7 or MIL-PRF-13830B. However, the limited optical resolution of the human eye turns the inspection of critical samples like high power optics or sensor cover glasses into a time-consuming and tiring task with inherently subjective results. Machine vision systems provide an objective alternative with reliable and reproducible results and additional advantages: Documentation in the form of inspection reports can be generated at no extra cost and data can be collected to analyze typical problems in the manufacturing process. Automated inspection systems need high resolution and well-designed illumination for the inspected part to ensure visibility of smallest imperfections with sizes as small as few micrometers. At the same time, short duty cycles of the inspection system are a key user requirement. Manual-visual is performed in dark field conditions. In automated inspection systems, a dark field optical setup is also advantageous, giving the best imperfection contrast. Generating good dark field conditions when inspecting a curved optical, i.e. specular, surface is not trivial. Using a multitude of illumination angles is necessary for good (and orientation-independent) imperfection visibility, but every additional illumination direction increases the risk of specular reflections into the camera, leading to blind spots in the images. We have developed two approaches to deal with this problem: In the first, reflections are avoided by design, in the second, reflections are treated in data processing. Our commercially available ARGOS system is using a line-scan camera in combination with a rotation stage. The light source is illuminating from almost every direction, except for the direction parallel to the line sensor of the camera itself. This gives excellent imperfection visibility while avoiding direct reflections and maintaining dark-field conditions on rotationally symmetric samples with almost arbitrary curvature. The second approach that has been successfully implemented is suitable also for matrix cameras: Multiple illumination configurations are used sequentially, each generating direct reflections in different portions of the image. These image-sets are then combined into a dark-field image without reflections, by applying suitable filter masks to remove specular reflections from the images. The ARGOS-approach with the line-scan camera has the additional advantage, that very high image resolution is possible by rotating the sample while observing a line aligned with the sample radius. In the standard version, image resolution is up to 250 mega pixels; in the high-resolution version up to 1 giga pixel is possible – in scan times of a few seconds. On the other hand, the requirement to rotate the sample is not ideal for an automated inspection of a batch of samples. In cases when the large resolution is not required, e.g. for smaller elements, using a matrix camera is more efficient, as the acquisition of a sufficient number of images with different illumination conditions takes only a fraction of a second, allowing for short overall inspection times.

Automated image inspection system to quantify thermal spray splat morphology

There are many methods and solutions to improve any process, and stay within the established control limits. Statistically analyzed data, received from inspection sensors and other devices, are one of them. Automated inspection sensors and systems are effective tools for controlling variation and obtaining process related knowledge. Automated inspection methods, discussed in this paper, represent an important, nevertheless not the only methods, that lead to process improvement, the ultimate goal of total quality management and control (TQM/TQC). Inspection, as part of the feedback control loop of the overall TQM/TQC process, involves the continual satisfaction of customer requirements at lowest cost by harnessing the efforts of everybody in the company. The key question in any inspection system is as follows: Are the measured values within tolerance, or not, and if they are outside the tolerance limits, why did we produce those parts in the first place?

Industrial automatic fabric inspection system, a critical technology in the industry, enhances both total production quantity and quality compared to conventional inspection techniques. This study aims to create a reliable and effective real-time automated visual inspection system for fabrics, focusing on defect detection. The goals of the study can be stated as; installing a system with advanced technology for capturing and processing images swiftly, the development and deployment of a system capable of autonomously learning and scanning fabrics in use, and the creation of a smart framework for accurate fabric defect detection and classification. We focus on the development of unsupervised fabric defect detection using a convolutional autoencoder model, and defect classification using a convolutional neural network model, which takes input as the feature vector generated by the convolutional autoencoder. The experimental outcomes have displayed significant success rates in both detecting defects and classifying them, confirming the effectiveness of the framework in real-time visual inspection systems.

The development of a laboratory-based tailor-welded blank (TWB) inspection system using the principles of magnetic flux leakage (MFL) is presented. The effects of variations in inspection system operating parameters are quantified to allow for optimized system performance. The parameters examined include the applied magnetic field strength, inspection scanning velocity, spatial resolution of acquired signals, specimen end effects, and pole piece liftoff. The results indicate that this inspection method is relatively insensitive to operating parameters and is therefore robust.

Vehicle inspection at ports of entry is a critical component of border security. One part of the vehicle screening process involves customs and border protection (CBP) personnel performing a preliminary inspection of the underside of random vehicles by looking under the vehicle through a mirror mounted on a stick, searching for anomalies or foreign objects present on the undercarriage structure and components of a vehicle. If any deviances are detected, the vehicle is directed to a secondary and more thorough inspection. This paper presents a project that aims at automating this preliminary undercarriage inspection by using an automatic under-vehicle inspection system (AUVIS) and image processing algorithms to assist personnel in identifying vehicles for secondary inspection. The inspection system will image the undercarriage of every car in a lane as they approach the CBP agent and use a novel change detection algorithm to compare the captured image to a corresponding reference image of the vehicle's make and model. The software will detect and highlight any differences between the two images to provide a rapid and objective recommendation for secondary inspection.

Unmanned aerial vehicles (UAVs) have become important tools for power transmission line inspection. Cameras installed on the platforms can efficiently obtain aerial images containing information about power equipment. However, most of the existing inspection systems cannot perform automatic real-time detection of transmission line components. In this paper, an automatic transmission line inspection system incorporating UAV remote sensing with binocular visual perception technology is developed to accurately detect and locate power equipment in real time. The system consists of a UAV module, embedded industrial computer, binocular visual perception module, and control and observation module. Insulators, which are key components in power transmission lines as well as fault-prone components, are selected as the detection targets. Insulator detection and spatial localization in aerial images with cluttered backgrounds are interesting but challenging tasks for an automatic transmission line inspection system. A two-stage strategy is proposed to achieve precise identification of insulators. First, candidate insulator regions are obtained based on RGB-D saliency detection. Then, the skeleton structure of candidate insulator regions is extracted. We implement a structure search to realize the final accurate detection of insulators. On the basis of insulator detection results, we further propose a real-time object spatial localization method that combines binocular stereo vision and a global positioning system (GPS). The longitude, latitude, and height of insulators are obtained through coordinate conversion based on the UAV’s real-time flight data and equipment parameters. Experiment results in the actual inspection environment (220 kV power transmission line) show that the presented system meets the requirement of robustness and accuracy of insulator detection and spatial localization in practical engineering.

Optical film is an important component for LCDs, cell phones and other mobile devices. It is crucial to ensure an optical films is defect-free. However, derive from the inherent translucent characteristic, many images of defects of optical films are low contrast. Low-contrast defects are notorious for their difficult to be detected. As some low-contrast defects are barely visible to human being without special training, it is indeed a challenging task to capture low-contrast defect images. To overcome the difficulty, we thoroughly investigated various of imaging and lighting technologies. After images have been captured successfully, we devided the captured defects, based on their sizes, into two categories, i.e. macro defects and micro defects. To detect both large and small defects, we set up a macro inspection system and a micro inspection system, respectively. In the study, we provide an easy to understand approach to reveal low-contrast defects. The experimental results showed that the proposed inspection system and method is capable of detecting low-contrast defects such as uneven coatings, streaks, white spots and foreign particles.