• Design & Manufacturing
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• v.18 no.2
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• pp.35-40
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• 2024

Injection molding is a process widely used across various industries for molding plastics, and it is the most commonly applied process in root industries utilizing molds. Among the different types of injection molding, insert injection molding, where busbars are used as inserts, is increasingly being applied in the electric vehicle industry. However, currently, the insert injection molding process is manually performed, with workers placing insert components by hand before injection molding. This results in issues related to productivity, safety, and quality. Additionally, there is a growing demand for automation of such production lines due to hazardous working conditions, economic difficulties in the manufacturing industry, and the decline in the labor force caused by an aging population. This study focuses on the application of an automated system for the insert injection molding process used in electric vehicles. The development of an automated system for the transport and insertion of insert components, as well as the inspection and stacking processes after injection, has resulted in over a 25% improvement in productivity and more than a 27% reduction in defect rates.

• Proceedings of the Korean Institute of Resources Recycling Conference
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• 2005.05a
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• pp.57-61
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• 2005

In this study, the machine vision system for inspection using color recognition method have been designed and developed to automatically sort out a specified material such as Cu scraps or other non-ferrous metal scraps mixed in Fe scraps. The system consists of a CCD camera, light sources, a frame grabber, conveying devices and an air nozzled ejector, and is program-controlled by a image processing algorithm. The ejector is designed to be operated by an I/O interface communication with a hardware controller. The sorting examination results show that the efficiency of separating Cu scraps from the Fe scraps mixed with Cu scraps is around 90 % at the conveying speed of 15 m/min. and the system is proven to be excellent in terms of its efficiency. Therefore, it is expected that the system can be commercialized in shredder firms, if the high-speed automated sorting system will be realized.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.101-106
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• 2020

This research is related to a development of the ultrasonic detector for an internal defect detection of various assembly part's welding zone. In this research, measurement S/Ws including system's motion control, S/W ultrasonic transmitter/receiver control, defect judgment standard setting, etc. have been designed for ultrasonic detection, and welding defects sample network, etc. were also designed for comparison between products in good condition and defective products. Through this kind of system, automatic detection function can be performed for the depth and the defect location of the assembly parts welding zone, and the system is able to make a judgment of internal defect detection which is used to be performed by an expert in the past.

• Smart Structures and Systems
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• v.29 no.1
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• pp.221-235
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• 2022

Manual inspection of steel box girders on long span bridges is time-consuming and labor-intensive. The quality of inspection relies on the subjective judgements of the inspectors. This study proposes an automated approach to detect and segment cracks in high-resolution images. An end-to-end cascaded framework is proposed to first detect the existence of cracks using a deep convolutional neural network (CNN) and then segment the crack using a modified U-Net encoder-decoder architecture. A Naïve Bayes data fusion scheme is proposed to reduce the false positives and false negatives effectively. To generate the binary crack mask, first, the original images are divided into 448 × 448 overlapping image patches where these image patches are classified as cracks versus non-cracks using a deep CNN. Next, a modified U-Net is trained from scratch using only the crack patches for segmentation. A customized loss function that consists of binary cross entropy loss and the Dice loss is introduced to enhance the segmentation performance. Additionally, a Naïve Bayes fusion strategy is employed to integrate the crack score maps from different overlapping crack patches and to decide whether a pixel is crack or not. Comprehensive experiments have demonstrated that the proposed approach achieves an 81.71% mean intersection over union (mIoU) score across 5 different training/test splits, which is 7.29% higher than the baseline reference implemented with the original U-Net.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.38.3-38.3
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• 2019

We present newly discovered dwarf galaxy candidates in deep wide-field images of NGC 1291 obtained with KMTNet. We identify 15 dwarf galaxy candidates by visual inspection within the virial radius of NGC 1291. Using imaging simulations, we demonstrate that our imaging data is complete up to 26 mag arcsec^-2 or -10 abs.mag with > 70% of the completeness rate. We also apply automated detection method to find the dwarfs. However, the completeness and the reliability are relatively low compared to the visual inspection. We find that structural and photometric properties of dwarf candidates such as effective radius, central surface brightness, Sérsic index, and absolute magnitude appear to be consistent with those of known dwarf galaxies in nearby groups and clusters, except for color. NGC 1291, residing in a relatively isolated environment, tends to accompany bluer dwarf galaxies (≃0.58) than those in denser environment. It shows that the quenching of dwarfs is susceptible to the environment.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.211-219
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• 2023

The structural health of a pipeline is usually assessed by visual inspection. In addition to the fact that this method is expensive and time consuming, inspection of the whole structure is not possible due to limited access to some points. Therefore, adopting a damage detection method without the mentioned limitations is important in order to increase the safety of the structure. In recent years, vibration-based methods have been used to detect damage. These methods detect structural defects based on the fact that the dynamic responses of the structure will change due to damage existence. Therefore, the location and extent of damage, before and after the damage, are determined. In this study, fuzzy genetic algorithm has been used to monitor the structural health of the pipeline to create a fuzzy automated system and all kinds of possible failure scenarios that can occur for the structure. For this purpose, the results of an experimental model have been used. Its numerical model is generated in ABAQUS software and the results of the analysis are used in the fuzzy genetic algorithm. Results show that the system is more accurate in detecting high-intensity damages, and the use of higher frequency modes helps to increase accuracy. Moreover, the system considers the damage in symmetric regions with the same degree of membership. To deal with the uncertainties, some error values are added, which are observed to be negligible up to 10% of the error.

• Journal of Food Hygiene and Safety
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• v.27 no.1
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• pp.103-107
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• 2012

We compared between an automated most-probable-number technique $TEMPO^{(R)}$TVC and traditional plating methods $Petrifilm^{TM}$ for estimating populations of total aerobic bacteria in various livestock products. 257 samples randomly selected in local retail stores and 87 samples inoculated with $E.$ $coli$ ATCC 25922, $Staphylococcus$ $aureus$ ATCC 12868 were tested in this study. The degree of agreement was estimated according to the CCFRA (Campden and Chorleywood Food Research Association Group) Guideline 29 and the agreement indicates the difference of two kinds methods is lower than 1 log base 10($log_{10}$). The samples of hams, jerky products, ground meat products, milks, ice creams, infant formulas, and egg heat formed products were showed above 95% in the agreement of methods. In contrast, proportion of agreement on meat extract products, cheeses and sausages were 93.1%, 92.1%, 89.1%, respectively. One press ham and five sausages containing spice and seasoning, two pork cutlets containing spice and bread crumbs, two meat extract product and two natural cheeses and one processing cheese with a high fat content, and one ice cream containing chocolate of all samples showed the discrepancy. Our result suggest that $TEMPO^{(R)}$TVC system is efficient to analyses total aerobic bacteria to compare manual method in time-consuming and laborious process except livestock products having limit of detection.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.729-735
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• 2021

Recently Artificial Intelligence(AI) has been developed and used in various fields. Especially AI recognition technology can perceive and distinguish images so it should plays a significant role in quality inspection process. For stability of autonomous driving technology, semiconductors inside automobiles must be protected from external electromagnetic wave(EM wave). As a shield film, a thin polymeric material with hole shaped micro-patterns created by a laser processing could be used for the protection. The shielding efficiency of the film can be increased by the hole structure with appropriate pitch and size. However, since the sensitivity of micro-machining for some parameters, the shape of every single hole can not be same, even it is possible to make defective patterns during process. And it is absolutely time consuming way to inspect all patterns by just using optical microscope. In this paper, we introduce a AI inspection system which is based on web site AI tool. And we evaluate the usefulness of AI model by calculate Area Under ROC curve(Receiver Operating Characteristics). The AI system can classify the micro-patterns into normal or abnormal ones displaying the text of the result on real-time images and save them as image files respectively. Furthermore, pressing the running button, the Hardware of robot arm with two Arduino motors move the film on the optical microscopy stage in order for raster scanning. So this AI system can inspect the entire micro-patterns of a film automatically. If our system could collect much more identified data, it is believed that this system should be a more precise and accurate process for the efficiency of the AI inspection. Also this one could be applied to image-based inspection process of other products.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.1
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• pp.94-101
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• 2014

Recently, in order to reduce the time required to replace an optical jumper cord, many researchers are using a field-installable connector and applying the ferrule polishing method, ferrule mechanical contact method, or ferrule fusion contact method. However, the process of arranging the length of the optical fiber, i.e., inserting the optical fiber into the ferrule by hand and checking its cross section, takes 60% of the time required for the entire process, which increases the overall cost. Therefore, in order to make this task more cost-effective, we will develop an automated inspection system with automatic cross-sectional arrangement of a field-installable connector. This system will be able to decrease the failure rate from 10% to 2% compared with the conventional method when cutting the optical fiber inserted into the ferrule. It will also improve the productivity by decreasing the test time by 28% compared with the conventional method. Our studies showed that it was possible to reduce the production costs and improve the quality of a field-installable connector, and we expect it to dominate the market.

• Journal of Information Technology Services
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• v.11 no.4
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• pp.153-170
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• 2012

When companies or institutes manage data, in order to utilize it as useful resources for decision-making, it is essential to offer precise and reliable data. While most small and medium-sized enterprises and public institutes have been investing a great amount of money in management and maintenance of their data systems, the investment in data management has been inadequate. When public institutions establish their data systems, inspection has been constantly carried out on the data systems in order to improve safety and effectiveness. However, their capabilities in improving the quality of data have been insufficient. This study develops an automatic tool to diagnose the quality of data in a way to diagnose the data quality condition of the inspected institute quantitatively at the stage of design and closure by inspecting the data system and proves its practicality by applying the automatic tool to inspection. As a means to diagnose the quality, this study categorizes, in the aspect of quality characteristics, the items that may be improved through diagnosis at the stage of design, the early stage of establishing the data system and the measurement items by the quality index regarding measurable data values at the stage of establishment and operation. The study presents a way of quantitative measurement regarding the data structures and data values by concretizing the measurement items by quality index in a function of the automatic tool program. Also, the practicality of the tool is proved by applying the tool in the inspection field. As a result, the areas which the institute should improve are reported objectively through a complete enumeration survey on the diagnosed items and the indicators for quality improvement are presented quantitatively by presenting the quality condition quantitatively.