• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.45 no.2
• /
• pp.48-55
• /
• 2022

The color image of the brand comes first and is an important visual element that leads consumers to the consumption of the product. To express more effectively what the brand wants to convey through design, the printing market is striving to print accurate colors that match the intention. In 'offset printing' mainly used in printing, colors are often printed in CMYK (Cyan, Magenta, Yellow, Key) colors. However, it is possible to print more accurate colors by making ink of the desired color instead of dotting CMYK colors. The resulting ink is called 'spot color' ink. Spot color ink is manufactured by repeating the process of mixing the existing inks. In this repetition of trial and error, the manufacturing cost of ink increases, resulting in economic loss, and environmental pollution is caused by wasted inks. In this study, a deep learning algorithm to predict printed spot colors was designed to solve this problem. The algorithm uses a single DNN (Deep Neural Network) model to predict printed spot colors based on the information of the paper and the proportions of inks to mix. More than 8,000 spot color ink data were used for learning, and all color was quantified by dividing the visible light wavelength range into 31 sections and the reflectance for each section. The proposed algorithm predicted more than 80% of spot color inks as very similar colors. The average value of the calculated difference between the actual color and the predicted color through 'Delta E' provided by CIE is 5.29. It is known that when Delta E is less than 10, it is difficult to distinguish the difference in printed color with the naked eye. The algorithm of this study has a more accurate prediction ability than previous studies, and it can be added flexibly even when new inks are added. This can be usefully used in real industrial sites, and it will reduce the attempts of the operator by checking the color of ink in a virtual environment. This will reduce the manufacturing cost of spot color inks and lead to improved working conditions for workers. In addition, it is expected to contribute to solving the environmental pollution problem by reducing unnecessarily wasted ink.

• Journal of Dental Rehabilitation and Applied Science
• /
• v.40 no.2
• /
• pp.72-81
• /
• 2024

Purpose: This study aimed to assess the marginal and internal fit of 3-unit monolithic zirconia fixed partial dentures (FPDs) fabricated via computer-aided design and computer-aided manufacturing (CAD/CAM) from solid working casts and removable die system. Materials and Methods: The tooth preparation protocol for a zirconia crown was executed on the mandibular right first premolar and mandibular right first molar, with the creation of a reference cast featuring an absent mandibular right second premolar. The reference cast was duplicated using polyvinyl siloxane impression, from which 20 working casts were fabricated following typical dental laboratory procedures. For comparative analysis, 10 FPDs were produced from a removable die system (RD group) and the remaining 10 FPDs from the solid working casts (S group). The casts were digitized using a dental desktop scanner to establish virtual casts and design the FPDs using CAD. The definitive 3-unit monolithic zirconia FPDs were fabricated via a CAM milling process. The seated FPDs on the reference cast underwent digital evaluation for marginal and internal fit. The Mann-Whitney U test was applied for statistical comparison between the two groups (α = 0.05). Results: The RD group showed significantly higher discrepancies in fit for both premolars and molars compared to the S group (P < 0.05), particularly in terms of marginal and occlusal gaps. Color mapping also highlighted more significant deviations in the RD group, especially in the marginal and occlusal regions. Conclusion: The study found that the discrepancies in marginal and occlusal fits of 3-unit monolithic zirconia FPDs were primarily associated with those fabricated using the removable die system. This indicates the significant impact of the fabrication method on the accuracy of FPDs.

• The Journal of Korean Academy of Prosthodontics
• /
• v.52 no.3
• /
• pp.246-251
• /
• 2014

In case of prosthesis fabrication by CAD/CAM, location, area and contour of occlusal contacts can be adjusted so more functional occlusion can be acquired. Also, errors in a manufacturing process is reduced compared to cast metal prostheses and porcelain fused metal prostheses fabricated by conventional methods such as casting and porcelain build up. Therefore, prostheses by CAD/CAM show superior occlusion accuracy. Recently, virtual articulator function has been introduced to CAD/CAM system, which reproduces mandibular movement against maxilla. Thus, it is possible to consider occlusal interference in anterior/lateral movement as well as closing movement. There have been many studies on the marginal and internal fit of prostheses using zirconia but the occlusal fit of zirconia crown fabricated by CAD/CAM has not been researched as much. In this case report, 7 zirconia crowns were designed and fabricated by CAD/CAM for total 5 patients. The models of zirconia crowns before and after occlusal adjustment during intraoral try-in were scanned for occlusal contacts, which were compared to evaluate accuracy of prostheses and understand patterns of occlusal adjustment. Most of the occlusal adjustments were done on functional cusps and slopes of zirconia crown, and the magnitude of occlusal adjustment ranged from $15{\mu}m$ to $60{\mu}m$. In the zirconia crown fabricated with CAD/CAM systems, the occlusal adjustment is a necessary procedure, so additional procedures will be needed for compensating reduced mechanical properties.

• KIPS Transactions on Computer and Communication Systems
• /
• v.9 no.8
• /
• pp.171-180
• /
• 2020

Smart Factory consists of digital automation solutions throughout the production process, including design, development, manufacturing and distribution, and it is an intelligent factory that installs IoT in its internal facilities and machines to collect process data in real time and analyze them so that it can control itself. The smart factory's equipment works in a physical combination of numerous hardware, rather than a virtual character being driven by a single object, such as a game. In other words, for a specific common goal, multiple devices must perform individual actions simultaneously. By taking advantage of the smart factory, which can collect process data in real time, if reinforcement learning is used instead of general machine learning, behavior control can be performed without the required training data. However, in the real world, it is impossible to learn more than tens of millions of iterations due to physical wear and time. Thus, this paper uses simulators to develop grid sortation systems focusing on transport facilities, one of the complex environments in smart factory field, and design cooperative multi-agent-based reinforcement learning to demonstrate efficient behavior control.

• Nuclear Engineering and Technology
• /
• v.39 no.5
• /
• pp.603-616
• /
• 2007

Roy Huddle, having invented the coated particle in Harwell 1957, stated in the early 1970s that we know now everything about particles and coatings and should be going over to deal with other problems. This was on the occasion of the Dragon fuel performance information meeting London 1973: How wrong a genius be! It took until 1978 that really good particles were made in Germany, then during the Japanese HTTR production in the 1990s and finally the Chinese 2000-2001 campaign for HTR-10. Here, we present a review of history and present status. Today, good fuel is measured by different standards from the seventies: where $9*10^{-4}$ initial free heavy metal fraction was typical for early AVR carbide fuel and $3*10^{-4}$ initial free heavy metal fraction was acceptable for oxide fuel in THTR, we insist on values more than an order of magnitude below this value today. Half a percent of particle failure at the end-of-irradiation, another ancient standard, is not even acceptable today, even for the most severe accidents. While legislation and licensing has not changed, one of the reasons we insist on these improvements is the preference for passive systems rather than active controls of earlier times. After renewed HTGR interest, we are reporting about the start of new or reactivated coated particle work in several parts of the world, considering the aspects of designs/ traditional and new materials, manufacturing technologies/ quality control quality assurance, irradiation and accident performance, modeling and performance predictions, and fuel cycle aspects and spent fuel treatment. In very general terms, the coated particle should be strong, reliable, retentive, and affordable. These properties have to be quantified and will be eventually optimized for a specific application system. Results obtained so far indicate that the same particle can be used for steam cycle applications with $700-750^{\circ}C$ helium coolant gas exit, for gas turbine applications at $850-900^{\circ}C$ and for process heat/hydrogen generation applications with $950^{\circ}C$ outlet temperatures. There is a clear set of standards for modem high quality fuel in terms of low levels of heavy metal contamination, manufacture-induced particle defects during fuel body and fuel element making, irradiation/accident induced particle failures and limits on fission product release from intact particles. While gas-cooled reactor design is still open-ended with blocks for the prismatic and spherical fuel elements for the pebble-bed design, there is near worldwide agreement on high quality fuel: a $500{\mu}m$ diameter $UO_2$ kernel of 10% enrichment is surrounded by a $100{\mu}m$ thick sacrificial buffer layer to be followed by a dense inner pyrocarbon layer, a high quality silicon carbide layer of $35{\mu}m$ thickness and theoretical density and another outer pyrocarbon layer. Good performance has been demonstrated both under operational and under accident conditions, i.e. to 10% FIMA and maximum $1600^{\circ}C$ afterwards. And it is the wide-ranging demonstration experience that makes this particle superior. Recommendations are made for further work: 1. Generation of data for presently manufactured materials, e.g. SiC strength and strength distribution, PyC creep and shrinkage and many more material data sets. 2. Renewed start of irradiation and accident testing of modem coated particle fuel. 3. Analysis of existing and newly created data with a view to demonstrate satisfactory performance at burnups beyond 10% FIMA and complete fission product retention even in accidents that go beyond $1600^{\circ}C$ for a short period of time. This work should proceed at both national and international level.

• KIPS Transactions on Computer and Communication Systems
• /
• v.10 no.10
• /
• pp.261-268
• /
• 2021

Cloud computing is a computing paradigm in which users can utilize computing resources in a pay-as-you-go manner. In a cloud system, resources can be dynamically scaled up and down to the user's on-demand so that the total cost of ownership can be reduced. The Modeling and Simulation (M&S) technology is a renowned simulation-based method to obtain engineering analysis and results through CAE software without actual experimental action. In general, M&S technology is utilized in Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Multibody dynamics (MBD), and optimization fields. The work procedure through M&S is divided into pre-processing, analysis, and post-processing steps. The pre/post-processing are GPU-intensive job that consists of 3D modeling jobs via CAE software, whereas analysis is CPU or GPU intensive. Because a general-purpose desktop needs plenty of time to analyze complicated 3D models, CAE software requires a high-end CPU and GPU-based workstation that can work fluently. In other words, for executing M&S, it is absolutely required to utilize high-performance computing resources. To mitigate the cost issue from equipping such tremendous computing resources, we propose HEMOS-Cloud service, an integrated cloud and cluster computing environment. The HEMOS-Cloud service provides CAE software and computing resources to users who want to experience M&S in business sectors or academics. In this paper, the economic ripple effect of HEMOS-Cloud service was analyzed by using industry-related analysis. The estimated results of using the experts-guided coefficients are the production inducement effect of KRW 7.4 billion, the value-added effect of KRW 4.1 billion, and the employment-inducing effect of 50 persons per KRW 1 billion.