• Journal of Korea Foundry Society
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• v.41 no.5
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• pp.419-433
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• 2021

Al-Si-Cu alloys benefit from the addition of copper for better hardness and strength through precipitation hardening, which results in remarkably strong alloys. However, the addition of copper expands the solidification range of Al-Si-Cu alloys, and due to this, these alloys become more prone to hot tearing, which is one of the most common and serious fracture phenomena encountered during solidification. The conventional evaluation method of the hot tearing properties of an alloy is a relative and qualitative analysis approach that does not provide quantitative data about this phenomenon. In the present study, the mold itself part of a device developed in Instone et al. was partially modified to obtain more reliable quantitative data pertaining to the hot tearing properties of an Al-Si-Cu casting alloy. To assess the influence of Cu element, four levels of Cu contents were tested (0.5, 1.0, 3.0, and 5.0 wt.%) in the Al-Si-Cu system alloy and the hot tearing properties were evaluated in each case. As the Cu content was increased, the hot tearing strength decreased to 2.26, 1.53, 1.18, and 1.04 MPa, respectively. At the moment hot tearing occurred, the corresponding solid fraction and solidification rate decreased at the same temperature due to the increase in the solid-liquid coexistence range as the Cu content increased. The morphology of the fracture surfaces was changed from dendrites to dendrites covered with residual liquid, and CuAl₂ phases were observed in the vicinity of hot tearing.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.9
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• pp.1-8
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• 2003

Velocity data were acquired at a series of stations in the chordwise direction above a delta wing with leading edge extension, using a triple axis hot film anemometry. Surveys normal to planform yield velocity field data at incidence angle of 24$^{\circ}$and 32$^{\circ}$at a centerline chord Reynolds number of $1.76{\times}10^6$. Experimental results of velocity measurements of mean velocity of three components gave a confidence to quantitative investigate the vortical flow field over a LEX-delta wing with this probe. The present experiments indicated the existence of both wing and LEX vortex where the local mean axial velocity is maximum. It also shown the development of secondary vortex of opposite sign of rotating above the wing surface near the leading edge. The insertion of probe across the flow field was found to have little influence on the position of the vortex core.

• Journal of Korean Society for Quality Management
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• v.45 no.1
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• pp.25-38
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• 2017

Purpose: Hot strip rolling mill consists of a lot of mechanical and electrical units. In condition monitoring and diagnosis phase, various units could be failed with unknown reasons. In this study, we propose an effective method to detect early the units with abnormal status to minimize system downtime. Methods: The early warning problem with various units is defined. K-means and PAM algorithm with Euclidean and Manhattan distances were performed to detect the abnormal status. In addition, an performance of the proposed algorithm is investigated by field data analysis. Results: PAM with Manhattan distance(PAM_ManD) showed better results than K-means algorithm with Euclidean distance(K-means_ED). In addition, we could know from multivariate field data analysis that the system reliability of hot strip rolling mill can be increased by detecting early abnormal status. Conclusion: In this paper, clustering-based monitoring and fault detection algorithm using Manhattan distance is proposed. Experiments are performed to study the benefit of the PAM with Manhattan distance against the K-means with Euclidean distance.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.75-86
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• 2019

Purpose: It is essential for the steel industry to produce steel products without unexpected downtime to reduce costs and produce high quality products. A hot strip rolling mill consists of many mechanical and electrical units. In condition monitoring and diagnosis, various units could fail for unknown reasons. Methods: In this study, we propose an effective method to detect units with abnormal status early to minimize system downtime. The early warning problem with various units was first defined. An autoencoder was modeled to detect abnormal states. An application of the proposed method was also implemented in a simulated field-data analysis. Results: We can compare images of original data and reconstructed images, as well as visually identify differences between original and reconstruction images. We confirmed that normal and abnormal states can be distinguished by reconstruction error of autoencoder. Experimental results show the possibility of prediction due to the increase of reconstruction error from just before equipment failure. Conclusion: In this paper, hot strip roughing mill monitoring method using autoencoder is proposed and experiments are performed to study the benefit of the autoencoder.

• Smart Media Journal
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• v.4 no.3
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• pp.16-23
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• 2015

As the IT technology advances, data processing system is required to handle and process large amounts of data. However, the existing On-Disk system has limit to process data which increase rapidly. For that reason, the In-Memory system is being used which saves and manages data on the fast memory not saving data into hard disk. Although it has fast processing capability, it is necessary to use the fault tolerance techniques in the In-Memory system because it has a risk of data loss due to volatility which is one of the memory characteristics. These fault tolerance techniques lead to performance degradation of In-Memory system. In this paper, we classify the data into Hot and Cold data in consideration of the data usage characteristics in the In-Memory system and propose compound backup technique to ensure data persistence. The proposed technique increases the persistence and improves performance degradation.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2449-2452
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• 2007

Hot spots on thin wafers of IC packages are becoming important issues in thermal and electrical engineering fields. To investigate these hot spots, we developed a Diode Temperature Sensor Array (DTSA) that consists of an array of 32 ${\times}$32 diodes (1,024 diodes) in a 8 mm ${\times}$ 8 mm surface area. To know specifically the hot spot temperature which is affected by the chip thickness and a generated power, we made the DTSA chips, which have 21.5 ${\mu}m$, 31 ${\mu}m$, 42 ${\mu}m$, 100 ${\mu}m$, 200 ${\mu}m$, and 400 ${\mu}m$ thickness using the CMP process. And we conducted the experiment using various heater power conditions (0.2 W, 0.3 W, 0.4 W, 0.5 W). In order to validate experimental results, we performed a numerical simulation. Errors between experimental results and numerical data are less than 4%. Finally, we proposed a correlation for the hot spot temperature as a function of the generated power and the wafer thickness based on the results of the experiment. This correlation can give an easy estimate of the hot spot temperature for flip chip packaging when the wafer thickness and the generated power are given.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.9 s.180
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• pp.2211-2219
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• 2000

Near net shape forming of 316L stainless steel powder was investigated under hot isostatic pressing. To simulate densification and deformation of a powder compact in a container during hot isostatic pressing, the constitutive model of Abouaf and co-workers was implemented into a finite element analysis. An optimal design technique based on the design sensitivity was applied to the container design during hot isostatic pressing. The optimal shape of the container was predicted from the desired final shape of a powder compact by iterative calculations. Experimental data of 316L stainless steel powder showed that the optimally designed container allowed precise forming of the desired powder compact during hot isostatic pressing.

• Transactions of Materials Processing
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• v.25 no.4
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• pp.235-241
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• 2016

Dimensional accuracy of hot coil is improved by precise control of thickness profiles, flatness, width and winding profile. Especially, precise width control is important because yield could be increased significantly. Precise width control can be improved by predicting the amount of width spread. The purpose of this study is to develop the advanced prediction model for width spread in hot finish rolling for controlling width precisely. FE-simulations were performed to investigate the effect of process variables on width spread such as reduction ratio, forward and backward tension and initial width at each stand. From the statistical analysis of simulated data, advanced model was developed based on the existing models for strip width spread. The experimental hot rolling trials showed that newly developed model provided fairly accurate predictions on the strip width spread during the whole hot finishing rolling process.

• Journal of the Korean Society of Food Science and Nutrition
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• v.27 no.5
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• pp.908-913
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• 1998

Chemical components and some physical proeprties of fresh, defrosted and dried yams(Dioscoreab batatas) were determined to obtain basic data for high quality yam processing. Fresh yam showed 81.79% moisture and 15.24% N-free extract. The cohesiveness and adhesiveness of defrosted yam homogenate were significantly higher than those of fresh yam homogenate. Free sugars of yam were mainly composed of sucrose, rhamnose, fructose and glucose, and the contents of fructose and glucose were apparently decreased by hot air drying. Linoleic(45.64%), oleic(8.32%), and arachidic acids(7.40%) were major fatty acids of yam. Hot air drying caused a decrease in unsaturated fatty acids and an increase in saturated acids. Hot air-dried yam powder showed higher gelatinization properties than freeze-dryed yam powder, such asinitial pasting temperature, temperature at maximum viscosity, and viscosity at different parameters. Hot air-dried yam powder showed higher Hunter parameter b and ΔE values and lower L value than freeze-dried one.

• The Journal of Bigdata
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• v.8 no.2
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• pp.97-107
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• 2023

Small business owners are relatively likely to be alienated from various benefits caused by the change to a big data/AI-based society. To support them, we would like to detect a hot place based on the floating population to support small business owners' decision-making in the start-up area. Through various studies, it is known that the population size of the region has an important effect on the sales of small business owners. In this study, inland regions were extracted from the Incheon floating population data from January 2019 to June 2022. the Data is consisted of a grid of 50m intervals, central coordinates and the population for each grid are presented, made image structure through imputation to maintain spatial information. Spatial outliers were removed and imputated using LOF and GAM, and temporal outliers were removed and imputated through LOESS. We used ConvLSTM which can take both temporal and spatial characteristics into account as a predictive model, and used AutoEncoder structure, which performs outliers detection based on reconstruction error to define an area with high MAPE as a hot place.