• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.18 no.6
• /
• pp.98-105
• /
• 2019

In order to improve the fuel economy and dynamic behavior of automobiles, the weight reduction tendency of automobile parts is obvious. Also, in order to maximize assembly and maintenance convenience, various parts are integrated and modularized. Multi-piece methods require many manufacturing processes and become a factor of lowering the strength of parts. It is advantageous to overcome the disadvantages by integrally manufacturing to reduce the processing steps and ensure the strength of the parts. However, when it is necessary to process in a narrow space inside the part, it is impossible to process with the existing spindle. The angle head spindle is only a component of a machine tool, but it is a core part that requires high technology and is highly utilizable in products requiring high precision machining. Therefore, various and continuous studies needs for angle head spindles in areas such as vibration absorption, operational safety, excellent dimensional stability, and strength. In this paper, we propose an optimal design for angle head spindle by performing structural analysis and shape optimization for angle head spindle gear and case.

• Journal of the Korean Society of Propulsion Engineers
• /
• v.24 no.5
• /
• pp.21-33
• /
• 2020

In this study, the program for system analysis of an expander cycle rocket engine using liquid hydrogen as a fuel was developed. The properties of hydrogen were considered by the ratio of isomers with temperature. The analysis procedure was established with the open and closed types of the expander cycle engine and the simulation methods were suggested for each component. To validation of the analysis program, we compared the performance of the engine operating point and the analysis results performed overseas for Vinci and SE-21D, which are expander cycle engines. As a result of the analysis, the main performance factors of the system, such as the mass flow of the propellant, specific thrust, and power, except for some of the inaccurate input information, showed high accuracy with an error of around 1-2%.

• Mass Spectrometry Letters
• /
• v.14 no.2
• /
• pp.42-47
• /
• 2023

Carthamus tinctorius L. (known as safflower) is a valuable oil plant whose importance is increasing rapidly in the world due to its high adaptation to arid regions. The seeds of this unique plant are especially used in edible oil, soap, paint, varnish and lacquer production. Its flowers are used in vegetable dye production and medicinal purposes beside its features as a coloring and flavoring in food. After the oil is removed, the remaining pulp and plant parts are used as animal feed, and dry straw residues are used as fuel. Beside all these features, its usage as a herbal medicinal plants for various diseases has gained importance on recent years. In this study, it was designed a plant metabolomic approach which transfers all the recent data processing strategies of untargeted metabolomics in clinical applications to the present study. Q-TOF LC/MS-based analysis of the extracts (70% ethanol, hexane, and chloroform) for both seed and flowers was performed using a C18 column (Agilent Zorbax 1.8 µM, 100 × 2.1 mm). Differences were observed in seed and fruit extracts and these differences were visualized using principal component analysis (PCA) plots. The total number and intersections of the peaks in the extracts were visualized using peak count comparison graph. Based on the experimental results, the number of the detected peaks for seeds was higher than the ones for the flowers for all solvent systems to extract the samples.

• International Journal of Computer Science & Network Security
• /
• v.23 no.9
• /
• pp.1-7
• /
• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
• /
• v.23 no.8
• /
• pp.210-216
• /
• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Tribology and Lubricants
• /
• v.39 no.5
• /
• pp.183-189
• /
• 2023

A bearing is a mechanical component that transmits rotation and supports loads. According to the type of rotating mechanism, bearings are categorized into ball bearings and tapered roller bearings. Tapered roller bearings have higher load-bearing capabilities than ball bearings. They are used in applications where high loads need to be supported, such as wheel bearings for commercial vehicles and trucks, aircraft and high-speed trains, and heavy-duty spindles for heavy machinery. In recent times, the demand for reducing the driving friction torque in automobiles has been increasing owing to the CO₂ emission regulations and fuel efficiency requirements. Accordingly, the research on the driving friction torque of bearings has become more essential. Researchers have conducted various studies on the lubrication, friction, and contact in tapered roller bearings. Although researchers have conducted numerous studies on the friction in the lips and on roller misalignment and skew, studies considering the influence of roller shape, specifically roller shape errors including lips, are few. This study investigates the driving friction torque of tapered roller bearings considering roller geometric uncertainties. Initially, the study calculates the driving friction torque of tapered roller bearings when subjected to axial loads and compares it with experimental results. Additionally, it performs Monte Carlo simulations to evaluate the influence of roller geometric uncertainties (i.e., the effects of roller geometric deviations) on the driving friction torque of the bearings. It then analyzes the results of these simulations.

• Journal of Korea Society of Industrial Information Systems
• /
• v.29 no.1
• /
• pp.31-48
• /
• 2024

The domestic automotive industry must consider a strategic shift from traditional automotive component manufacturing to align with future trends such as connectivity, autonomous driving, sharing, and electrification. This research conducted topic modeling on R&D projects in the future automotive sector funded by the Ministry of Trade, Industry, and Energy from 2013 to 2021. We found that topics such as sensors, communication, driver assistance technology, and battery and power technology remained consistently prominent throughout the entire period. Conversely, topics like high-strength lightweight chassis were observed only in the first period, while topics like AI, big data, and hydrogen fuel cells gained increasing importance in the second and third periods. Furthermore, this research analyzed the areas of concentrated investment for each period based on topic-specific government investment amounts and investment growth rates.

• Tribology and Lubricants
• /
• v.40 no.4
• /
• pp.111-117
• /
• 2024

Due to global CO₂ emission reductions and fuel efficiency regulations, the trend toward transitioning from internal combustion engine vehicles to electric vehicles (EVs) has accelerated. Consequently, the problem of EV failures has become a focal point of active research. The parasitic capacitance generated during motor-shaft rotation induces voltage that deteriorates the raceway and ball surfaces of bearings, causing electrical damage in EVs. Despite numerous attempts to address this issue, most studies have been conducted under high viscosity lubricant and low load conditions. However, due to factors such as high-speed operation, rapid acceleration and deceleration, motor heating, and motor system-decelerator integration, current EV applications have shown diminished stability in lubrication films of motor bearings, thereby leveraging the investigation to address the risk of electrical damage. This study investigates the electrical damage to rolling bearing elements in EV motor drive systems. The experimental analysis focuses on the effects of electric currents and operational loads on bearing integrity. A test rig is designed to generate high-rate voltage specific to a motor system's parasitic capacitance, and bearing samples are exposed to these currents for specified durations. Component evaluation involves visual inspections and vibration measurements. In addition, a predictive model for electrical failure is developed based on accumulated data, which demonstrates the ability to predict the likelihood of electrical failure relative to the duration and intensity of current exposure. This in turn reduces uncertainties in practical applications regarding electrical erosion modes.

• Journal of Korean Society for Atmospheric Environment
• /
• v.32 no.5
• /
• pp.501-512
• /
• 2016

The study evaluated methods to measure condensable fine particles in flue gases and measured particulate matter by fuel and material to get precise concentrations and quantities. As a result of the method evaluation, it is required to improve test methods for measuring Condensable Particulate Matter (CPM) emitted after the conventional Filterable Particulate Matter (FPM) measurement process. Relative Standard Deviation (RSD) based on the evaluated analysis process showed that RSD percentages of FPM and CPM were around 27.0~139.5%. As errors in the process of CPM measurement and analysis can be caused while separating and dehydrating organic and inorganic materials from condensed liquid samples, transporting samples, and titrating ammonium hydroxide in the sample, it is required to comply with the exact test procedures. As for characteristics of FPM and CPM concentrations, CPM had about 1.6~63 times higher concentrations than FPM, and CPM caused huge increase in PM mass concentrations. Also, emission concentrations and quantities varied according to the characteristics of each fuel, the size of emitting facilities, operational conditions of emitters, etc. PM in the flue gases mostly consisted of CPM (61~99%), and the result of organic/inorganic component analysis revealed that organic dusts accounted for 30~88%. High-efficiency prevention facilities also had high concentrations of CPM due to large amounts of $NO_x$, and the more fuels, the more inorganic dusts. As a result of comparison between emission coefficients by fuel and the EPA AP-42, FPM had lower result values compared to that in the US materials, and CPM had higher values than FPM. For the emission coefficients of the total PM (FPM+CPM) by industry, that of thermal power stations (bituminous coal) was 71.64 g/ton, and cement manufacturing facility (blended fuels) 18.90 g/ton. In order to estimate emission quantities and coefficients proper to the circumstances of air pollutant-emitting facilities in Korea, measurement data need to be calculated in stages by facility condition according to the CPM measurement method in the study. About 80% of PM in flue gases are CPM, and a half of which are organic dusts that are mostly unknown yet. For effective management and control of PM in flue gases, it is necessary to identify the current conditions through quantitative and qualitative analysis of harmful organic substances, and have more interest in and conduct studies on unknown materials' measurements and behaviors.

• Korean Chemical Engineering Research
• /
• v.53 no.1
• /
• pp.31-38
• /
• 2015

At present, carbon dioxide ($CO_2$) emission, which causes global warming, is a major issue all over the world. To reduce $CO_2$ emission directly, commercial deployment of $CO_2$ separation processes has been attempted in industrial plants, such as power plant, oil refinery and steelmaking plant. Besides, several studies have been done on indirect reduction of $CO_2$ emission from recycle of reducing gas (carbon monoxide or hydrogen containing gas) in the plants. Unlike many competing gas separation technologies, pressure swing adsorption (PSA) and membrane filtration are commercially used together or individually to separate a single component from the gas mixture. However, there are few studies on operation of sequential separation process of multi-component gas which has more than two target gas products. In this paper, process simulation model is first developed for two available configurations: $CO_2$ PSA-CO PSA-$H_2$ PSA and $CO_2$ PSA-CO PSA-$H_2$ membrane. Operation optimization and economic evaluation of the processes are also performed. As a result, feed gas contains about 14% of $H_2$ should be used as fuel than separating $H_2$, and $CO_2$ separation should be separated earlier than CO separation when feed gas contains about 30% of $CO_2$ and CO. The simulation results can help us to find an optimal process configuration and operation condition for separation of multicomponent gas with $CO_2$, CO, $H_2$ and other gases.