급변하는 환경에서 지속 가능한 경쟁우위를 확보하기 위해 기업들은 끊임없이 새로운 지식을 획득해야 한다. 인수합병이나, 지식재산권의 구매 등이 이를 위한 대표적인 수단이나, 합작회사의 설립도 보완자산을 내재화하기 위한 효과적인 지식 획득 방법으로 주 목받고 있다. 그러나 모든 합작회사가 새로운 지식을 성공적으로 획득하는 것은 아닌데, 이는 기업들이 획득하고자 하는 지식의 특성에 맞는 적절한 학습전략 및 조직 구조를 갖추지 못했기 때문인 경우가 많다. 본 연구는 이 같은 문제의식 하에 상황이론 관점에서 지식의 특성을 다차원 적으로 구분하고 지식 특성에 맞는 학습전략과 조직구조의 필요성을 자동차 부품분야의 국제 합작회사 사례를 통해 살펴보았다. 하드웨어 기술에 최적화된 사례 회사(국제 합작회사)는 다른 성격의 지식인 소프트웨어 기술을 습득하기 위해 차별화된 학습 전략과 조직구조를 갖추지 못했고, 이러한 미스매치로 인해 합작회사를 통한 새로운 지식(엔진제어시 스템 기술)의 흡수에 실패하였다. 본 연구는 기업이 성공적 지식흡수를 위해 고려해야 할 요소들을 이론적 프레임워크를 통해 제시하고 실증분석을 통해 이를 검증함으로써 합작회사 설립, 인수합병 등 조직 변화를 통해 동태적 역량을 확보하고자 하는 기업들에게 실무적 시사점을 제공하고 있다.

This study wanted to optimize the radiator tank's deformation assembled on the automotive engine block. Among the experimental planning methods, the Taguchi method was used to find optimal molding conditions to minimize plastic covers' deformation. The four main factors used in the Taguchi method were selected as the main factors: resin temperature, pressure time, coolant temperature, and cooling time. The number of cycles for each factor was divided into five stages, and a total of 25 experiments were conducted. The experiment used the Moldflow program, an injection molding analysis program. The maximum deformation obtained under the existing molding conditions was about 1.318mm. Still, the deformation of the mold applied with the optimal molding conditions obtained using the Taguchi method was approximately 1.273mm, which showed that the maximum deformation was reduced by 3.4% compared to the existing molding conditions.

In this paper, each controller's unique ID and PICO oscilloscope were used to measure the voltage waveform of each CAN communication line, and compare and analyze the serial decoding results. Using the voltage change level of the CAN communication line, it was possible to check whether the CAN-High line and the Low line were disconnected. And it was possible to infer the circuit disconnection point between the controller and the controller only with the unique ID information of each controller. And when the CAN-High circuit was disconnected, the voltage of the high line was measured at the same voltage level as the Low line.

PURPOSES : In this study, the factors influencing traffic accidents of commercial vehicles in the transportation industry were examined. The evidence observed in this study showed how it contributes to the establishment of safety management policies. METHODS : Safety management data obtained from the Korean Transportation Safety Authority were integrated. A multi-regression analysis was performed by comparing data for the past three years of traffic accident data. RESULTS : Through multi-regression analysis, items that significantly influenced the safety evaluation index (the number of traffic accidents per vehicle owned and the number of dangerous driving actions per 100 km per person) were analyzed. Items with similar patterns observed for various commercial vehicle industries were also analyzed. Monthly average drop off rate, number of law violations per driver, percentage of drivers above 65 years old, percentage of drivers that have completed the compulsory education, number of vehicles owned, and driving distance per vehicle are the variables that influenced the safety evaluation index. CONCLUSIONS : There is a growing need to establish safety management policies and management measures to enhance the voluntary safety management capabilities of the industry. Safety management should be conducted through the analysis of traffic accident impact factors for commercial vehicles.

Domestic as well as global automobile manufacturers are making greater efforts in cost reduction to strengthen the competitiveness. In this study, we developed a program to effectively manage standard time of automobile assembly line, and confirm based on A-automobile factory data. For the purpose, we develop the system which is possible to manage standard time as well as calculate man-hour for automobile assembly line.

The objective of this study is to identify the emission characteristics of VOCs from small-scale painting facilities, such emissions being pollutants that impact nearby living areas and to devise improvement measures to enhance management plans regarding pollutant emissions from painting facilities. VOCs emissions from painting facilities were estimated according to Clean Air Policy Support System (CAPSS) data based on the National Institute of Environmental Research (NIER)'s emission list in 2017. Three automotive painting facilities in Seoul were chosen for evaluation of the adsorption system. We analyzed the characteristics of VOCs generated by type of different operation and measured the removal efficiency of the adsorption system. Therefore, we analyzed current emissions of VOCs from automotive painting facilities based on field measurements. According to such detailed analysis, a systematic management plan was proposed.

The emission of particulate matter and volatile organic compounds (VOCs) from a motor vehicle painting booth was quantitatively evaluated. Most particulate matter was emitted during the spraying process, in which the PM10 concentration was 16.5 times higher than that of the drying process. When the paint was being sprayed, the particles with a diameter of 1.0~2.5 μm accounted for 39.4% and particles greater than 2.5 μm in diameter accounted for 30.6% of total particles. On the other hand, small particles less than 0.5 μm in diameter accounted for 52.4% of total particles during the drying process. In contrast to the particulate matter, high concentrations of VOCs were emitted during both spraying and drying processes. Butyl acetate, xylene, toluene, and m-ethyltoluene were the most abundant VOCs emitted from the motor vehicle painting booth. Additionally, xylene, butyl acetate, toluene, and 1,2,3-trimethylbenzene were the dominant ozone precursors. Especially, xylene exhibited the highest ozone production contribution (32.5~44.4%) among 34 species of the ozone precursors. The information obtained in this study can be used to establish a suitable management strategy for air pollutants from motor vehicle painting booths.

This study was performed to analyze the effect on driving performance by identifying the flow characteristics of the rear diffuser such as drag coefficient, lift coefficient, velocity vector, velocity streamlines, turbulence kinetic energy and vortex-core region according to the angle of the sedan rear diffuser and the shape of the divider. The angle of the diffuser was analyzed at 2° intervals from 0° to 18°, and the divider was analyzed by changing from one to five. The three-dimensional modeling was performed using CATIA V5 and the vehicle model was selected as a sedan car in the form of an LF Sonata. The CFD analysis was performed in order to identify the flow characteristics of rear diffuser using ANSYS CFX 14.5.7. For each model, the analysis was performed under each condition with speeds of 80km/h, 100km/h, 120km/h, 140km/h. The results of the flow analysis showed that the rear diffuser angle was the best result in driving stability at 6°. The results of the study on the number of dividers showed the best result value in driving stability when the rear diffuser angle was 6° and the divider was 3 and selected as the optimal shape.

In this paper, we compare and analyze the injector defects of P-ENG and S-ENG with normal injectors by measuring current waveforms, voltage waveforms, exhaust gases and driving fuel economy. In the case of FTS failure, the S-ENG reduced the overall injection time by 3.7% and the main injection by 3.5% compared to the normal engines. In the case of AFS failure, the overall injection time increased by 45.7% and the main injection time increased by 24.1% compared to the normal engine. The rest data showed that fuel economy of S-ENG had 25.9% higher than P-ENG, NOX had 162.5% higher than that of P-ENG, and CO2 of S-ENG had 26.7% lower than P-ENG.

In this study, the flow analyses were carried out on three kinds of front wing models. The down forces of front wings which influence the stability, cornering at driving were investigated with three models. At model 1, the maximum pressure shown on the main plate of front wing is 3177.539Pa. The maximum pressures at models 2 and 3 are shown to be 3429.677Pa and 3506.494Pa, respectively. The higher the pressure, the more resistance. So, the lower the pressure, the less resistance the model gets. At model 1, the maximum velocity of stream that flows under the front wing was shown to be the smallest among three models. In case of all three models, the pressure at which the air passes through the front wing is high in the upper part of the front wing. Among three models, model 1 is thought to be the most appropriate model to give the effect of the down force while reducing the flow resistance at driving. By utilizing this study result, the flow velocity and pressure are investigated without the flow experiment at driving due to the configuration of automotive front wing, and the flow resistance can be seen.

In this paper, the injectors with normal quantity, over quantity of +10%, under quantities of -10% and –30%, were mounted on S-ENG and P-ENG in order to measure the voltage energy, current energy and power supplied to the injectors and the fuel economy under several speed of rpm conditions. The voltage and current energy of S-ENG was greater than P-ENG, and the power of S-ENG was measured and analyzed 4.8 times higher than that of P-ENG at all injectors, and the tendency of carbon dioxide emissions calculated from fuel efficiency measurement results was not significantly affected by the type of injectors, but P-ENG was measured to be slightly affected by the type of injectors. It is assumed that the model year and mileage of the test vehicle affects this tendency.

초록 close In this study, the structural analyses were carried out with the old and new wheel models installed at the automotive tires. The stress and deformation at the middle circumference of wheel were seen to be greatest at two models. Also, the stress and deformation were smallest at the edge of wheel. The maximum deformation of the old model B is about five times larger than that of the new model A. The maximum equivalent stress of the old model B can be seen to be twice as large as the new model A. Also, it can be seen that the new model A is more stable than the old model B in terms of strength. It can be seen that the deformation energy of the old model B is 19 times larger than that of the new model A. And it is thought that the new model A is much more durable than the old model B in terms of impact. By utilizing this study result, the stress and deformation are investigated without the strength test of wheel installed at the automotive tire, and the durability can be seen.

자동차 고급화 추세에 따라 소비자의 차량 실내 환경에 대한 관심이 증가함에 따라 자동차의 기본적 성능뿐만 아니라 실내 쾌적성 향상에 관심이 증대되고 있다. 또한 자동차 실내 쾌적성에 대한 연구는 운전자에게 만족을 제공하 는데 그치지 않고, 운전자의 불쾌지수 및 스트레스를 낮추어서 교통사고의 위험을 줄이는데 기여할 수 있기 때문에 매우 중요한 연구 주제이다. 따라서 본 연구에서는 운전자의 뇌파측정을 통해 통풍시트의 온도변화에 따른 쾌적감 변화와 쾌적온도를 알아보고, 온도변화에 따른 남녀간 쾌적감에 대한 차이를 탐색하고자 하였다. 연구결과 첫째 통풍시 트의 온도가 22℃, 25℃, 28℃에서 각각의 실험군을 비교한 결과 28℃보다 25℃에서 통계적으로 유의하게 쾌적감이 더 높게 나타났다. 둘째 통풍시트 온도 변화에 따른 남녀간 쾌적감에 대한 실험결과 남성과 여성이 온도에 따라 느끼는 쾌적감은 통계적으로 유의한 차가 없는 것으로 나타났다. 향후 자동차의 실내온도와 통풍시트의 온도 변화에 따른 운전자의 쾌적감 변화를 파악하여 상관관계를 분석한다면, 운전자의 쾌적성을 확보하여 휴먼에러로 인한 교통사고를 낮출수 있을 뿐만 아니라 자동차의 전기에너지의 사용량을 줄일 수 있을 것이다.

This research explores how imported automobile companies can develop their strategies to improve the outcome of their recalls. For this, the researchers analyzed patterns of recall demand, classified recall types based on the demand patterns and examined response strategies, considering plans on how to procure parts and induce customers to visit workshops, recall execution capacity and costs. As a result, recalls are classified into four types: U-type, reverse U-type, L- type and reverse L-type. Also, as determinants of the types, the following factors are further categorized into four types and 12 sub-types of recalls: the height of maximum demand, which indicates the volatility of recall demand; the number of peaks, which are the patterns of demand variations; and the tail length of the demand curve, which indicates the speed of recalls. The classification resulted in the following: L-type, or customer-driven recall, is the most common type of recalls, taking up 25 out of the total 36 cases, followed by five U-type, four reverse L-type, and two reverse U-type cases. Prior studies show that the types of recalls are determined by factors influencing recall execution rates: severity, the number of cars to be recalled, recall execution rate, government policies, time since model launch, and recall costs, etc. As a component demand forecast model for automobile recalls, this study estimated the ARIMA model. ARIMA models were shown in three models: ARIMA (1,0,0), ARIMA (0,0,1) and ARIMA (0,0,0). These all three ARIMA models appear to be significant for all recall patterns, indicating that the ARIMA model is very valid as a predictive model for car recall patterns. Based on the classification of recall types, we drew some strategic implications for recall response according to types of recalls. The conclusion section of this research suggests the implications for several aspects: how to improve the recall outcome (execution rate), customer satisfaction, brand image, recall costs, and response to the regulatory authority.

In this study, the failure characteristic of the center floor of a front-wheel drive vehicle was investigated according to material. UHSS, Al6061-T6, CFRP, and CFRP-Al were used as materials. As the analysis condition, a fixed support was applied to the rear surface of the center floor and a forced displacement of 2 mm/sec was applied to the front surface. As the result, when comparing with the equivalent stress and strain energy according to the material, it was found that UHSS, Al6061-T6, CFRP, and CFRP-Al were higher in the order. Also, when comparing with the equivalent strain due to the material, it was shown that the equivalent strain was high in the order of Al6061-T6, UHSS, CFRP and CFRP-Al. As for the damage characteristic of the center floor according to the material, it was found that the highest structural stability was obtained when UHSS was used. However, it was found that it was good to utilize Al6061-T6 in order to acquire the structural stability along with the structure with the lighter weight.

Spot welding is a representative process in automotive welding and the application of intelligent systems is accelerating. In particular, in the case of welding electrode management, the timing of electrode wear and dressing was determined by continuous spot welding evaluation, however there is concerned that errors in welding equipment or processes may work in a complex manner. In this study, a dynamic resistance waveform sensing and image measurement system that greatly affects the nugget formation, which is important to the quality of spot welding, was fabricated and used. Based on the experimental data of the galvanized steel sheet, an electrode life prediction algorithm for electrode wear was derived through CNN(Convolutional Neural Network) model of machine learning training.