• 월간 기계설비
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• no.8 s.205
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• pp.49-52
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• 2007

벽산건설(주)은 최근 '열병합발전과 개별난방 통합 시스템’을 개발, 특허를 획득했다. 이 기술은 아파트에 소형 열병합발전기를 설치하는 중앙난방방식과 기존 개별난방방식이 합쳐진 통합 시스템 이다. 이 시스템은 열병합발전 시스템과 개별난방 시스템을 동시에 실시, 열병합발전에서 발생하는 폐열을 이용해 급탕수 온도를 높여 이를 직접 배출시키거나 개별보일러로 공급해 개별보일러의 연료 소모량을 절감토록 한 것이다. 벽산건설(주)이 개발한 '열병합발전과 개별난방 통합 시스템’에 대해 살펴본다.

• 월간 기계설비
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• s.39
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• pp.99-104
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• 1993

• 월간 기계설비
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• s.32
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• pp.78-90
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• 1993

• 월간 기계설비
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• s.92
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• pp.76-78
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• 1998

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.2
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• pp.431-439
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• 1996

New design principles nad necessary conditions for a mechanical system have been suggested to be kept in the design process using a washing machine transmission as a model. The necessary conditions are funcitnal requirement condition and spatial arrangement condition. The design principles to satisfy the necessary conditions are the principle of sequence and the principle of expansion. Decision sequence for state variables and design varibles of various mechanicla elements have been formulated. New automatic design program for washing machine transmission has been developed observing the necessary conditions and design principles investigated in this study. It was verified to be very effective to follow the design conditions, principles nad formulated decision sequence in mechanical system design process.

• Journal of the Earthquake Engineering Society of Korea
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• v.2 no.2
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• pp.1-12
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• 1998

The seismic behaviour of rotating machine-foundation systems subjected to six-component nonstationary earthquake ground accelerations is analyzed. The rotating machine-foundation system is idealized by using discs, rotating shaft, fluid-film journal bearings, pedestals, and space frame foundation. Thus, governing equations of motion for the rotating machine-foundation system are obtained by considering Gyroscopic effect, Coriolis effect, dynamic characteristics of fluid-film journal bearings, and translational and rotational motions of seismic rigid base. The influences due to Gyroscopic effects, Coriolis effects, and rotational motions of seismic base on the overall structural response are demonstrated by a numerical example. The results show that the inclusion of base rotations and Gyroscopic effects contributes significantly to the system response.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.42-47
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• 2021

A one-touch descending lifeline that can easily be installed and rapidly evacuated in the event of a fire accident in high-rise buildings was proposed to overcome difficulties of conventional descending lifeline such as complex installation methods and procedures. However, this lifeline exhibits limitations such as restrictions in installation location and large apparatus size. Therefore, this paper proposes a sliding-type descending lifeline, which has a similar operation to that of current one-touch descending lifeline and solves the aforementioned limitations. A double square link mechanism including a sliding four-bar linkage is proposed and the descending lifeline support is redesigned to unfold in two different planes, allowing 3D movement. Additionally, the shape of the support frame is designed to obtain two attachment surfaces that can be attached to a wall, irrespective of the angle between the window and the inner wall. FEA analysis using ABAQUS is performed to ensure that the robustness of the presented support complies with the Fire Control Act Enforcement Decree. Finally, the feasibility of the proposed sliding one-touch descending lifeline is verified through fabrication.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.12
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• pp.85-91
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• 2021

In our daily life, artificial intelligence performs simple and complicated tasks like us, including operating mobile phones and working at homes and workplaces. Artificial intelligence is used in industrial technology for diagnosing various types of equipment using the machine learning technology. This study presents a fault mode effect analysis (FMEA) of start motors using machine learning and big data. Through multiple data collection, we observed that the primary failure of the start motor was caused by the melting of the magnetic switch inside the start motor causing it to fail. Long-short-term memory (LSTM) was used to diagnose the condition of the magnetic locations, and synthetic data were generated using the synthetic minority oversampling technique (SMOTE). This technique has the advantage of increasing the data accuracy. LSTM can also predict a start motor failure.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.6
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• pp.91-97
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• 2018

Chemical mechanical polishing(CMP) realizes a surface planarity through combined mechanical and chemical means. In CMP process, Preston equation is known as one of the most general approximation of the removal rate. Effects of pressure and relative speed on the mechanical property of Cu CMP has been investigated. On the other hand, The amount of abrasion also increased with changes in pressure and speed, resulting in a proportional increase of temperature during CMP. Especially this temperature is an important factor to change chemical reaction in a Cu CMP. However, when the slurry temperature became higher than $70^{\circ}C$, the removal rate went lower due to abrasives aggregation and scratching occurred on the Cu film. Therefore, it was found that the slurry temperature should not exceed $70^{\circ}C$ during Cu CMP. Finally, authors could increase the pressure, speed and slurry temperature up to a ceratin level to improve the removal rate without surface defects.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.11
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• pp.94-101
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• 2020

In the age of industry 4.0, artificial intelligence is being widely used to realize machinery condition monitoring. Due to their excellent performance and the ability to handle large volumes of data, machine learning techniques have been applied to realize the fault diagnosis of different equipment. In this study, we performed the failure mode effect analysis (FMEA) of an aluminum electrolytic capacitor by using deep learning and big data. Several tests were performed to identify the main failure mode of the aluminum electrolytic capacitor, and it was noted that the capacitance reduced significantly over time due to overheating. To reflect the capacitance degradation behavior over time, we employed the Vanilla long short-term memory (LSTM) neural network architecture. The LSTM neural network has been demonstrated to achieve excellent long-term predictions. The prediction results and metrics of the LSTM and Vanilla LSTM models were examined and compared. The Vanilla LSTM outperformed the conventional LSTM in terms of the computational resources and time required to predict the capacitance degradation.