• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.5
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• pp.413-421
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• 2012

In industry, various rotating machinery such as pumps, gas turbines, compressors, electric motors, generators are being used as an important facility. Due to the industrial development, they make high performance(high-speed, high-pressure). As a result, we need more intelligent and reliable machine condition diagnosis techniques. Diagnosis technique using hidden Markov-model is proposed for an accurate and predictable condition diagnosis of various rotating machines and also has overcame the speed limitation of time/frequency method by using compensation of the rotational speed of rotor. In addition, existing artificial intelligence method needs defect state data for fault detection. hidden Markov model can overcome this limitation by using normal state data alone to detect fault of rotational machinery. Vibration analysis of step-up gearbox for wind turbine was applied to the study to ensure the robustness of diagnostic performance about compensation of the rotational speed. To assure the performance of normal state alone method, hidden Markov model was applied to experimental torque measuring gearbox in this study.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.7 s.172
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• pp.122-129
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• 2005

Shoe with leather upper such as safety and golf shoe requires a roughing process where the upper is roughed fur helping outsole to be cemented well. It is an important and basic process for production of leather shoe but is not automated yet. Thus, there are problems that the defect rate is high and the quality of roughed surface is not uniform. In order to solve such problems, the interest in automation of roughing process is being increased and this paper introduces CAM system for 5-axis automatic roughing machine as one part of automation of roughing process. The CAM system developed interpolates a B-spline curve using points measured from the Roughing Path Measurement System. The B-spline curve is used to generate the tool path and orientation data fer a roughing tool which has not only stiffness but also flexibility to rough the inclined surface efficiently. For productivity, the upper of shoe is machined by side of the roughing tool and tool offset is applied to the roughing tool for machining of inclined surface. The generated NC code was applied to 5-axis polishing machine for the test. The upper of shoe was roughed well along the roughing path data from CAM and the roughed surface was proper fur cementing of the outsole.

• Journal of Drive and Control
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• v.17 no.3
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• pp.9-14
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• 2020

A special purpose machine with two manipulators and quadruped crawler system is being developed to work at disaster sites where it is intended to quickly respond in the initial stages after the event. In this study, a terrain recognition module is developed so that the above special purpose machine can quickly obtain ground information to help choose its path while recognizing objects in its way, this is intended to enhance the remote driver's limited situational awareness. Terrain recognition modules were developed for two tasks (real-time path guidance, precision terrain measurements). The real-time path guidance analyzes terrain and obstacles while moving, while the precision terrain measurement feature provides more accurate terrain information by precisely measuring the ground in front of the vehicle while stationary. In this study, an air-cooled sensor protection module was developed so that the terrain recognition module can continue its vital tasks in the event of exposure to foreign substances, including scattered dust, mist and rainfall, as well as high temperatures.

• Journal of Magnetics
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• v.16 no.4
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• pp.431-434
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• 2011

The reduction of the Nb content in the $(Fe_{0.75}B_{0.20}Si_{0.05})_{96}Nb_4$ bulk metallic glass (BMG) has been studied. The glass-forming ability (GFA) is reduced by decreasing the Nb content, but it can be enhanced by replacing partially Fe by Co. Furthermore, the saturation magnetization of the $(Fe_{0.8}Co_{0.2})_{76}B_{18}Si_3Nb_3$ BMG is 1.35 T, being with 13% larger than that of the base alloy $(Fe_{0.75}B_{0.20}Si_{0.05})_{96}Nb_4$. $(Fe_{0.8}Co_{0.2})_{76}B_{18}Si_3Nb_3$ BMG exhibits slightly larger $B_{800}$ (the magnetic flux density at 800 A/m) and smaller core losses (20%-30%) compared with the commercial Fe-6.5 mass% Si steel.

• Industrial Engineering and Management Systems
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• v.3 no.1
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• pp.52-58
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• 2004

This report examines the issue of designing an efficient production system by increasing several types of flexibility. Increasing manufacturing flexibility is a key strategy for efficiently improving market responsiveness in the face of uncertain market demand for final products. The manufacturing system comprises multiple plants, of which individual plants have multiple manufacturing lines that are designed to produce limited types of products in accordance with their size and materials. Imbalance in the workload occurs among plants as well as among manufacturing lines because of fluctuations in market demand for final products. Thereby, idleness of some manufacturing lines and longer lead times in some manufacturing lines occur as a result of the high workload. We clarify how these types of flexibility affect manufacturing performance by improving only one type of flexibility or by improving multiple types of flexibility simultaneously. The average lead time and the imbalance in workload are adopted as measures of manufacturing performance. Three types of manufacturing flexibility are interrelated: machine flexibility, routing flexibility, and process flexibility. Machine flexibility refers to the various types of operations that a machine can perform without requiring the prohibitive effort of switching from one order to another. Routing flexibility is the capability of processing a given set of part types using more than one line (alternative line) in the plant. Process flexibility results from being able to build different types of final products at the same plant.

• IE interfaces
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• v.19 no.1
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• pp.43-52
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• 2006

In order to make best use of NC machine tools with minimal labor costs, they need to be in operation 24 hours a day without being attended by human operators except for setup and tool changes. Thus, unattended machining is becoming a dream of every modern machine shop. However, without a proper mechanism for real-time monitoring of the machining processes, unattended machine could lead to a disaster. Investigated in this paper are ways to using PC camera as a real-time monitoring system for unattended NC milling operations. This study defined five machining states READY, NORMAL MACHINING, ABNORMAL MACHINING, COLLISION and END-OF-MACHINING and modeled them with DEVS (discrete event system) formalism. An image change detection algorithm has been developed to detect the table movements and a flame and smoke detection algorithm to detect unstable cutting process. Spindle on/off and cutting status could be successfully detected from the sound signals. Initial experimentation shows that the PC camera could be used as a reliable monitoring system for unattended NC machining.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.5
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• pp.38-45
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• 2004

As optical communication is being substituted for telecommunication, the demand of a large variety of fiber optic components is increasing. V-groove substrates, one of the module components, are used to connect optical fibers to optical planar circuits and to arrange fibers. Their applications are multi-channel optical connectors and optical waveguide fiber coupling, etc. Because these substrates are a critical part of the splitter in a multiplexer and a multi fiber connector, precise and reliable fabrication process is required. For precisely aligning core pitch between fibers, machined core pitch tolerance should be within sub-microns. Therefore, these are generally produced by state-of-the-art micro-fabrication like MEMS. However, most of the process equipment is very expensive. It is also difficult to change the process line for custom designs to meet specific requirements using various materials. For various design specifications such as different values of the V angle and low-priced process, the fabrication method should be flexible and low cost. To achieve this goal, we have suggested a miniaturized machine tool with high accuracy positioning system. Through this study, it is shown that this cutting process can be applied to produce V-groove subtracts. We also show the possibility of using a miniaturized machining system for producing small parts.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.53-59
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• 2021

In this paper, we propose a system that can perform dumbbell exercise by recognizing the weight of dumbbells without wearing and device. With the development of virtual reality technnology, many studies are being conducted to simulate the pysical feedback of the real world in the virtual world. Accurate motion recognition is important to the elderly for rehabilitation exercises. They cannot lift heavy dumbbells. For rehabilitation exercise, correct body movement according to an appropriate weight must be performed. We use a machine learning algorithm for the accuracy of motion data input in real time. As an experiment, we was test three types of bicep, double, shoulder exercise and verified accuracy of exercise. In addition, we made a virtual gym game to actually apply these exercise in virtual reality.

• 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.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.3
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• pp.1-7
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• 2021

With the advent of industry 4.0, big data and machine learning techniques are being widely adopted in the maintenance domain. Inverters are widely used in many engineering applications. However, overloading and complex operation conditions may lead to various failures in inverters. In this study, failure mode effect analysis was performed on inverters and voltages collected to investigate the over-voltage effect on capacitors. Several features were extracted from the collected sensor data, which indicated the health state of the inverter. Based on this correlation, the best features were selected for classification. Moreover, random forest classifiers were used to classify the healthy and faulty states of inverters. Different performance metrics were computed, and the classifiers' performance was evaluated in terms of various health features.