• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2004.10a
• /
• pp.475-478
• /
• 2004

In this paper, a magnet-type automatic pipe cutting machine that binds itself to the surface of the pipe using magnetic force and executes unmanned cutting process is proposed. During pipe cutting process when the machine moves around the pipe laid vertical to the gravitational field, the gravity acting on the pipe cutting machine widely varies as the position of the machine varies. That is, with same driving force from the driving motor the cutting machine moves faster when it climbs down the surface of the pipe and moves slower when it climbs up to the top of the pipe. To maintain a constant velocity of the pipe cutting machine and improve the cutting quality, the authors adopted a conventional PID controller with a feedforward effort designed based on the encoder measurement of the driving motor. It is, however, impossible for the encoder at the motor to measure the absolute position and consequently the absolute velocity of the cutting machine in the case where the slip between the surface of the pipe and wheel of the cutting machine is not negligible. As an attempt to obtain a better estimation of the absolution angular position/velocity of the machine the authors proposes the use of the MEMS-type accelerometer which can measure static acceleration as well as dynamic acceleration. The estimated angular velocity of the cutting machine using the MEMS-type accelerometer measurement is experimentally obtained and it indicates the significant slipping of the machine during the cutting process.

• Textile Coloration and Finishing
• /
• v.10 no.4
• /
• pp.37-44
• /
• 1998

The purpose of this study was to elucidate the effect of weight loss of polyethylene terephthalate(PET) fabrics on the mechanical properties such as bending and shear. In order to compare the effect of treatment machine on the mechanical properies of treated PET fabrics, PET fabrics were hydrolyzed with NaOH aqueous solution using Tank machine and Liquor flow machine, respectively. The results were as follows ： 1. The bending rigidity and shear stiffness of hydrolyzed PET fabric decreased markedly up to about 10％ weight loss regardless of treatment machines. At the above 10％ weight loss, the variation of these properties is nearly unchanged. In addition, the bending hysteresis and shear hysteresis also showed similar trend. 2. Weft density change of PET fabrics treated with Liquor flow machine decreased by 1pick/inch. It is assumed that this is attributed to the tension during the treatment of Liquor flow machine. On the other hand, the weft density change of PET fabrics treated with Tank machine is scarcely influeneced by the weight loss. While warp density of PET fabrics treated with Liquor flow machine had no change with weight loss, warp density of PET fabrics treated with Tank machine decreased by 6pick/inch due to the tension. 3. The bending rigidity and shear stiffness of PET fabrics hydrolyzed with liquor flow machine slightly higher than with Tank m/c at the above 10％ weight loss. It is assumed that this is caused by the increasement of the crossing pressure of warp and weft yarn and contact points of filaments in the yarns. Also, the bending and shear hysteresis of PET fabrics treated with Tank machine were higher than that of liquor flow machine.

• Journal of the Korean Society for Precision Engineering
• /
• v.29 no.2
• /
• pp.156-160
• /
• 2012

Recently, the demands of the large scale machine tools gradually increase to machine the large parts, such as large scale crankshaft, yaw and pitch bearings for the wind power generator and the vehicle or aircraft components. But the high technology is necessary in order to develop the huge machine tools. Furthermore, the global market of it has been monopolized by a few companies. So, we need to develop the large scale machine tools and study its core technology to rush into the increasing market. In this study, we carried out the researches for the important core technology of a multi-tasking, machine tool; a large scale 5-axis machine tool of gantry type for multi-task machining. This study is focused on the design of large size gantry type multi-axis machine. In the case of large size of machine the cross rail deflection in the X-axis is significant. To reduce the deflection due to the eccentric spindle head, a special hollow type design in the cross rail with outside ram is adapted in this study. Also, the Zig-Zag motion in the Y-axis is inevitable with the gantry geometry, which is by the un-balancing, different motion at the left and the right columns moving. We tried to reduce the influence of Zig-Zag motion using FEM with different loading conditions at the left and the right side column.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.3
• /
• pp.1202-1211
• /
• 2018

In this paper, induction machine operation efficiency and torque is improved using Machine Learning based Gene Optimization (ML-GO) Technique is introduced. Optimized Genetic Algorithm (OGA) is used to select the optimal induction machine data. In OGA, selection, crossover and mutation process is carried out to find the optimal electrical machine data for induction machine design. Initially, many number of induction machine data are given as input for OGA. Then, fitness value is calculated for all induction machine data to find whether the criterion is satisfied or not through fitness function (i.e., objective function such as starting to full load torque ratio, rotor current, power factor and maximum flux density of stator and rotor teeth). When the criterion is not satisfied, annealed selection approach in OGA is used to move the selection criteria from exploration to exploitation to attain the optimal solution (i.e., efficient machine data). After the selection process, two point crossovers is carried out to select two crossover points within a chromosomes (i.e., design variables) and then swaps two parent's chromosomes for producing two new offspring. Finally, Adaptive Levy Mutation is used in OGA to select any value in random manner and gets mutated to obtain the optimal value. This process gets iterated till finding the optimal value for induction machine design. Experimental evaluation of ML-GO technique is carried out with performance metrics such as torque, rotor current, induction machine operation efficiency and rotor power factor compared to the state-of-the-art works.

• Geomechanics and Engineering
• /
• v.25 no.1
• /
• pp.1-16
• /
• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Journal of Power Electronics
• /
• v.15 no.5
• /
• pp.1244-1255
• /
• 2015

This paper presents a new multi-machine robust control based on an electric differential system for electric vehicle (EV) applications which is composed of four in-wheel permanent magnet synchronous motors. It is based on a new master-slave direct torque control (DTC) algorithm, which is used for the control of bi-machine traction systems based on a speed model reference adaptive system observer. The use of an electric differential in the design of a new EV constitutes a technological breakthrough. A classical system with a multi-inverter and a multi-machine comprises a three-phase inverter for each machine to be controlled. Another approach consists of only one three-phase inverter for several permanent magnet synchronous machines. The control of multi-machine single-inverter systems is the subject of this study. Several methods have been proposed for the control of multi-machine single-inverter systems. In this study, a new master-slave based DTC strategy is developed to generate an electric differential system. The entire system is simulated by Matlab/Simulink. The simulation results show the effectiveness of the new multi-machine robust control based on an electric differential system for use in EV applications.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.8 no.2
• /
• pp.3-9
• /
• 2009

When washing machine is rotated in the laundry, it tends to lean toward one side. This tendency causes a serious vibration. The balance of washing machine plays an important role in order to reduce the vibration by injecting the sand or the salt water into the balance of washing machine. The hot plate welder is used to prevent from outflow of contents. The hot plate welder brings about many problems which is concerned with accidents. The direction recognition and location information of the balance are required in this system. In this paper, the recognition direction of balance in washing machine using machine vision system is studied. The template matching algorithm compares sub-image with original image acquired in real-time to obtain a center point of balance image. The mid points and the edges of balance are estimated by the edge detection and gauging algorithms. The data acquired by these results is used for recognition direction of balance. The automation software for image processing is developed by using LabVIEW.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2005.06a
• /
• pp.1887-1892
• /
• 2005

In the future, a machine-tool will be more improved in the form of a knowledge evolution based device. In order to develop the knowledge evolution based machine-tool, this paper proposes the structure of standard M2M(Machine To Machine) and the scheme of agent communication in environment. The communication agent such as dialogue agent has a role of interfacing with another machine for cooperation. To design of the communication agent module in M2M environment, FIPA(Foundation of Intelligent Physical Agent) and ping agent based on JADE(Java Agent Development Framework) or FIPA-OS(Open Source) are analyzed in this study. Through this, it is expected that the agent communication can be more efficiently designed and the knowledge evolution based machine-tool can be hereafter more easily implemented.

• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.2 s.95
• /
• pp.201-210
• /
• 1999

One of the major limitations of productivity and quality in metal cutting is the machining accuracy of machine tools. The machining accuracy is affected by geometric and thermal errors of the machine tools. Therefore, a key requirement for improving te machining accuracy and product quality is to reduce the geometric and thermal errors of machine tools. This study models geometric error for error analysis and develops on-machine measurement system by which the volumetric erors are measured. The geometric error is modeled using form shaping function(FSF) which is defined as the mathematical relationship between form shaping motion of machine tool and machined surface. The constant terms included in the error model are found from the measurement results of on-machine measurement system. The developed on-machine measurement system consists of the spherical ball artifact (SBA), the touch probe unit with a star type stylus, the thermal data logger and the personal computer. Experiments, performed with the developed measurement system, show that the system provides a high measuring accuracy, with repeatability of ${\pm}2{\mu}m$ in X, Y and Z directions.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1993.04b
• /
• pp.108-113
• /
• 1993

The material for the machine tool structure should have high static stiffiness and damping in its property to improve both the static and dynamic performances. The static stiffness of a machine tool can be inceased by using either higher modulus material in the structure of a machine tool. However, the machine tool structrue with high stiffness but low damping is vulnerable to vibration at the resonance frequencies of the structure . For the high precision and highsped machine tool structure, therefore, the high damping capacity is most important in order to suppress vibration. The damping of a machine tool can not be increased by increasing the static stiffness. The best way to increase the damping capacity of the machine tool structure is to use a composite material which is composed of on material with high stiffness with low damping and another material with low stiffness with high damping. Therefore, in this paper, the bed of the ultra high precision grinding machine for mirror surface machining of brittle materials such as ceramics and composite materials was designed and manufactured with the epoxy concrete material. The epoxy concrete material was prepared by mixing epoxy resin with different size sands and gravels. The modulus, compressive strength, coefficient of thermal expansion, specific heat, and damping factor were measured by varying the compaction ratio, sizes and contents of the ingredients to assess the effect of the processing parameters on the mechanical properties of the material. Based of the measured properties, the prototype epoxy resin concrete bed for the mirror surface CNC grinding machine was designed and manufactured.