• Transactions of Materials Processing
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• v.30 no.4
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• pp.186-194
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• 2021

Finite element simulation is a widely applied method for practical purpose in various metal forming process. However, in the simulation of elasto-plastic behavior of porous material or in crystal plasticity coupled multi-scale simulation, it requires much calculation time, which is a limitation in its application in practical situations. A machine learning model that directly outputs the constitutive equation without iterative calculations would greatly reduce the calculation time of the simulation. In this study, we examined the possibility of artificial intelligence based constitutive equation with the input of existing state variables and current velocity filed. To introduce the methodology, we described the process of obtaining the training data, machine learning process and the coupling of machine learning model with commercial software DEFROM^TM, as a preliminary study, via rigid plastic finite element simulation.

• Proceedings of the KSR Conference
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• 2005.05a
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• pp.48-57
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• 2005

A lot of manufacturing knowledge and method have applied to increase manufacturing efficiency in industry field. DES(Discrete Event Simulation) is one of solution to deal with manufacturing problems in factory. Beginning of research, old maintenance system of KNR ( Korea National Railroad) and its technical problems are basically investigated. KNR has maintained railway vehicle with their own solution based on experience. Very advanced railway vehicles such as KTX (Korea Train Express) and TTX(Tilting Train Express) will be difficult to maintain with their old maintenance method. In order to apply knowledge of DES, maintenance field of railway must be considered. Imaginary maintenance machine are selected to variable of DES. Maintenance capability of each machine will be evaluated base on imaginary data from imaginary machine. The machine could be very expensive as well as difficult to replace. Target of research is minimization of number of machine in railway workshop. So basic knowledge of discrete event simulation is introduced. Then five essential stages of discrete event simulation are provided. Each maintenance case defined as event. Each event is discrete and simulated base on different case such as one maintenance line with one machine and one maintenance line with two machines in railway workshop. simple maintenance method, discrete event simulation, will be come out very powerful in complicate maintenance system and will be helpful to reduce maintenance cost as well as maintenance labor.

• 한국금형공학회:학술대회논문집
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• 2008.06a
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• pp.61-66
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• 2008

The hydraulic system have been used much in a heavy machine which high power source is desired. In the case of the heavy press machine and the injection molding machine, the use of the hydraulic power is essential especially for increasing productivity and getting the good products. Because the hydraulic circuit is very complex and the system parameters are uncertain, the development of the simulation model for hydraulic system is not easy in the heavy machine. In this case, Many researchers have used a commercial program for analysis and development in a major field of study. In this paper, the aim is to develop the simulation model of the hydraulic system with various commercial program for trajectory tracking. And adaptive control method is applied to the simulation model for the trajectory tracking of a cylinder motion. Load on the cylinder is modeled in ADAMS program, the hydraulic circuit including pump, spool valve and cylinder is modeled in AMESim program and a controller is designed in MatLab/simulink program. The suggested model is applied for the tracking of a cylinder motion, and through computer simulation, its trajectory tracking performance is illustrated.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.211-217
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• 2007

A nano-imprinting stage has been widely used in various fields of nanotechnology. In this study, an analysis method of a nano-imprinting stage machine using FEM and flexible multi-body kinematics and dynamics has been presented. We have developed a virtual imprinting machine to evaluate the prototype design in the early design stage. The simulation using CAE for the imprinting machine is not only to analyze static and dynamic characteristics of the machine but also to determine design parameters of the components for the imprinting machine, such as dimensions and specifications of actuators and sensors. Structural components as the upper plate, the rotator, the shaft and the translator have been modeled with finite elements to analyze flexibility effects during the precision stage motion. In this paper flexible multi-body dynamic simulation is executed to support robust design of the precision stage mechanism. In addition, we made the 4-axis stage model to compare the dynamic behavior with that of 3-axis stage model.

• International Journal of CAD/CAM
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• v.12 no.1
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• pp.1-8
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• 2012

This study analyzes the characteristics of two different kinds of squat exercise through physical experiments and a computer simulation, i.e. one with a free weight and the other with a Smith machine are studied. This study also proposes a new design for the Smith machine, which has both the advantages of each type based on the results of the analysis. The muscle force and level of stimulation of the lower extremities during squatting were calculated by running an inverse dynamics analysis program on a musculoskeletal model together with the measured motion data. The calculated results were verified by comparing with the measured EMG data. The analysis showed that squatting using free weight is more effective than squatting using the Smith machine. Meanwhile, in order to design an improved Smith machine, which is the final goal of this study, the trajectory of the barbell of the subjects during free weight squatting was measured on the sagittal plane. The measurement showed that the average slope of the trajectory of the barbell is tilted backward by $10.7^{\circ}$. Based on this measurement, this study proposes a tilted design for an improved Smith machine.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.455-456
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• 2006

Multi-tasking machines are widely used in machine tool industries nowadays. This study focuses on the effect of load on the structure stabilities of laser multi-tasking machine which is comprehensively combined turning center and laser machine. For design of the machine, simulation of structural analysis is carried out varying number of elements. The analysis is carried out by FEM simulation using the commercial software, CATIA V5. This method showed a proper number of elements can be selected to obtain good result by reduced computation time.

• Journal of the Korean Society of Industry Convergence
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• v.4 no.3
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• pp.281-287
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• 2001

Traditional job shop scheduling is supposed that machine has a fixed processing job type. But actually the machine has a highly utilization or long processing time is occurred delay. Therefore product system is difficult to respond quickly to the change of products or loads or machine failure etc. Here we use flexible job shop which is supposed that a machine has several jobs by tool change. The heuristic for the flexible job shop scheduling has to solve two problems. One is a routing problem which is determine a machine to process job. The other is sequencing problem which is determine processing sequence. The approach to solve two problems arc a hierarchical approach which is determined routing and then schedule, and a concurrence approach which is solved concurrently two problems by considering routing when it is scheduled. In this study, we simulate for flexibility efficiency fo flexible job shop scheduling with machine failure using hierarchical approach.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.422-432
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• 2019

A hybrid kernel function of support vector machine is proposed to improve the classification performance of power quality disturbances. The kernel function mathematical model of support vector machine directly affects the classification performance. Different types of kernel functions have different generalization ability and learning ability. The single kernel function cannot have better ability both in learning and generalization. To overcome this problem, we propose a hybrid kernel function that is composed of two single kernel functions to improve both the ability in generation and learning. In simulations, we respectively used the single and multiple power quality disturbances to test classification performance of support vector machine algorithm with the proposed hybrid kernel function. Compared with other support vector machine algorithms, the improved support vector machine algorithm has better performance for the classification of power quality signals with single and multiple disturbances.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.56-61
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• 2021

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.50.2-50.2
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• 2020

The dark matter (DM) only simulations have been exploited to study e.g. the large scale structures and properties of a halo. In a baryon side, the high-resolution hydrodynamic simulation such as IllustrisTNG has helped extend the physics of gas along with stars and DM. However, the expansive computational cost of hydrodynamic simulations limits the size of a simulated universe whereas DM-only simulations can generate the universe of the cosmological horizon size approximately. I will introduce a pipeline to estimate baryonic properties of a galaxy inside a dark matter (DM) halo in DM-only simulations using a machine trained on high-resolution hydrodynamic simulations. An extremely randomized tree (ERT) algorithm is used together with multiple novel improvements such as a refined error function in machine training and two-stage learning. By applying our machine to the DM-only simulation of a large volume, I then validate the pipeline that rapidly generates a galaxy catalog from a DM halo catalog using the correlations the machine found in hydrodynamic simulations. I will discuss the benefits that machine-based approaches like this entail, as well as suggestions to raise the scientific potential of such approaches.