• International Journal of Precision Engineering and Manufacturing
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• v.7 no.3
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• pp.3-7
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• 2006

For the purpose to improve and to automate designing of high-speed spindle units (SU's), we have developed the mathematical models and software to estimate SU performance characteristics, including the run-out of spindles running on ball bearings. In order to understand better the mechanics of high-speed SUs, the dynamic interaction of ball bearings and SU, and the influence of the bearing imperfections and SU's operational conditions on the run-out, we have carried out computer simulation and experimental studies. Through the study of SU's, we have found out that run-out of SU can vary drastically with variation of rpm. The influences of rotation speed and of accuracy parameters of bearings on the SU accuracy have the greatest importance. The influence of bearing preload has a secondary importance. Comparison of the results of these studies has demonstrated adequacy of the models and software developed to the real SU's.

• Journal of Korean Association for Spatial Structures
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• v.22 no.2
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• pp.39-46
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• 2022

Recently, machine learning is widely used to solve optimization problems in various engineering fields. In this study, machine learning is applied to development of a control algorithm for a smart control device for reduction of seismic responses. For this purpose, Deep Q-network (DQN) out of reinforcement learning algorithms was employed to develop control algorithm. A single degree of freedom (SDOF) structure with a smart tuned mass damper (TMD) was used as an example structure. A smart TMD system was composed of MR (magnetorheological) damper instead of passive damper. Reward design of reinforcement learning mainly affects the control performance of the smart TMD. Various hyper-parameters were investigated to optimize the control performance of DQN-based control algorithm. Usually, decrease of the time step for numerical simulation is desirable to increase the accuracy of simulation results. However, the numerical simulation results presented that decrease of the time step for reward calculation might decrease the control performance of DQN-based control algorithm. Therefore, a proper time step for reward calculation should be selected in a DQN training process.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.5
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• pp.59-64
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• 2006

The automation of hot rolling process requires the developments of several mathematical models for simulation and quantitative description of the industrial operations involved in order to achieve the continuously increasing productivity, flexibility and quality(dimensional accuracy, mechanical properties and surface properties). The mathematical modeling of hot rolling process has long been recognized to be a desirable approach to investigate rolling operating practice and design of mill requirement. To achieve this objectives, a new teaming method with neural network to improve the accuracy of rolling force prediction in hot rolling mill is developed. Also, Genetic Algorithm(GA) is applied to select the optimal structure of the neural network and compared with that of engineers experience. It is shown from this research that both structure selection methods can lead to similar results.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.363-366
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• 2003

The hard turning is a turning operation performed in high strength alloy steels (HRC>30) in order to reach surface roughness close to those obtained in grinding. This is possible because of availability of improved tool materials (polycrystalline cubic boron nitride. PCBN), ad more rigid machine tools. According to many previous work of hard turning mechanism, the maximum temperature of cutting can be raised up to 100$0^{\circ}C$. As the heat generation rate is very high, the thermal displacement of tool holder cannot be negligible. Therefore, the aim of this paper is to analyze effects of high heat generation at CBN tool tip to the thermal displacement of a tool holder in hard turning and finally geometric accuracy. The thermal behavior of a CBN tool holder is investigated by numerical simulation and experiment, and the result shows thermal elongation of microns order is possible during hard turning process.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.2
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• pp.168-175
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• 2011

Contouring accuracy of CNC machine tools is very important for high-speed and high-precision machining. In particular, large contour error may occur during corner tracking. In order to reduce the corner contouring error, acceleration and deceleration control or tool-path planning methods have been suggested. However, they do not directly control the corner contouring error. In the meantime, network servo systems are widely used because of their easiness of building and cost effectiveness. Communication latency between the master controller and servo drives, however, may deteriorate contouring accuracy especially during corner tracking. This paper proposes a control strategy that can accurately calculate and directly control the corner contouring error. A prediction control is combined with the above control to cope with communication latency. The proposed control method is evaluated through computer simulation and experiments. The results show its validity and usefulness.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.83-89
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• 2000

This paper presents a dynamic modeling and a sliding mode controller for the high-speed / high-accuracy position control system. Selected target system is the wire bonder head assembly which is used in semiconductor assembly process. This system is a reciprocating one around the pivot point that consists of VCM(voice coil motor) as a actuator and transducer horn as a bonding tool. For the modeling elements, the system is divided into electrical circuit, magnetic circuit and mechanical system. Each system is modeled by using the bond graph method and united into the full system. Two major aims are considered in the design of the controller. The first one is that the horn must track the given reference trajectory. The second one is that the controller must be realizable by using the DSP board. Computer simulation and experimental results show that the designed sliding mode controller provides better performance than the PID controller.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.41-46
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• 2002

In the face of global competition, the requirements fer the continuously increasing productivity, flexibility and quality (dimensional accuracy, mechanical properties and surface properties) have imposed a major change on steel manufacturing industries. The automation of hot rolling process requires the developments of several mathematical models for simulation and quantitative description of the industrial operations involved. To achieve this objectives, a new loaming method with neural network to improve the accuracy of rolling force prediction in hot rolling mill is developed. Also, Genetic Algorithm(GA) is applied to select the optimal structure of the neural network and compared with that of engineers experience. It is shown from this research that both structure selection methods can lead to similar results.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.91-99
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• 2017

In order to meet the thermal performance analysis accuracy requirements of high density permanent magnet synchronous motor (PMSM), a method of multi physical domain coupling thermal analysis based on control circuit, electromagnetic and thermal is presented. The circuit, electromagnetic, fluid, temperature and other physical domain are integrated and the temperature rise calculation method that considers the harmonic loss on the frequency conversion control as well as the loss non-uniformly distributed and directly mapped to the temperature field is closer to the actual situation. The key is to obtain the motor parameters, the realization of the vector control circuit and the accurate calculation and mapping of the loss. Taking a 48 slots 8 poles high density PMSM as an example, the temperature rise distribution of the key components is simulated, and the experimental platform is built. The temperature of the key components of the prototype machine is tested, which is in agreement with the simulation results. The validity and accuracy of the multi physical domain coupling thermal analysis method are verified.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.593-606
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• 2016

Computer simulation is widely used in a variety of computational science and engineering fields, including computational fluid dynamics, nano physics, computational chemistry, structural dynamics, and computer-aided optimal design, to simulate the behavior of a system. As the demand for the accuracy and complexity of the simulation grows, however, the cost of executing the simulation is rapidly increasing. It, therefore, is very important to lower the total execution time of the simulation especially when that simulation makes a huge number of repetitions with varying values of input parameters. In this paper we develop a simulation service system that provides the ability to predict the result of the requested simulation without actual execution for that simulation: by recording and then returning previously obtained or predicted results of that simulation. To achieve the goal of avoiding repetitive simulation, the system provides two main functionalities: (1) storing simulation-result records into database and (2) predicting from the database the result of a requested simulation using statistical machine learning techniques. In our experiments we evaluate the prediction performance of the system using real airfoil simulation result data. Our system on average showed a very low error rate at a minimum of 0.9% for a certain output variable. Using the system any user can receive the predicted outcome of her simulation promptly without actually running it, which would otherwise impose a heavy burden on computing and storage resources.

• Journal of Electrochemical Science and Technology
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• v.15 no.2
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• pp.299-313
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• 2024

Electrochemical machining (ECM) is an effective manufacturing method for difficult-to-machine materials and is widely used in the precision manufacturing of aerospace components. In recent years, the requirements for the machining accuracy and surface integrity of ECM have become increasingly stringent. To further improve the machining quality, this work investigated the intricate laws between electrolyte filtration accuracy and machining quality. Electrolytes with different filtration accuracies were compared, and a numerical simulation was used to evaluate the change in temperature and bubble rate of the flow field in the machining area. Experiments were conducted on ECM of Ti-6Al-4V (TC4) alloy workpieces using electrolytes with different filtration accuracy. The workpiece machining accuracy and surface quality were analyzed, and the repetition accuracy of the workpiece was evaluated. The intricate laws between electrolyte filtration accuracy and machining quality were explored. It was found that when the electrolyte filtration accuracy is improved, so too is the machining quality of the ECM. However, once the filtration accuracy has reached a certain value, the machining quality has extremely limited improvement. By evaluating the repetition accuracy of processed workpieces in electrolytes with different filtration accuracies, it was found that when the filtration accuracy reaches a certain value, there is no positive correlation between the repetition accuracy and filtration accuracy. The result shows that, for the workpiece material and conditions considered in this paper, an electrolyte with 0.5㎛ filtration accuracy is suitable for the wide application of precision ECM.