• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1999.11a
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• pp.19-24
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• 1999

Understanding of wear mode and prediction of wear rate of parts in sliding contact are very important in field of meterial design relating with wear resistant. This paper has been undertaken to analyze the possibility of elucidation of wear mode and prediction of wear rate for annealed steel in sliding contact using the X-ray diffraction. The sliding wear test with various velocities using pin-on-disc machine and the X-ray diffraction test on the worn surface have been carried out. The results have been shown that the magnitude of residual stress and half-value breadth on the worn surface have a good correlation with wear mode. The difference between before and after test of half-value breadth on worn surface has been shown to be exponential relation with wear rate in the same wear mode.

• Transactions of Materials Processing
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• v.23 no.1
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• pp.29-34
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• 2014

The damage level of the die surface was predicted by estimating the surface roughness with a finite element analysis and the wear characteristics. Wear and friction tests were conducted to compare the wear characteristics for three kinds of surface treatments - CrN, TiAlN and AlCrN coatings. A prediction model was derived from the surface roughness results with respect to contact pressure and sliding speed which were obtained from the wear test. Surface roughness values for the damage regions of the die surface were compared between the experiments and the prediction model, which shows fairly good agreement with each other.

• Tribology and Lubricants
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• v.28 no.3
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• pp.124-129
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• 2012

The prediction of fretting wear is a significant issue for the design of contacting mechanical components such as flexible couplings and splines, jointed structures and so on. In our earlier study, we developed a numerical model to predict the fretting wear using boundary element method. The developed algorithm needs experimental fretting wear coefficients and friction coefficients between two moving materials to get more reliable results. In this study, therefore, we demonstrated the measurement method of the fretting wear coefficients and friction coefficients using disk on plate tribometer with piazo actuator and gap sensor. For four different material combinations, the fretting wear coefficients and friction coefficients are acquired through the fretting wear experiment and the analysis of the measured values. Thess results are useful to predict the quantative fretting wear rate in the developed algorithm.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1997.10a
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• pp.164-167
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• 1997

The die wear is one of the main factors affecting die accuracy and tool lifetime. It is desired to reduce die wear by developing simulation method to predict wear based on process parameters, and then optimize the process. Therefore, this paper describes disign methodology of preform for minimizing wear of finisher die in multi-stage cold forging processes. The finite element method is combined with the routine of wear prediction and the cold forging processes. The finite element method is combined with the routine of wear prediction and the cold forging process is analyzed. In order to obtain preform to minimize die wear, the FPS algorithm is applied and the optimal preform shape is found from iterative deformation analysis and wear calculation.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.90-96
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• 2021

As real-time data of factories can be collected using various sensors, the adaptation of intelligent unmanned processing systems is spreading via the establishment of smart factories. In intelligent unmanned processing systems, data are collected in real time using sensors. The equipment is controlled by predicting future situations using the collected data. Particularly, a technology for the prediction of tool wear and for determining the exact timing of tool replacement is needed to prevent defected or unprocessed products due to tool breakage or tool wear. Directly measuring the tool wear in real time is difficult during the cutting process in milling. Therefore, tool wear should be predicted indirectly by analyzing the cutting load of the main spindle, current, vibration, noise, etc. In this study, data from the current and acceleration sensors; displacement data along the X, Y, and Z axes; tool wear value, and shape change data observed using Newroview were collected from the high-speed, two-edge, flat-end mill machining process of SKD11 steel. The support vector machine technique (machine learning technique) was applied to predict the amount of tool wear using the aforementioned data. Additionally, the prediction accuracies of all kernels were compared.

• Design & Manufacturing
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• v.7 no.1
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• pp.28-33
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• 2013

During the cold forming, due to high working pressure acting on the die surface, failure mechanics must be considered before die design. One of the main reasons of die failure in industrial application of metal forming technologies is wear. The mechanisms of wear are consisted of adhesion, abrasion, erosion and so on. Die wear affects the tolerances of formed parts, metal flow, and costs of process. The only way to control these failures is to develop a prediction method on die wear suitable in the design state in order to optimize the process. The wear system is used to analyse 'operating variables' and 'system structure'. In this study, with AISI D2, AISI 1020, AISI 304SS materials, a series of the wear experiments of pin-on-disk type to obtain the wear coefficients from Archard's wear model and the upsetting processes are carried out to observe the wear phenomenon during the cold forming process. The analysis of upsetting processes are performed by the rigid-plastic finite element method. The result of the analysis is used to investigate the die wear the processes, and the analysis simulated die wear profiles are compared with the experimental measured die wear profiles.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.317-322
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• 2004

This paper deals with a prediction method of tool life in view of the reliability assessment. In this study, the flank wear was studied among multi-factors deciding the tool wear state. Firstly, tool lift was predicted by correlation between flank wear and cutting time, based on the extended Taylor tool life equation of turning data, including parameters of cutting speed, feed rate, and cutting depth. Secondly, each of cutting conditions of endmilling was equivalently converted to apply ball endmill data to the extended Taylor equation. The web-based reliability prediction program for tool lift is being developed as one of reliability assessment programs to for the machine tools.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.03a
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• pp.172-177
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• 1998

An experimental study on the prediction of grinding wheel wear by dual detection methods was conducted by the laser displacement and acoustic emission(AE) system. The laser displacement sensor was located above the head of the grinding wheel and the AE sensor was set under the workpiece, where the wheel were condition can be detected. It was found that the dual detection methods by laser displacement system and AE system made it possible to predict the wheel wear. From the experiments, the root mean square(RMS) values both methods was found to be proportional to the grinding wheel wear.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1996.10a
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• pp.122-130
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• 1996

During the cold forming, due to high working pressure acting on the die surface, failure mechanics must be considered before die design. One of the main reasons of die failure in industrial application of metal forming technologies is wear. Die wear affects the tolerances of formed parts, metal flow and costs of process etc. The only way to control these failures is to develop methods which allow prediction of die wear and costs of process etc. The only way to control these failures is to develop methods which allow prediction of die wear and which are suited to be used in the design state in order to optimize the process. In this paper, the wear experiments to abtain the wear coefficients and the upsetting processes was accomplished to observe the wear phenomenon during the cold forming process. The analysis of upsetting processes was accomplished to observe the wear phenomenon during the cold forming process. The analysis of upsetting processes was accomplished by the rigid-plastic finite element method. The result from the deformation analysis was used to analyse the die wear during the processes and the predicted die wear profiles were compared with the measured die wear profiles.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.1-10
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• 2016

Magnetic disks have been used for decades in auxiliary storage devices of computer systems. In recent years, the use of NAND flash memory, which is called SSD, is increased as auxiliary storage devices. However, NAND flash memory, unlike traditional magnetic disks, necessarily performs the erase operation before the write operation in order to overwrite data and this leads to degrade the system lifetime and performance of overall NAND flash memory system. Moreover, NAND flash memory has the lower endurance, compared to traditional magnetic disks. To overcome this problem, this paper proposes EPET (Enhanced Prediction and Elapsed Time) wear leveling technique, which is especially efficient to portable devices. EPET wear leveling uses the advantage of PET (Prediction of Elapsed Time) wear leveling and solves long-term system failure time problem. Moreover, EPET wear leveling further improves space efficiency. In our experiments, EPET wear leveling prolonged the first bad time up to 328.9% and prolonged the system lifetime up to 305.9%, compared to other techniques.