• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.4
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• pp.137-142
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• 2018

Recycled synthetic resin materials produced from waste vinyl and waste plastic contain many foreign substances. Plastic products made from this recycled resin materials containing foreign substances are of poor quality, with reduced the strength and rigidity. Foreign substances include heavy metals, cement, foil, dyed paper and dust. In this study, the scratch-Dies process; which remove foreign sbustances, with precision and automation, through a three-stage mesh filter, is designed. The process is evaluated with finite element analysis according to vibration loading and make. After installing the manufactured equipment, recycled resin was producde, and its heavy metal content was evaluated. Recycled synthetic resin materials were also used plastic products and evaluate their strength. In addition, the change in production was assessed.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1998.03a
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• pp.151-154
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• 1998

Stainless steel sheets are widely used to produce electrical appliances. But there are various problems occured in forming stainless steel sheets such as scratch, local fracture, earing. So the productivity of electrical appliances made of stainless steel sheets is decreased. And it is very important to improve the formability of stainless steel sheets. In this study, rectangular cup drawing tests have been carried out to obtain optimum process parameters for improving the formablity of stainless steel sheets. In the tests, selected process parameters are materials of dies and punches, lubricating conditions, and blank holding force. From the test results, we suggest the appropriate conditions to be applicable to the actual manufacturing processes.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2007.11a
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• pp.140-141
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• 2007

The arc ion plating (AIP) technique has been used widely for thin coating in the area of surface engineering. The TiN coating is important in the field of dies, cutting tools and other mechanical parts. When forming the TiN films by AIP technique, the processing parameters such as arc power, bias voltage, working pressure, temperature of substrate and pre-treatment affected the adhesion respectively. The results of scratch test revealed that the adhesion strength was influenced by arc power most strongly. And a sequence of the importance of each parameters has been obtained. The crystal structure and cross-section of TiN films are also be investigated.

• Tribology and Lubricants
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• v.31 no.2
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• pp.56-61
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• 2015

In this study, we investigated the effectiveness of an ultrasonic nanocrystal surface modification (UNSM) technique on the mechanical and wear properties of tungsten carbide (WC). The UNSM technique is a newly developed surface modification technique that increases the mechanical properties of materials by severe plastic deformation. The objective of this study was to improve the wear resistance of press die made of WC by applying the UNSM technique. We observed the microstructures of the untreated and UNSM-treated specimens using a scanning electron microscope (SEM), and energy-dispersive X-ray spectroscopy (EDX) was used to investigate the chemical composition. The SEM observations showed the pore size and the number of pores decreased after the UNSM treatment. We assessed the wear behavior of both the untreated and UNSM-treated specimens using a scratch test. The test results showed that the wear resistance of the UNSM-treated specimens increased by about 46% compared with the untreated specimens. This may be attributed to increased hardness, reduced surface roughness, induced compressive residual stress, and refined grain size following the application of the UNSM technique. In addition, we found that the UNSM treatment increased the carbon concentration to 63% from 33%. We expect that implementing the findings of this study will lead to an increase in the life of press dies.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.407-419
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• 2022

The semiconductor fabrication process is complex and time-consuming. There are sometimes errors in the process, which results in defective die on the wafer bin map (WBM). We can detect the faulty WBM by finding some patterns caused by dies. When one manually seeks the failure on WBM, it takes a long time due to the enormous number of WBMs. We suggest a two-step approach to discover the probable pattern on the WBMs in this paper. The first step is to separate the normal WBMs from the defective WBMs. We adapt a hierarchical clustering for de-noising, which nicely performs this work by wisely tuning the number of minimum points and the cutting height. Once declared as a faulty WBM, then it moves to the next step. In the second step, we classify the patterns among the defective WBMs. For this purpose, we extract features from the WBM. Then machine learning algorithm classifies the pattern. We use a real WBM data set (WM-811K) released by Taiwan semiconductor manufacturing company.