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- 저자 : Jae-SeobKwak (검색결과 2 건)
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A study on magnetic abrasive deburring of dual micro pattern
Dong-Hyun Jin,Jae-SeobKwak 대한기계학회 2016 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.30 No.12
Studies on dual micro pattern are not established because of difficulty of its fabrication and deburring technology. In this investigation, a dual micro pattern which consists of a net pattern and a lenticular pattern was fabricated on a cylindrical workpiece by turning process. Magnetic abrasive deburring (MAD) was proposed as a deburring process in this study. Burr height defined by difference of its height and theoretical pattern height was measured as about 1 μm. It is one of the non-traditional machining methods utilizing flexible tool which consists of ferrous particle and abrasive powder. Hence, the aim of this investigation is to remove generated burr on the dual micro pattern. Burr at the dual micro pattern was removed through MAD, and a prediction equation of machined pattern height was derived. A deburring condition was optimized and verified by experiments. As a result, it was confirmed that dual micro pattern which has high shape accuracy was fabricated using MAD.
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Jiyoung Yu,Yongho Sohn,박영환,Jae-SeobKwak 대한기계학회 2016 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.30 No.10
Lightweight metals have been used to manufacture the body panels of cars to reduce the weight of car bodies. Typically, aluminum sheets are welded together, with a focus on weld quality assurance. A weld quality prediction system for the laser welding of aluminum was developed in this research to maximize welding production. The behavior of the plasma was also analyzed, dependent on various welding conditions. The light intensity of the plasma was altered with heat input and wire feed rate conditions, and the strength of the weld and sensor signals correlated closely for this heat input condition. Using these characteristics, a new algorithm and program were developed to evaluate the weld quality. The design involves a combinatory algorithm using a neural network model for the prediction of tensile strength from measured signals and a fuzzy multi-feature pattern recognition algorithm for the weld quality classification to improve predictability of the system.
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