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폴리카보네이트/신디오탁틱 폴리스티렌 블렌드의 상용성 및 Encapsulation 상용화
박은주,조수봉,이무성,Park, Eun Ju,Jo, Soo Bong,Lee, Moo Sung 한국섬유공학회 2013 한국섬유공학회지 Vol.50 No.4
The miscibility of the blends of polycarbonate (PC) and syndiotactic polystyrene (s-PS) was investigated by using differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA). The blends exhibit two different glass transition temperatures ($T_g$) close to those of the pure components, indicating immiscibility at the melt mixing temperature of $280^{\circ}C$. Morphological studies of the blends shows large dispersed particles and significant cavities between the matrix and the dispersed phase, especially for the PC matrix blends. To reduce such detrimental effects, poly(methyl methacrylate) (PMMA) or styrene-maleic anhydride copolymer (SMA) was added as a compatibilizer to the PC/s-PS blends. Both polymers contribute to enhancing the interfacial adhesion between PC and s-PS and reduce the particle size of the dispersed phase. Especially when PMMA was added, morphological studies showed that PMMA encapsulates the dispersed s-PS phase in the PC matrix. The elastic modulus of the PC/s-PS blend increased on adding PMMA or SMA, mainly because of enhanced interfacial adhesion. The pencil hardness of PC increased at least three times from that of neat PC as a result of compatibilization and surface enrichment of the s-PS phase, whose surface tension is lower than that of PC.
이지웅,최현정,남정수,조수봉,김문현,이상원 대한기계학회 2017 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.31 No.12
This paper addresses the development of an online tool condition monitoring and diagnosis system for a milling process. To establish a tool condition monitoring and diagnosis system, three modeling algorithms – an Adaptive neuro fuzzy inference system (ANFIS), a Back-propagation neural network (BPNN) and a Response surface methodology (RSM) – are considered. In the course of modeling, the measured milling force signals are processed, and critical features such as Root mean square (RMS) values and node energies are extracted. The RMS values are input variables for the models based on ANFIS and RSM, and the node energies are those for the BPNNbased model. The output variable is the confidence value, which indicates the tool condition states – initial, workable and dull. The tool condition states are defined based on the measured flank wear values of the endmills. During training of the models, numerical confidence values are assigned to each tool condition state: 0 for the initial, 0.5 for the workable and 1 for the dull. An experimental validation was conducted for all three models, and it was found that the RSM-based model is best in terms of lowest root mean square error and highest diagnosis accuracy. Finally, the RSM-based model was used to build an online system to monitor and diagnose the tool condition in the milling process in a real-time manner, and its applicability was successfully demonstrated.