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神經回路網에 의한 機械驅動系의 作動狀態 豫知 및 判定에 관한 硏究
이충엽 동의공업대학 1999 論文集 Vol.25 No.1
Wear debris can be collected from the lubricants of operating machinery and its morphology is directly related to the damage to the interacting surfaces from which the particles originated. The morphology of the wear particles are therefore directly indicative of wear processes occurring in machinery. This paper was undertaken to identify the wear debris by neural network link for condition monitoring of the lubricated machine surface. The wear test of pin on disk type was carried out under different experimental conditions, In order to describe the characteristics of wear debris with various shapes and sizes, the four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction condition of five values. The results obtained were as follows: 1. It is easily distinguished the morphology of wear debris on driving condition of the lubricated machinery through the four shape parameters of wear debris with computer image processing. 2. In order to identify morphology of wear particles more easily with computer image analysis, it is necessary to divide total wear debris in small classes of every 200 wear debris and available for identification of wear debris on driving condition to use its average value, It was possible to calculate the presumed wear loss using software developed through this study. 3. The morphology of wear debris depend on mechanical Properties of the specimen and boundary film of lubricants. As the applied load increase, friction coefficient decrease due to shear stress of lubricant atoms. 4. It is shown that identification results rely upon the ranges of these shape parameters learned. The three kinds of the wear debris had a different pattern and recognized very well between relation the friction condition and materials with neural network. We discussed how the network determines difference in wear debris feature, and this approach can be applied to foreseeability and decision for machine condition monitoring. 5. It was able to presume friction coefficient of the lubricated machine through relation between morphology of wear particles and measured friction coefficient with neural network.
계면 제어를 이용한 높은 과포하 하에서의 응축 성능 향상
이충엽,서동현,심재환,문병윤,이경준,이주영,남영석 한국공업화학회 2020 한국공업화학회 연구논문 초록집 Vol.2020 No.-
Superhydrophobic (SHPo) surfaces can provide high condensation heat transfer due to facilitated droplet removal. However, such high performance has been limited to low supersaturation conditions due to surface flooding. Here, we suggest effective anti-flooding strategies through tailoring the nanoscale coating heterogeneity and structure length scale. Experimental verification is conducted using CuO nanostructures having different length scales combined with hydrophobic coatings with different nanoscale heterogeneities. The proposed antiflooding SHPo can provide a ∼130% enhanced average heat transfer coefficient with ∼14% larger supersaturation range for droplet jumping compared to a previous CuO SHPo.