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논문 기본 정보

자료유형
학술저널
저자정보
Reddy, N. Subba (School of Materials Science and Engineering, Gyeongsang National University) Baek, Yong-Hyun (School of Materials Science and Engineering, Gyeongsang National University) Kim, Seong-Gyeong (School of Materials Science and Engineering, Gyeongsang National University) Hur, Bo Young (School of Materials Science and Engineering, Gyeongsang National University)
저널정보
한국주조공학회 한국주조공학회지 (주조) 한국주조공학회지 제34권 제3호
발행연도
2014.1
수록면
107 - 111 (5page)

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Permeability is the ability of a material to transmit fluid/gases. It is an important material property and it depends on mould parameters such as grain fineness number, clay, moisture, mulling time, and hardness. Modeling the relationships among these variable and interactions by mathematical models is complex. Hence a biologically inspired artificial neural-network technique with a back-propagation-learning algorithm was developed to estimate the permeability of green sand. The developed model was used to perform a sensitivity analysis to estimate permeability. The individual as well as the combined influence of mould parameters on permeability were simulated. The model was able to describe the complex relationships in the system. The optimum process window for maximum permeability was obtained as 8.75-10.5% clay and 3.9-9.5% moisture. The developed model is very useful in understanding various interactions between inputs and their effects on permeability.

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