• Journal of Powder Materials
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• v.22 no.1
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• pp.1-5
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• 2015

Powder injection molding is an important manufacturing technology to mass produce superalloy components with complex shape. Injection molding step is particularly important for realizing a desired shape, which requires much time and efforts finding the optimum process condition. Therefore computer aided engineering can be very useful to find proper injection molding conditions. In this study, we have conducted a finite element method based simulation for the spiral mold test of superalloy feedstock and compared the results with experimental ones. Sensitivity analysis with both of simulation and experiment reveals that the melt temperature of superalloy feedstock is the most important factor for the full filling of mold cavity. The FEM based simulation matches well the experimental results. This study contributes to the optimization of superalloy powder injection molding process.

• Korea Real Estate Review
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• v.27 no.3
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• pp.71-86
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• 2017

The purpose of this study was to apply the deep running method to real estate price index predicting and to compare it with the time series analysis method to test the possibility of its application to real estate market forecasting. Various real estate price indices were predicted using the DNN (deep neural networks) and LSTM (long short term memory networks) models, both of which draw on the deep learning method, and the ARIMA (autoregressive integrated moving average) model, which is based on the time seies analysis method. The results of the study showed the following. First, the predictive power of the deep learning method is superior to that of the time series analysis method. Second, among the deep learning models, the predictability of the DNN model is slightly superior to that of the LSTM model. Third, the deep learning method and the ARIMA model are the least reliable tools for predicting the housing sales prices index among the real estate price indices. Drawing on the deep learning method, it is hoped that this study will help enhance the accuracy in predicting the real estate market dynamics.