This work has investigated filling characteristics during injection molding of a thin‐plate with microchannel patterns on it. To conduct molding experiments, an injection mold and mold inserts with microchannel patterns were designed and built. The experiments were conducted for three different channel sizes, and the results are analyzed. An unstable filling flow was observed in microscopic images for the smallest channel size. The front advanced with continuous creation and merge of microballs, which looked like teeth on the gum. We have analyzed this phenomenon and argued that there exists a critical channel width where such instability takes places. POLYM. ENG. SCI., 2010. © 2010 Society of Plastics Engineers

Rubber compounds have high viscoelastic property. One of the viscoelastic behaviors shown in profile extrusion is an extrudate swell and circulation flow at the corner of inside of die. Application of viscoelastic model to a capillary extrusion has been investigated in this study. Experiments and simulations have been performed using Fluidity Tester and commercial computational fluid dynamics (CFD) code, Polyflow respectively. Die swell of rubber compounds in a capillary die were predicted using a non-linear differential viscoelastic model, Phan-Thien and Tanner (PTT) model for various relaxation times and relaxation modes. As relaxation time and number of relaxation mode increase, die swell increases. The results of simulations were compared with the experiment. Pressure and velocity distributions, and circulation flows at the corner of reservoir have been analyzed through computer simulation. Two and three relaxation modes with large range of relaxation time examined in this study showed good agreement with experimental results of die swell and well represented circulation flow at the corner of reservoir in the capillary die.

This paper develops a bankruptcy prediction model on an Artificial Neural Network for hotel management. A bankruptcy prediction model has a specific feature to predict a bankruptcy of the whole hotel business after evaluate bankruptcy possibility on the basis of business performance data of each branch. here are many traditional statistical models for bankruptcy prediction such as Multivariate Discriminant Analysis or Logit Analysis. However, we chose Artificial Neural Network because the method has accuracy rates of prediction better than those of other methods. We first selected 100 good enterprises and 100 bankrupt enterprises as experimental data and set up a bankruptcy prediction model by use of a tool for Artificial Neural Network, NeuroShell. The model and its experiments, which