チタン鋳造における鋳型温度の影響

The purpose of this study was to evaluate the titanium castability with a vacuum pressured type casting machine. We tested ethyl-silicate bonded investment "TITAVESTPS" of metal frame work. Four different mold temperatures (room temperature, 300℃, 600℃, and 900℃) were prepared for the present study, and casting was done in five times in each condition. When the mold temperature increased, high percentage of castability was gained. Mold temperature showed a highly significant (p<0.001) correlation on the castability. These results indicate that high performance of castability on the titanium was achieved when the mold temperature increased by using vacuum pressured type casting machine

Self-Reconfigurable Multi-Layer Neural Networks with Genetic Algorithms

This paper addresses the issue of reconfiguring multi-layer neural networks implemented in single or multiple VLSI chips. The ability to adaptively reconfigure network configuration for a given application, considering the presence of faulty neurons, is a very valuable feature in a large scale neural network. In addition, it has become necessary to achieve systems that can automatically reconfigure a network and acquire optimal weights without any help from host computers. However, self-reconfigurable architectures for neural networks have not been studied sufficiently. In this paper, we propose an architecture for a self-reconfigurable multi-layer neural network employing both reconfiguration with spare neurons and weight training by GAs. This proposal offers the combined advantages of low hardware overhead for adding spare neurons and fast weight training time. To show the possibility of self-reconfigurable neural networks, the prototype system has been implemented on a field programmable gate array