%0 Journal Article %A Sachin Man Bajimaya and SangChul Park and Gi-Nam Wang %D 2007 %J International Journal of Mechanical and Mechatronics Engineering %B World Academy of Science, Engineering and Technology %I Open Science Index 11, 2007 %T Predicting Extrusion Process Parameters Using Neural Networks %U https://publications.waset.org/pdf/5742 %V 11 %X The objective of this paper is to estimate realistic principal extrusion process parameters by means of artificial neural network. Conventionally, finite element analysis is used to derive process parameters. However, the finite element analysis of the extrusion model does not consider the manufacturing process constraints in its modeling. Therefore, the process parameters obtained through such an analysis remains highly theoretical. Alternatively, process development in industrial extrusion is to a great extent based on trial and error and often involves full-size experiments, which are both expensive and time-consuming. The artificial neural network-based estimation of the extrusion process parameters prior to plant execution helps to make the actual extrusion operation more efficient because more realistic parameters may be obtained. And so, it bridges the gap between simulation and real manufacturing execution system. In this work, a suitable neural network is designed which is trained using an appropriate learning algorithm. The network so trained is used to predict the manufacturing process parameters. %P 644 - 648