AJOU Central Library Repository: Neural Network-based Prediction of Aluminum Extrusion Process Parameters

Neural Network-based Prediction of Aluminum Extrusion Process Parameters

Alternative Abstract: The objective of this research is to estimate realistic principal extrusion process parameters by means of artificial neural network. Conventionally, finite element analysis is used to derive extrusion 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 results and the parameters required by a real manufacturing execution system. In this work, the feasibility of making use of neural networks to predict realistic principal extrusion process parameters is studied. In the course, a suitable neural network is designed which is trained using an appropriate learning algorithm. The network so trained is used to predict the extrusion process parameters and finally, the predicting performance of the network is evaluated.

Appears in Collections:: Special Graduate Schools > Graduate School of Science and Technology > Department of Industrial Engineering > 3. Theses(Master)

qrcode

STATISTICS: Total Visit :3,738,322; Total Download :1,818; Today View :15,981

AJOU Central Library Repository는 국립중앙도서관 OAK 보급사업으로 구축되었습니다.