In this present work attempts have been made to develop an integrated neural network system for prediction of process parameters
such as injection pressure and injection time in metal injection moulding (MIM) process. The current system has been developed by
integrating the different aspects of MIM process. The aspects that are addressed in this system are the physical model of MIM filling
stage based on governing equations of mould filling, and process parameters for debinding and sintering stages generated by
experimentation. In this work the feed forward type of neural network has been used, which was initially trained with the analytical
data before incorporating as part of an integrated system. In this work Gauss training method has been incorporated for the usage of
function approximation. This integrated system has been implemented in MatLAB environment by using neural networks toolbox. This
integrated system was successfully tested to solve the real world problems ofMIM process. The analytical algorithm based on governing
equations of mould filling process first produces a feasible injection time for the MIM process. Injection time data is then used to train
the neural network system. In order to validate the results generated by the neural network system are checked with the simulation
results of the ‘‘Moldflow’’ software and found that the results generated by integrated neural network system are not different from the
simulated results.
[1]
H. Kudo,et al.
A PC-based expert system for forming sequence design
,
1990
.
[2]
Prasad K. Yarlagadda,et al.
Prediction of die casting process parameters by using an artificial neural network model for zinc alloys
,
2000
.
[3]
Sang-Gook Kim,et al.
Knowledge based synthesis system for injection molding
,
1987
.
[4]
Gary P. Maul,et al.
Optimal injection velocity profiling
,
1989
.
[5]
I. Pandelidis,et al.
Optimization of injection molding design. Part II: Molding conditions optimization
,
1990
.
[6]
Prasad K. Yarlagadda,et al.
Prediction of processing parameters for injection moulding by using a hybrid neural network
,
2001
.
[7]
J. Terrisse,et al.
Modeling the packing stage in injection molding of thermoplastics
,
1988
.
[8]
S. F. Shen,et al.
A finite-element/finite-difference simulation of the injection-molding filling process
,
1980
.
[9]
Stefanos Kollias,et al.
An adaptive least squares algorithm for the efficient training of artificial neural networks
,
1989
.
[10]
W. L. Wilkinson,et al.
Non-isothermal developing flow of a power-law fluid in a converging slit
,
1983
.