Optimisation of Plastic Injection Moulding Process with Soft Computing

The paper presents a hybrid strategy in a soft computing paradigm for the optimisation of the plastic injection moulding process. Various plastic injection molding process parameters, such as mold temperature, melt temperature, injection time and injection pressure are considered. The hybrid strategy combines numerical simulation software, a genetic algorithm and a multilayer neural network to optimise the process parameters. An approximate analysis model is developed using a Back-propagation neural network in order to avoid the expensive computation resulting from the numerical simulation software. According to the characteristic of the optimisation problem, a nonbinary genetic algorithm is applied to solve the optimisation model. The effectiveness of the improved strategy is shown by an example.

[1]  J. C. Lin,et al.  Optimum Gate Design of FreeForm Injection Mould using the Abductive Network , 2001 .

[2]  M. Gutierrez,et al.  Estimating hidden unit number for two-layer perceptrons , 1989, International 1989 Joint Conference on Neural Networks.

[3]  Sankar K. Pal,et al.  Staging of cervical cancer with soft computing , 2000, IEEE Transactions on Biomedical Engineering.

[4]  Zhou Zhen.  Research on Integrated Design Techniques for Injection Mold Runner System , 2000 .

[5]  Sankar K. Pal,et al.  Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing , 1999 .

[6]  B. Lee,et al.  Optimization of Part Wall Thicknesses to Reduce Warpage of Injection-Molded Parts Based on The Modified Complex Method , 1995 .

[7]  R. G Song,et al.  Heat treatment technique optimization for 7175 aluminum alloy by an artificial neural network and a genetic algorithm , 2001 .

[8]  Wang Rong,et al.  A genetic algorithm methodology for data mining and intelligent knowledge acquisition , 2001 .

[9]  Elias Siores,et al.  An intelligent system for plastic injection molding process design , 1996 .

[10]  Bin Wu,et al.  An improved strategy for GAs in structural optimization , 1996 .

[11]  Hecht-Nielsen.  Theory of the backpropagation neural network , 1989 .

[12]  B.H.M. Sadeghi,et al.  A BP-neural network predictor model for plastic injection molding process , 2000 .

[13]  Lotfi A. Zadeh,et al.  Fuzzy logic, neural networks, and soft computing , 1993, CACM.

[14]  Prasad K. Yarlagadda,et al.  A neural network system for the prediction of process parameters in pressure die casting , 1999 .