Neuro-Knowledge-Based Expert System （NKBES） for Optimal Scheming of Die Casting Process

Qiaodan HU, Peng LUO, Yi YANG and Liliang CHENSchool of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Manufacturing Science and Engineering, Sichuan University, Chengdu 610065, ChinaSchool of Mechanical Engineering and Automation, Guizhou University of Technology, Guiyang 550003, China

摘 要：<正> We develop a neuro-knowledge-based expert system （NKBES） frame in this work. The system mainly concerns with decision of gating system and die casting machine based on a neuro-inference engine launched under the MATLAB software environment. For enhancement of reasoning agility, an error back-propagation neural network was applied. A rapidly convergent adaptive learning rate （ALR） and a momentum-based error back-propagation algorithm was used to conduct neuro-reasoning. The working effect of the system was compared to a conventional expert system that is based on a two-way （forward and backward） chaining inference mechanism. As the reference, the present paper provided the neural networks sum-squared error （S5E） and ALR vs iterative epoch curves of process planning case mentioned above. The study suggests that the neuro-modeling optimization application to die casting process design has good feasibility, and based on that a novel and effective intelligent expert system can be launched at low cost.

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