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

材料科学技术（英）, 2004, 20(5): 622-626.

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1.(1.School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan,430074, China;School of Manufacturing Science and Engineering, Sichuan University, Chengdu,610065, China

2.School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan,430074, China;School of Mechanical Engineering and Automation, Guizhou University of Technology, Guiyang,550003, China

3.School of Manufacturing Science and Engineering, Sichuan University, Chengdu,610065, China

4.School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan,430074, China

基金项目: This work was supported by the China Scholarship Council under the grant number(21852035)

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

Keywords： Die casting

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 (SSE) 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.

参考文献

[1]	XiaoguangWU;Ruixiang LIU;Liliang CHEN;Hantong LIN .[J].Foundry,2003,52(04):272.
[2]	SHYue;GXWang;FYin .[J].Journal of Materials Processing Technology,2003,139:465.
[3]	DingfangCHEN;Huaiguo CHEN;Zhichao CHENG .[J].China Mechanical Engineering,1994,5(06):17.
[4]	PHairy;YHemon;JYMarmier.in Proc of the NADCA 16th Die Casting Cong[J].Detroit United States,1991:137.
[5]	CALoong.in Proc of the NADCA 17th Die Casting CongClevel[J].United States,1993:62.
[6]	LiulingDAI;Xianjia MENG.[J].Special Casting & Nonferrous Alloys,2002(01):18.
[7]	WeishanZHANG;Shoumei XIONG;Baicheng LIU .[J].Journal of Materials Processing Technology,1997,63:707.
[8]	CAGWester .[J].Journal of Materials Processing Technology,1995,55:296.
[9]	Jiadi WANG,Yongfeng JIANG,Chen LU,Wenjiang DING.Expert Network for Die Casing Defect Analysis[J].材料科学技术学报(英文版),2003(04):320-323.
[10]	PKDVYarlagadda;ECWChiang .[J].Journal of Materials Processing Technology,1999,89:583.
[11]	DThomas;J Allen;YAloimonos.Artificial Intelligence:Theory[M].北京:电子工业出版社,2003
[12]	ASCh;rashekara;TAnanthapadmanabha;ADAKulkarni .[J].International Journal of Electrical Power & Energy Systems,1999,21:309.
[13]	K. W. Chau;C. Chuntian;C. W. Li .Knowledge management system on flow and water quality modeling[J].Expert Systems with Application,2002(4):321-330.
[14]	CTLin;CSGLee.Neural Fuzzy Systems: a Neuro-Fuzzy Synergism to Intelligent System[M].Prentice-Hall PTR,New Jersey,1996
[15]	PYPapalamos;DJWilde.Principles of Optimal Design:Modeling[M].Computation, Cambridge Univ Press, Cambridge,2000
[16]	HDemuth;MBeale.Neural Network Toolbox for Use with MATLAB: User's guide[M].The MATHWORKS Inc,1993

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

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