Applying an artificial neural network to the control system for electrochemical gear-tooth profile modifications

被引:0
作者
Yi Jianjun [1 ,2 ,3 ]
Guan Yifeng [1 ]
Ji Baiyang [2 ]
Yu Bin [1 ]
Dong Jinxiang [3 ]
机构
[1] E China Univ Sci & Technol, Dept Mech Engn, Shanghai 200237, Peoples R China
[2] HangZhou Sunyard Syst Engn Co Ltd, Hangzhou 310053, Zhejiang, Peoples R China
[3] Zhejiang Univ, Coll Comp Sci, Hangzhou 310027, Peoples R China
关键词
back-propagation technique; real-time control; electrochemical gear-tooth modifications; control system;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Gears, crucial components in modern precision machinery for power transmission mechanisms, are required to have low contacting noise with high torque transmission, which makes the use of gear-tooth profile modifications and gear-tooth surface crowning extremely efficient and valuable. Due to the shortcomings of current techniques, such as manual rectification, mechanical modification, and numerically controlled rectification. we propose a novel electrochemical gear-tooth profile modification method based on an artificial neural network control technique. The fundamentals of electrochemical tooth-profile modifications based on real-time control and a mathematical model of the process are discussed in detail. Due to the complex and uncertain relationships among the machining parameters of electrochemical tooth-profile modification processes, we used an artificial neural network to determine the required processing electric current as the tooth-profile modification requirements were supplied The system was implemented and a practical example was used to demonstrate that this technology is feasible and has potential applications in the production of precision machinery.
引用
收藏
页码:27 / 32
页数:6
相关论文
共 22 条
[1]   ADAPTIVE CONTROLLERS FOR INTELLIGENT MONITORING [J].
BELLAZZI, R ;
SIVIERO, C ;
STEFANELLI, M ;
DENICOLAO, G .
ARTIFICIAL INTELLIGENCE IN MEDICINE, 1995, 7 (06) :515-540
[2]  
Chang-Kyu Song, 2005, International Journal of Precision Engineering and Manufacturing, V6, P31
[3]   ADAPTIVELY CONTROLLING NONLINEAR CONTINUOUS-TIME SYSTEMS USING MULTILAYER NEURAL NETWORKS [J].
CHEN, FC ;
LIU, CC .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1994, 39 (06) :1306-1310
[4]  
DONAT S, 1991, INT J CONTROL, V54, P1453
[5]  
DRAEGER A, 1995, IEEE CONTROL SYSTEMS, V3, P61
[6]  
HORNG RH, 2006, INT J PRECIS ENG MAN, V7, P18
[7]  
KIM JY, 2004, INT J PRECISION ENG, V5, P11
[8]  
Ro Seung-Kook, 2005, International Journal of Precision Engineering and Manufacturing, V6, P19
[9]   MODEL-PREDICTIVE CONTROL OF AN INDUSTRIAL PACKED-BED REACTOR USING NEURAL NETWORKS [J].
TEMENG, KO ;
SCHNELLE, PD ;
MCAVOY, TJ .
JOURNAL OF PROCESS CONTROL, 1995, 5 (01) :19-27
[10]   Solving the control problem for electrochemical geartooth-profile modification using an artificial neural network [J].
Yi, J ;
Zheng, J ;
Yang, T ;
Xia, D ;
Hu, D .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2002, 19 (01) :8-13