Predicting the grinding force of titanium matrix composites using the genetic algorithm optimizing back-propagation neural network model

被引:41
作者
Zhou, Huan [1 ]
Ding, Wen-Feng [1 ]
Li, Zheng [1 ]
Su, Hong-Hua [1 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Nanjing 210016, Jiangsu, Peoples R China
基金
美国国家科学基金会;
关键词
Grinding forces; neural network; Genetic algorithm optimizing back-propagation neural network; specific grinding energy; SURFACE-ROUGHNESS; WEAR; GRINDABILITY; PERFORMANCE; SUPERALLOY; BEHAVIOR; ENERGY; ALLOY;
D O I
10.1177/0954405418780166
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A back-propagation neural network BP model and a genetic algorithm optimizing back-propagation neural network (GA-BP) model are proposed to predict the grinding forces produced during the creep-feed deep grinding of titanium matrix composites. These models consider quantitative and non-quantitative grinding parameters (e.g. up-grinding mode and down-grinding mode) as inputs. Comparative results show that the GA-BP model has better prediction accuracy (e.g. up to 95%) than the conventional regression model and the BP model. Specific grinding energy was calculated against the grinding parameters and grinding modes based on the grinding forces predicted by the GA-BP model.
引用
收藏
页码:1157 / 1167
页数:11
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