An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations

被引:0
|
作者
PoTsang B. Huang
机构
[1] Chung-Yuan Christian University,Department of Industrial and Systems Engineering
来源
Journal of Intelligent Manufacturing | 2016年 / 27卷
关键词
Intelligent neural-fuzzy model; In-process surface roughness monitoring; End milling operations; Neural networks; Fuzzy logic;
D O I
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中图分类号
学科分类号
摘要
In this research, a new intelligent neural-fuzzy in-process surface roughness monitoring (INF-SRM) system for an end milling operation was developed. The success of the INF-SRM system depends on an accurate decision-making algorithm, which can analyze the input factors and then generate an accurate output. A new neural-fuzzy model was proposed and implemented as decision-making algorithm for the INF-SRM system. The objective of the new model is to achieve higher accuracy for surface roughness prediction and solve the disadvantages of both neural networks and fuzzy logic. The neural-assisted method was implemented to generate the fuzzy IF-THEN rules for the model. To evaluate the performance of the new neural-fuzzy model, a neural networks model was applied to develop another surface roughness monitoring system for comparison. A statistical method was finally employed to analyze the accuracy between these systems.
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页码:689 / 700
页数:11
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