An in-process neural network-based surface roughness prediction (INN-SRP) system using a dynamometer in end milling operations

被引：55

作者：

J.C. Chen

B. Huang

机构：

[1] Dept. of Indust. Educ. and Technol., Iowa State University, Ames

来源：

Chen, J.C. (cschen@iastate.edu) | 1600年 / Springer-Verlag London Ltd卷 / 21期

关键词：

Absolute average force; Average resultant peak force; Backpropagation; INN-SRP system; Pearson correlation;

D O I：

10.1007/s001700300039

中图分类号：

学科分类号：

摘要：

Surface roughness is influenced by the machining parameters and other uncontrollable factors resulting from the cutting tool in end milling operations. To perform the in-process surface roughness prediction (ISRP) system accurately, the uncontrollable factors must be monitored. In this paper, an empirical approach using a statistical analysis was employed to discover the proper cutting force to represent the uncontrollable factors in end milling operations. Furthermore, an in-process neural network-based surface roughness prediction (INN-SRP) system was developed. A neural network associated with sensing technology was applied as a decision-making system to predict the surface roughness for a wide range of machining parameters. The good accuracy of the results for a wide range of machining parameters indicates that the system is suitable for application in industry.

引用

页码：339 / 347

页数：8

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