Multiple regression and committee neural network force prediction models in milling frp

被引:57
|
作者
Sheikh-Ahmad, Jamal
Twomey, Janet
Kalla, Devi
Lodhia, Prashant
机构
[1] Petr Inst, Mech Engn Program, Abu Dhabi, U Arab Emirates
[2] Wichita State Univ, Dept Ind & Mfg Engn, Wichita, KS USA
基金
美国国家科学基金会;
关键词
chip thickness; committee network; fiber orientation; FRP; specific cutting energy;
D O I
10.1080/10910340701554873
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This work utilizes the mechanistic modeling approach for predicting cutting forces and simulating the milling process of fiber-reinforced polymers (FRP) with a straight cutting edge. Specific energy functions were developed by multiple regression analysis (MR) and committee neural network approximation (CN) of milling force data and a cutting model was developed based on these energies and the cutting geometry. It is shown that both MR and CN models are capable of predicting the cutting forces in milling of unidirectional and multidirectional composites. Model predictions were compared with experimental data and were found to be in good agreement over the entire range of fiber orientations from 0 to 180 degrees. Furthermore, CN model predictions were found to greatly outperform MR model predictions.
引用
收藏
页码:391 / 412
页数:22
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