MULTIPLE REGRESSION AND COMMITTEE NEURAL NETWORK FORCE PREDICTION MODELS IN MILLING FRP

Abstract

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°. Furthermore, CN model predictions were found to greatly outperform MR model predictions.

Keywords:

• Chip thickness
• Committee network
• Fiber orientation
• FRP
• Specific cutting energy

ACKNOWLEDGMENT

The authors would like to acknowledge the support of the National Science Foundation for the funding of this research (DMI-973347).