In this paper, a method for robust design of a neural network (NN) model for prediction of delamination (Da), damage width (Dw), and hole surface roughness (Ra) during drilling in carbon fiber reinforced epoxy (BMS 8-256) is presented. This method is based on a parametric analysis of neural network models using a design of experiments approach. The effects of number of neurons (N), hidden layers (L), activation function (AF), and learning algorithm (LA) on the mean square error (MSE) of model prediction are quantified. Using the aforementioned method, a robust NN model was developed that predicted process-induced damage with high accuracy.
Bibliographical noteFunding Information:
This work was supported by The University of Missouri Research Board, The Boeing Company, and The National Science Foundation. These contributions are greatly appreciated. Our appreciations are extended to Mr. Barton Moenster, Director Advanced Manufacturing R&D, and Mr. John Griffith, Technical Fellow Advanced Manufacturing R&D, and Dr. Hamid Razi, Technical Fellow Structural Methods and Allowable of the Boeing Company for their valuable discussions of the results.
Copyright 2008 Elsevier B.V., All rights reserved.
- Composite material
- Drilling process
- Neural network model
- Process-induced damage