Ht. Fan et Sm. Wu, CASE-STUDIES ON MODELING MANUFACTURING PROCESSES USING ARTIFICIAL NEURAL NETWORKS, Journal of engineering for industry, 117(3), 1995, pp. 412-417
The modeling capability of an artificial neural network is studied thr
ough three different manufacturing processes. The first case study is
a linear separable pattern classification problem in manufacturing pro
cess diagnosis. The performance between the neural network and the pro
bability voting classifier is compared. The second case study uses a d
esign of experiment to study an SMC compression molding process. Model
ing and predicting performances between a regression model and a neura
l network model are compared in linear as well as nonlinear cases. The
third case study investigates correlation models between the operatin
g conditions and product quality defects of an automotive painting pro
cess. Results from a neural network model are compared with those of a
probability voting classifier. An ad hoc modification named focused l
earning paradigm on the back-propagation algorithm is also introduced
to speed up network learning.