TITLE:
Neural Network Modeling and Prediction of Surface Roughness in Machining Aluminum Alloys
AUTHORS:
N. Fang, N. Fang, P. Srinivasa Pai, N. Edwards
KEYWORDS:
Artificial Neural Network, Modeling, Prediction, Surface Roughness, Machining, Aluminum Alloys
JOURNAL NAME:
Journal of Computer and Communications,
Vol.4 No.5,
May
13,
2016
ABSTRACT: Artificial neural network is a powerful
technique of computational intelligence and has been applied in a variety of
fields such as engineering and computer science. This paper deals with the
neural network modeling and prediction of surface roughness in machining
aluminum alloys using data collected from both force and vibration sensors. Two
neural network models, including a Multi-Layer Perceptron (MLP) model and a
Radial Basis Function (RBF) model, were developed in the present study. Each
model includes eight inputs and five outputs. The eight inputs include the
cutting speed, the ratio of the feed rate to the tool-edge radius, cutting
forces in three directions, and cutting vibrations in three directions. The
five outputs are five surface roughness parameters. Described in detail is how
training and test data were generated from real-world machining experiments
that covered a wide range of cutting conditions. The results show that the MLP
model provides significantly higher accuracy of prediction for surface
roughness than does the RBF model.