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.
Highlights
As a powerful technique of computational intelligence, artificial neural network (ANN) has been applied in a variety of fields such as engineering and computer science [1]-[3]
The present study deals with the neural network modeling and prediction of surface roughness in machining aluminum alloys using data collected from both force and vibration sensors
Based on a back-propagation algorithm, Multi-layer Perceptron (MLP) neural networks are among the most popular networks and have been widely applied in engineering research involving the modeling of metal machining [4] [6] [13] [14]
Summary
As a powerful technique of computational intelligence, artificial neural network (ANN) has been applied in a variety of fields such as engineering and computer science [1]-[3]. The present study deals with the neural network modeling and prediction of surface roughness in machining aluminum alloys using data collected from both force and vibration sensors. The overall goal of the present study is to determine which particular ANN technique—Multi-layer Perceptron (MLP) or Ra-dial Basis Function (RBF)—offers higher accuracy in predicting surface roughness in machining aluminum alloys. Both MLP and RBF are common neural net-work techniques with wide application in a variety of engineering and computer science fields. A substantial amount of experimental data by many other researchers [9]-[11] demonstrates that tool-edge radius significantly affects cutting forces, cutting vibrations, machined surface roughness, and other machining performance measures.
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