Neural Network Modeling and Prediction of Surface Roughness in Machining Aluminum Alloys

Abstract

Artificial neural network is a powerful technique of computational intelligence and has been ap-plied 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 us-ing 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 expe-riments that covered a wide range of cutting conditions. The results show that the MLP model pro-vides significantly higher accuracy of prediction for surface roughness than does the RBF model.