Multiple regression and neural networks analyses in composites machining
作者： J.T. Lin , D. Bhattacharyya , V. Kecman
DOI: 10.1016/S0266-3538(02)00232-4
关键词: Tool wear 、 Nonlinear system 、 Composite material 、 Materials science 、 Artificial neural network 、 Radial basis function 、 Linear regression 、 Machining 、 Cutting tool 、 Regression analysis
摘要: The machining forces-tool wear relationship of an aluminium metal matrix composite has been studied in this paper using multiple regression analysis (MRA) and generalised radial basis function (GRBF) neural network. results show that the force-wear equation derived from MRA is a fairly accurate way predicting attainment prescribed tool wear. However, use network can further improve accuracy prediction particularly when functional dependency nonlinear. It evident feed force data more than cutting force.
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