Fiber reinforced polymers are widely used in the transportation, aerospace and chemical industries. In rare instances these materials are produced net-shape, and secondary processing such as machining and assembly may be required to produce a finished product. Because fiber reinforced polymers are heterogeneous materials, they do not machine in a similar way to metals. Thus, the theory of metal machining is not valid for the analysis of machining of fiber- reinforced composites. Previous attempts in modeling this problem have adopted Merchant's theory from metal cutting by assuming that chip formation takes place in a shear plane which inclination angle is determined by the minimum energy principle. This class of models showed that model predictions are valid only for fiber orientations less than 60°. The work presented here focuses on providing predictive models for the cutting forces in unidirectional composites. The models are based on the specific cutting energy principle and account for a wide range of fiber orientations and chip thickness. Results from two forms of non-linear modeling methods, non-linear regression and committee neural networks, were compared. It was found that committee neural networks provide better prediction capability by smoothing and capturing the inherent non-linearity in the data. The model predictions were found to be in good agreement with experimental results over the entire range of fiber orientations from 0 to 180°.
Conference Committee Involvement (1)
Intelligent Systems in Design and Manufacturing VI