Neural Network-based Model Parameter Estimation for End Milling of Carbon Fiber Reinforced Polymer (CFRP) Composites

Abstract

A predictive cutting force model is critical during the end milling of Carbon Fiber Reinforced Polymer (CFRP) composites to achieve optimum fiber delamination, tool wear, and surface accuracy. The mechanistic force model showed robust prediction abilities; however, cutting constant estimation is challenging due to the involvement of multiple variables resulting in higher-order mathematical formulations. This paper presents augmenting the Feed Forward Neural Network (FFNN) with a mechanistic force model to approximate independent relationships of the chip load and edge contact length with shearing and rubbing constants. The prediction abilities of the proposed approach are compared with the traditional Fourier fitting model and experimentally measured values over diverse cutting conditions. It has been demonstrated that the proposed approach can accurately estimate cutting forces during the end milling of CFRPs.