Analytical model of workpiece temperature in end milling in-situ TiB2/7050Al metal matrix composites. (December 2018) - Xiong, Yifeng; Wang, Wenhu; Jiang, Ruisong; Lin, Kunyang
Highlights: An attempt was made to establish a temperature model for predicting cutting temperature of a new kind of metal matrix composites (MMCs), whose analytical temperature models do not exist at present. The temperature model of workpiece, which was machined by the bottom cutting edge of a flat end mill in end milling process, was proposed. With validation, the temperature model proposed in this study was able to predict the temperature of workpiece surface machined by the bottom cutting edge during end milling process. The heat partition ratio Bs increased linearly as thermal number increased, which seems to be different from other researches. This analytical model will be of great significance for both the temperature modeling work of end milling process and analytical modeling study of machining particle reinforced aluminum metal matrix composites. Abstract: At present, analytical models to predict cutting temperature of metal matrix composites do not exist. The objective of this study is to establish an analytical temperature model of workpiece machined by the bottom cutting edge during end milling in-situ TiB2 /7050Al metal matrix composites. In this model, the moving heat source method was applied with taking into consideration the tool flank wear effect, complex tool geometry of bottom cutting edge, dynamic heat flux and heat partition. Meanwhile, to meet the actual cutting facts, the effect of heating time was also considered. A dynamic cutting force model and the shear angle relationship were built for heat flux calculation and temperature modeling. With theoretical calculation and experimental data, the heat partition ratio, which stands for the heat conducted into workpiece, was found to increase linearly as thermal number increased. With validation, the proposed cutting temperature model was proved to be of good reliability and accuracy with the relative errors between prediction model and experiments being smaller than 18%. This analytical model will be of great significance for both the temperature modeling work of end milling process and analytical temperature modeling study of machining particle reinforced aluminum metal matrix composites. Graphical abstract: Image, graphical abstract