An Improved Mathematical Model of Cutting Temperature in End Milling Al7050 Based on the Influence of Tool Geometry Parameters and Milling Parameters

Abstract

Excessively high temperature during milling will shorten the life of the milling tools, reduce the surface quality of the workpiece, and increase production costs. In this paper, a novel cutting temperature prediction model for milling Al7050 is proposed, which considers both tool geometry parameters and milling parameters. The aim is to adjust the milling conditions in advance according to the predicted temperature, thereby prolonging the tool life and improving the machining quality. Through single-factor experiments, it is found that there is a polynomial relationship between tool geometric parameters and cutting temperature. Based on the empirical formula of cutting temperature, an improved mathematical model of cutting temperature is proposed. In this model, eight variables including tool geometry parameters and milling parameters are considered. The coefficients of the mathematical model are determined by the cyclic fitting method. The simulation data and experimental data show that compared with the temperature prediction model considering only milling parameters, this model has better prediction accuracy. This idea can also be used to establish the cutting temperature prediction model of other materials.

Numerical Modelling, Simulation, and Analysis of the End-Milling Process Using DEFORM-3D with Experimental Validation

“…ey are the most flexible milling tools and are commonly used in facing, profiling, slotting, shouldering, slabbing, and plunging operations [19,20]. e diameter, corner radius, length of the flute, and length of the end-mill must always complement the measurements of the machined pocket.…”

Multi-input fuzzy inference system based model to predict the cutting temperature when milling AISI 1060 steel

“…From this comparison it is clear that the chosen technique succeeded in predicting the cutting temperature with a very low prediction error compared to the published works. Works that should be considered competitive with the literature are the RSM method of (Tamilarasan et al, 2016), the RSM method coupled with gray relational analysis of (Tamilarasan & Marimuthu, 2014) and Polynomial Regression by Ji et al 2021, and the ANN-GA method of (Kumar et al, 2018).…”