Numerical modeling and simulation of macro- to microscale chip considering size effect for optimum milling characteristics of AA2024T351

Abstract

This research work deals with the numerical modeling and analysis of macro-to microscaled milling of a strain rate-sensitive alloy AA2024T351. The milling computations of a semicircular slot are performed in Abaqus/Explicit by employing the Johnson-Cook thermo-elasto viscoplastic material damage model. The Coulomb friction model is applied at the contact interfaces of work piece and cutting tool. Due to the simultaneous effect of cutting feed and angular speed (ω r), the geometry of uncut chip is modeled with variable section thickness to adapt the trochoidal path. The configuration of the uncut chip undertakes the macro-to micromilling effect. This research effort uniquely explains a comprehensive modeling and milling computations at macro-to microscales to identify the imperative milling characteristics. The presented formulation in form of the modified Johnson-Cook constitutive equation explains the size effect during micromilling. A parametric sensitivity analysis is performed with four different cutting speeds (500, 600, 700, and 800 m/min) and two contact friction coefficients (0.2 and 0.3) while keeping the feed rate constant (0.2 mm/teeth). It was observed that cutting speed and contract friction coefficient play a pivotal role in cutting reaction force and chip-tool interface temperature. A temperature in the range of 141-167 °C is predicted with different cutting speeds. The percentage deviations of the simulated cutting forces from the published experimental results are found promising. The estimated deviation may be because of compromising some imperative factors in numerical model, such as cutter vibrations, tool wear effect, tool run-out, and limitations, to develop an exact trochoidal trajectory.

Analysis and Prediction of the Machining Force Depending on the Parameters of Trochoidal Milling of Hardened Steel

“…Materials harder to process are widely used in the aircraft, automotive, shipbuilding and energy industries for the production of complex and varied components; therefore, materials based on nickel, titanium and steels with improved mechanical properties have been investigated in order to understand the factors degrading the machining process thoroughly. In recent decades, various milling methods have been investigated and analysed to increase productivity [1,[13][14][15], the subject of the investigation also included the following aspects: high-speed machining with respect to the removed material [2,13,14]; simulation methods aiming to implement different control models and the real behaviour of machines to eliminate machine failure and downtime [16,17]; adjusting tool feeds aiming to optimize the production cycle time; process monitoring to evaluate tool wear for titanium or nickel-based alloys [1,18]. The possibilities of the predictive model are control of the machining process in order to reduce vibrations, increase the stability of the cut and efficiency of the cutting process [19]; and trochoidal milling in connection with finish operations in machining superalloys based on nickel [20][21][22].…”

Numerical Modeling and Analysis of Ti6Al4V Alloy Chip for Biomedical Applications

“…In a milling simulation study, it is imperative to consider the effect of tool edge radius, the trochoidal trajectory of the cutting flute, and the tool-spindle run out [ 46 ]. Saleem et al [ 47 ] worked on the macro and micro-scale numerical modeling by analyzing the effects of various cutting parameters (cutting speed, feed rate, tool geometry, etc.) in order to consistently control the tool-chip interaction.…”