Study of Cutting Forces in Drilling of Aluminum Alloy 2024-T351

Abstract

Duralumin 2024-T351 is an alloy characterized by a good mechanical strength, relatively high hardness and corrosion resistance frequently used in the aeronautical, automotive, defense etc. industries. In this paper, the variation of axial forces and torques when drilling aluminum alloy 2024-T351 was investigated, analyzing the measured values for different cutting regimes. Experimental data on the forces and moments generated during the drilling process were collected using specialized equipment, and these data were preprocessed and analyzed using MatLab R218a. The experimental plan included 27 combinations of the parameters of the cutting regime (cutting depth, cutting speed, and feed), for which energetic cutting parameters were measured, the axial force and the torsion moment, respectively Based on these data, a neural network was trained, using the Bayesian regularization algorithm, in order to predict the optimal values of the cutting energy parameters. The neural model proved to be efficient, providing predictions with a relative error below 10%, indicating a good agreement between measured and simulated values. In conclusion, neural networks offer an accurate alternative to classical analytical models, being more suitable for materials with complex behavior, such as aluminum alloys.