ARTIFICIAL NEURAL NETWORK-BASED SPECIFIC CUTTING ENERGY MODEL FOR THE ROTARY TURNING MOLD STEEL

Tóm tắt

The self-propelled rotary tool turning (SPRT) process is an effective solution
for machining hardened steels. In this investigation, the specific cutting energy
(SCE) model was developed in terms of the inclination angle (I), depth of cut (D),
feed rate (f), and spindle speed (S). A set of experiments was performed for the
SKD 61 material to obtain experimental data. The Bayesian regularized feed-
forward neural network was applied to develop the SCE model. The results
indicated that the model’s precision was acceptable due to the small deviations
between the predictive and actual data. Moreover, the proposed correlation was
primarily affected by the depth of cut, feed rate, spindle speed, and inclination
angle, respectively. Finally, the developed SPRT operation could be utilized for
machining difficult-to-cut materials.