On modelling Nd:Yag nanosecond laser milling process by neural network and multi response prediction methods

In this paper, the milling effects on AISI 316 L steel by nanosecond pulsed laser was studied under different parameters as laser pulse peak power, laser repetition rate, scanning speed and focal point. Based on a full factorial design, 108 tests were conducted. MD varied from 15.58 to 60.14 µm; MRR from 0.0028 to 0.0195 mm3/s; Ra from 0.13 to 1.49 µm. The study showed that the laser power and scanning speed have a great influence on material removal. When the laser spot was focused on the upper sample surface, the removing material process was improved. Moreover, Response Surface Methodology and Artificial Neural Network were used to analyze and predict the milling depth, the material removal rate, and the surface roughness. The predictive capabilities of the methods were compared. The predicted R-square for RMS model were 99.70%, 77.79% and 67.73%, while for FF-BPNN were 99.99%, 99.85% and 81.98% for MD, MRR and Ra respectively. Therefore, both models demonstrated strong ability to match the responses. Neural network model exhibited superior predictive power.