Surface Feature Prediction for Laser Ablated 40Cr13 Stainless Steel Based on Extreme Learning Machine.

Determining an optimal combination of laser process parameters can significantly improve the efficiency and quality of 40Cr13 steel surface processing. In this study, two machine learning models (ELMSS and ELMPS) were proposed to predict the processing results of surface features to optimize process parameters. The prediction accuracies of the proposed models were always higher than those of traditional back propagation (BP) and radial basis function (RBF) neural networks, and the calculation time of the proposed models was significantly reduced. In comparison, the prediction accuracy ranking for ablation depth was ELMSS (92.6%), BP (89.8%), and RBF (89.6%), and for the ablation width, it was ELMSS (98.3%), BP (97.4%), and RBF (96.1%). The material removal rate was 92.4%, 91.1%, and 89.1% for ELMSS, BP, and RBF, respectively. Finally, the prediction accuracy ranking for surface roughness was 86.8%, 80.7%, and 79.5% for ELMPS, BP, and RBF, respectively. After optimization by the genetic algorithm, the prediction accuracies of the proposed models for the depth, width, material removal rate, and surface roughness reached 94.0%, 99.0%, 93.2%, and 91.2%, respectively. With the support of ELMSS and ELMPS, the results of the surface features can be predicted before machining and the appropriate process parameters can be selected in advance.

Depth Model and 5-Axis Variable-Angle Laser Engraving Experiment Based on the Energy Conservation Principle.

To ensure the consistency of laser engraving depth in chemical milling, the precise control of 5-axis variable-angle laser engraving was the focus of research. Based on the energy conservation principle, the depth model of 5-axis variable-angle laser engraving is established, and the relationships among the laser engraving depth, laser power, scanning velocity, and beam axis angle are proposed. A depth-constraint real-time adaptive control method of laser power is proposed considering the variable scanning velocity and beam axis angles. The depth model parameters are identified by an orthogonal experiment, and a variable-angle laser engraving experiment with adaptive control of laser power is carried out. The coefficient of determination of the proposed depth model is 0.977, which means that the engraving depth model established in this paper predicts the engraving depth effectively and reliably. The depth-constraint adaptive control method of laser power obtains stable and uniform machining results under abrupt changes in scanning velocity and beam axis angles.