Abstract
Microstructured steel 40Cr13, which is considered a hard-to-machine steel due to its high mechanical strength and hardness, has wide applications in the dies industry. This study investigates the influence of three process parameters of a 355 nm nanosecond pulse laser on the ablation results of 40Cr13, based on analysis of variance (ANOVA) and back propagation (BP) neural network. The ANOVA results show that laser power has the greatest influence on the ablation depth, width, and material removal rate (MRR), with influence levels of 52.5%, 60.9%, and 70.4%, respectively. The scan speed affects the ablation depth and width to a certain extent, and the influence of the pulse frequency on the ablation depth and MRR is non-negligible. BP neural network models with 3-8-3, 3-10-3, and 3-12-3 structures were applied to predict the ablation results. The results show that the prediction accuracy is relatively high for the ablation width and MRR, with average prediction accuracies of 96.0% and 93.5%. The 3-8-3 network model has the highest prediction accuracy for the ablation width, and the 3-10-3 network model has the highest prediction accuracy for the ablation depth and MRR.
Highlights
This study used analysis of variance (ANOVA) to analyse the influence of three process parameters on the nanosecond pulse laser ablation of 40Cr13 and used back propagation (BP) neural networks to predict the ablation results
Laser power had the strongest influence on D, W, and the material removal rate (MRR); the influence levels were 52.5%, 60.9%, and 70.4%, respectively
The main reason is that laser power mainly determines the single-pulse energy, which is the decisive factor for the ablation results
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ANN has been successfully applied in the industrial field to realize the prediction and improvement of device accuracy [11,12]. Another important application of it is to assist the selection of process parameters in laser processing, which cannot be achieved by traditional technical methods [13]. In this study, we used BP neural networks to predict laser ablation results of 40Cr13 die steel. This study presents the first application of BP neural networks for the laser ablation results prediction of hard-to-process 40Cr13 steel.
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