# Study on Nanosecond Laser Ablation of 40 Cr13 Die Steel Based on ANOVA and BP Neural Network
## 5 Conclusions and Future Work  
This study used ANOVA to analyse the influence of three process parameters on the nanosecond pulse laser ablation of 40 Cr13 and used BP neural networks to predict the ablation results. The main conclusions are summarised as follows:  
1. Laser power had the strongest influence on D, W, and the 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. 2. The scan speed had an effect on D and W to a certain extent; the influence levels were 28.9% and 30.6%, respectively. However, the effect of the scan speed on the MRR was \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Group** \\ **No.** \\ \end{tabular} } & \multicolumn{4}{c}{**Ablation Depth (\(\mu\)m)**} \\ \cline{2-5}  & \multicolumn{4}{c}{Prediction Values} & \multicolumn{1}{c}{Measurement Value} \\ \cline{2-5}  & 3-8-3 & 3-10-3 & 3-12-3 & \\ \hline
46 & 6.110 & 5.390 & 5.172 & 6.590 \\ \hline
47 & 10.361 & 12.843 & 9.912 & 15.415 \\ \hline
48 & 10.223 & 10.085 & 10.710 & 10.545 \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Group** \\ **No.** \\ \end{tabular} } & \multicolumn{4}{c}{**Ablation Width (\(\mu\)m)**} \\ \cline{2-5}  & \multicolumn{4}{c}{Prediction Values} & \multicolumn{1}{c}{Measurement Value} \\ \cline{2-5}  & 3-8-3 & 3-10-3 & 3-12-3 & \\ \hline
46 & 36.437 & 36.530 & 37.548 & 36.341 \\ \hline
47 & 43.052 & 49.423 & 44.389 & 41.633 \\ \hline
48 & 42.592 & 41.995 & 41.581 & 41.798 \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Group** \\ **No.** \\ \end{tabular} } & \multicolumn{4}{c}{**MRR (mm\({}^{3}\)/min)**} \\ \cline{2-5}  & \multicolumn{4}{c}{Prediction Values} & \multicolumn{1}{c}{Measurement Value} \\ \cline{2-5}  & 3-8-3 & 3-10-3 & 3-12-3 & \\ \hline
46 & 1.618 & 1.634 & 1.324 & 1.730 \\ \hline
47 & 3.106 & 3.470 & 3.693 & 3.591 \\ \hline
48 & 3.101 & 3.208 & 3.149 & 3.144 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Prediction results of testing set data. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Indexes**} & \multicolumn{4}{c}{**Network Models**} \\ \cline{2-4}  & 3-8-3 & 3-10-3 & 3-12-3 \\ \hline \(D\) (\(\mu\)m) & 2.937 & 1.660 & 3.283 \\ \hline \(W\) (\(\mu\)m) & 0.940 & 4.500 & 1.742 \\ \hline \(MRR\) (mm\({}^{3}\)/min) & 0.289 & 0.096 & 0.237 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: RMSEs of testing set data. only minor, with an influence level of 2.1%. The main reason is that scan speed and pulse frequency jointly determine the pulse coincidence rate, which is another factor that affects the ablation results. 3. The influence of pulse frequency on D and the MRR was non-negligible; the influence levels were 18.6% and 27.4%, respectively. The pulse frequency had little effect on W; the influence level was 8.5%. 4. Three types of BP neural network models were used to predict the ablation results. The results show that the prediction accuracy for the W was the highest, followed by MRR and D. The average prediction accuracies reached 96.0%, 93.5% and 84.4%, respectively. 5. The network model with the 3-10-3 structure had the highest prediction accuracy for D and the MRR, the minimum RMSEs reached 0.096 and 1.660, the network model with the 3-8-3 structure had the highest prediction accuracy for W with the minimum RMSE of 0.940. Although only 40 Cr13 was tested in this study, the abovementioned methods should be applicable to the laser ablation processing of other die steel materials as well. These techniques can be used to guide the setting of process parameters during laser ablation processing, which will help improve processing accuracy and efficiency. However, the experimental data in this study is based only on the results of single-pass processing. In the future, our group will conduct research on the quality control and prediction of multi-pass processing or even the processing of entire surfaces, which is a more complex task.