Study on Nanosecond Laser Ablation of 40Cr13 Die Steel Based on ANOVA and BP Neural Network
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 40Cr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/21/10331 |
| _version_ | 1797512791477714944 |
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| author | Zhenshuo Yin Qiang Liu Pengpeng Sun Jian Wang |
| author_facet | Zhenshuo Yin Qiang Liu Pengpeng Sun Jian Wang |
| author_sort | Zhenshuo Yin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:06:40Z |
| format | Article |
| id | doaj.art-8ad7923b30f94a0fbe3aa69debb9345c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T06:06:40Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8ad7923b30f94a0fbe3aa69debb9345c2023-11-22T20:31:11ZengMDPI AGApplied Sciences2076-34172021-11-0111211033110.3390/app112110331Study on Nanosecond Laser Ablation of 40Cr13 Die Steel Based on ANOVA and BP Neural NetworkZhenshuo Yin0Qiang Liu1Pengpeng Sun2Jian Wang3School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, ChinaSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, ChinaSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, ChinaSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, ChinaMicrostructured 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.https://www.mdpi.com/2076-3417/11/21/10331nanosecond pulse laser40Cr13ANOVABP neural network |
| spellingShingle | Zhenshuo Yin Qiang Liu Pengpeng Sun Jian Wang Study on Nanosecond Laser Ablation of 40Cr13 Die Steel Based on ANOVA and BP Neural Network Applied Sciences nanosecond pulse laser 40Cr13 ANOVA BP neural network |
| title | Study on Nanosecond Laser Ablation of 40Cr13 Die Steel Based on ANOVA and BP Neural Network |
| title_full | Study on Nanosecond Laser Ablation of 40Cr13 Die Steel Based on ANOVA and BP Neural Network |
| title_fullStr | Study on Nanosecond Laser Ablation of 40Cr13 Die Steel Based on ANOVA and BP Neural Network |
| title_full_unstemmed | Study on Nanosecond Laser Ablation of 40Cr13 Die Steel Based on ANOVA and BP Neural Network |
| title_short | Study on Nanosecond Laser Ablation of 40Cr13 Die Steel Based on ANOVA and BP Neural Network |
| title_sort | study on nanosecond laser ablation of 40cr13 die steel based on anova and bp neural network |
| topic | nanosecond pulse laser 40Cr13 ANOVA BP neural network |
| url | https://www.mdpi.com/2076-3417/11/21/10331 |
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