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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Autores principales:	Zhenshuo Yin, Qiang Liu, Pengpeng Sun, Jian Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	nanosecond pulse laser 40Cr13 ANOVA BP neural network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/8ad7923b30f94a0fbe3aa69debb9345c
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spelling	oai:doaj.org-article:8ad7923b30f94a0fbe3aa69debb9345c2021-11-11T15:21:15ZStudy on Nanosecond Laser Ablation of 40Cr13 Die Steel Based on ANOVA and BP Neural Network10.3390/app1121103312076-3417https://doaj.org/article/8ad7923b30f94a0fbe3aa69debb9345c2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10331https://doaj.org/toc/2076-3417Microstructured 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.Zhenshuo YinQiang LiuPengpeng SunJian WangMDPI AGarticlenanosecond pulse laser40Cr13ANOVABP neural networkTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10331, p 10331 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	nanosecond pulse laser 40Cr13 ANOVA BP neural network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	nanosecond pulse laser 40Cr13 ANOVA BP neural network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Zhenshuo Yin Qiang Liu Pengpeng Sun Jian Wang Study on Nanosecond Laser Ablation of 40Cr13 Die Steel Based on ANOVA and BP Neural Network
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.
format	article
author	Zhenshuo Yin Qiang Liu Pengpeng Sun Jian Wang
author_facet	Zhenshuo Yin Qiang Liu Pengpeng Sun Jian Wang
author_sort	Zhenshuo Yin
title	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_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_sort	study on nanosecond laser ablation of 40cr13 die steel based on anova and bp neural network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8ad7923b30f94a0fbe3aa69debb9345c
work_keys_str_mv	AT zhenshuoyin studyonnanosecondlaserablationof40cr13diesteelbasedonanovaandbpneuralnetwork AT qiangliu studyonnanosecondlaserablationof40cr13diesteelbasedonanovaandbpneuralnetwork AT pengpengsun studyonnanosecondlaserablationof40cr13diesteelbasedonanovaandbpneuralnetwork AT jianwang studyonnanosecondlaserablationof40cr13diesteelbasedonanovaandbpneuralnetwork
_version_	1718435377521360896

Study on Nanosecond Laser Ablation of 40Cr13 Die Steel Based on ANOVA and BP Neural Network

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