A rapid classification method of aluminum alloy based on laser-induced breakdown spectroscopy and random forest algorithm

Liuyang ZHAN (詹浏洋); Xiaohong MA (马晓红); Weiqi FANG (方玮骐); Rui WANG (王锐); Zesheng LIU (刘泽生); Yang SONG (宋阳); Huafeng ZHAO (赵华凤)

doi:10.1088/2058-6272/aaf7bf

Plasma Science and Technology > 2019 > 21(3): 34018-034018. > DOI: 10.1088/2058-6272/aaf7bf

Liuyang ZHAN (詹浏洋), Xiaohong MA (马晓红), Weiqi FANG (方玮骐), Rui WANG (王锐), Zesheng LIU (刘泽生), Yang SONG (宋阳), Huafeng ZHAO (赵华凤). A rapid classification method of aluminum alloy based on laser-induced breakdown spectroscopy and random forest algorithm[J]. Plasma Science and Technology, 2019, 21(3): 34018-034018. DOI: 10.1088/2058-6272/aaf7bf

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A rapid classification method of aluminum alloy based on laser-induced breakdown spectroscopy and random forest algorithm

Department of Electronic Engineering, Tsinghua University, Beijing 100084, People’s Republic of China

Funds: This work was supported by National High Technology Research and Development Program of China (863 Program. No. 2013AA102402).

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Received Date: August 13, 2018

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Abstract

Abstract

As an important non-ferrous metal structural material most used in industry and production, aluminum (Al) alloy shows its great value in the national economy and industrial manufacturing. How to classify Al alloy rapidly and accurately is a significant, popular and meaningful task. Classification methods based on laser-induced breakdown spectroscopy (LIBS) have been reported in recent years. Although LIBS is an advanced detection technology, it is necessary to combine it with some algorithm to reach the goal of rapid and accurate classification. As an important machine learning method, the random forest (RF) algorithm plays a great role in pattern recognition and material classification. This paper introduces a rapid classification method of Al alloy based on LIBS and the RF algorithm. The results show that the best accuracy that can be reached using this method to classify Al alloy samples is 98.59%, the average of which is 98.45%. It also reveals through the relationship laws that the accuracy varies with the number of trees in the RF and the size of the training sample set in the RF. According to the laws, researchers can find out the optimized parameters in the RF algorithm in order to achieve, as expected, a good result. These results prove that LIBS with the RF algorithm can exactly classify Al alloy effectively, precisely and rapidly with high accuracy, which obviously has significant practical value.
- laser-induced breakdown spectroscopy (LIBS),
- random forest (RF),
- aluminum (Al) alloy,
- classification

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References (31)

References

[1]	Rusak D A et al 1997 Crit. Rev. Anal. Chem. 27 257
[2]	Pasquini C et al 2007 J. Braz. Chem. Soc. 18 463
[3]	El Haddad J, Canioni L and Bousquet B 2014 Spectrochim. Acta B 101 171
[4]	Galbács G 2015 Anal. Bioanal. Chem. 407 7537
[5]	Rakovsky J et al 2014 Spectrochim. Acta B 101 269
[6]	Wang Z et al 2014 Front. Phys. 9 419
[7]	Wang Z Z et al 2016 Front. Phys. 11 114213
[8]	Singh V K, Kumar V and Sharma J 2015 Laser Med. Sci. 30 1763
[9]	Zhang S et al 2018 Front. Phys. 13 135201
[10]	Zhang Y et al 2012 Front. Phys. 7 714
[11]	Li X F, Zhou W D and Cui Z F 2012 Front. Phys. 7 721
[12]	Zhang Y et al 2016 Front. Phys. 11 115205
[13]	Zhang L et al 2012 Front. Phys. 7 690
[14]	Bi Y F et al 2015 Plasma Sci. Technol. 17 923
[15]	El Rakwe M et al 2017 J. Chemom. 31 e2869
[16]	Cravetchi I V et al 2006 Anal. Bioanal. Chem. 385 287
[17]	Mohamed W T Y 2008 Opt. Laser Technol. 40 30
[18]	Wang Q Q et al 2012 Front. Phys. 7 701
[19]	Wang Z et al 2012 Front. Phys. 7 708
[20]	Dastjerdi M V et al 2018 Iran. J. Sci. Technol. 42 959
[21]	Aberkane S M et al 2017 Anal. Methods 9 3696
[22]	Moros J et al 2013 Talanta 110 108
[23]	Remus J and Dunsin K S 2012 Appl. Opt. 51 B49
[24]	Breiman L 2001 Mach. Learn. 45 5
[25]	Liaw A and Wiener M 2002 R. News 2 18
[26]	Zhang T L et al 2014 J. Anal. At. Spectrom. 29 2323
[27]	Wu S et al 2015 Anal. Methods 7 2425
[28]	Tang H S et al 2015 Anal. Methods 7 9171
[29]	Sheng L W et al 2015 J. Anal. At. Spectrom. 30 453
[30]	Zhang T L et al 2016 Chemom. Intell. Lab. Syst. 157 196
[31]	Yu Q et al 2014 Spectrosc. Spect. Anal. 34 3095 (in Chinese)

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