基于原位观测及机器学习的7050铝合金腐蚀行为表征

doi:10.13228/j.boyuan.issn1001-0777.20230035

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (0 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The in-situ observation of corrosion behavior of T7451 and as-cast 7050 aluminum alloys in 3.5% NaCl solution was performed. The corrosion morphology feature and precipitated phase composition were analyzed by scanning electron microscopy (SEM), and the relationship between grain orientation and corrosion behavior was analyzed statistically by combining with EBSD. Based on 196 pieces of historical literature data, the importance ranking of corrosion behavior characteristics were conducted by Pearson correlation screening and Backforward algorithm. It was found that the corrosion behavior of 7050 aluminum alloy started around the precipitated phase, and the corrosion depth and range increased with time, which was accompanied with crack initiation. Among them, the precipitated phases were most distributed in the crystal faces of <112>, <114> and <324>. Potentiodynamic scan showed that the self-corrosion current density increased with the precipitated phase density. In addition, the importance ranking of SEM and machine learning showed that Cu, Ti, Fe and Mg all appeared in the precipitated phase, and their influence on the corrosion behavior was most significant. This study could effectively guide the corrosion resistance design of aluminum alloys.

Key words： 7050 aluminum alloy grain orientation corrosion machine learning precipitated phase

Received: 05 May 2023

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	NIU Tong
	ZHANG Na
	XIONG Xilin

Cite this article:

NIU Tong,ZHANG Na,XIONG Xilin. Corrosion behavior characterization of 7050 aluminum alloy based on in-situ observation and machine learning[J]. PHYSICS EXAMINATION AND TESTING, 2024, 42(1): 1-7.

URL:

http://www.chinamet.cn/Jweb_wlcs/EN/10.13228/j.boyuan.issn1001-0777.20230035 OR http://www.chinamet.cn/Jweb_wlcs/EN/Y2024/V42/I1/1

[1]	Shan D, Zhen L. Aging Behavior and Microstructure Evolution in the Processing of Aluminum Alloys[M]. Cambridge: Woodhead Publishing, 2012.
[2]	Burleigh T D. The postulated mechanisms for stress corrosion cracking of aluminum alloys: A review of the literature 1980-1989[J]. Corrosion, 1991, 47(2): 89.
[3]	Landkof M, Gal-Or L. Stress corrosion cracking of Al-Zn-Mg alloy AA-7039[J]. Corrosion, 1980, 36(5): 241.
[4]	Knight S P, Birbilis N, Muddle B C, et al. Correlations between intergranular stress corrosion cracking, grain-boundary microchemistry, and grain-boundary electrochemistry for Al-Zn-Mg-Cu alloys[J]. Corrosion Science, 2010, 52(12): 4073.
[5]	曹鑫宇. 基于机器学习方法的7XXX系铝合金设计及抗应力腐蚀性能与机理研究[D]. 西安:西南交通大学, 2020.
[6]	曲志豪, 胡丽华, 李夏侨, 等. 基于海底管道服役数据和机器学习方法的管道腐蚀风险预测[C]//第十一届全国腐蚀与防护大会论文摘要集. 沈阳:中国腐蚀与防护学会,2021:939.
[7]	张舒研, 高洋洋, 张志彬, 等. 高熵非晶合金耐腐蚀性能研究进展[J]. 材料工程,2021, 49(1):11.
[8]	徐迪,杨小佳,李清,等. 材料大气环境腐蚀试验方法与评价技术进展[J]. 中国腐蚀与防护学报,2022,42(3):11.
[9]	夏志敏,张大发,陈永红. 基于SVM的核动力管道腐蚀状态评估方法研究[J]. 武汉理工大学学报(交通科学与工程版),2010,34(5):4.
[10]	Zamin M. The role of Mn in the corrosion behavior of Al-Mn alloys[J]. Corrosion, 1981, 37(11): 627.