Coarse Grain Layer on Stress Corrosion Cracking Resistance of Al–Zn–Mg Alloy

Ye, Lingying; Yao, Xuebin; Lin, Huaqiang; Liu, Shengdan; Deng, Yunlai; Zhang, Xinming

doi:10.1007/978-981-13-0104-9_35

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…The mechanical properties of the profiles were significantly deteriorated by surface recrystallization [21]- [23]. However, Ye et al indicated that the softening effect no longer exists when the thickness of the surface recrystallization layer is less than 200 µm [24]. In addition, Zhang et al indicated that the recrystallized region of aluminum alloys exhibits low corrosion susceptivity than other region [25].…”

Section: Introductionmentioning

confidence: 99%

Image Deep Learning Assisted Prediction of Mechanical and Corrosion Behavior for Al-Zn-Mg Alloys

Sun

et al. 2022

IEEE Access

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The use of metallographic images to predict the mechanical properties of materials and their corrosion behavior is helpful in achieving nondestructive detection and quality control. However, after a long-term attempt, the traditional methods cannot accurately correlate the mechanical properties and corrosion behavior of materials with the corresponding microstructure images. In this study, we propose a deep learning strategy to predict the mechanical property and corrosion behavior of large-scale extruded aluminum profiles using surface optical microstructure images. The proposed models with remarkable properties were established through experimental dataset collection, dataset preprocessing, deep learning network modification, and key parameter screening. Taking extruded Al-Zn-Mg alloys with different surface microstructures as example materials, 4,800 sets of ''metallographic image -hardness (HV) -corrosion potential (E corr )'' data were experimentally collected to establish the HV and E corr models with prediction accuracies of 90% and 82%, respectively. The proposed HV and E corr models exhibit great generalization ability with mean average errors of 1.8 HV and 7.0 mV on experimental validation sets, respectively. The proposed model can accurately correlate the metallographic images, mechanical property, and corrosion behavior, which can provide theoretical support for intelligent and nondestructive testing methods to further prevent unexpected material failure.

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Section: Introductionmentioning

confidence: 99%

Image Deep Learning Assisted Prediction of Mechanical and Corrosion Behavior for Al-Zn-Mg Alloys

Sun

et al. 2022

IEEE Access

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Enhanced Strength and Toughness in Al-Mg-Si Alloys with Addition of Cr, Mn, and Cu Elements

Lin

Dang

Wang

et al. 2022

J. of Materi Eng and Perform

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Exfoliation behavior of EN AW 7020 with T6, step aging and ultrasonic impact peening processes

Yurdgülü,

Sadeler,

Koç

2024

Materials Testing

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In this study the effect of T6 heat treatment, two-step aging treatment and ultrasonic impact peening on the exfoliation corrosion behavior of welded EN AW 7020 was investigated. Exfoliation tests were performed according to the ASTM G34-01 standard. As expected, as a result of the tests, the base material region of all three samples were showed better corrosion resistance than the heat-affected region. Ultrasonic impact peening and two-step aging treatment was observed that improved EXCO sensitivity in the weld area. When sorting was made by considering the resistance to corrosion of the heat affected zone of the samples, it was observed that the best resistance was shown by the sample with two-step aging treatment, and the worst resistance was shown by the sample with T6 heat treatment.

show abstract

Coarse Grain Layer on Stress Corrosion Cracking Resistance of Al–Zn–Mg Alloy

Cited by 3 publications

References 4 publications

Image Deep Learning Assisted Prediction of Mechanical and Corrosion Behavior for Al-Zn-Mg Alloys

Image Deep Learning Assisted Prediction of Mechanical and Corrosion Behavior for Al-Zn-Mg Alloys

Enhanced Strength and Toughness in Al-Mg-Si Alloys with Addition of Cr, Mn, and Cu Elements

Exfoliation behavior of EN AW 7020 with T6, step aging and ultrasonic impact peening processes

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