The influence of metallurgical factors on corrosion fatigue strength of stainless steels

Ebara, Ryuichiro

doi:10.1016/j.prostr.2016.06.067

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…This allowed comparison at selected test durations which was most suitable as corrosion and other environmental phenomena are time-dependent. 4–5,7,21,29–30 It also negated the differences in strength between the two grain size variants. Such an assessment suggested that a strength debit of 3–7% was observed for both FG and CG material.…”

Section: Resultsmentioning

confidence: 94%

Corrosion-fatigue testing of Ni-based superalloy RR1000

Child

Meldrum²,

Onwuarolu³

2017

Materials Science and Technology

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Operation of gas turbines at high temperatures can lead to corrosion of turbine materials. A programme was designed to understand the corrosion-fatigue behaviour of two grain size variants of RR1000 under cyclic loading at elevated. Tests were conducted on cylindrical specimens under a range of loading conditions. Results indicated that salted specimens tested in air-SO x had shorter fatigue lives than unsalted specimens tested in air, and this deficit was dependent on stress and/or test duration. Examination of specimens showed that the extent of corrosion was dependent on exposure time and stress. Additional salt loading resulted in faster initial pit growth which led to lower fatigue lives at higher stresses. This paper is part of a thematic issue on the 9th International Charles Parsons Turbine and Generator Conference. All papers have been revised and extended before publication in Materials Science and Technology.

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Section: Resultsmentioning

confidence: 94%

Corrosion-fatigue testing of Ni-based superalloy RR1000

Child

Meldrum²,

Onwuarolu³

2017

Materials Science and Technology

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“…The corrosion values obtained at room temperature and 37°C were expected to be similar, due to 316L SS high temperature resistance. Although significant temperature changes have shown to affect corrosion values for 316L SS in a multitude of studies [34, 35, 36], the difference between 37°C and room temperature is not significant to affect 316L SS in short tests. Tests conducted at 0 RPM-300 RPM in both the dynamic and control bench were comparable, as expected.…”

Section: Resultsmentioning

confidence: 99%

Preliminary validation of a dynamic electrochemical biodegradation test bench in pseudo-physiological conditions

Gayle

Mahapatro

Lundin

2017

Materials Technology

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There is a growing interest in the development of next generation stent materials. In vitro tests that accurately predict in vivo conditions, are needed for a full evaluation of a material’s corrosion in vivo. In this manuscript a novel approach for the design of a dynamic electrochemical test bench is evaluated in hopes to later characterize and model biodegradable metallic stent materials. This dynamic test bench design allows for real-time corrosion testing with easy variation of temperature, shear stress, and simulated body fluids (SBF), with minimal complications of test sample fabrication. Preliminary tests have shown Tafel generation stable. Further testing of the stability of the test bench were conducted with the incorporation SBF, shear stress, and temperature. Shear stress was applied through variation in fluid velocities at 0 m/s, 0.127 m/s, 0.245 m/s, 0.372 m/s, 0.489 m/s at 37°C. Incorporation of the different SBFs showed no significant difference in corrosion readings; however, variances were observed higher in DMEM and PBS, than in Hanks, respectively. This dynamic test bench showed to be relatively stable under temperature and SBF modification; however, further optimization is needed to decrease variances seen throughout fluid velocity analysis.

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“…Subsequent researchers proposed other modification models [5], but they are only effective for material strength at lower levels. Additionally, material strength is influenced by factors such as composition [6], microstructure [7], and processing [8]. Establishing a predictive model between fatigue strength and other properties cannot reveal the most essential characteristics, thus having certain limitations.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Fatigue Strength of Steel Based on Interpretable Machine Learning

Liu,

Wang,

Cai

et al. 2023

Materials

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Most failures in steel materials are due to fatigue damage, so it is of great significance to analyze the key features of fatigue strength (FS) in order to improve fatigue performance. This study collected data on the fatigue strength of steel materials and established a predictive model for FS based on machine learning (ML). Three feature-construction strategies were proposed based on the dataset, and compared on four typical ML algorithms. The combination of Strategy Ⅲ (composition, heat-treatment, and atomic features) and the GBT algorithm showed the best performance. Subsequently, input features were selected step by step using methods such as the analysis of variance (ANOVA), embedded method, recursive method, and exhaustive method. The key features affecting FS were found to be TT, mE, APID, and Mo. Based on these key features and Bayesian optimization, an ML model was established, which showed a good performance. Finally, Shapley additive explanations (SHAP) and symbolic regression (SR) are introduced to improve the interpretability of the prediction model. It had been discovered through SHAP analysis that TT and Mo had the most significant impact on FS. Specifically, it was observed that 160 < TT < 500 and Mo > 0.15 was beneficial for increasing the value of FS. SR was used to establish a significant mathematical relationship between these key features and FS.

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The influence of metallurgical factors on corrosion fatigue strength of stainless steels

Cited by 11 publications

References 10 publications

Corrosion-fatigue testing of Ni-based superalloy RR1000

Corrosion-fatigue testing of Ni-based superalloy RR1000

Preliminary validation of a dynamic electrochemical biodegradation test bench in pseudo-physiological conditions

Prediction of the Fatigue Strength of Steel Based on Interpretable Machine Learning

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