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Hydrogen Assisted Cracking in Pearlitic Steel Rods: The Role of Residual Stresses Generated by Fatigue Precracking.

ABSTRACT: Stress corrosion cracking (SCC) of metals is an issue of major concern in engineering since this phenomenon causes many catastrophic failures of structural components in aggressive environments. SCC is even more harmful under cathodic conditions promoting the phenomenon known as hydrogen assisted cracking (HAC), hydrogen assisted fracture (HAF) or hydrogen embrittlement (HE). A common way to assess the susceptibility of a given material to HAC, HAF or HE is to subject a cracked rod to a constant extension rate tension (CERT) test until it fractures in this harsh environment. This paper analyzes the influence of a residual stress field generated by fatigue precracking on the sample's posterior susceptibility to HAC. To achieve this goal, numerical simulations were carried out of hydrogen diffusion assisted by the stress field. Firstly, a mechanical simulation of the fatigue precracking was developed for revealing the residual stress field after diverse cyclic loading scenarios and posterior stress field evolution during CERT loading. Afterwards, a simulation of hydrogen diffusion assisted by stress was carried out considering the residual stresses after fatigue and the superposed rising stresses caused by CERT loading. Results reveal the key role of the residual stress field after fatigue precracking in the HAC phenomena in cracked steel rods as well as the beneficial effect of compressive residual stress.

SUBMITTER: Toribio J

PROVIDER: S-EPMC5459094 | biostudies-other | 2017 May

REPOSITORIES: biostudies-other

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Project description:BackgroundOptimal sports performance requires a balance between intensive training and adequate rest. IMUs provide objective, quantifiable data to analyze performance dynamics, despite the challenges in quantifying athlete training loads. The ability of AI to analyze complex datasets brings innovation to the monitoring and optimization of athlete training cycles. Traditional techniques rely on subjective assessments to prevent overtraining, which can lead to injury and underperformance. IMUs provide objective, quantitative data on athletes' physical status during action. AI and machine learning can turn these data into useful insights, enabling data-driven athlete performance management. With IMU-generated multivariate time series data, this paper uses AI to construct a robust model for predicting fatigue and stamina.Materials and methodsIMUs linked to 19 athletes recorded triaxial acceleration, angular velocity, and magnetic orientation throughout repeated sessions. Standardized training included steady-pace runs and fatigue-inducing techniques. The raw time series data were used to train a supervised ML model based on frequency and time-domain characteristics. The performances of Random Forest, Gradient Boosting Machines, and LSTM networks were compared. A feedback loop adjusted the model in real time based on prediction error and bias estimation.ResultsThe AI model demonstrated high predictive accuracy for fatigue, showing significant correlations between predicted fatigue levels and observed declines in performance. Stamina predictions enabled individualized training adjustments that were in sync with athletes' physiological thresholds. Bias correction mechanisms proved effective in minimizing systematic prediction errors. Moreover, real-time adaptations of the model led to enhanced training periodization strategies, reducing the risk of overtraining and improving overall athletic performance.ConclusionsIn sports performance analytics, the AI-assisted model using IMU multivariate time series data is effective. Training can be tailored and constantly altered because the model accurately predicts fatigue and stamina. AI models can effectively forecast the beginning of weariness before any physical symptoms appear. This allows for timely interventions to prevent overtraining and potential accidents. The model shows an exceptional ability to customize training programs according to the physiological reactions of each athlete and enhance the overall training effectiveness. In addition, the study demonstrated the model's efficacy in real-time monitoring performance, improving the decision-making abilities of both coaches and athletes. The approach enables ongoing and thorough data analysis, supporting strategic planning for training and competition, resulting in optimized performance outcomes. These findings highlight the revolutionary capability of AI in sports science, offering a future where data-driven methods greatly enhance athlete training and performance management.

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Hydrogen Assisted Cracking in Pearlitic Steel Rods: The Role of Residual Stresses Generated by Fatigue Precracking.

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