Prediction Fatigue Life of Aluminum Alloy 7075 T73 Using Neural Networks and Neuro-Fuzzy Models

Abdullatef, Mustafa S.; AlRazzaq, Nazhat .; Hasan, Mustafa Moayad

doi:10.30684/etj.2016.112624

Cited by 5 publications

(5 citation statements)

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“…MATLAB2021b was used to develop, train, and test the models [19]. Several models' performances were also assessed using a variety of statistical metrics, and the rootmean-square error (RMSE) was mostly used to improve the neurons in the hidden layer [20]:…”

Section: Experimental Techniquementioning

confidence: 99%

“…We can enhance the quality of the data correction by using the inflection point method developed by Palmgren and Stromeyer with two parameters. The Corson equation, which has three parameters (A, E, and d), and the Weibull equation, which has four parameters, are both significantly inaccurate and inconsistent [2]. Since achieving the fatigue S-N curve has been extremely challenging; therefore it is an imperative objective for the designer to obtain the curve fully and consistently.…”

Section: Introductionmentioning

confidence: 99%

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Fatigue Life Estimation of High Strength 2090-T83 Aluminum Alloy under Pure Torsion Loading Using Various Machine Learning Techniques

Abdullatef¹,

Alzubaidi²,

Al-Tamimi³

et al. 2023

Fluid Dynamics &Amp; Materials Processing

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The ongoing effort to create methods for detecting and quantifying fatigue damage is motivated by the high levels of uncertainty in present fatigue-life prediction approaches and the frequently catastrophic nature of fatigue failure. The fatigue life of high strength aluminum alloy 2090-T83 is predicted in this study using a variety of artificial intelligence and machine learning techniques for constant amplitude and negative stress ratios (R ¼ À1). Artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support-vector machines (SVM), a random forest model (RF), and an extreme-gradient tree-boosting model (XGB) are trained using numerical and experimental input data obtained from fatigue tests based on a relatively low number of stress measurements. In particular, the coefficients of the traditional force law formula are found using relevant numerical methods. It is shown that, in comparison to traditional approaches, the neural network and neuro-fuzzy models produce better results, with the neural network models trained using the boosting iterations technique providing the best performances. Building strong models from weak models, XGB helps to predict fatigue life by reducing model partiality and variation in supervised learning. Fuzzy neural models can be used to predict the fatigue life of alloys more accurately than neural networks and traditional methods.

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Section: Experimental Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fatigue Life Estimation of High Strength 2090-T83 Aluminum Alloy under Pure Torsion Loading Using Various Machine Learning Techniques

Abdullatef¹,

Alzubaidi²,

Al-Tamimi³

et al. 2023

Fluid Dynamics &Amp; Materials Processing

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show abstract

“…After several investigations 26,27 were conducted on the life estimation of composite materials, Wan et al 28 dealt with the use of an SVM both to regress a linear S-N curve and to build an SVM model to predict the endurable number of cycles for a given stress amplitude. Abdullatef et al 29 compared several ANN architectures and different activation functions for predicting the endurable stress of an aluminum alloy at a given cycle number. The use of the inverse of the often used description of cycles as a function of stress can be beneficial in accurately predicting the relationship of those two quantities.…”

Section: Literature On Machine Learning In Connection To Fatiguementioning

confidence: 99%

New approaches for a reliable fatigue life prediction of powder metallurgy components using machine learning

Leininger

Reissner

Baumgartner

2022

Fatigue Fract Eng Mat Struct

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Up to now, no consistent fatigue assessment approach of powder metallurgy (PM) components is available. For some materials and for some parameters, such as the density, a relationship to the fatigue strength is known; however, for other materials, such relationships are unknown. Based on an extensive data set with 828 test series, the present work addresses this problem by conceiving and applying five machine learning (ML)-based approaches to increase the accuracy of the prediction of the fatigue life as well as to predict the scatter of unknown data as precisely as possible. With the elaborated procedure, on the one hand, a scatter range of T S ¼ 1 : 1:40 can be achieved on completely unknown data. On the other hand, by using a newly defined loss function, the standard deviation of unknown data can be predicted very accurately. The findings provide the basis for further research on cost and efficiency optimized design of PM components through better estimation of fatigue life.

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“…Data science methods were employed to estimate the fatigue characteristics of aluminum alloys. In a study, Abdullatef et al [13] assessed the accuracy of different machine learning (ML) and artificial intelligence (AI) approaches in predicting the fatigue lifetime of an aluminum alloy based on bending fatigue data. They compared artificial neural networks, support vector machines (SVM) with different kernels, extreme gradient boosting (XGBoost), random forest (RF), and additive neuro-fuzzy inference.…”

Section: Introductionmentioning

confidence: 99%

Interpretation of fatigue lifetime prediction by machine learning modeling in piston aluminum alloys under different manufacturing and loading conditions

Azadi,

Matin

2024

Frattura Ed Integrità Strutturale

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Various input variables, including corrosion time, fretting force, stress, lubrication, heat-treating, and nano-particles, were evaluated by modeling of stress-controlled fatigue lifetimes in AlSi12CuNiMg aluminum alloy of the engine pistons with different machine learning (ML) techniques. Bending fatigue experiments were conducted through cyclic loading with zero mean stress, and then experimental data was predicted by five different ML-based models. Moreover, when the optimal ML prediction model was found, it was analyzed using the Shapley additive explanation (SHAP) values method. Results illustrated that extreme gradient boosting (XGBoost) had superior data for estimations, with average training coefficients of determination of at least 63% and 90%, respectively for fatigue lifetime and its logarithmic value. The SHAP values interpretation of the XGBoost model revealed that fretting force, stress, and corrosion time were the most significant inputs in estimating the logarithm values of fatigue lifetimes, respectively.

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Prediction Fatigue Life of Aluminum Alloy 7075 T73 Using Neural Networks and Neuro-Fuzzy Models

Cited by 5 publications

References 0 publications

Fatigue Life Estimation of High Strength 2090-T83 Aluminum Alloy under Pure Torsion Loading Using Various Machine Learning Techniques

Fatigue Life Estimation of High Strength 2090-T83 Aluminum Alloy under Pure Torsion Loading Using Various Machine Learning Techniques

New approaches for a reliable fatigue life prediction of powder metallurgy components using machine learning

Interpretation of fatigue lifetime prediction by machine learning modeling in piston aluminum alloys under different manufacturing and loading conditions

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