Low cycle fatigue life prediction of titanium alloy using genetic algorithm-optimized BP artificial neural network

Wang, Yanju; Zhu, Zhenqi; Sha, Aimin; Hao, Wenfeng

doi:10.1016/j.ijfatigue.2023.107609

Cited by 36 publications

(13 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study uses the outcomes of numerical simulations based on the Seeger fatigue life theory and an enhanced version of the Lemaitre damage evolution model to inform the ML process. The Seeger fatigue life approximation formula serves as a key component in this approach (Wang et al , 2023a): …”

Section: Methodologiesmentioning

confidence: 99%

“…In the given equation, N f represents the cycle count leading to failure, σ′ f denotes the exponent of fatigue strength, ε′ f corresponds to the coefficient of ductility and b and c represent exponents governing fatigue strength and fatigue ductility, respectively. The enhanced formulation for Lemaitre damage evolution is as follows (Wang et al , 2023a; Malcher and Mamiya, 2014; Xiao et al , 2011; Ladani and Razmi, 2009): …”

Section: Methodologiesmentioning

confidence: 99%

“…It is important to note that in this study, the FEM model used for data collection has undergone prior validation in Ref. (Wang et al , 2023a) through direct comparison with experimental data. As a result, in the context of this current work, we have deliberately refrained from revalidating the FEM data through experimentation to streamline the focus and avoid unnecessary complexity.…”

Section: Methodologiesmentioning

confidence: 99%

“…Subsequently, simulation calculations are performed on three SAC structural parts with varying initial conditions using the calibrated parameters. It is important to note that in this study, the FEM model used for data collection has undergone prior validation in Ref (Wang et al, 2023a). through direct comparison with experimental data.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Low-cycle fatigue life assessment of SAC solder alloy through a FEM-data driven machine learning approach

Ruiz-Jacinto,

Gutiérrez-Valverde,

Aslla-Quispe

et al. 2023

SSMT

View full text Add to dashboard Cite

Purpose This paper aims to present the novel stacked machine learning approach (SMLA) to estimate low-cycle fatigue (LCF) life of SAC305 solder across structural parts. Using the finite element simulation (FEM) and continuous damage mechanics (CDM) model, a fatigue life database is built. The stacked machine learning (ML) model's iterative optimization during training enables precise fatigue predictions (2.41% root mean square error [RMSE], R2 = 0.975) for diverse structural components. Outliers are found in regression analysis, indicating potential overestimation for thickness transition specimens with extended lifetimes and underestimation for open-hole specimens. Correlations between fatigue life, stress factors, nominal stress and temperature are unveiled, enriching comprehension of LCF, thus enhancing solder behavior predictions. Design/methodology/approach This paper introduces stacked ML as a novel approach for estimating LCF life of SAC305 solder in various structural parts. It builds a fatigue life database using FEM and CDM model. The stacked ML model iteratively optimizes its structure, yielding accurate fatigue predictions (2.41% RMSE, R2 = 0.975). Outliers are observed: overestimation for thickness transition specimens and underestimation for open-hole ones. Correlations between fatigue life, stress factors, nominal stress and temperature enhance predictions, deepening understanding of solder behavior. Findings The findings of this paper highlight the successful application of the SMLA in accurately estimating the LCF life of SAC305 solder across diverse structural components. The stacked ML model, trained iteratively, demonstrates its effectiveness by producing precise fatigue lifetime predictions with a RMSE of 2.41% and an “R2” value of 0.975. The study also identifies distinct outlier behaviors associated with different structural parts: overestimations for thickness transition specimens with extended fatigue lifetimes and underestimations for open-hole specimens. The research further establishes correlations between fatigue life, stress concentration factors, nominal stress and temperature, enriching the understanding of solder behavior prediction. Originality/value The authors confirm the originality of this paper.

show abstract

Section: Methodologiesmentioning

confidence: 99%

Section: Methodologiesmentioning

confidence: 99%

Section: Methodologiesmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Low-cycle fatigue life assessment of SAC solder alloy through a FEM-data driven machine learning approach

Ruiz-Jacinto,

Gutiérrez-Valverde,

Aslla-Quispe

et al. 2023

SSMT

View full text Add to dashboard Cite

show abstract

“…11 Furthermore, traditional model predictions must also be measured directly under different stress cycles that are expected to be carried out during their service life, which leads to high test costs and long cycle times in the fatigue prediction process. 12 In this context, machine learning serves as a solution, which could overcome empirical and theoretical limitations. The artificial neural network (ANN) in particular offers a powerful tool as a nonlinear computing system composed of interconnected neurons, exhibiting excellent nonlinearity, noise tolerance, and robust learning capabilities.…”

mentioning

confidence: 99%

Fatigue life prediction of composite bolted joints based on finite element model and machine learning

Ma,

Tian,

Sun

et al. 2024

Fatigue Fract Eng Mat Struct

View full text Add to dashboard Cite

This study proposes a fatigue life prediction method for composite bolted joints, which combines algorithm optimization‐based hybrid neural networks with finite element modeling. First, based on the Hashin failure criterion of physical mechanism, a finite element model for fatigue life prediction of composite bolted joints is established, and the simulation calculations have been conducted using various initial conditions. Then, by integrating the simulation and experiment data, we have established a fatigue life database that serves machine learning training and prediction. Finally, the data undergo a comprehensive process of deep feature extraction through the utilization of a convolutional neural network (CNN). The resulting deep features are utilized as inputs for training the backpropagation neural network (BPNN) to predict fatigue life. The results indicate this synergistic combination of CNN and BPNN results in a substantial improvement in prediction accuracy and has remarkable superiority in predicting the fatigue life of composite bolted joints.

show abstract

Prediction flow behavior of Mg-12Gd-3Y-0.6Zr alloy during hot deformation based on Arrhenius and BP-ANN models: a comparative study

Li,

2024

Appl. Phys. A

View full text Add to dashboard Cite

Low cycle fatigue life prediction of titanium alloy using genetic algorithm-optimized BP artificial neural network

Cited by 36 publications

References 29 publications

Low-cycle fatigue life assessment of SAC solder alloy through a FEM-data driven machine learning approach

Low-cycle fatigue life assessment of SAC solder alloy through a FEM-data driven machine learning approach

Fatigue life prediction of composite bolted joints based on finite element model and machine learning

Prediction flow behavior of Mg-12Gd-3Y-0.6Zr alloy during hot deformation based on Arrhenius and BP-ANN models: a comparative study

Contact Info

Product

Resources

About