Modeling the Mechanical Properties of Heat-Treated Mg-Zn-RE-Zr-Ca-Sr Alloys with the Artificial Neural Network and the Regression Model

Fu, Yu; Shao, Zhiwen; Liu, Chen; Wang, Yinyang; Xu, Yating; Zhu, Xiurong

doi:10.3390/cryst12060754

Cited by 4 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers have increasingly focused on utilizing machine learning methods to predict material performance, leveraging their capacity to extract high-dimensional features from raw data [11][12][13][14][15]. By effectively capturing the nonlinear relationships between material parameters and mechanical properties, machine learning models offer valuable insights into the complex interplay, thus guiding experimental efforts [16].…”

Section: Introductionmentioning

confidence: 99%

Improving Support Vector Regression for Predicting Mechanical Properties in Low-Alloy Steel and Comparative Analysis

Che,

Peng

2024

Mathematics

View full text Add to dashboard Cite

Low-alloy steel is widely employed in the aviation industry for its exceptional mechanical properties. These materials are frequently used in critical structural components such as aircraft landing gear and engine mounts, where a high strength-to-weight ratio is crucial for optimal performance. However, the mechanical properties of low-alloy steel are influenced by various components and their compositions, making identification and prediction challenging. Accurately predicting these mechanical properties can significantly reduce the development time of new alloy steel, lower production costs, and offer valuable insights for design analysis. support vector regression (SVR) is known for its superior learning and generalization capabilities. However, optimizing SVR performance can be challenging due to the significant impact of the penalty factor and kernel parameters. To address this issue, a hybrid method called SMA-SVR is proposed, which combines the Slime Mould Algorithm (SMA) with SVR. This hybrid approach aims to efficiently and accurately predict two crucial mechanical parameters of low-alloy steel: tensile strength and 0.2% proof stress. Detailed descriptions of the modeling processes and principles that are involved in the hybrid method are provided. Furthermore, three other popular hybrid models for comparison are introduced. To evaluate the performance of these models, four statistical measures are utilized: Mean Absolute Error, Root Mean Square Error, R-Squared, and computational time. Using data from the NIMS database and from material tests conducted on a universal testing machine, experiments were carried out to compare the performance of these models. The results indicate that SMA-SVR outperforms the other methods in terms of accuracy and computational efficiency.

show abstract

Section: Introductionmentioning

confidence: 99%

Improving Support Vector Regression for Predicting Mechanical Properties in Low-Alloy Steel and Comparative Analysis

Che,

Peng

2024

Mathematics

View full text Add to dashboard Cite

show abstract

Multiobjective Optimization of the Difficult-to-Machine Material TC18 Based on AVOA-SVR and MOAVOA

Chen,

Mu,

Liao

et al. 2024

JOM

View full text Add to dashboard Cite

Plastic Workability and Rheological Stress Model Based on an Artificial Neural Network of SiCp/Al-7.75Fe-1.04V-1.95Si Composites

Feng,

Chen,

Tang

et al. 2024

Materials

View full text Add to dashboard Cite

SiCp/Al-Fe-V-Si composites exhibit complex deformation behaviors at both room and high temperatures because of the presence of SiC reinforcement particles and numerous fine dispersed Al12(Fe, V)3Si heat-resistant phases. In this work, an artificial neural network (ANN) constitutive model was established to study the deformation behavior of SiCp/Al-7.75Fe-1.04V-1.95Si composites over a wide temperature range based on uniaxial compression. Then, microstructural observation, finite element analysis, and processing maps were utilized to investigate the plastic workability. The results showed that the ANN model fit the experimental stress–strain curves with high accuracy, achieving an R2 value of 0.999. The ANN model was embedded into finite element software to study plastic deformation behaviors, which indicated that this model could accurately compute the plastic and mechanical response during the compressing process. Finally, a thermomechanical processing diagram was developed, revealing that the optimal processing parameters of the SiCp/Al-7.75Fe-1.04V-1.95Si composites were a deformation temperature of 450–500 °C and a deformation rate of 0.1–0.2 s−1.

show abstract

Modeling the Mechanical Properties of Heat-Treated Mg-Zn-RE-Zr-Ca-Sr Alloys with the Artificial Neural Network and the Regression Model

Cited by 4 publications

References 24 publications

Improving Support Vector Regression for Predicting Mechanical Properties in Low-Alloy Steel and Comparative Analysis

Improving Support Vector Regression for Predicting Mechanical Properties in Low-Alloy Steel and Comparative Analysis

Multiobjective Optimization of the Difficult-to-Machine Material TC18 Based on AVOA-SVR and MOAVOA

Plastic Workability and Rheological Stress Model Based on an Artificial Neural Network of SiCp/Al-7.75Fe-1.04V-1.95Si Composites

Contact Info

Product

Resources

About