Data on mechanical properties of open-cell AlSi10Mg materials and open-cell AlSi10Mg-SiC composites with different pore sizes and strain rates

Kolev, Mihail; Drenchev, Ludmil; Simeonova, Tatiana; Krastev, Rumen; Kavardzhikov, Vasil

doi:10.1016/j.dib.2023.109461

Cited by 3 publications

(10 citation statements)

References 2 publications

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“…The core focus of this study centers around modeling the stress-strain curves of open-cell AlSi10Mg materials and open-cell AlSi10Mg-SiC composites previously reported as open data in the work by Kolev et al [26]. In this work, such data is used for the prediction of stress response in these materials utilizing neural-networks-based models.…”

Section: Alsi10mg Alloys Datamentioning

confidence: 99%

“…According to Kolev et al [26], the AlSi10Mg and AlSi10Mg-SiC materials underwent compression tests using a Zwick-Rockwell HA-250 servohydraulic testing machine. The data encompass the material type (either AlSi10Mg or AlSi10Mg-SiC composite), specific pore sizes, and strain rates applied during compressive test (0.01 and 0.001 s −1 ).…”

Section: Alsi10mg Alloys Datamentioning

confidence: 99%

“…Table 1. Specifications of the material properties and their testing conditions in Kolev et al [26]. Where: 'Avg.'…”

Section: Alsi10mg Alloys Datamentioning

confidence: 99%

“…As described by the authors of such data, the machine's software recorded the load-displacement data, which was subsequently normalized based on the specimen's initial dimensions. Further specifications of such data can be consulted in Kolev et al [26].…”

Section: Alsi10mg Alloys Datamentioning

confidence: 99%

“…These materials, with varying pore sizes and strain rates, are modeled using feedforward neural networks. Utilizing data from Kolev et al [26], the construction and analysis of feedforward neural network architectures for studying compressive stress responses in these materials are conducted. Additionally, the implementation of specific interpretability techniques enhances the scientific interpretation of the models.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of compressive stress in AlSi10Mg alloys using feed-forward neural networks

Rodríguez-Sánchez,

Acevedo-Alvarado

2024

Eng. Res. Express

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This study addresses the challenge of modeling compressive stress in AlSi10Mg foams by introducing a method that employs feedforward artificial neural networks (ANNs) and their interpretability, which helps to simulate and analyze material behavior under various conditions. The main objective is to develop a predictive ANN model that can effectively simulate material responses under several factors, incorporating diverse testing parameters and material specifications related with its synthesis. An optimized ANN model, featuring eleven neurons in its hidden layer, was used and demonstrated high predictive accuracy, achieving $R^{2}$ values exceeding 0.94. Additionally, a SHAP interpretability analysis was conducted to assess the influence of key factors such as strain and material conditions on the stress response. The results highlight the significant role of material synthesis processes, compared to the strain rate, in the stress response. In conclusion, this method presents a comprehensive tool for studying complex stress behaviors in AlSi10Mg-based foams, offering insights that could guide future material development and research.

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Section: Alsi10mg Alloys Datamentioning

confidence: 99%

Section: Alsi10mg Alloys Datamentioning

confidence: 99%

“…Table 1. Specifications of the material properties and their testing conditions in Kolev et al [26]. Where: 'Avg.'…”

Section: Alsi10mg Alloys Datamentioning

confidence: 99%

Section: Alsi10mg Alloys Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modeling of compressive stress in AlSi10Mg alloys using feed-forward neural networks

Rodríguez-Sánchez,

Acevedo-Alvarado

2024

Eng. Res. Express

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Open-Cell AlSn6Cu-SiC Composites: Fabrication, Dry-Sliding Wear Behavior, and Machine Learning Methods for Wear Prediction

Kolev,

Drenchev,

Petkov

et al. 2023

Materials

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Open-cell AMMCs are high-strength and lightweight materials with applications in different types of industries. However, one of the main goals in using these materials is to enhance their tribological behavior, which improves their durability and performance under frictional conditions. This study presents an approach for fabricating and predicting the wear behavior of open-cell AlSn6Cu-SiC composites, which are a type of porous AMMCs with improved tribological properties. The composites were fabricated using liquid-state processing, and their tribological properties are investigated by the pin-on-disk method under different loads (50 N and 100 N) and with dry-sliding friction. The microstructure and phase composition of the composites were investigated by scanning electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray diffraction. The mass wear and coefficient of friction (COF) of the materials were measured as quantitative indicators of their tribological behavior. The results showed that the open-cell AlSn6Cu-SiC composite had an enhanced tribological behavior compared to the open-cell AlSn6Cu material in terms of mass wear (38% decrease at 50 N and 31% decrease at 100 N) while maintaining the COF at the same level. The COF of the composites was predicted by six different machine learning methods based on the experimental data. The performance of these models was evaluated by various metrics (R2, MSE, RMSE, and MAE) on the validation and test sets. Based on the results, the open-cell AlSn6Cu-SiC composite outperformed the open-cell AlSn6Cu material in terms of mass loss under different loads with similar COF values. The ML models that were used can predict the COF accurately and reliably based on features, but they are affected by data quality and quantity, overfitting or underfitting, and load change.

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Fabrication and Dry-Sliding Wear Characterization of Open-Cell AlSn6Cu–Al2O3 Composites with LSTM-Based Coefficient of Friction Prediction

Kolev,

Drenchev,

Petkov

et al. 2024

Metals

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This study investigates the fabrication, wear characterization, and coefficient of friction (COF) prediction of open-cell AlSn6Cu–Al2O3 composites obtained by a liquid-state processing technique. Focusing on wear behavior under varying loads using the pin-on-disk method, this research characterizes microstructure and phase composition via SEM, EDS, and XRD analyses. A novel aspect of this research is the application of an LSTM recurrent neural network model for the fast and accurate prediction of the COF of the composites, eliminating the need for extensive experimental work. Additionally, feature importance analysis using Random Forest regressors is conducted to ascertain the relative contribution of each input variable to the output variable, enhancing our understanding of the wear mechanisms in these materials. The results demonstrate the effectiveness of the composite’s reinforcement in improving wear resistance, highlighting the critical role of mechanical stress and the reinforcement’s hardness in the wear process. The quantitative findings related to the wear behavior include a mass-wear reduction in the open-cell AlSn6Cu–Al2O3 composite from 8.05 mg to 1.90 mg at 50 N and a decrease from 17.55 mg to 8.10 mg at 100 N, demonstrating the Al2O3 particles’ effectiveness in improving wear resistance under different loads.

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Data on mechanical properties of open-cell AlSi10Mg materials and open-cell AlSi10Mg-SiC composites with different pore sizes and strain rates

Cited by 3 publications

References 2 publications

Modeling of compressive stress in AlSi10Mg alloys using feed-forward neural networks

Modeling of compressive stress in AlSi10Mg alloys using feed-forward neural networks

Open-Cell AlSn6Cu-SiC Composites: Fabrication, Dry-Sliding Wear Behavior, and Machine Learning Methods for Wear Prediction

Fabrication and Dry-Sliding Wear Characterization of Open-Cell AlSn6Cu–Al2O3 Composites with LSTM-Based Coefficient of Friction Prediction

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