Prediction of mechanical properties of aluminium metal matrix hybrid composites synthesized using Stir casting process by Machine learning

Rajput, Rutuj; Raut, Abhishek; Setti, Srinivasu Gangi

doi:10.1016/j.matpr.2022.04.316

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…Equation ( 2) displays the RMSE, or root mean square error, which is the average root squared difference between the actual and predicted values, while Equation (3) displays the coefficient of determination (R 2 or R-squared), which can be interpreted as the proportion of the variance in the dependent variable that can be predicted from the independent variables (accuracy). Based on all model performance evaluations, the model with the highest R 2 and the smallest error was chosen as the best model [16,29,51]. To determine which model offers the most accurate and reliable predictions, an evaluation of model performance was conducted.…”

Section: Methodsmentioning

confidence: 99%

“…The independent parameters can be obtained from several factors that affect the output parameter. The output parameter, YS (Yield Strength), is a continuous variable; hence, a regression model is suitable to predict continuous output [29][30][31]. Previous research using machine learning, as shown in Table 1, provides an overview of the current state of research on magnesium matrix composites, their production techniques, and recent advancements in the use of machine learning to predict mechanical properties.…”

Section: Machine Learning Regression Algorithm Modelmentioning

confidence: 99%

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Predicting Mechanical Properties of Magnesium Matrix Composites with Regression Models by Machine Learning

Huang,

Adityawardhana,

Sanjaya

2023

J. Compos. Sci.

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Magnesium matrix composites have attracted significant attention due to their lightweight nature and impressive mechanical properties. However, the fabrication process for these alloy composites is often time-consuming, expensive, and labor-intensive. To overcome these challenges, this study introduces a novel use of machine learning (ML) techniques to predict the mechanical properties of magnesium matrix composites, providing an innovative and cost-effective alternative to conventional methods. Various regression models, including decision tree regression, random forest regression, extra tree regression, and XGBoost regression, were employed to forecast the yield strength of magnesium alloy composites reinforced with diverse materials. This approach leverages existing research data on matrix type, reinforcement type, heat treatment, and mechanical working. The XGBoost Regression model outperformed the others, exhibiting an R2 value of 0.94 and the lowest error rate. Feature importance analysis from the best model indicated that the reinforcement particle form had the most significant influence on the mechanical properties. Our research also identified the optimized parameters for achieving the highest yield strength at 186.99 MPa. This study successfully demonstrated the effectiveness of ML as a valuable, novel tool for optimizing the production parameters of magnesium matrix composites.

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Section: Methodsmentioning

confidence: 99%

Section: Machine Learning Regression Algorithm Modelmentioning

confidence: 99%

Predicting Mechanical Properties of Magnesium Matrix Composites with Regression Models by Machine Learning

Huang,

Adityawardhana,

Sanjaya

2023

J. Compos. Sci.

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“…[Guo et al 2021] utilizaram 10 propriedades de mistura do material, juntamente de 4 propriedades físicas de compósitos cimentícios reforc ¸ados com fibra de alto desempenho. [Rajput et al 2022] utilizaram 8 parâmetros dos materiais e 3 parâmetros do processo de fabricac ¸ão de compósitos de matriz de metal híbrido à base de alumínio.…”

Section: Introduc ¸ãOunclassified

Machine Learning Algorithms to Estimate Composite Mechanical Properties

Gomide,

Vignoli,

Macedo

2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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Structures made of composite materials have been implemented in several sectors such as transport, civil construction, maritime and aerospace. The focus of this study is unidirectional composites that have 5 independent properties. Obtaining these properties can be done experimentally, numerically and analytically. In this article we propose an alternative way to estimate these properties, using machine learning algorithms. The goal of this paper is to evaluate machine learning algorithms to generate the estimation of these 5 properties of composites. Experiments were carried out with two distinct data sets and the results obtained were satisfactory.

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Microstructure and electrical contact behavior of Al2O3–Cu/30W3SiC(0.5Y2O3) composites

Zheng¹,

Zhou²,

Zhang³

et al. 2023

Journal of Materials Research and Technology

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Prediction of mechanical properties of aluminium metal matrix hybrid composites synthesized using Stir casting process by Machine learning

Cited by 8 publications

References 19 publications

Predicting Mechanical Properties of Magnesium Matrix Composites with Regression Models by Machine Learning

Predicting Mechanical Properties of Magnesium Matrix Composites with Regression Models by Machine Learning

Machine Learning Algorithms to Estimate Composite Mechanical Properties

Microstructure and electrical contact behavior of Al2O3–Cu/30W3SiC(0.5Y2O3) composites

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