Wear Resistance Prediction of AlCoCrFeNi-X (Ti, Cu) High-Entropy Alloy Coatings Based on Machine Learning

Kang, Jiqiang; Niu, Yi; Zhou, Yong-kuan; Yang, Fan; Guo, Ma

doi:10.3390/met13050939

Cited by 9 publications

(4 citation statements)

References 41 publications

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“…The role of the GBRT states that it combines additional trees by correcting the errors made by the previous base models, which potentially enhances the precision of the predictions [80]. Similarly, the testing result of AlCoCrFeNi-X (Ti, Cu) HEA coating applied by GBRT exhibited the best prediction of wear resistance, evidenced by performance parameters with R 2 value of 0.96, the error values of RMSE is 0.09 and MAE is 0.72 [31].…”

Section: Performance Of Models In the Prediction Of Wear Ratementioning

confidence: 99%

“…In the prediction of wear behavior in AlCoCrFeNiX (X = Ti, Cu) HEA coatings, ML algorithms namely LR, RF, the , GBRT and Classification and Regression Tree (CART) are utilized. The GBRT model and CART model made the best performance in predicting the wear performance of HEA coatings, scoring an R 2 value almost equal to 1, with a small volume of database [31]. XGBoost algorithm is developed for predicting the COF of FeCoCrNiAlN HEA coatings, which displays the best accuracy with the high R 2 value of 0.8 while predicting COF [32].…”

Section: Introductionmentioning

confidence: 99%

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Predictive analytics of wear performance in high entropy alloy coatings through machine learning

Sivaraman,

Radhika

2024

Phys. Scr.

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High-entropy alloys (HEAs) are increasingly renowned for their distinct microstructural compositions and exceptional properties. These HEAs are employed for surface modification as coatings exhibit phenomenal mechanical characteristics including wear and corrosion resistance which are extensively utilized in various industrial applications. However, assessing the wear behaviour of the HEA coatings through conventional methods remains challenging and time-consuming due to the complexity of the HEA structures. In this study, a novel methodology has been proposed for predicting the wear behaviour of HEA coatings using Machine Learning (ML) algorithms such as Support Vector Machine (SVM), Linear Regression (LR), Gaussian Process Regression (GPR), Least Absolute Shrinkage and Selection Operator (LASSO), Bagging Regression (BR), Gradient Boosting Regression Tree (GBRT), and Robust regressions (RR). The analysis integrates of 75 combinations of HEA coatings with processing parameters and wear test results from peer-reviewed journals for model training and validation. Among the ML models utilized, the GBRT model was found to be more effective in predicting wear rate and Coefficient of Friction (COF) with the highest correlation coefficient of R2 value of 0.95 ∼ 0.97 with minimal errors. The optimum model is used to predict the unknown wear properties of HEA coatings from the conducted experiments and validate the results, making ML a crucial resource for engineers in the materials sector.

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Section: Performance Of Models In the Prediction Of Wear Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictive analytics of wear performance in high entropy alloy coatings through machine learning

Sivaraman,

Radhika

2024

Phys. Scr.

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“…Classification provides discrete-valued output. It is helpful to predict a characterization for a particular case; for a naive example, machine learning in tribology can predict if wear is coming from a specific type of coated surface or an uncoated surface, which is a discrete-valued output [25].…”

Section: Machine Learning Types and Processesmentioning

confidence: 99%

Recent Progress of Machine Learning Algorithms for the Oil and Lubricant Industry

2023

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Machine learning (ML) algorithms have brought about a revolution in many industries where otherwise operation time, cost, and safety would have been compromised. Likewise, in lubrication research, ML has been utilized on many occasions. This review provides an in-depth understanding of seven ML algorithms from a tribological perspective. More specifically, it presents a comprehensive overview of recent advancements in ML applied to lubrication research, organized into four distinct categories. The first category, experimental parameter prediction, highlights the significant contributions of artificial neural networks (ANNs) in accurately forecasting operating conditions related to friction and wear. These predictions offer valuable insights that aid in forensic preparation. Discriminant analysis, Bayesian modeling, and transfer learning approaches have also been used to predict experimental parameters. Second, to predict the lubrication film thickness and identify the lubrication regime, algorithms such as logistic regression and ANN were useful. Such predictions provide up to 99.25% accuracy. Third, to predict the friction and wear for a given experimental condition, support vector machine (SVM), polynomial regression, and ANN offered an accuracy above 93%. Finally, for condition monitoring for bearings, gearboxes, gear trains, and similar critical situations where regular in-person inspection is difficult, Naïve Bayes, SVM, decision trees, and ANN were utilized to predict the safe life of lubricants. This review highlighted these four aspects with state-of-the-art examples and discussed the current situation and projected future possibilities of lubricant design facilitated by ML techniques.

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“…Design and development of the non-equiatomic HEAs with excellent strength and toughness has been the focus of many studies in recent years. One approach to solving these issues is the adding or variation of alloying elements (e.g., titanium (Ti), aluminum (Al), silicon (Si), and copper (Cu)) to equiatomic or near-equiatomic HEAs, which can effectively improve combination properties, while maintaining simple solid solution phases [13][14][15][16][17][18][19]. Hu et al [15] used the powder metallurgy technique to study microstructure and corrosion properties of AlxCuFeNiCoCr (x = 0.5, 1.0, 1.5, 2.0) high-entropy alloys, and the results confirmed that the microstructure and corrosion properties of these alloys are closely related to its Al content.…”

Section: Introductionmentioning

confidence: 99%

Microstructure and Properties of Non-Equiatomic Ni10Cr6WFe9TiAlx High-Entropy Alloys Combined with High Strength and Toughness

Yang

He²,

Li³

et al. 2023

Metals

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High-entropy alloys (HEAs) with excellent mechanical properties have broad application scope and application prospects. However, it is difficult to obtain the optimized element composition, based on the traditional equiatomic or near-equiatomic statistical analysis of the phase selection rules. The non-equiatomic HEAs have abundant constituents combination by optimizing the type and content of elements. In this study, Ni10Cr6WFe9TiAlx (x = 0, 1.0 and 1.5, at.%) HEAs were prepared by vacuum arc melting. The effect of Al content x on microstructure and mechanical properties of HEAs was systematically studied. The results show that the HEAs are composed mainly of face-centered cubic (FCC) with hexagonal Al2W phase. The increase of Al content promotes the formation of the hexagonal Al2W phase. When the Al mole content is 1.0, the Ni10Cr6WFe9TiAl HEA material has achieved superior mechanical properties. The alloy exhibited a high ultimate tensile strength of 741 MPa and a large total elongation of 46%. The improvement in the mechanical properties of the Ni10Cr6WFe9TiAl HEA is mainly attributed to the precipitation strengthening of the high-density Al2W phase. This work provides a reference for the future design of Al2W precipitation-strengthened non-equiatomic HEAs with ideal properties.

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Wear Resistance Prediction of AlCoCrFeNi-X (Ti, Cu) High-Entropy Alloy Coatings Based on Machine Learning

Cited by 9 publications

References 41 publications

Predictive analytics of wear performance in high entropy alloy coatings through machine learning

Predictive analytics of wear performance in high entropy alloy coatings through machine learning

Recent Progress of Machine Learning Algorithms for the Oil and Lubricant Industry

Microstructure and Properties of Non-Equiatomic Ni10Cr6WFe9TiAlx High-Entropy Alloys Combined with High Strength and Toughness

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