A study on microstructural, mechanical properties and optimization of wear behavior of friction stir processed AlCrCoFeNi High Entropy Alloy reinforced SS410 using response surface methodology

Ragunath, S.; Radhika, N.; Krishna, S. Aravind; Rajeshkumar, L.

doi:10.1016/j.heliyon.2024.e24429

Cited by 18 publications

(1 citation statement)

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“…The advantages of the singlephase solid solution of multicomponent HEAs and its extraordinary structure, HEAs are considered to safeguard turbine blades, engine parts, cutting tools, and various other components. Wear and friction are the usual occurrences of component breakdowns, and the augmenting wear resistance can enhance the lifespan of the parts [3][4][5]. The surface properties of metal components are significantly affected by wear and friction which cause gradual removal or deformation of material, leading to material loss and increasing maintenance costs [6][7][8].…”

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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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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