Machine Learning-Based Prediction of Hardness in Electrodeposited Nickel-Tungsten Alloy Coatings

Sen Zhai; Kenjiro Sugio; Gen Sasaki

doi:10.4028/p-lMBI6Y

Paper Titles

Preface

Stress Corrosion Cracking of Aluminum in Chloride-Containing Solution
p.3

Fourier Neural Operator for Predicting the Growth of Precipitates in a Binary Alloy
p.17

Machine Learning-Based Prediction of Hardness in Electrodeposited Nickel-Tungsten Alloy Coatings
p.25

Effect of Zinc Content and Surface Roughness on the Wettability of Magnesium Alloys
p.31

Influence of Magnetron Sputtering Parameters on Speckle Characteristics for Application in Microscale DIC of Maraging Steel
p.37

Effect of Pre-Strain Condition on the Mechanical Properties of Steel Wire and its Aging Behavior
p.43

Effect of Lamellae Modification in Eutectic Al_82.70Cu_17.30 Alloy on its Electrochemical Performance as Anode Using Symmetric Cell Electrochemical Impedance Spectroscopy
p.51

Relationship between Yield Strength and Cluster Formation of Multi-Component Aluminum Alloys by Monte Carlo Simulation
p.57

HomeMaterials Science ForumMaterials Science Forum Vol. 1154Machine Learning-Based Prediction of Hardness in...

Machine Learning-Based Prediction of Hardness in Electrodeposited Nickel-Tungsten Alloy Coatings

Abstract:

This study aims to predict the hardness of electrodeposited Ni-W alloy coatings by combining machine learning methods based on a small dataset, with the goal of streamlining the trial-and-error process and reducing experimental costs. In this study, 11 features comprised of electrolyte compositions and process parameters (including current density, pH value, bath temperature, and agitation) were utilized as input parameters, with coating hardness serving as the target value. Two machine learning models (KNN and Elastic-Net) were employed to predict coating hardness, and hyperparameters were tuned using Randomized-Search CV (CV=5). The results demonstrate that the KNN model exhibits the highest predictive accuracy, with R²=0.942 and RMSE=0.0658. The SHAP method was used to analyze the importance of features and their impact on hardness. It is found that bath temperature, current density, and ammonium chloride concentration have the most significant influence on coating hardness. This study demonstrates how machine learning can enhance electrodeposition to predict coating hardness, offering insights for improving Ni-W alloy coatings in mechanical applications.

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Periodical:

Materials Science Forum (Volume 1154)

Pages:

25-30

DOI:

https://doi.org/10.4028/p-lMBI6Y

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Online since:

June 2025

Authors:

Sen Zhai, Kenjiro Sugio*, Gen Sasaki

Keywords:

Electrodeposited Ni-W, Hardness, KNN Model, Machine Learning, Prediction

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* - Corresponding Author

References

[1] M.H. Allahyarzadeh, M. Aliofkhazraei, A.R. Rezvanian, V. Torabinejad, and A.S. Rouhaghdam: Surface and Coatings Technology Vol. 307 (2016), p.978–1010.