Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Naveena, B.E.; Santhosh, K.; Keshavamurthy, R.; Shrivathsa, T.V.; Ganesha, B.B.; Sahil; Mahesh, B.R. Title A name given to the resource Performance comparison of tree-based and neural network models for wear prediction in coated and uncoated Al6061 Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Results in Surfaces and Interfaces;Volume;23;Issue;;Article No.;100788; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.rsurfi.2026.100788" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.rsurfi.2026.100788</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033608245?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033608245?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Naveena B.E., Department of Automobile Engineering, Dayananda Sagar College of Engineering, Karnataka, Bangalore, 560078, India; Santhosh K., Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India; Keshavamurthy R., Department of Mechanical and Automobile Engineering, CHRIST University, Karnataka, Bengaluru, 560074, India; Shrivathsa T.V., Department of Artificial Intelligence and Machine Learning, Shri Madhwa Vadiraja Institute of Technology &amp; Management, Bantakal, Karnataka, 574115, India; Ganesha B.B., Department of Mechanical Engineering, Vidyavardhaka College of Engineering, Karnataka, Mysuru, 570002, India; Sahil, Department of Automobile Engineering, Dayananda Sagar College of Engineering, Karnataka, Bangalore, 560078, India; Mahesh B.R., Department of Automobile Engineering, Dayananda Sagar College of Engineering, Karnataka, Bangalore, 560078, India Description An account of the resource This study compares the performance of machine learning (ML) and deep learning (DL) models in predicting the dry sliding wear of uncoated Al6061, plasma-sprayed flyash-Al2O3 and flyash-SiC coatings. Ensemble models, including random forest (RF), XGBoost and LightGBM, along with neural network models such as multilayer perceptron (MLP) regressors, backpropagation neural networks (BPNN) and deep neural networks (DNN), were trained on experimental data that varied load, sliding speed and sliding distance. The dataset was scaled and split into training (80%) and testing (20%) subsets. Model performance was evaluated using the coefficient of determination (R2), mean absolute error (MAE) and root mean square error (RMSE). ML regressors accurately predicted the properties of uncoated alloys, with R2 scores above 0.97, though their performance decreased on coated samples. RF experienced the largest decline in accuracy, particularly flyash-SiC (R2 = 0.736). Gradient boosting models exhibited improved robustness, with LightGBM achieving R2 values of 0.977, 0.936 and 0.794 for uncoated, flyash-Al2O3 and flyash-SiC samples, respectively. Neural networks outperformed tree-based methods for coated systems, with MLP and DNN attaining R2 values up to 0.992, alongside lower MAE and RMSE. SEM analysis corroborated the predictions, showing severe wear in uncoated alloys, minimal surface damage in flyash-Al2O3 coatings and cracking and delamination in flyash-SiC coatings. 2026 The Authors. Subject The topic of the resource Aluminum alloys; Deep learning; Flyash; Machine learning; Neural network; Plasma coating Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 26668459; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online