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Analyzing the mechanisms of Al2O3-TiO2 coating for enhanced slurry erosion resistance on AISI410 stainless steel

Analyzing the mechanisms of Al2O3-TiO2 coating for enhanced slurry erosion resistance on AISI410... In this research, the optimization of D-gun process parameters for depositing Al2O3-TiO2 coatings on SS410 steel has been explored, with a specific focus on enhancing resistance to slurry erosion. A hybrid intelligent approach based on an artificial neural network (ANN) is employed to quantitatively analyze the influence of jet velocity, impingement angle, and slurry concentration on erosion performance. The findings reveal the significant impact of these parameters, with higher jet velocities and slurry concentrations, coupled with lower impingement angles, leading to increased mass loss, underscoring the need for precise parameter optimization. The ANN model has been developed which is further optimized by the amended slime mold algorithm (ASMA) for accurate predictions for optimal parameter selection to enhance coating durability. Additionally, metallurgical and mechanical characterizations offer quantitative insights into material properties, including porosity percentage, microhardness, surface roughness, and bond strength, all of which play critical roles in erosion resistance. Through SEM imaging, erosion mechanisms such as lip and crater formation, plows, erosive grooves, and crowded pits are quantitatively identified, shedding light on erosive wear patterns. Comparative analysis of coated samples quantitatively underscores the varying levels of erosive damage and resistance, emphasizing the essential role of coating composition and process parameters. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Advanced Manufacturing Technology Springer Journals

Analyzing the mechanisms of Al2O3-TiO2 coating for enhanced slurry erosion resistance on AISI410 stainless steel

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References (39)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0268-3768
eISSN
1433-3015
DOI
10.1007/s00170-024-13077-x
Publisher site
See Article on Publisher Site

Abstract

In this research, the optimization of D-gun process parameters for depositing Al2O3-TiO2 coatings on SS410 steel has been explored, with a specific focus on enhancing resistance to slurry erosion. A hybrid intelligent approach based on an artificial neural network (ANN) is employed to quantitatively analyze the influence of jet velocity, impingement angle, and slurry concentration on erosion performance. The findings reveal the significant impact of these parameters, with higher jet velocities and slurry concentrations, coupled with lower impingement angles, leading to increased mass loss, underscoring the need for precise parameter optimization. The ANN model has been developed which is further optimized by the amended slime mold algorithm (ASMA) for accurate predictions for optimal parameter selection to enhance coating durability. Additionally, metallurgical and mechanical characterizations offer quantitative insights into material properties, including porosity percentage, microhardness, surface roughness, and bond strength, all of which play critical roles in erosion resistance. Through SEM imaging, erosion mechanisms such as lip and crater formation, plows, erosive grooves, and crowded pits are quantitatively identified, shedding light on erosive wear patterns. Comparative analysis of coated samples quantitatively underscores the varying levels of erosive damage and resistance, emphasizing the essential role of coating composition and process parameters.

Journal

The International Journal of Advanced Manufacturing Technology – Springer Journals

Published: Feb 1, 2024

Keywords: Al2O3-TiO2; SS410; ANN; SEM

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