Optimization of Coating Thickness Parameters and Prediction of Optimal Range in the Cold Spray Process

This research investigates the optimization of electro-conductive coatings using the cold spray process, employing a Taguchi L18 experimental design. The study focuses on the influence of various process parameters on coating thickness, including substrate material, powder feeding arrangement, stagnation gas temperature, stagnation gas pressure, and standoff distance. Three levels of each parameter were tested: substrate materials (Aluminum alloy, Brass, and Ni alloy), powder feeding types (Gravity and Argon feeders), stagnation pressures (98, 104, and 108 psi), stagnation temperatures (360°C, 380°C, and 400°C), and standoff distances (2, 4.5, and 7 mm). The results indicate that the optimal coating thickness is achieved with an Argon powder feeder, Aluminum alloy substrate, stagnation pressure of 110 psi, stagnation temperature of 400°C, and a standoff distance of 4.5 mm. The typical coating thickness for the low-pressure cold spray process was found to be 1.63 mm, within the predicted confidence interval. Statistical analysis, including Analysis of Variance (ANOVA) and Signal-to-Noise (S/N) ratio, was performed to identify the significant factors influencing coating thickness. The percentage contribution of each process parameter was determined, revealing that the most influential factors, in decreasing order of importance, are standoff distance, stagnation gas temperature, substrate material, stagnation gas pressure, and powder feeding arrangement. These findings provide valuable insights into optimizing the cold spray process for high-quality electro-conductive coatings.

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Publication Details

Published
2024-12-21
DOI
https://doi.org/10.46254/ba07.20240103
Primary Topic
High-Temperature Coating Behaviors
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article
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article

Optimization of Coating Thickness Parameters and Prediction of Optimal Range in the Cold Spray Process

Md. Rahad Islam Bhuiyan, Rotan Kumar Saha, Mahbub Hasan, M Kazi et al.
High-Temperature Coating Behaviors
article

Optimization of Coating Thickness Parameters and Prediction of Optimal Range in the Cold Spray Process

Md. Rahad Islam Bhuiyan, Rotan Kumar Saha, Mahbub Hasan, M Kazi, Md. Nazmul Hasan, Safayet Hossain, Tuhin Hosen
article en

Abstract

This research investigates the optimization of electro-conductive coatings using the cold spray process, employing a Taguchi L18 experimental design. The study focuses on the influence of various process parameters on coating thickness, including substrate material, powder feeding arrangement, stagnation gas temperature, stagnation gas pressure, and standoff distance. Three levels of each parameter were tested: substrate materials (Aluminum alloy, Brass, and Ni alloy), powder feeding types (Gravity and Argon feeders), stagnation pressures (98, 104, and 108 psi), stagnation temperatures (360°C, 380°C, and 400°C), and standoff distances (2, 4.5, and 7 mm). The results indicate that the optimal coating thickness is achieved with an Argon powder feeder, Aluminum alloy substrate, stagnation pressure of 110 psi, stagnation temperature of 400°C, and a standoff distance of 4.5 mm. The typical coating thickness for the low-pressure cold spray process was found to be 1.63 mm, within the predicted confidence interval. Statistical analysis, including Analysis of Variance (ANOVA) and Signal-to-Noise (S/N) ratio, was performed to identify the significant factors influencing coating thickness. The percentage contribution of each process parameter was determined, revealing that the most influential factors, in decreasing order of importance, are standoff distance, stagnation gas temperature, substrate material, stagnation gas pressure, and powder feeding arrangement. These findings provide valuable insights into optimizing the cold spray process for high-quality electro-conductive coatings.

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High-Temperature Coating Behaviors
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Optimization of Coating Thickness Parameters and Prediction of Optimal Range in the Cold Spray Process — Md. Rahad Islam Bhuiyan, Rotan Kumar Saha, et al. · (2024) | TGRS Research Map | TGRS