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Published June 30, 2001 | public
Journal Article

Optimization of small-particle plasma-sprayed alumina coatings using designed experiments

Abstract

Statistically designed experiments and multiple regression analysis have been used to determine the effects of processing parameters on three properties of small particle plasma sprayed alumina coatings – permeability, hardness, and thickness. First, an initial screening study was conducted with six variables at two levels each. Based on the results from this preliminary set, a second set of experiments was then conducted to probe the effects of four variables at three levels each. Multiple regression analysis was used to create models describing the effects of the variables on the three coating properties. The models were then used to identify the optimum conditions for producing coatings with the desired properties. It was found that the optimum conditions for low permeability did not correlate with those for high hardness due to defect structures that formed in the hardest coatings. The morphology of the defect structures was such that they did not affect indentation measurements, but did affect permeability. These defect structures formed as a result of copious splashing during splat formation and correlated to coatings that had high surface roughness.

Additional Information

Copyright © 2001 Elsevier. Received 10 July 2000; received in revised form 17 November 2000. The authors thank the following people for their contributions to this work: Rick Marzec of the Advanced Coating Technology Group at Northwestern University for his assistance in spraying the samples, Keith Legg of Rowan Catalyst, Inc. for guidance on the project, Mike Wieczorek of the Advanced Coating Technology Group at Northwestern University for conducting particle size measurements of the powder feedstock, and Ramille Capito, Benjamin Goldsmith, Min Yan, and Shawn Doherty of the Materials Science and Engineering Department at Northwestern University, for working out the three-level design. This work was supported by AFOSR contract #F49620-96-1-0298.

Additional details

Created:
August 21, 2023
Modified:
October 17, 2023