Predictive Modeling and Experimental Optimization of the Electrodeposition–Sintering Process for Functional Ceramic Coatings

This study focuses on optimizing a sol-gel based electrodeposition-sintering process for producing yttria-stabilized zirconia (YSZ) ceramic coatings on stainless steel substrates. Four key process variables-precursor concentration, current density, sintering time, and temperature-were evaluated in terms of two response variables: R (electrodeposition yield) and S (sintering yield). A fractional factorial design was used to reduce the number of experiments while enabling robust statistical modeling. Multiple linear regression analysis revealed that precursor concentration and current density were the most influential factors for both R and S, whereas sintering time and temperature had a lesser effect. Under central conditions (42.9 g·L<sup>-1</sup>, 1.5 A·cm<sup>2</sup>, 500 °C, 20 min), coatings exhibited yields of ~3.9 mg·cm<sup>2</sup> and superior morphological uniformity. Higher current density (3 A·cm<sup>2</sup>) increased R to 6.9 mg·cm<sup>2</sup> but induced porosity and cracking. Compared to conventional sol-gel derived coatings, the proposed methodology enables a more controlled microstructure with a trade-off between mass deposition and structural integrity. This predictive, statistically validated approach facilitates the optimization of electrodeposition parameters to obtain defect-minimized ceramic coatings, particularly suited for protective and thermal barrier applications in demanding environments.

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Publication Details

Journal
Materials
Published
2025-08-20
DOI
https://doi.org/10.3390/ma18163893
Primary Topic
Electrophoretic Deposition in Materials Science
Type
article
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article

Predictive Modeling and Experimental Optimization of the Electrodeposition–Sintering Process for Functional Ceramic Coatings

Antonio Díaz-Parralejo, Jesús M. Rodríguez-Rego, Antonio Macı́as-Garcı́a, Laura Mendoza-Cerezo et al.
Materials
Electrophoretic Deposition in Materials Science
article

Predictive Modeling and Experimental Optimization of the Electrodeposition–Sintering Process for Functional Ceramic Coatings

Antonio Díaz-Parralejo, Jesús M. Rodríguez-Rego, Antonio Macı́as-Garcı́a, Laura Mendoza-Cerezo, Juan Pablo Carrasco-Amador
article en

Abstract

This study focuses on optimizing a sol-gel based electrodeposition-sintering process for producing yttria-stabilized zirconia (YSZ) ceramic coatings on stainless steel substrates. Four key process variables-precursor concentration, current density, sintering time, and temperature-were evaluated in terms of two response variables: R (electrodeposition yield) and S (sintering yield). A fractional factorial design was used to reduce the number of experiments while enabling robust statistical modeling. Multiple linear regression analysis revealed that precursor concentration and current density were the most influential factors for both R and S, whereas sintering time and temperature had a lesser effect. Under central conditions (42.9 g·L<sup>-1</sup>, 1.5 A·cm<sup>2</sup>, 500 °C, 20 min), coatings exhibited yields of ~3.9 mg·cm<sup>2</sup> and superior morphological uniformity. Higher current density (3 A·cm<sup>2</sup>) increased R to 6.9 mg·cm<sup>2</sup> but induced porosity and cracking. Compared to conventional sol-gel derived coatings, the proposed methodology enables a more controlled microstructure with a trade-off between mass deposition and structural integrity. This predictive, statistically validated approach facilitates the optimization of electrodeposition parameters to obtain defect-minimized ceramic coatings, particularly suited for protective and thermal barrier applications in demanding environments.

MaterialsVol. 18(16)
Universidad de Extremadura (ES)
European Commission, Junta de Extremadura, European Regional Development Fund
Openalex Percentile: Top 80%
Electrophoretic Deposition in Materials Science
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Predictive Modeling and Experimental Optimization of the Electrodeposition–Sintering Process for Functional Ceramic Coatings — Antonio Díaz-Parralejo, Jesús M. Rodríguez-Rego, et al. · Materials (2025) | TGRS Research Map | TGRS