Bioactivity Improvement of Zirconia Substrate by Hydroxyapatite Coating Using Room Temperature Spray Processing

Zirconia ceramics has a bioinert property with low bioactivity. So, it is necessary to improve its low bioactivity by the surface modification using effective coating methods. In this study, we fabricated the hydroxyapatite-coated zirconia substrate by room temperature spray processing to improve the bioactivity of the zirconia implant and investigated its coating effect on the biological performance of zirconia substrate via an in vitro test in simulated body fluid (SBF) solution. Before the room temperature spray coating was completed, size-controlled hydroxyapatite powder that had an average size of 4.5 μm, was obtained by the calcination and milling of a commercial powder. By controlling the processing parameters, such as spraying distance, and deposition cycles, we fabricated homogeneous and dense hydroxyapatite coatings on zirconia substrate. Surface morphology, coating thickness, and microstructure were dependent on deposition cycles, and were related to surface roughness and bioactivity. Zirconia substrates with wave-patterned and roughened hydroxyapatite coatings demonstrated high bioactivity in their in vitro tests. Via the immersion test in an SBF solution, surface dissolution and new precipitates of hydroxyapatite were observed on coated zirconia substrate, indicating the degree of bioactivity.

Prediction of Thermal Barrier Coatings Microstructural Features Based on Support Vector Machine Optimized by Cuckoo Search Algorithm

Microstructural features have a vital effect on the comprehensive performance of thermal barrier coatings (TBCs) and highly depend on the thermal spray processing parameters. Herein, a novel hybrid machine-learning method was proposed to predict the microstructural features of TBCs using thermal spray processing parameters based on a support vector machine method optimized by the cuckoo search algorithm (CS-SVM). In this work, atmospheric-plasma-sprayed (APS) TBCs samples with multifarious microstructural features were acquired by modifying the spray powder size, spray distance, and spray power during thermal spray processing. The processing parameters were used as the inputs for the CS-SVM model. Then, the porosity, the pore-to-crack ratio, the maximum Feret’s diameter, the aspect ratio, and the circularity were counted and treated as the targets for the CS-SVM model. After optimization and training procedure of the CS-SVM model, the predicted results were compared to the results of experimental data, as a result, the squared correlation coefficient (R2) of CS-SVM model showed that the prediction accuracy reached by over 95%, and the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were less than 0.1, which also verified the reliability of the CS-SVM model. Finally, this study proposed a novel and efficient microstructural feature prediction that could be potentially employed to improve the performance of TBCs in service.

Cold-Sprayed Al6061 Coatings: Online Spray Monitoring and Influence of Process Parameters on Coating Properties

Process optimization and quality control are important issues in cold spraying and coating development. Because the cold spray processing is based on high kinetic energy by high particle velocities, online spray monitoring of particle inflight properties can be used as an assisting process tool. Particle velocities, their positions in the spray jet, and particle size measurements give valuable information about spraying conditions. This, in turn, improves reproducibility and reliability of coating production. This study focuses on cold spraying of Al6061 material and the connections between particle inflight properties and coating characteristics such as structures and mechanical properties. Furthermore, novel 2D velocity scan maps done with the HW CS2 online spray monitoring system are presented as an advantageous powder and spray condition controlling tool. Cold spray processing conditions were similar using different process parameters, confirmed with the online spray monitoring prior to coating production. Higher particle velocities led to higher particle deformation and thus, higher coating quality, denser structures, and improved adhesions. Also, deposition efficiency increased significantly by using higher particle velocities.

Grey modeling for thermal spray processing parameter analysis

PurposeThis paper presents the application of grey modeling for thermal spray processing parameter analysis in less data environment.Design/methodology/approachBased on processing knowledge, key processing parameters of thermal spray process are analyzed and preselected. Then, linear and non-linear grey modeling models are integrated to mine the relationships between different processing parameters.FindingsModel A reveals the linear correlation between the HVOF process parameters and the characterization of particle in-flight with average relative errors of 9.230 percent and 5.483 percent for velocity and temperature.Research limitations/implicationsThe prediction accuracies of coatings properties vary, which means that there exists more complex non-linear relationship between the identified input parameters and coating results, or more unexpected factors (e.g. factors from material side) should be further investigated.Practical implicationsAccording to the modeling case in this paper, method has potential to deal with other diverse modeling problems in different industrial applications where challenge to collecting large quantity of data sets exists.Originality/valueIt is the first time to apply grey modeling for thermal spray processing where complicated relationships among processing parameters exist. The modeling results show reasonable results to experiment and existing processing knowledge.