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Go to Editorial ManagerThe aim of this work is to optimize EPD variables (voltage, time, and focus) using alternating current through the Taguchi Design of Experiment (DOE). Coating Nano hydroxyapatite (Nano-HA) on a Ti6Al4V substrate depends on thickness and roughness, then characterization of a coating layer to determine the optimum state. Hydroxyapatite (HAp) powder was deposited on a Ti-6Al-4V alloy substrate by electro-deposition with ethanol as a solvent under AC current, to improve the alloy surface quality based on coating thickness and maximum coating mass meeting the requirements of a biological orthopedics application. Ethanol was used as a solvent to precipitate ketazone and HAp on the base alloy. Taguchi's approach was used in order to determine the optimal conditions for EPD and subsequently to apply various criteria for depositing the biochemical coating. The surface and cross-section composition of the paint is described by characterization. Numerous tests and inspections; Zeta, XRD and SEM stability test, water contact angle and optical microscopes were used to describe the surface morphology of the HAp layer. The value of the optimum conditions for deposition of the HAp layer which is a simultaneous thickness and maximum coating mass, was predicted at a sedimentation voltage of 40 V, 2 min sedimentation time and 1 g / L for the concentration of the suspended solution at room temperature. The validity of the model resulting from the response surface methodology was assessed by comparing the expected results with the experimental results. In addition, close agreement was observed between the experimental results and the expected results. For the solution at room temperature, the results obtained with the highest value of the coating thickness of 41at the surface roughness of 0.94 and the contact angle of the alloy before coating is 67.489º reduced to. 38.132º after plating, which indicates an increase in the harmony of the metal implant and biocompatibility.
The aim of this work is to determine the optimum parameters for deposition of chitosan and mixture of chitosan and hydroxyapatite (HA) layers using electrophoretic deposition. The layers were on 316L stainless steel substrate. Taguchi approach was utilized to select the optimum parameters for both layers. The parameters used for deposition chitosan are voltage, time and temperature while the parameters used for HA and chitosan are voltage, time, concentration and temperature. Zeta potential tests were employed to measure the solutions stability. Coating layers were characterized for thickness, porosity and nanoroughness using optical microscopy (OM) and atomic force microscopy (AFM). The results from Taguchi design of experiments demonstrated that the best conditions for deposition of chitosan and HA layers are 50 V, 5 min, 3 g HA/L and 30°C. The corresponding thickness, % porosity, nanoroughness and microroughness for optimum conditions were 22 µm, 3.53, 4.48 nm and 3.85 µm respectively.
This study develops a decision-oriented uncertainty quantification methodology for analyzing as-built surface roughness in laser powder bed fusion/direct metal laser sintering (LPBF/DMLS) SS316L. A Taguchi L9 design was used to vary laser power (300-360 W), scan speed (800-1000 mm·s-1), and layer thickness (20-80 μm), producing nine process settings with three independently fabricated specimens per setting, resulting in 27 total specimens. Surface roughness was measured by contact stylus profilometry using arithmetic mean roughness (Ra), root mean square roughness (Rq), and maximum profile height (Rz). The mean roughness varied within narrow ranges, Ra as 5.748-5.952 μm, Rq as 6.673-6.811 μm, and Rz as 28.828-28.892 μm, while within-setting scatter remained non-negligible, particularly for Rz. Probabilistic regression models were evaluated using leave-one-setting-out validation, negative log predictive density, interval coverage, calibration diagnostics, and reliability-driven accept/reject analysis. For Ra and Rq, a low-capacity linear mean model with pooled variance achieved the best predictive density, indicating limited transportable heteroscedastic structure under setting-wise extrapolation. For Rz, a nonlinear mean model with pooled variance performed best. Unregularized two-stage variance learning produced unstable uncertainty estimates, whereas shrinkage regularization improved calibration and reduced spurious setting-dependent variance effects. The decision analysis showed that calibration strongly influences process acceptance, reliability thresholds sharply reduced the number of accepted settings, and shrinkage-stabilized uncertainty produced a conservative and consistent decision frontier. The main contribution of this work is the integration of grouped validation, probabilistic calibration, variance-shrinkage modelling, and reliability-aware decision analysis for surface roughness qualification in LPBF/DMLS SS316L.