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The purpose of this paper is to investigate the silt erosion performance of Bare, 75%Cr₂O₃ + 25%Al₂O₃ and 85%Cr₂O₃ + 15Al₂O₃-coated SS304 under various control parameters such as rotation speed, concentration of silt and particle size of silt used for making slurry. This can provide insight for using chromia and alumina-based coatings for hydro-turbines.

Taguchi approach was used to identify the effect of three input parameters on the bare and coated alloys. L₁₆ orthogonal array is used for determining the signal-to-noise (S/N) ratio for each process parameter. For each level of parameters taken into consideration about the erosion wear, the arithmetic mean of the S/N ratio is calculated. On the essence of the results of S/N ratios, it is possible to determine the effect of the most dominating parameters of the erosion wear.

Results show that the erosion increases with an increase in silt concentration (Wt.%). It has been analyzed that the rotational speed has the most significant effect followed by the particle size and concentration on erosion wear for all uncoated and coated SS-304 samples. Maximum resistance to erosion is provided by 85%Cr₂O₃ + 15%Al₂O₃. The least erosion wear for process parameters has occurred at the optimal parametric combination of rotational speed (N) = 415 rev/min, concentration (C) = 15 Wt.% and particle size range as <53 µm for uncoated and coated stainless steel.

The study clearly shows the silt erosion performance of chromia and alumina coatings of different compositions at different input parameters.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0028/

The purpose of this paper is to investigate the erosion performance of high velocity oxy fuel- (HVOF) sprayed Al₂O₃–Cr₂O₃ composite coatings under silt slurry conditions.

The requisite HVOF composite coatings has been deposited on the stainless steel substrate (SS-304). The slurry erosion pot tester of make Ducom was used for conducting the silt slurry erosion tests on the required substrates. The comprehensive experiments were conducted at different particle size of silt in the range 212–250, 150–212, 53–106 µm, and the concentration of the silt ranged from 10%–40% by weight. The rotational speed of the pot tester has been varied between 500 and 1,500 revolutions per minute, and the test duration has been kept to 4 h.

The erosion wear resistance of the uncoated SS-304 has been greatly enhanced by the application of HVOF-sprayed Al₂O₃–Cr₂O₃ composite coatings. The addition of CeO₂ has a significant impact in reducing the erosive wear caused by silt slurry. The composite coating powder composition of 65%Cr₂O₃ + 34.5%Al₂O₃ + 0.5%CeO₂ has shown the highest erosion resistance.

The developed coatings have the potential to be used for hydro turbines as subjected to silt slurry conditions.

The erosion wear experiments are conducted comprehensively for coated and uncoated samples and the scanning electron micrographs supports the findings.

Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to experiment with different printing settings. The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates.

The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models such as linear regression, AdaBoost regression, gradient boosting regression (GBR), random forest, decision trees and k-nearest neighbors were trained for predicting tensile strength and flexural strength. Model performance was assessed using root mean square error (RMSE), coefficient of determination (R²) and mean absolute error (MAE).

Traditional experimentation with various settings is both time-consuming and expensive, emphasizing the need for alternative approaches. Among the models tested, GBR model demonstrated the best performance in predicting both tensile and flexural strength and achieved the lowest RMSE, highest R2 and lowest MAE, which are 1.4778 ± 0.4336 MPa, 0.9213 ± 0.0589 and 1.2555 ± 0.3799 MPa, respectively, and 3.0337 ± 0.3725 MPa, 0.9269 ± 0.0293 and 2.3815 ± 0.2915 MPa, respectively. The findings open up opportunities for doctors and surgeons to use GBR as a reliable tool for fabricating patient-specific bone plates, without the need for extensive trial experiments.

The current study is limited to the usage of a few models. Other machine learning-based models can be used for prediction-based study.

This study uses machine learning to predict the mechanical properties of FDM-based distal ulna bone plate, replacing traditional design of experiments methods with machine learning to streamline the production of orthopedic implants. It helps medical professionals, such as physicians and surgeons, make informed decisions when fabricating customized bone plates for their patients while reducing the need for time-consuming experimentation, thereby addressing a common limitation of 3D printing medical implants.

The implications of metallic biomaterials involve stress shielding, bone osteoporosis, release of toxic ions, poor wear and corrosion resistance and patient discomfort due to the need of second operation. This study aims to use additive manufacturing (AM) process for fabrication of biodegradable orthopedic small locking bone plates to overcome complications related to metallic biomaterials.

Fused deposition modeling technique has been used for fabrication of bone plates. The effect of varying printing parameters such as infill density, layer height, wall thickness and print speed has been studied on tensile and flexural properties of bone plates using response surface methodology-based design of experiments.

The maximum tensile and flexural strengths are mainly dependent on printing parameters used during the fabrication of bone plates. Tensile and flexural strengths increase with increase in infill density and wall thickness and decrease with increase in layer height and wall thickness.

The present work is focused on bone plates. In addition, different AM techniques can be used for fabrication of other biomedical implants.

Studies on application of AM techniques on distal ulna small locking bone plates have been hardly reported. This work involves optimization of printing parameters for development of distal ulna-based bone plate with high mechanical strength. Characterization of microscopic fractures has also been performed for understanding the fracture behavior of bone plates.