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Selection of optimum process parameters is vital for performing a sound grinding operation on Inconel 751 alloy. This paper co-relates the relationship between the most influential input parameters like cutting velocity, depth of cut, feed rate, and environmental conditions to the output parameters, namely, tangential grinding forces, normal grinding forces, temperature, and surface roughness. Three types of machine-learning (ML) algorithms such as support vector machine (SVM), Gaussian process regression (GPR), and boosted tree ensemble techniques are employed to develop a ML model for predicting the output variables during grinding operation of Inconel 751. In order to develop a better ML model, K-fold technique is employed on a total of 81 datasets which are extracted from experimental studies. ML models developed from different algorithms are compared based on performance metrics like R2 score and root-mean-square error (RMSE). GPR algorithm exhibits best results with relatively better R2 score and RMSE value in predicting grinding forces and temperature at wheel work interface. From analyzing the ML models, it is found that cooling environments determined the output grinding parameters to a greater extent when compared with the input grinding parameters. [ABSTRACT FROM AUTHOR]

Magnesium (Mg) alloys are gaining more attention in recent times as biodegradable materials. However, two major problems with Mg alloy implants are bacterial infections and poor corrosion resistance. In this context, a composite surface (Mg–Zn–Dy–Al2O3/HA) is developed using surface modification techniques. First, Al2O3 + HA composite powder is coated on Mg–Zn–Dy alloy to attain coated surface (C-AHa). Next, the C-AHa surface is subjected to friction stir processing to develop composite surface (F-AHa). Microstructural characterization reveals that, the Al2O3 + HA particles were distributed evenly into the Mg–Zn–Dy substrate. Antimicrobial activities against Escherichia coli and Staphylococcus aureus reveal low adhesion of bacteria on the F-AHa sample surface due to low surface energy (37.83 ± 0.22 mN/m) and low surface roughness (0.36 ± 0.1 µm). Further, the cytotoxicity tests confirm that the F-AHa sample shows significant improvement in cell viability (98%) after 7 days and non-toxic against the mouse osteoblast cells. In Vitro corrosion study observations demonstrate that the corrosion rate for the F-AHa sample is decreased by 72% compared to the C-AHa sample. Thus, the results of this study for the fabricated composites are promising for antimicrobial, biocompatible and bioabsorbable temporary implants. [ABSTRACT FROM AUTHOR]