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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]

The galvanic corrosion behavior of as‐received and ECAPed ZE41 Mg alloy coupled with Al7075 alloy is investigated using zero resistance ammeter in three different corrosive environments, 0, 0.1, and 1 M NaCl, to mimic the conditions experienced in engineering applications. The mechanism of galvanic corrosion for the ZE41 Mg–Al7075 aluminum alloy is explained. It is observed that a robust surface film containing a composite layer of oxide/hydroxide of magnesium and aluminum is established in deionized water (0 M). However, only a single layer of magnesium oxide/hydroxide is detected in chloride‐containing environments. Equal channel angular pressing (ECAP) improved the resistance to galvanic corrosion by 58% and 54% when compared with the as‐cast counterparts in 0 and 1 M NaCl solution, respectively. In contrast, galvanic corrosion resistance decreased by 26% in 0.1 M NaCl after ECAP while the as‐received samples evinced pits unfavorable to be used in engineering applications. ECAP is a promising method to combat galvanic corrosion encountered by ZE41 magnesium alloy used in automobiles and components of military vehicles. [ABSTRACT FROM AUTHOR]