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The production of gear components includes numerous manufacturing operations which are carried out to ensure proper surface characteristics of components to deal with wear and fatigue. Surface shot peening is one way to increase the compressive residual stresses on the surface and thus ensure better wear and fatigue resistance. An experimental plan for shot peening was conducted to produce samples with varying surface characteristics. Residual stress profile and Barkhausen noise measurements were carried out for the samples. The objective of the study was to evaluate the interactions between the shot peening parameters studied, the residual stress profiles and the Barkhausen noise measurements. A multivariable regression analysis was applied for the task. Some remarkable correlations were found between the shot peening parameters, residual stress profile and Barkhausen noise features. The most important finding was that when the shot peening intensity was high enough, over 0.5 mmA, it dominated the shot peening coverage density parameter and thus no correlations could be gained. On the other hand, if the intensity parameter was lower than the limit of 0.5 mmA, the correlation between residual stress and Barkhausen noise measurements was remarkable. This means that the surface Barkhausen noise measurements could be used for the evaluation of the stress gradient in the shot peening process.

Sulfur is considered as one of the main impurities in hot metal and hot metal desulfurization is often carried out using injection of fine-grade desulfurization reagent. The selection of variables used for predicting the course of hot metal desulphurization requires expert knowledge. However, it is difficult to model the complex interactions in the process and to evaluate a high number of possible variable subsets with manual variable selection techniques. As the amount of data gathered from the process increases, manual variable selection becomes too time-consuming and might lead to a suboptimal prediction model. The objective of this work is to execute an automatic variable selection procedure for prediction of hot metal desulfurization based on an industrial scale data set. The variable selection problem is formulated as a constrained optimization problem, in which the objective function is formulated based on repeated leave-multiple-out cross-validation. The implemented solution strategy is a binary-coded genetic algorithm (GA). By making use of the developed model, the effect of the main production variables on the rate and efficiency of primary hot metal desulfurization is quantified. The variables related to properties of the reagent and the injection parameters were found to be of great importance.