In this research, the authors set out to propose a new method of predicting and adjusting the main cutting parameters of chip machining, namely spraying on the substrate. In the experiments, a total of 28 turning cuts on a Stellite 6 coating layer were realized. The base materials of the Stellite 6 layers were EN S235JRG1 and carbide WC-Co. This work aimed to find out and optimize the influence of different combinations of cutting parameters. Due to the coating solution, the work is focused on the evaluation of the roughness Ra (μm) of the turned substrate, the grain size Dgr (μm) of the spraying, the normal stress σrz (MPa), the residual stress σrez (MPa), the specific cutting force Fc (N), the quality of adhesion to the substrate Adhmp (MPa) and other parameters. For the sake of a comprehensive solution to the problem, several new predictive equations and subsequent suggestions for solutions have been derived from this research topic based on the newly obtained experimental results. The proposed models and procedures make it possible to get new results and insights into coating technologies.

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The high-velocity oxy-fuel spraying process was used to investigate and improve the surface properties of a workpiece. The research was focused on the spherical surface of a workpiece made of high-strength steel, a ball and socket assembly. After spraying with a nickel alloy, the surface was machined by milling. The coating was carried out as a process in which a very thin layer of coating of the required thickness and the required specific properties, i.e., high Vickers hardness, adhesion to the surface, wear resistance and other important characteristics, which must be respected in other machining methods, was applied to the already finished, heat-prepared metal substrate. This article deals with the milling of complex surfaces of steel substrate EN 10060 after spraying with NiCrBSi alloy. After spraying, a total of 15 milling experiments were performed in order to determine precisely the optimal cutting parameters of milling and surface adhesion, based on newly acquired prediction relations. The main presented results are new relations for the determination of optimal technological milling conditions based on the identification of adhesive sections using derived equations. The new relations were verified and also compared with the current literature in the field.

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