Experimental Analysis of Effect of Machined Material on Cutting Forces during Drilling.

Current research studies devoted to cutting forces in drilling are oriented toward predictive model development, however, in the case of mechanistic models, the material effect on the drilling process itself is mostly not considered. This research study aims to experimentally analyze how the machined material affects the feed force () during drilling, alongside developing predictive mathematical-statistical models to understand the main effects and interactions of the considered technological and tool factors on Ff. By conducting experiments involving six factors (feed, cutting speed, drill diameter, point angle, lip relief angle, and helix angle) at five levels, the drilling process of stainless steel AISI1045 and case-hardened steel 16MnCr5 is executed to validate the numerical accuracy of the established prediction models (AdjR = 99.

Thermodynamics and Mechanics of Thermal Spraying of Steel EN 10060 Substrate with NiCrBSi Alloy after Milling.

The objective of this paper is to present a new way of identifying and predicting the relationship between thermodynamic and physical-mechanical parameters in the formation of a layer after spraying on a substrate with NiCrBSi alloy and its subsequent processing by milling. The milling of the spherical surface of the EN 10060 material after spraying was performed on the DMU 40 eVolinear linear milling centre. The experimental part of the article is focused on investigating the influence of cutting parameters when machining a selected combination of materials (substrate-coating: EN 10060 steel-NiCrBSi alloy).

Investigation of Surface Roughness and Predictive Modelling of Machining Stellite 6.

The aim of the paper was to examine the influence of cutting conditions on the roughness of surfaces machined by longitudinal turning, namely of surfaces coated with Stellite 6 prepared by high-velocity oxygen fuel (HVOF) technology and applied onto a standard structural steel substrate. From the results of measurements of the cutting parameters, a prediction model of the roughness parameters was created using mathematical and statistical methods. Based on a more detailed analysis and data comparison, a new method for prediction of parameters of longitudinal turning technology was obtained.