This article deals with the problems that arise during the evaluation of the surface of the components, which can have a fundamental impact on the functionality and service life of the component as well as the entire product. A fast method for estimating the condition and maximum error of the surface tester used for assessing the surface roughness of components was proposed. Using the secondary surface roughness standard, a uniform distribution law of the measured data was experimentally identified as a suitable model, according to which it is then possible to determine the coverage factor for determining the measurement uncertainty. The design of surface roughness measurement methodology for Ra determination is critical when selecting optimal settings of conditions and measurement parameters for various types of component surface treatment technologies. This is mainly due to the effect of the selection of the evaluation standard, the effect of the measurement speed, and the effect of setting the cut-off filter for the evaluation of the quantity Ra. Etalon samples of surface roughness realized by different technologies were used for experimental verification of these phenomena. Large sets of measurements were carried out on selected samples from each technology, and, accordingly, a methodology was proposed for determining the optimal number of measurements for determining the surface roughness of the investigated component, which represents an important factor for achieving measurement results with minimal measurement uncertainties at low financial costs.

Metal machining production faces a myriad of demands encompassing ecology, automation, product control, and cost reduction. Within this framework, an exploration into employing a direct inspection of the machined area within the work zone of a given machine through a confocal chromatic sensor was undertaken. In the turning process, parameters including cutting speed (A), feed (B), depth of cut (C), workpiece length from clamping (D), and cutting edge radius (E) were designated as input variables. Roundness deviation (Rd) and tool face wear (KM) parameters were identified as output factors for assessing process performance. The experimental phase adhered to the Taguchi Orthogonal Array L27. Confirmatory tests revealed that optimizing process parameters according to the Taguchi method could enhance the turning performance of C45 steel. ANOVA results underscored the significant impact of cutting speed (A), feed (B), depth of cut (C), and workpiece length from clamping (D) on turning performance concerning Rd and KM. Furthermore, initial regression models were formulated to forecast roundness variation and tool face wear. The proposed parameters were found to not only influence the machined surface but also affect confocal sensor measurements. Consequently, we advocate for the adoption of these optimal cutting conditions in product production to bolster turning performance when machining C45 steel.