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The article is devoted to the study of the effect of cryogenic cooling on the tool wear in thread turning tests. The tool wear and its influence on the thread accuracy were investigated. Two different grades of titanium alloys were used for comparative purposes. The excellent performance characteristics of titanium alloys pose machining problems, causing high unit forces at the edge of the tool leading to chipping and premature tool failure. In turn, the low thermal conductivity of pure titanium affects the heat distribution in the cutting zone. The heat is not absorbed by the material being machined but accumulates in the tool, causing an increase in diffusion and chemical wear. The results of cutting tests using liquid nitrogen showed lower values of wear on the major and minor tool flank. The edge reduction of the tool was also significantly less during cryogenic machining. The analysis of the formation of wear marks and the blade wear mechanisms was carried out for the tool rake face. The tests were carried out using the SEM method and confirmed by EDS analyses. In order to compare the course of tool wear over time, a mathematical model was developed, which results from the course of phenomena during cutting. It consists of two complementary equations. The first equation is characteristic for the first cutting phase and results from the loads imposed on the blade and aims at thermodynamic equilibrium. It is a period of stable tool operation and constant wear intensity. The second equation concerns crossing the equilibrium point followed by the process of accumulation of elementary wear phenomena. These phenomena accumulate until the blade is completely worn-out. The use of blade wear development models to determine the expected blade life allowed to confirm the beneficial effect of cryogenic cooling on the course of the blade wear process when cutting threads for two different titanium alloys.

The objective of the paper was to show various options of using by author an automated stand with computer image analysis for control of plant germination on the example of cauliflower Brassica oleracea L. 'Pionier" variety. The developed system consisted of a mobile platform equipped with the acquisition and image processing system based on Raspberry PL processor. Germination of cauliflower seeds was the object of observation, which in one case were sown to soil after dressing them with plant extracts (sweet flag Acorus calamus L., great burdock roots Arctium lappa L.). In the other case, undressed seeds were sown in the place of previous application of the abovementioned extracts. The use of a robot for monitoring plant germination enabled the automated analysis of the investigated material with higher frequency than it has been possible so far. Simultaneously, higher germination was reported when seeds were treated with macerates and extracts from great burdock roots.

This article describes a system for measuring and compensating for errors resulting from the cutting process in order to improve the accuracy of the workpiece. Measurements were performed by means of an automatic measurement unit. The diameter of the workpiece was measured at two points, and at the same time, the temperature at the end face of the workpiece was measured. These measurements were used in Statistical Process Control (SPC). Based on the measured values, the process stability was checked and an error correction value was determined for the next item. Moreover, the value of the correction was influenced by the assumed value of tool wear, in accordance with the adopted model, and the possibility of achieving the assumed surface quality. The diameter of the workpiece for SPC purposes was measured under industrial conditions using an automatic measurement unit, which indicates that the temperature of the workpiece during the measurement was significantly higher than the reference temperature. The study focuses on the possibility of identifying a workpiece temperature compensation model in measurements of the workpiece diameter for the purpose of introducing an additional change in the correction value. It was found that a model with a constant correction value and a linear model poorly reflect the nature of the changes. On the other hand, the Autoregressive with Extra Input (ARX) model and the Nonlinear Autoregressive with Extra Input (NLARX) model, with a neural network, are able to map the inertia of the system and map the process with greater accuracy. In this way, measurements performed in industrial conditions can more accurately determine the possibility of achieving the assumed tolerance of the finished product. At the same time, the research shows that the temperature compensation model is nonlinear, and that the maximum possible machining accuracy of the workpiece can be achieved thanks to the repeatable measurement and compensation technique.