The article presents the results of strength tests of screw-nut threaded connections made of polymeric materials such as: ABS, PLA, PET-G and RGD720. In order to make physical models, three 3D printing techniques were used: Fused Deposition Modeling (FDM), Fused Filament Fabrication (FFF), and PolyJet. The tests took into account the stresses caused by the axial force generated when the bolt is screwed into the nut or other structural element. Due to the complexity of the issue, the presented studies are only a starting point for further research.

The relevant problem is searching for up-to-date methods to improve tools and machine parts’ performance due to the hardening of surface layers. This article shows that, after the magnetic-pulse treatment of bearing steel Cr15, its surface microhardness was increased by 40–50% compared to baseline. In this case, the depth of the hardened layer was 0.08–0.1 mm. The magnetic-pulse processing of hard alloys reduces the coefficient of microhardness variation from 0.13 to 0.06. A decrease in the coefficient of variation of wear resistance from 0.48 to 0.27 indicates the increased stability of physical and mechanical properties. The nitriding of alloy steels was accelerated 10-fold that of traditional gas upon receipt of the hardened layer depth of 0.3–0.5 mm. As a result, the surface hardness was increased to 12.7 GPa. Boriding in the nano-dispersed powder was accelerated 2–3-fold compared to existing technologies while ensuring surface hardness up to 21–23 GPa with a boride layer thickness of up to 0.073 mm. Experimental data showed that the cutting tool equipped with inserts from WC92Co8 and WC79TiC15 has a resistance relative to the untreated WC92Co8 higher by 183% and WC85TiC6Co9—than 200%. Depending on alloy steel, nitriding allowed us to raise wear resistance by 120–177%, boriding—by 180–340%, and magneto-pulse treatment—by more than 183–200%.

The paper deals with implementation of the optimizing process into multi-axial rainflow analysis and cumulative damage calculation. It’s presented computational program FEA_FAT compiled in MATLAB. Stress analysis is realized by finite element procedure and the cumulative damage can be calculated by using two fundamental ways – critical plane approach and so called integral approach. Testing example presents random stress analysis and damage prediction of a simple FE model with non-proportional loading.

Materials based on basalt fiber are widely used as thermal insulating material. These materials have a number of advantages, including their low thermal conductivity and fire resistance due to their natural composition. However, there is a significant drawback in that the material contain non-fibrous inclusions. The solution to this problem would significantly improve the working conditions of workers engaged in the production of materials from basalt fiber, as well as workers engaged in construction and installation works. In addition, the research will help to make completely new products, such as special fireproof paper and sterile medical materials. This article focuses on the reasons for the formation of non-fibrous inclusions in the production of this kind of material. The technology of producing canvases from superthin fiber in the duplex way is studied. The analysis of the production process is made. Certain technological and structural parameters of the influence on the formation of such inclusions are identified. Experiments are carried out and conclusions are drawn given formation of non-fibrous inclusions of various geometric shapes for various factors. A mathematical model of the process under consideration is built. The article draws conclusion on the application of these developments in the production cycle of creating materials based on basalt fiber.

In this paper we are finding input-output dependencies of feed-forward neural network which usually behaves as black box. It is very important and difficult to find or evaluate those dependencies especially for multi-input/output data approximation. We will use small neural network which will be trained on a given data in MATLAB Mathworks. Network will be simulated in standalone .NET application.