This paper deals with the verification of thermoplastic flowing when manufacturing plastic products by injection moulding used in the consumer industry. An analysis was performed by Plastic Insight, which is used for the determination of flow properties of thermoplastic materials. A good knowledge of flow properties of these materials enables a very good starting position for the design of thermoplastic products made by the injection moulding technology [1,2]. The task of this simulation was the comparison of injection parameters of a mould, with and without cooling of channels. Simulated thermoplastic material is used for the manufacture of ribs in washing machine drums. The tested material was polypropylene.

The effectiveness of a machining process can be determined by analysing the quality of the generated surface and the rate of tool wear. Stellite is highly challenging to machine, which is why it is primarily processed through grinding methods. This study concentrates on the impact of cutting parameters and tool wear (VB_b, KB_b) on the created surface roughness surface (Rt, Ra, Rz) during the orthogonal cutting of Stellite 6, which is overlaid on a steel surface, C45, prepared by means of HP/HVOF (JP-5000). The results indicate that the dominant influence on the change in the total roughness profile height value (Rt) is the mutual interaction of cutting speed and depth of cut at 16% (p < 0.000). The greatest impact on the change in the mean arithmetic deviation of the roughness profile (Ra) value is the interaction of cutting speed, tool front angle, and depth of cut with a 15% share (p < 0.000), as well as on the change in the Rz value (15%) and tool wear VB_b (25%). This investigation lays the groundwork for potentially substituting the processing of flat surfaces with hardened layers created by thermal spraying (such as Stellite 6) with grinding or methods that offer greater efficiency from both economic and technological perspectives.

Creation of wear resistant boride layers on selected steel grades in electric arc remelting process

SPD (several plastic deformations) methods make it possible to obtain an ultrafine-grained structure (UFG) in larger volumes of material and thus improve its mechanical properties. The presented work focuses on the structural and mechanical changes of aluminium alloy AlMgSi0.5 (EN AW 6060) during processing by repeated extrusion through the ECAP rectangular channel. After a four-pass extrusion, the samples’ microstructures were observed using an optical microscope, where refinement of the material grains was confirmed. Tensile tests determined the extrusion forces and allowed interpretation of the changes in the mechanical properties of the stressed alloy. The grain size was refined from 28.90 μm to 4.63 μm. A significant improvement in the strength of the material (by 45%) and a significant deterioration in ductility (to 60%) immediately after the first extrusion was confirmed. The third pass through the die appeared to be optimal for the chosen deformation path, while after the fourth pass, micro-cracks appeared, significantly reducing the strength of the material. Based on the measurement results, new analytical equations were formulated to predict the magnitude or intensity of the volumetric and shape deformations of the structural grain size and, in particular, the adequate increase in the strength and yield point of the material.

This article deals with studying the relationships among the chemical composition, microstructure and properties (hardness, micro-hardness, wear resistance) of powder additives based on NiCrBSi after their deposition. Tested materials reached a relatively wide range of hardness after deposition, which corresponds to their chemical composition and microstructure. The abrasive wear resistance of materials was tested on an emery cloth. The results indicate that both the hardness and abrasive wear resistance of tested materials depend especially on the content of carbon and chromium. Microstructural analysis indicates that the structure of tested materials is formed by the γ-Ni solid solution and intermediate phases based on boron, silicon and carbon. Those mostly form eutectics (three types), or are excluded by precipitation. There was also found a significant effect of chromium, but especially carbon content on the ratio between the solid solution and eutectics in the microstructure of tested materials. These different ratios of solid solution and eutectics were markedly reflected in micro-hardness behaviours in deposited layers. Micro-hardness values confirmed also the presence of carbidic particles in the layers with carbon content higher than 0.7 wt%.

The production of parts by pressing and subsequent welding is commonly used in the automotive industry. The disadvantage of this method of production is that inaccuracies arising during pressing significantly affect the final dimension of the part. However, this can be corrected by the choice of the technological parameters of the following operation—welding. Suitably designed parameters make it possible to partially eliminate inaccuracies arising during pressing and thus increase the overall applicability of this technology. The paper is focused on the upper arm geometry of a car produced in this manner. There have been two neural networks proposed in which the optimal welding parameters are determined based on the stamped dimensions and the desired final dimensions. The Levenberg–Marquardt back-propagation algorithm and the Bayesian regularised back-propagation algorithm were used as the learning algorithm for ANNs in multi-layer feed-forward networks. The outputs obtained from the neural networks were compared with a linear prediction model based on a on the design of experiment methodology. The mean absolute percentage error of the linear regression model on the entire dataset was 3 × 10⁻³%. A neural network with Levenberg–Marquardt back-propagation learning algorithm had a mean absolute percentage error of 4 × 10⁻³. Similarly, a neural network with a Bayesian regularised back-propagation learning algorithm had a mean absolute percentage error of 3 × 10⁻³%.