The article presents the method of magnetron sputtering for the deposition of conductive emitter coatings in semiconductor structures. The layers were applied to a silicon substrate. For optical investigations, borosilicate glasses were used. The obtained layers were subjected to both optical and electrical characterisation, as well as structural investigations. The layers on silicon substrates were tested with the four-point probe to find the dependence of resistivity on the layer thickness. The analysis of the elemental composition of the layer was conducted using a scanning electron microscope equipped with an EDS system. The morphology of the layers was examined with the atomic force microscope (AFM) of the scanning electron microscope (SEM) and the structures with the use of X-ray diffraction (XRD). The thickness of the manufactured layers was estimated by ellipsometry. The composition was controlled by selecting the target and the conditions of the application, i.e. the composition of the plasma atmosphere and the power of the magnetrons. Based on the obtained results, this article aims to investigate the influence of the manufacturing method and the selected process parameter on the optical properties of thin films, which should be characterised by the highest possible value of the transmission coefficient (>85–90%) and high electrical conductivity.

Coagulation is a process during which a flocculent suspension may sediment. It is characterized by its polydisperse structure. There are three main fractions of sedimentation particles after coagulation: spherical, non-spherical and porous agglomerates. Each of the fractions sediments in a different manner, for different forces act on them, due to interactions between the particles, inhibition or entrainment of neighboring particles. The existing sedimentation models of polydisperse suspension do not consider the flocculation process, i.e. the change of one particle into another during sedimentation, resulting from their agglomeration. The presented model considers the shape of particles and flocculation, which is a new approach to the description of the mathematical process of sedimentation. The velocity of sedimentation depends on the concentration of particles of a given fraction in a specific time step. Following the time step, the heights of individual fractions are calculated. Subsequently, new concentration values of individual fractions are determined for the correspondingly reduced volume of occurrence of a given fraction in the volume analyzed, taking particle flocculation into consideration. The new concentration values obtained in this way allow to recalculate the total sedimentation rates for the next time step. Subsequent iterations allow for numerical simulation of the sedimentation process.

This study proposes a new integrated analytical-field design method for multi-disc magnetorheological (MR) clutches. This method includes two stages, an analytical stage (composed of 36 algebraic formulas) and a field stage based on the finite element method (FEM). The design procedure is presented systematically, step-by-step, and the results of the consecutive steps of the design calculations are depicted graphically against the background of the entire considered clutch. The essential advantage of the integrated method with this two-stage structure is the relatively high accuracy of the first analytical stage of the design procedure and the rapid convergence of the second field stage employing the FEM. The essence of the new method is the introduction of a yoke factor kY (the concept of which is based on the theory of induction machines) that determines the ratio of the total magnetomotive force required to magnetise the entire magnetic circuit of the clutch to the magnetomotive force required to magnetise the movement region. The final value, the yoke factor kY is determined using loop calculations. The simplicity of the developed design method predisposes its use in optimisation calculations. The proposed method can also be adapted to other MR devices analysed in shear mode.

The goal of the research was to analyze the acoustic emission signal recorded during heat treatment. On a special stand, samples prepared from 27MnCrB5-2 steel were tested. The steel samples were heated to 950°C and then cooled continuously in the air. Signals from phase changes occurring during cooling were recorded using the system for registering acoustic emission. As a result of the changes, Widmanstätten ferrite and bainite structures were observed under a scanning microscope. The recorded acoustic emission signal was analyzed and assigned to the appropriate phase transformation with the use of artificial neural networks.

Numerous examples of physically unjustified neural networks, despite satisfactory performance, generate contradictions with logic and lead to many inaccuracies in the final applications. One of the methods to justify the typical black-box model already at the training stage involves extending its cost function by a relationship directly inspired by the physical formula. This publication explains the concept of Physics-guided neural networks (PGNN), makes an overview of already proposed solutions in the field and describes possibilities of implementing physics-based loss functions for spatial analysis. Our approach shows that the model predictions are not only optimal but also scientifically consistent with domain specific equations. Furthermore, we present two applications of PGNNs and illustrate their advantages in theory by solving Poisson’s and Burger’s partial differential equations. The proposed formulas describe various real-world processes and have numerous applications in the area of applied mathematics. Eventually, the usage of scientific knowledge contained in the tailored cost functions shows that our methods guarantee physics-consistent results as well as better generalizability of the model compared to classical, artificial neural networks.

The paper presents the results of experimental verification on using a zero-sum differential game and H ∞ control in the problems of tracking and stabilizing motion of a wheeled mobile robot (WMR). It is a new approach to the synthesis of input-output systems based on the theory of dissipative systems in the sense of the possibility of their practical application. This paper expands upon the problem of optimal control of a nonlinear, nonholonomic wheeled mobile robot by including the reduced impact of changing operating condtions and possible disturbances of the robot’s complex motion. The proposed approach is based on the H∞ control theory and the control is generated by the neural approximation solution to the Hamilton-Jacobi-Isaacs equation. Our verification experiments confirm that the H∞ condition is met for reduced impact of disturbances in the task of tracking and stabilizing the robot motion in the form of changing operating conditions and other disturbances, which made it possible to achieve high accuracy of motion.