One of the crucial problems in the dynamics and automatic control theory is balancing of an invertedpendulum robot by moving a cart along a horizontal path. This task is often used as a benchmark for di erentmethod comparison. In the practical use of the LQR method, the key problem is how to choose weight matricesQ and R correctly. To obtain satisfying results the experiments should be repeated many times with di erentparameters of weight matrices. These LQR parameters can be tuned by a Genetic Algorithm (GA) techniquefor getting better results. In our paper, the LQR parameters weight matrices Q and R which were tuned usingthe Genetic Algorithm. The simulations of the control problem are designed using MATLAB script code andMATLAB Simulink on an inverted pendulum model. The results show that the Genetic Algorithm is suitablefor tuning the parameters to give an optimal response. The control problem of the inverted pendulum was solvedsuccessfully.

The current digitization of industrial processes is leading to the development of smart machines and smart applications in the field of engineering technologies. The basis is an advanced sensor system that monitors selected characteristic values of the machine. The obtained data need to be further analysed, correctly interpreted, and visualized by the machine operator. Thus the machine operator can gain a sixth sense for keeping the machine and the production process in a suitable condition. This has a positive effect on reducing the stress load on the operator in the production of expensive components and in monitoring the safe condition of the machine. The key element here is the use of a suitable classification model for data evaluation of the monitored machine parameters. The article deals with the comparison of the success rate of classification models from the MATLAB Classification Learner App. Classification models will compare data from the frequency and time domain, the data source is the same. Both data samples are from real measurements on the CNC vertical machining center (CNC-Computer Numerical Control). Three basic states representing machine tool damage are recognized. The data are then processed and reduced for the use of the MATLAB Classification Learner app, which creates a model for recognizing faults. The article aims to compare the success rate of classification models when the data source is a dataset in time or frequency domain and combination.

The article deals with the possibility of using machine learning in vibrodiagnostics to determine the type of fault of rotating machine. The data source is real measured data from the vibrodiagnostic model. This model allows simulation of some types of faults. The data is then processed and reduced for the use of the Matlab Classication learner app, which creates a model for recognizing faults. The model is ultimately tested on new samples of data. The aim of the article is to verify the ability to recognize similarly rotary machine faults from real measurements in the time domain.

The increasing digitization of manufacturing and the integration of CNC and industrial control systems into the industry 4.0 environment have introduced new cybersecurity risks that directly affect operational reliability. Traditional deterministic risk-assessment methods used for securing ICS—such as SCADA, PLC, and CNC systems—struggle to address uncertainty, dynamic operating conditions, and complex dependencies between technical and organizational factors. To overcome these limitations, this study develops a Bayesian Network (BN) model that captures probabilistic relationships between machine-level configuration parameters, network conditions, and potential security incidents. The model is applied to a CNC machining center (ZPS MCG1000i), where it supports scenario-based prediction of cybersecurity risks and provides interpretable outputs suitable for operator decision-making and human–machine interaction. The results demonstrate that BNs are effective in environments with limited data availability and high uncertainty, offering transparent and quantifiable insights into how specific misconfigurations—such as active remote access or irregular firmware updates—elevate overall system exposure. The proposed approach aligns with current regulatory and standardization requirements, including the NIS2 Directive (EU 2022/2555), ISO/IEC 27001:2022, ISO/IEC 27005:2022, and Regulation (EU) 2024/2847 (Cyber Resilience Act), which define cybersecurity obligations for products with digital elements. The study provides a reproducible and future-oriented methodology for integrating cybersecurity into machinery-safety evaluation in modern industrial environments.

The paper deals with the practical problem of the intelligent weighing and his real solution by means of unprecedented new progression. The project was concerned with handling and weighing goods with different weights of individual items. The basic part of the paper is a demonstration of automatic control of the intelligent process of weighing goods using mostly hardware and software resources by Phoenix Contact and standard DC motor platform. The basic idea of an intelligent weighing is based on the fact that the movement dynamics depends on the weight of the goods handled. In the process of movement control, detailed position and motor current values are measured to calculate the desired weight of the goods to be transported (handled).