This paper investigates the data-driven modelling of electrical power consumption in industrial robots performing different types of motion at various velocity levels. Three representative trajectory classes were analysed: joint motion, linear motion, and circular joint motion. Each class was executed at several commanded speeds. Instantaneous electrical power was measured during experimental runs, pre-processed, and used to identify parametric models relating power demand to selected motion-related variables. The identified model parameters are highly statistically significant, confirming the relevance of the adopted structure. Separate models calibrated for individual motion types achieved very good agreement with the empirical data. For joint and linear motion, correlation coefficients of approximately =0.95 and =0.93 were obtained, with mean absolute percentage errors below 2

The main aim of the work is to determine the impact of regeneration of metal elements using epoxies that have been subjected to the bonding process and to determine the strength of the adhesive joints containing regenerated elements. The subject of the research was single-lap adhesive joints of sheet metal in various variants: solid sheets and sheets with a hole, which were subjected to a regeneration process by filling the defect with epoxy mass. Two types of the epoxy adhesive prepared on the basis of bisphenol A-based epoxy resin (Epidian 53) and two types of curing agent: polyamide and amine in appropriate proportions were used to make the adhesive joints and regenerate the joined elements. In order to determine the adhesive joints strength, experimental tests were carried out according to EN DIN 1465 standard. The joints made using the adhesive contains Epidian 53 epoxy resin with PAC curing agent with two regenerated holes were characterized by the highest strength. The lowest strength was observed in the case of adhesive joints made using E53/Z–1 epoxy adhesive. When E53/Z–1 was used to make adhesive joints, there was no effect of the regeneration holes on the strength of the adhesive joints. Taking into account the influence of the type of the adhesive on the strength of the adhesive joints made of C45 steel, it is concluded that better results were obtained when using E53/Z–1 epoxy adhesive.

This work presents an integrated approach combining experimental testing and mathematical modeling to analyze fuel consumption and pollutant emissions in a spark-ignition engine vehicle. Experimental data were obtained from chassis dynamometer tests under the WLTP driving cycle, including time series of vehicle speed, energy consumption, and CO2, CO, THC, and other compounds emissions. Two classes of artificial neural networks were implemented to capture the complex, nonlinear relationships between driving dynamics and emission profiles: Multi-layer perceptrons (MLP) and self-associative neural networks (SANN). These models were trained on real-world time series data to predict vehicle speed and energy consumption as functions of emission parameters and vice versa. The models demonstrated high accuracy, especially in the validation phase, confirming their potential for forecasting and environmental performance assessment. The neural network models underwent training, validation, and testing processes, allowing for the assessment of their effectiveness in predicting energy consumption under various system operating scenarios. The results demonstrated high prediction accuracy, confirming the usefulness of ANN as a tool for analyzing complex relationships between emissions and energy efficiency. The best-performing model achieved a mean absolute error (MAE) of 0.034 MJ/km and a coefficient of determination (R2) of 0.91 for energy consumption prediction. The study developed models identifying the relationships between emission parameters and energy consumption characteristics, enabling precise modeling of combustion processes. The input data included key emission indicators such as carbon monoxide (CO), carbon dioxide (CO2), and hydrocarbons (HC, NMHC and CH4), as well as operational parameters of energy systems. Additionally, the observed patterns in energy use were interpreted through a physical lens, considering the thermodynamics and chemical kinetics of combustion processes under different driving conditions. This hybrid methodology-combining data-driven AI with domain-specific physical insight-provides a robust framework for predicting the environmental impacts of internal combustion engines and optimizing their operation. The proposed approach applies to broader engineering contexts, including emission control strategy design, digital twin development for powertrains, and intelligent vehicle energy management systems. The proposed approach represents a significant step toward leveraging modern artificial intelligence methods to improve energy efficiency and develop emission reduction strategies through combustion condition optimization. The obtained results can serve as a foundation for further refining industrial processes in the context of sustainable development and environmental protection.

The Lean Office philosophy is a modern approach to process management in non-production environments, focusing on the elimination of waste and enhancement of efficiency in administrative and service-oriented operations. The foundation for the development of the Lean Office concept was the Lean Manufacturing philosophy, which was born in Toyota in the second half of the 20th century. This paper presents the fundamental principles of Lean methodology as applied in office settings, details tools and methods used in implementation, and discusses observed benefits and challenges based on practical examples and case studies. The research underscores the adaptability of Lean principles beyond manufacturing and highlights their impact on organizational culture and performance.

The manuscript analyzes the impact of the HVOF (high-velocity oxygen fuel) coating spraying technology on a substrate made of a light and high-specific-strength magnesium casting alloy from the AZ31 series. Among others, the following were examined: the influence of the spraying distance of coatings using commercial cermet powders (WC–Co, WC–Co–Cr, and WC–Cr3C2–Ni) on their resistance to erosive wear. It is worth emphasizing the energy savings resulting from the possibility of spraying on the surfaces of existing machine parts to protect or regenerate them. Energy savings result from the possibility of recycling the substrate material (AZ31), as well as from extending the functionality of an existing element without the need to dispose of it and the energy-intensive production of a new component. Tests have shown that the best resistance to the destructive effects of erodent in the form of hard corundum particles is characterized by a WC–Co–Cr coating sprayed at a distance of 320 mm.

The article presents and describes the implementation of research on the detection of a drone in an urban environment using of the sound features. The methods of drone detection were recognized on the basis of modeling and evaluation of the features of the audio and acoustic signal. The authors proposed the use of a neural network model for the needs of drone detection taking into account acoustic measurements in an anechoic chamber and in an urban environment. The final part presents the obtained results of the drone detection. For the purposes of detection, a neural network model was used in order to recognize the obtained images of the spectograms of sound sources.

A method of tribological testing of models with such sliding friction using a simple pin-on-disc mechanism was presented. Wear resistance indicators of unfilled polyamides PA6, PA66 and composites based on polyamide PA6+30GF, PA6+30CF, PA6+MoS2, PA6 and oil coupled with steel C45 are determined. They, as polymeric materials with the property of self-lubrication, they are often used in metal-polymer dry friction bearings. Based on them, wear resistance characteristics of these polymeric materials at sliding friction are established. They are used as basic parameters for developed by authors mathematical model of material wear kinetics at sliding friction and analytical research method of metal-polymer sliding bearings research. For comparative assessment of wear resistance of the investigated polymeric materials, their wear resistance diagrams are constructed. Thef show the functional dependence of wear resistance on specific friction forces. It is proved that the wear resistance of materials nonlinearly depends on specific pressure, i.e., the specific friction forces. Qualitative and quantitative influence of the type and structure of fillers (which improve the tribological properties of the base polymer PA6) on their wear resistance has been established. The forecast estimation of durability of metal polymer bearings made of the specified polyamides by the author's method of calculation taking into account their various wear resistance, characteristics of elasticity and conditions of dry friction is carried out. The research results are presented graphically, which facilitates their understanding and analyses.

This paper reports the results of research on the influence of the compliance of the technological system used in grinding low-stiffness shafts on the shape accuracy of the workpieces. The level of accuracy achieved using passive compliance compensation was assessed, and technological assumptions were formulated to further increase the shape accuracy of the low-stiffness shafts obtained in the grinding process. Taking into account the limitations of passive compliance compensation, a method for the active compensation of the compliance of the elastic technological system during the machining process was developed. The experiments showed that the accuracy of grinding was most effectively increased by adjusting the compliance and controlling the bending moments, depending on the position of the cutting force (grinding wheel) along the part. The experimental results were largely consistent with the results of the theoretical study and confirmed the assumptions made. Adjusting the compliance in the proposed way allows for the significant improvement in the accuracy and productivity of machining of low-stiffness shafts.

The aim of the research was to analyse the possibility of using neural networks to determine the parameters of the chemical composition of exhaust gases as a function of engine performance parameters obtained from the on-board diagnostics system such as crankshaft speed and engine load index.The subject of the study was a Fiat Panda car equipped with a 1.3 Multijet diesel engine and powered by pure diesel.The tests used the MAHA MET 6.3 exhaust gas analyser and the on-board diagnostics system OBD II.The obtained values of NO x ,O 2 ,CO 2 and PM measured behind the DPF were analysed.For the purpose of building a neural network model, preliminary studies were carried out in non-urban traffic (high-speed route).On the basis of the data obtained, processes of learning neural network structures with approximate properties with backward propagation of errors were carried out.Subsequently, tests were performed on the operational parameters of the vehicle and the chemical composition of exhaust gases in urban traffic.Analysis of the obtained values of the average parameters obtained during the measurement and on the basis of the prepared neural models allows determining the relative differences at the level of not more than 10 percent.

The beginning of the 21st century resulted in the emergence and development of the concept known as the fourth industrial revolution. At present, due to the initiatives for sustainable development and the need for breakthrough innovations, more and more attention is being paid to the "human factor". This became the basis for the creation of the so-called Industry 5.0. Although the idea of Industry 5.0 is more and more widely described in the literature it lacks a synthetic statement of assumptions and challenges that are associated with this concept. Therefore, the aim of this article is to indicate the challenges in the development of the assumptions of the Industry 5.0 concept. In order to achieve the above goal, a literature review was carried out in a way that allowed to identify of current directions and challenges related to the development of Industry 5.0. On this basis, the most probable directions of future research in this area have been indicated.

The proposed changes to the legislation on diesel cars require intensification of work on the possibilities of reducing emissions of harmful substances into the atmosphere by these vehicles. The subject of experimental research included in the manuscript was the Skoda Octavia with a 1.9 TDI (turbocharged direct injection) compression ignition engine (type 1Z). Light absorption measurements of smokiness of the exhaust gases emitted after combustion of various biofuels (conventional diesel, pure hydrotreated vegetable oil, hydrotreated vegetable oil, biobutanol) and their blends with fossil diesel fuel were studied. The measured light absorption coefficient is the reciprocal of the thickness of the layer, after passing through which the light has a ten times lower intensity. Its unit is the reciprocal of the meter (1/m or m−1). The results obtained by means of a standard smokiness meter indicate that the use of biofuels or their blends, in general, reduces smoke formation.

This paper presents results of a study investigating worm gears consisting of polymer worm wheels and steel invo lute and Archimedes worms. The author uses his own calculation method to predict polymer wheel wear, gear life and maximum contact pressure in mesh. The effect of tooth correction and wear on gear life and contact pressure is considered. Cases of double and triple tooth engagement are analysed. The worm wheel is made of non-reinforced polyamide PA6. Quantitative and qualitative relationships are established between the maximum initial contact pressure along tooth profile and the tooth correction coefficient. Tooth wear causes a considerable decrease in contact pressure, with the highest decrease observed at the exit of engagement. The maximum contact pressure is generated at the exit of engagement. The same trend is observed for tooth wear. The minimum gear life is observed at the exit of engagement. It increases linearly with increasing the coefficient of tooth correction. The gear life significantly increases (by approx. 56%) in triple tooth engagement compared to double tooth engagement.

The article presents original technological methods that allow the improvement of the accuracy of the turning and grinding of elastic-deformable shafts by increasing their stiffness by controlling the state of elastic deformations. In particular, the adaptive control algorithm of the machining process that allows the elimination of the influence of the cutting force vibration and compensates for the bending vibrations is proposed. Moreover, a novel technological system, equipped with the mechanism enabling the regulation of the stiffness and dedicated software, is presented. The conducted experimental studies of the proposed methods show that, in comparison with the passive compliance equalization, the linearization control ensures a two-fold increase in the shape accuracy. Compared to the uncontrolled grinding process of shafts with low stiffness, the programmable compliance control increases the accuracy of the shape by four times. A further increase in the accuracy of the shape while automating the processes of abrasive machining is associated with the proposed adaptive control algorithm. Moreover, the initial experiments with the adaptive devices prove that it is possible to reduce the longitudinal shape inaccuracy even by seven times.

The aim of the present study was to investigate the effects of the application of hydrotreated vegetable oil (HVO) mixed with pure duck fat (F100) as fuel, replacing the conventional fossil diesel fuel (D100). The tests were performed using a four-stroke direct injection CI engine diesel engine. Six fuel samples were used: D100, HVO100, F100, as well as three HVO–fat mixtures F25, F50, and F75. To further study the main characteristics of fuel combustion, the AVL BOOST software (Burn program) was applied. The results of experimental studies showed that with the addition of pure fat to HVO, the ignition delay phase increased with an increase in the amount of heat released during the premix combustion phase and the pressure and temperature rise in the cylinder increased; however, the mentioned parameters were not higher as compared to diesel fuel. It was found that as the concentration of fat in the HVO–fat mixtures increases, the viscosity and density increases, while LHV was decreased, which thereby increases brake specific fuel consumption and slightly decreases brake thermal efficiency in comparison to diesel fuel. A decrease of CO2, HC, NOx emissions, and smoke was established for all HVO–fat mixtures as compared to diesel fuel at all loads; however; under low loads, CO emissions increased.

During several recent years an increasing interest concerning behavior of various gaseous or liquid hydrocarbons mixtures with neat gaseous hydrogen is observed.The phase equilibria as well as flow properties have been studied and some practical implementations indicated.The main practical idea consists in a possibility to use such mixtures as a replacement for pure hydrocarbons actually used as the energy source.Application of hydrogen enriched mixtures seem to be a temporary solution for gradual decarbonization of energy resources.The advantage of such approach may consist in much smaller requirements for investment in various aspect of infrastructure needed for handling the fuels.The present work is devoted to computer simulation of carbon dioxide emissions for several scenarios based on different compositions of hydrogen mixtures with hydrocarbons.The simulations include estimation of calorific value of the mixture and composition of exhaust gases emitted after its combustion.The simulation, based on s.c.logistic function, evaluates several variants of time dependence of new fuels implementation, and consequently time dependence of changes in composition of emissions to atmosphere.The simulation can be used for choosing the ways of practical implementation management.

In the last decade, the paradigms concerning designing and enhancing manufacturing systems were changed due to an uncommonly fast development of computerization, automation and robotization. Unknown information technologies, such as virtual reality, artificial intelligence-based solutions and robots enter the industrial reality to an increasingly great extent. Development trend analysis allows one to assume that in the nearest future collaborative robots, capable of intelligent cooperation with people will constitute an element of manufacturing systems. The objective of this article is to present conditions that ought to be met in order to introduce intelligent collaborative robots as well as to employ Digital Twin technology in the process of cobot adaptation in terms of a certain manufacturing system.

The transport sector is one of the main barriers to achieving the European Union’s climate protection objectives. Therefore, more and more restrictive legal regulations are being introduced, setting out permissible limits for the emission of toxic substances emitted into the atmosphere, promoted biofuels and electromobility. The manuscript presents a computer tool to model the total energy consumption and carbon dioxide emissions of vehicles with an internal combustion engine of a 2018 Toyota Camry LE. The calculation tool is designed in the OpenModelica environment. Libraries were used for this purpose to build models of vehicles in motion: VehicleInterfaces, EMOTH (E-Mobility Library of OTH Regensburg). The tool developed on the basis of actual driving test data for the selected vehicle provides quantitative models for the instantaneous value of the fuel stream, the model of the instantaneous value of the carbon dioxide emission stream as a function of speed and the torque generated by the engine. In the manuscript, the tests were conducted for selected driving cycles tests: UDDS (EPA Urban Dynamometer Driving Schedule), HWFET (Highway Fuel Economy Driving Schedule), EPA US06 (Environmental Protection Agency; Supplemental Federal Test Procedure (SFTP)), LA-92 (Los Angeles 1992 driving schedule), NEDC (New European Driving Cycle), and WLTP (Worldwide Harmonized Light-Duty Vehicle Test Procedure). Using the developed computer tool, the impact on CO2 emissions was analyzed in the context of driving tests with four types of fuels: petrol 95, ethanol, methanol, DME (dimethyl ether), CNG (compressed natural gas), and LPG (liquefied petroleum gas).

Low-rigidity thin-walled parts are components of many machines and devices, including high precision electric micro-machines used in control and tracking systems. Unfortunately, traditional machining methods used for machining such types of parts cause a significant reduction in efficiency and in many cases do not allow obtaining the required accuracy parameters. Moreover, they also fail to meet modern automation requirements and are uneconomical and inefficient. Therefore, the aim of provided studies was to investigate the dependency of cutting forces on cutting parameters and flank wear, as well as changes in cutting forces induced by changes in heating current density and machining parameters during the turning of thin-walled parts. The tests were carried out on a specially designed and constructed turning test stand for measuring cutting forces and temperature at specific cutting speed, feed rate, and depth of cut values. As part of the experiments, the effect of cutting parameters and flank wear on cutting forces, and the effect of heating current density and turning parameters on changes in cutting forces were analyzed. Moreover, the effect of cutting parameters (depth of cut, feed rate, and cutting speed) on temperature has been determined. Additionally, a system for controlling electro-contact heating and investigated the relationship between changes in cutting forces and machining time in the operations of turning micro-machine casings with and without the use of the control system was developed. The obtained results show that the application of an electro-contact heating control system allows to machine conical parts and semi-finished products at lower cutting forces and it leads to an increase in the deformation of the thin-walled casings caused by runout of the workpiece.

Industrial robot work optimization has been extensively studied. The main reason for analysis is the growing number of robots implemented in the different manufacturing processes. In order to benefit from the implementation of industrial robots, each implementation process ought to be preceded by an in-depth analysis of the stand work. Often the integrator’s intuition is the only base for decisions. This work focuses on the need for individualized scheduling and analysis of robotic production tasks in the context of overall production scheduling. The method of alternative schedules analysis was presented. The paper presents a scheduling process for an industrial robot in the process of robot welding of thin-walled steel sheet structures under constraints caused by the process technology. The proposed method allowed to reduce the assumed time criterion at the level of 5.4% for one detail. The obtained value of technological operation time reduction resulted in increased time savings throughout the entire production process.

The continuous development of production processes is currently observed in the fourth industrial revolution, where the key place is the digital transformation of production is known as Industry 4.0. The main technologies in the context of Industry 4.0 consist Cyber-Physical Systems (CPS) and Internet of Things (IoT), which create the capabilities needed for smart factories. Implementation of CPS solutions result in new possibilities creation – mainly in areas such as remote diagnosis, remote services, remote control, condition monitoring, etc. In this paper, authors indicated the importance of Cyber-Physical Systems in the process of the Industry 4.0 and the Smart Manufacturing development. Firstly, the basic information about Cyber-Physical Production Systems were outlined. Then, the alternative definitions and different authors view of the problem were discussed. Secondly, the conceptual model of Cybernetic Physical Production System was presented. Moreover, the case study of proposed solution implementation in the real manufacturing process was presented. The key stage of the verification concerned the obtained data analysis and results discussion.