Mechanical Engineering Research Articles

A Review of Human-Centric AI in Industry 5.0: Integrating Data Science with Mechanical Automation

The shift towards Industry 5.0 from Industry 4.0 represents the paradigm shift in industry, not only highlighting automation and efficiency but also human-centered innovation, resilience, and sustainability. Central to this transformation is the synergy between Artificial Intelligence (AI) and Data Science with mechanical automation to produce intelligent, adaptive, and collaborative industrial environments. This review identifies the new frontier of human-centered AI in Industry 5.0 as the intersection of data-driven intelligence, mechanical engineering, and human-robot collaboration (HRC). It methodically examines how models of AI/Machine Learning (ML), such as explainable AI (XAI), prediction analytics, and systems of human-in-the-loop are redefining mechanical automation into cognitive, user-oriented settings. A systematic methodology based on major scientific databases was employed in order to choose more than 150 high-impact articles published between the years 2015 and 2025. Fundamental enabling technologies like collaborative robots (cobots), digital twins, cyber-physical systems, and edge AI are discussed in detail, with special emphasis on how they facilitate ergonomic, transparent, and secure interaction between humans and machines. In addition, the review discusses how data science frameworks are implemented to maximize the performance, trust, and well-being of humans in automated machinery systems. The paper also identifies some key missing gaps, such as the absence of scalable explainability of industrial AI, poor integration of ergonomic models with robotics, and difficulty in implementing real-time feedback systems from humans. In overcoming these challenges, this review provides a research and development pathway towards ethically oriented, resilient, and inclusive production. The research is expected to be used as a basis of reference by academics, engineers, and policy-makers who are leading the humanity-oriented shift of smart production systems.

Dedicating Science and War Books: Networks of Power Between Science and Politics in the Early Modern Period

The article explores the dedications to promoters and patrons found in military and mechanical engineering books between the last decades of the sixteenth century and the first half of the seventeenth century. These dedications are significant because they show how the authors of those books managed their strategies of self-promotion within the power of the court and weaved new relationships with promoters and patrons. Through their dedications, they attempted to guarantee both their status as authors and that of their books. Circulating as objects throughout elite political networks, these scientific books also enabled less tangible transactions of knowledge, prestige and power.

Integrating cumulative binomial probability into artificial bee colony algorithm for global optimization in mechanical engineering design

Integrating cumulative binomial probability into artificial bee colony algorithm for global optimization in mechanical engineering design

Applications of the Möbius strip (Review)

The subject of research is industrial and household equipment and devices, which as a whole or their structural elements are made in the form of a Möbius strip. The purpose of the research is a critical analysis of the use of the Möbius strip in engineering, technology and everyday life. One of the most important characteristics of elements of various machines, apparatuses, devices, and structures is their geometric shape, which is especially evident in mechanical engineering, aerodynamics and hydrodynamics, and heat and mass transfer. One of the geometric objects with atypical and unique properties is the Möbius strip (sheet, ring, Möbius loop) – a topological object that is the simplest unoriented surface with one side and one edge. The main advantage of the Möbius strip is precisely the presence of one surface, which is widely used in a wide variety of areas of human life, and primarily in engineering and technology. The Möbius strip has found its application in mechanical and instrument engineering, chemical technology and related industries, thermal power engineering, renewable energy, construction, light industry, agriculture, transport, aerospace and military technology, electronics, radio engineering, computer technology, medicine, advertising and jewelry, entertainment and recreation, personal hygiene products, as well as in everyday life. In developing innovative technical solutions, the use of the Möbius strip in various sectors of the economy and everyday life can provide significant assistance not only from the more than one and a half century of experience of scientists, engineers, and inventors in this matter, but also from the unique capabilities of artificial intelligence. This review may be useful to creators of new equipment and technology, because when creating new developments, a fund of technical, including geometric, effects is widely used, which is an integral part of the information fund of inventors, designers, and scientists and helps them create innovative solutions to technical problems.

Biomechanical Analysis and Design Optimization of the Human Knee Joint in Mechanical Engineering

The human knee joint is a kind of hinge joint that is a pivotal component of the musculoskeletal system, providing mobility and stability in daily human activity. Mechanical engineering plays a crucial role to make us understand, analyze, and optimize the human knee hinge joint for clinical and prosthetic applications. This article presents a comprehensive overview of the biomechanical structure of the human knee, methods of analysis, and strategies for design optimization. Emphasis is placed on computational modeling, finite element analysis (FEA), and biomimetic design. The goal is to enhance artificial knee joint design and therapeutic interventions for patients with knee disorders or injuries.

Технологическое обеспечение качества при роботизированной отделочной обработке на основе средств адаптации

Industrial robots are used for machining in mechanical engineering. This trend is associated with an increase in the geomet-ric complexity of parts and wider kinematic capabilities of industrial robots in comparison with classical CNC machines. The article analyzes the technological capabilities of using industrial robots in finishing machining operations, and pro-vides the reasons for limited robots introduction Schemes of construction of technological operations are given: "part in hand" and "tool in hand". The factors influencing the choice of the preferred machining are studied. The areas of effective application of passive and active adaptation tools are given. The article provides two main reasons for possible vibrations under robotic manipulation: e.g. low rigidity of the industrial robot structure and shape errors effect in the previous opera-tion. The problem of developing a sustainable code conversion for the amount of material removal is discussed. The problem statement is due to the fact that the model of the cutting process varies greatly depending on the cutting conditions. Force control work makes it possible to take into account the rigidity of the robot without sacrificing the running accuracy in six coordinates. The article discusses the use of a neural network and a genetic algorithm in the development of a robotic pol-ishing operation for a flat surface within limited access constraints. The authors of the article have developed a postproces-sor for controlling an industrial robot in case of variable tool overhang and uneven tolerance. Special technological equipment has been designed and manufactured for this purpose. Experiments on testing of the developed algorithmic and software have been conducted in the laboratory "Industrial Robots and Automation Tools"

MODELING OF ELECTROMAGNETIC PROCESSES IN A NONLINEAR CIRCUIT OF AN ELECTROLYZER FOR PULSE DEPOSITION OF METAL COATINGS

The primary objective of this study is to construct a comprehensive analytical model describing the electromagnetic processes in a nonlinear electrical circuit of an electrolyzer operating under pulsed electrodeposition conditions. Special attention is given to the influence of reversed rectangular voltage pulse parameters on the near-cathode voltage drop, deposition current, and the general electrochemical behavior of the system. The goal is to identify optimal operation modes of the pulse power supply that ensure high-quality metal coatings while minimizing energy consumption and structural defects. Methodology. The research employs the method of variable transformation to develop a set of differential equations describing the dynamic behavior of voltage and current in a nonlinear electrolyzer circuit during pulsed operation. The model accounts for critical circuit components: resistive, capacitive, and inductive elements of the electrolyte, as well as nonlinear conductances reflecting electrochemical properties. Analytical expressions for voltage drop and deposition current are derived for both the forward and reverse pulse intervals. The model includes the influence of electrolyte inductance–an often-neglected factor–which significantly alters the current front shape and ultimately affects deposition kinetics and coating characteristics. Numerical simulations are performed to validate the analytical solutions. Results. Closed-form analytical expressions were obtained for the time-dependent near-cathode voltage drop and deposition current under pulsed conditions. It was shown that the electrolyte inductance leads to a rounding of the current front, influencing both the amplitude and stability of the electrochemical reactions. A parametric analysis identified that the optimal pulse duty cycle lies in the range of τ/T ≈ 0.73 to 0.78. Within this interval, the maximum near-cathode voltage drop does not exceed 0.6 V, while the peak voltage and total current remain within safe technological limits (up to 13 V and 150 A, respectively). The average partial discharge current at the cathode remains stable at approximately 70 A, ensuring consistent deposition rates. Scientific novelty. This work introduces a novel analytical approach to modeling pulsed electrodeposition that, for the first time, includes the effect of electrolyte inductance. Unlike existing models relying heavily on numerical simulations, the proposed model allows for direct computation of key process parameters. It enables the derivation of precise criteria for determining the optimal ranges of input pulse parameters–both in amplitude and duration–necessary to avoid the formation of crystalline defects and to achieve uniform, high-quality coatings. These innovations significantly advance the theoretical understanding of pulsed electrochemical processes. Practical significance. The developed model provides a valuable tool for industries that utilize electrochemical metal deposition, including microelectronics, galvanics, mechanical engineering, and power systems. It offers a foundation for designing advanced pulse power supplies with optimized operation modes and supports the development of automated control systems for deposition processes. Furthermore, the model can aid in adapting laboratory results for industrial implementation, improving coating quality, process efficiency, and operational reliability.

Machine Learning Based Predictive Maintenance Framework for Rotating Machinery - A Case Study Using Vibration and Temperature Data

Predictive maintenance is essential for ensuring the reliability and efficiency of mechanical systems, particularly in industries where unexpected equipment failures can cause costly downtimes. Traditional approaches are often reactive or based on fixed schedules and lack real-time insights into system health. This study presents an interdisciplinary framework that integrates mechanical system diagnostics with machine learning to implement predictive maintenance. Sensor data, such as vibration and temperature, which are key indicators of mechanical condition, are analyzed using supervised learning algorithms including Support Vector Machines (SVM), Random Forest (RF), and Artificial Neural Networks (ANN). These models are trained to classify equipment states and predict potential failures. The proposed method demonstrates that combining mechanical domain knowledge with computational intelligence enables early fault detection and improves maintenance planning. The results highlight the effectiveness of this hybrid approach in delivering scalable, data-driven solutions for condition-based monitoring, emphasizing the synergy between mechanical engineering and computer science in solving realworld industrial challenges.

Teaching Differential Equations to Mechanical Engineering Students Using Excel VBA

The ability to solve differential equations is necessary for mechanical engineering students, as these equations model real-world engineering systems such as fluid flow, heat conduction, thermodynamics, and structural integrity. This aim of this study is to develop a complete teaching module by utilizing the Excel VBA to facilitate the numerical solution of differential equations in mechanical engineering. Unlike MATLAB or Python, Excel VBA provides an open platform for students with minimal coding experience, thus enabling them to understand numerical methods while working within a spreadsheet. The research follows the Design, Development, Implementation, and Evaluation (DDIE) framework to create an interactive and practical learning experience for the students. The study evaluates student engagement, understanding, and problem-solving ability by using both the qualitative and quantitative assessments. The module was tested in an undergraduate engineering classroom and demonstrated strong quantitative impact, receiving high validation scores from both the students and faculty members with a 35% average score improvement, 85% student preference for Excel VBA, and faculty reporting 30% increased confidence, 25% better problem-solving, 20% more experimentation, and 25% improved conceptual understanding. The results demonstrate that the Excel VBA is an effective tool for teaching numerical methods by providing the students with an intuitive and interactive approach to solve differential equations.

Planes de mejora y su influencia en los indicadores de aprendizaje en la enseñanza de la ingeniería

The purpose of this research was to analyse the influence of the improvement plans on the learning indicators measured in the subjects Electropneumatic Automation and Transformers and Transmission Lines taught in the professional career of Mechanical and Electrical Engineering at the National University of Jaén, Cajamarca, Peru. The improvement plans are aimed at strengthening the quality of the teaching-learning process and were implemented starting in 2020. The measurement and evaluation of learning was carried out according to the following stages: definition of the educational objectives of the programme, construction of the matrix of the contribution of the subjects in the learning indicators, selection of the subjects and elaboration and implementation of the improvement plans. The learning indicators obtained from the implementation of the improvement plans were processed following the established guidelines for an analysis of variance and Tukey's multiple comparisons test, obtaining evidence that indicates the existence of highly significant differences when comparing semesters and learning indicators with respect to the percentage of the level of achievement reached, which reflects the influence of the improvement plans that acquire a continuous character when applied semester after semester

Plasma-chemical purification of flue gases using pulsed electron beams: a review

Relevance. Currently, most industrialized countries face the problem of air pollution by emissions from thermal power plants and other industrial enterprises. Flue gases from thermal power plants are characterized by high volume emission rates and rather low concentrations of harmful impurities (nitrogen and sulfur oxides). Nowadays, the following flue gas cleaning technologies are mainly used: homogeneous (catalytic-free) reduction of nitrogen oxide with ammonia, catalytic selective reduction using ammonia, direct absorption of nitrogen oxide with simultaneous absorption of SO2, lime and limestone, magnesite and ammonia methods. Technological processes currently used in metallurgy, chemistry, power engineering, and mechanical engineering require new solutions aimed at increasing the specific productivity per unit volume of the reaction zone. An increase in the specific productivity of the above processes can occur when switching to ultra-high temperatures (over 5000°C) and high rates of reagent transfer through the interaction zone, which can be realized in the case of their gas transport with energy supply from an external source. This situation is realized in plasma-chemical systems. One of the most effective physical and chemical methods at present is the method of removing nitrogen and sulfur oxides from exhaust gases of thermal power plants using high-energy electron beams. Aim. Review of plasma-chemical methods of flue gas purification using pulsed electron beams, as well as identification of the advantages and disadvantages of existing methods. Results and conclusions. The paper presents the main chemical, electrophysical, catalytic and plasma-chemical methods of flue gas purification. Particular attention is paid to methods in which pulsed electron beams are used as plasma sources. Basic information on flue gas purification is presented with the name of the installations, accelerator parameters, and cleaning efficiency. This review contains useful information for specialists involved in developing flue gas cleaning methods.

ИНСТРУМЕНТЫ РАЗРАБОТКИ КОНСТРУКЦИЙ ИЗДЕЛИЙ МАШИНОСТРОЕНИЯ

To enhance the competitiveness of domestic machinery mechanical engineering, to reduce excessive diversity in component parts and production components, as well as to address challenges related to digital transformation of production processes, new efficient business processes for product creation are required. Improving quality, technological sophistication, and operational efficiency of the parts depends on their design perfection. To solve design tasks, the author proposes three main tools namely coupling nodes, mobility matrices, and gaps between component parts of a product. Using these tools within the framework of group design methodology allows developing customized products with low costs per unit produced and will contribute to solving the task of digitizing business processes. Being the most general concepts that reflect what is inherent in any product, the proposed tools can help formalize the process of “inventing” a construction. The paper illustrates applying the proposed tools by an example of modernizing the design of a movable connection. In the original product, a moving part must be replaced with another type of structure. As a result of the project work, it is established that this requires introducing an additional component and designing its structure.

Bioinspired Intelligent Soft Robotics: From Multidisciplinary Integration to Next‐Generation Intelligence

Abstract Soft robotics, distinguished by intrinsic compliance, biomimetic adaptability, and safe human‐environment interaction, has emerged as a transformative paradigm in next‐generation intelligent systems. Biological systems, refined through evolutionary optimization, exhibit unparalleled multifunctionality in unstructured environments, inspiring the development of soft robots with energy‐efficient reconfiguration and environmental responsiveness. This review presents a comprehensive analysis of intelligent soft robotics via multidisciplinary integration, covering key aspects from bioinspired design principles to advanced functional implementation. Recent breakthroughs across four interconnected domains are systematically examined: 1) biomimetic actuation mechanisms that enhance actuation efficiency through innovative structural configurations; 2) programmable materials enabling adaptive morphology and tunable mechanical properties; 3) multiscale manufacturing techniques for fabricating complex heterogeneous structures; and 4) closed‐loop control strategies integrating artificial intelligence algorithms. While highlighting emerging applications in biomedical engineering, environmental exploration, and human‐machine interfaces, challenges such as actuation efficiency, material degradation, manufacturing limitations, nonlinear‐control complexity, and sensing instability under real‐world conditions are discussed. Furthermore, strategic research directions are identified to guide the development of next‐generation soft robots endowed with embodied intelligence and adaptive functionalities. Notably, by synergizing advances in materials science, mechanical engineering, and computational intelligence, soft robotics is poised to redefine the boundaries of intelligent machines across healthcare, exploration, and human augmentation.

Beyond Boundaries: Integrating SolidWorks and eLamX in Designing, Simulating, and Validating a Mechanical Female Left Arm Low-Elbow Prosthesis for CrossFit Training

The objective of this work was to design a mechanical prosthesis tailored to below-elbow agenesis using a polymer matrix composite material (epoxy resin) reinforced with laminated carbon fiber (prepreg). The design process involved considering the patient's current condition and idealizing the potential tensional stress the component would endure during service conditions. SolidWorks, a computer-aided design software, facilitated the design process, while a customized material library of composite sheet materials, incorporating material parameters and orientations, was established manually. Using the eLamX software, the composite material properties were computed based on the micromechanical model developed by Chamis. Furthermore, to ensure the optimal performance of the prosthesis, a simplified simulation of its mechanical behaviour under service conditions was conducted. This holistic approach, integrating SolidWorks and eLamX, enabled a comprehensive assessment of the prosthesis's functionality and durability in real-world applications, particularly in the context of CrossFit training. Derived from a master's thesis in Materials Engineering (Rivas Hernández, 2023), this work was conducted collaboratively across disciplines, exemplifying a multidisciplinary approach integrating mechanical engineering and materials science, with valuable insights contributed by orthopaedic specialists to prosthesis design and simulation.

Technology Innovation in Mechanical Engineering Using Social Strategy for Sustainable Geothermal Business

To know the important role of technology innovation in a company achieving goals to support corporate, economic, social, & environment in geothermal business. Technology innovation could enhance resource assessment, resource development, & resource utilization & management. They could give a competitive advantage & sustainable business for the company that operates among communities. The geothermal field sometimes has different types of communities, the company should have a social strategy to make sure the operation will run as well as e xpectation. This research uses qualitative methods by undertaking social mapping, observation, interviews, analysis documents, & forum group discussion. Technology innovation in mechanical engineering that is applied by the company can give a competitive advantage & accelerate geothermal development. Social strategy is very important for geothermal developers so that communities can have a good impact to avoid issues of economic, social, & environmental. Technology innovation in the geothermal business is to choose the right power plant technology that use. By using the binary cycle power plant system, the company can sell the electricity power at a cheaper price because the grace period is shorter than others.

Outline of cybernetical neuroscience

The article discusses a new scientific field — cybernetical neuroscience — which studies mathematical models adopted in computational neuroscience using methods from cybernetics (the science of control and communication in living organisms, machines, and society). It also examines the practical application of results obtained from research on mathematical models. Key tasks, methods, and results in cybernetical neuroscience are outlined. As an example some results in neurointerface control and machine learning methods from the Institute for Problems in Mechanical Engineering, Russian Academy of Sciences (IPME RAS) are presented.

Analysis of Temperature Characteristics of Electrolytic-Plasma Discharge in Jet Processing of a Metal Anode

Introduction. Electrolytic plasma technologies used for dimensional and finishing processing of metal surfaces attract attention due to their high efficiency and precision. The key factor that determines the quality of processing is the temperature of the electrolytic-plasma discharge (EPD), which affects the ionization of the electrolyte and the properties of the surface. The lack of comprehensive studies of the temperature characteristics of jet EPD limits the optimization of processes. The research objective is to determine the distribution of temperatures and heat flows in the system “jet electrolytic cathode — metal anode” under various processing conditions.Materials and Methods. The study was conducted using an electrolyte jet with a diameter of 3 mm and a mass flow rate of 0.25–3.75 g/s at a voltage of 20–500 V. KhVG and 08Kh18N9T steels were used as anodes, and the electrolytes were aqueous solutions of NaCl, (NH4)2SO4, C6H8O7, with a concentration of 4–50 g/l. The temperature was measured with a chromel-alumel thermocouple, an infrared pyrometer, and a thermal imager.Results. A heat balance equation was developed, describing heat distribution among the metallic anode (MA), jet cathode, electrolyte, vapor, and radiation. The analysis of the volt-ampere characteristics (VAC) showed an increase in current at low electrolyte flow rates (0.75–1.2 g/s) followed by a decrease at 300–500 V, and a parabolic dependence with a maximum of 2.6 A at a flow rate of 2.37 g/s. The maximum MA temperature reached 100°С (NaCl, 4–35 g/L), decreasing to 82°С at 150 g/L, while the hollow cathode reached 158°С at an initial electrolyte temperature of 90°С. Vapor temperatures ranged from 67.3°С (high flow rates) to 87.5°С (low flow rates). Electrolyte loss due to evaporation reached 5,8 g at 300–340 The temperature at the periphery of the anode was 15–20% higher than in the center.Discussion and Conclusion. The main source of heat was the Joule-Lenz law, with the contribution of exothermic reactions of carbon oxidation up to 260 V. The maximum heat release was observed in the EPD zone, forming an ellipsoid. The data obtained and the heat balance equation create the basis for optimizing jet electrolytic-plasma polishing in mechanical engineering, medicine, and microelectronics.

Strain relaxation in halide perovskites via 2D/3D perovskite heterojunction formation.

Applying mechanical strain and strain engineering to halide perovskites has endowed them with intriguing properties. However, an in-depth understanding of mechanical strain, including residual strain in halide perovskites, remains incomplete, coupled with the critical challenge of decoupling strain effects from other interferences. Here, we examine the relaxation of residual tensile strain in three-dimensional (3D) halide perovskites through 2D/3D perovskite heterojunction formation. The 2D perovskite induces structural fragmentation in 3D perovskites, facilitating plastic relaxation of tensile strain. By isolating extrinsic crystalline phase interference and exciton-related optical disturbances, we observe that 3D perovskites retain high crystallinity only with moderate tensile strain relaxation. This moderate relaxation enhances optoelectronic properties in 3D perovskites, including broadened band-to-band absorption and prolonged charge carrier lifetime, markedly contributing to an increase in the maximum attainable power conversion efficiency in photovoltaic devices. Our findings outline conditions for strain relaxation that optimize optoelectronic properties, advancing strain engineering in halide perovskites.

Determining the rational technological processing modes for achieving optimal operational characteristics of the surface layer obtained by electric spark alloying using carbide electrodes

The object of this study is the wear resistance of surfaces after electric spark alloying in contact with elastically fixed abrasive grains. The task addressed relates to the lack of technological modes for electric spark processing, in particular for hard alloys T15K6 and VK8. A rational mode has been determined, under which samples with an optimal surface profile, a uniform structure, and minimal internal defects were obtained. The choice of the technique for machining high-wear surfaces by the electric spark alloying method is due to its simplicity and accessibility. Waste can be used as electrodes, specifically hard alloy plates of a cutting tool that have failed. At the same time, there are a number of unresolved issues related to the choice of optimal machining modes that would ensure high wear resistance of the machined surfaces. A technique for testing machined parts for wear has been proposed. It was found that the highest predicted wear resistance would be demonstrated by parts processed by the electric spark alloying (ESA) method using the VK8 electrode, with the capacitor bank capacity of 330 ± 30 μF and the electrode vibration frequency of 125 ± 25 Hz. They combine high surface microhardness (13.5 MPa) and residual compressive stresses in the deposited layer (–90 MPa). The results are attributed to the physical and mechanical processes occurring in the metal during electric spark alloying. These conditions were created by different values of technological parameters. A feature of the results is that it was established that not only the hardness of the deposited layer but also the magnitude of internal stresses in this layer have a significant impact on the operational parameters. Practical application implies that electric spark alloying could become an alternative technology for strengthening the surfaces of parts that work in contact with abrasives (mechanical engineering, medicine)

Numerical Methods in Fins with Excel and MATLAB

In this paper, numerical methods are applied through the implementation of finite difference routines using Excel and MATLAB to calculate the heating and cooling times of a square plate supporting fins using aluminum and steel as materials. Four cases are analyzed for the same fin model: the first one is heating with hot air, the second one is heating with hot air, the third case is heating with air and then with air but reducing the fin thickness by half, and the fourth case is cold air cooling, but in all cases the plate area remains unchanged. Numerical methods are used as the mathematical tool to solve the system of equations derived from applying an energy balance to the nodes in the fin. These solutions provide temperature as a function of time for each material, based on the fin design. MATLAB was used to develop the simulation code, and Excel was employed to implement the equations in each cell. The results obtained in this study align with findings from existing literature. This approach serves as a valuable tool for mechanical engineering students interested in simulating thermodynamic effects in materials and extending the analysis to estimate heating or cooling times, as well as the efficiency of fins made from other materials.