Abrasive water jet (AWJ) technology, predicated on the high-velocity mixing of air, water, and abrasive particles, is a critical technique for precision material removal. This study presents a comprehensive investigation into AWJ erosion mechanisms by integrating experimental observations with advanced Computational fluid dynamics (CFD) simulations. The inherent complexity of the three-phase erosion field, particularly regarding the evolution of stagnation zones at increased erosion depths, presents significant challenges for direct experimental observation. To overcome these limitations, an initial erosion channel profile obtained under controlled experimental conditions was employed as a boundary condition in CFD simulations to model the trajectory of abrasive particles accurately. The simulations facilitate the prediction of successive erosion channel profiles by elucidating the influence of stagnation zones on abrasive particle refraction during both normal and inclined multi-step erosion processes. Comparative analysis between the CFD results and experimental data confirms that stagnation zones play a pivotal role in modulating AWJ erosion energy. This work not only refines the predictive modeling of AWJ-induced erosion but also deepens the fundamental understanding of the erosion process through detailed examination of stagnation zone dynamics.
This study develops and empirically validates an integrated monitoring framework linking technological, operational, and financial performance indicators in automotive manufacturing. While process capability (Cpk), overall equipment effectiveness (OEE), and return on investment (ROI) are widely applied, their interrelationships are rarely examined within a unified empirical framework in real production environments. The proposed model is implemented within a business and technological processes monitoring system for the production of automotive daytime running lights (DRL), combining real-time measurement, automated data acquisition, and structured process optimization. A multi-phase implementation strategy enabled the transition from manual to fully automated monitoring, supported by more than 1,400 measurements collected across key technological operations in accordance with international standards. A longitudinal case study design was applied, and statistical analyses, including correlation and regression methods, were used to examine relationships between process capability, operational performance, and financial outcomes. The results show that systematic optimization increased equipment effectiveness from 78.36 % to 85.41 % and financial return from & euro;2.9 million to & euro;7.98 million, while achieving process capability levels above the required thresholds (Cpk > 2). A strong statistical relationship was identified between OEE and ROI, whereas the relationship between process capability Cpk and OEE was not statistically confirmed as a direct effect. The findings indicate that technological, operational, and financial indicators are interconnected but not strictly linear, highlighting the importance of integrated monitoring for understanding performance dynamics in manufacturing systems. The proposed framework provides an empirically grounded approach for linking process stability, operational efficiency, and financial outcomes, supporting performance evaluation and continuous improvement in automotive manufacturing.
The increasing reliance on wind energy necessitates a deeper understanding of the unsteady aerodynamic behavior of wind turbines operating under variable speed conditions. This study employs computational fluid dynamics (CFD) simulations to investigate the flow dynamics around a large wind turbine subjected to height-dependent wind speed variations common within the atmospheric boundary layer (ABL). Utilizing a fine computational mesh and the k-omega shear stress transport (SST) turbulence model, we explored the interaction between wind turbine rotor aerodynamics, wake structures formation and evolution, and power performance under nonuniform operational conditions. The obtained numerical results highlight the nonlinear relationship between power output and angular velocity, demonstrating peak power generation at an optimal rotational speed (1.25 rad/s) before aerodynamic losses reduce performance. Additionally, the study examines variations in power and thrust coefficients, emphasizing their dependence on tip speed ratio and wind inflow characteristics. The computed power coefficient curve matches closely the corresponding experimental one, thus validating the adopted numerical setup as well as the assigned boundary conditions. Velocity contour analyses reveal critical regions of flow deceleration and separation, intensive turbulence, and wake interactions, providing insights for optimizing turbine design and control strategies. The findings underscore the importance of accurate inflow conditions and turbulence modeling in improving the reliability of estimated wind turbine.
This paper presents an integrated artificial neural network-genetic algorithm (ANN-GA)-based framework for multi-parameter design optimization of a large-scale concrete 3-dimensional (3D) printer frame in a discrete design space. Based on practical design requirements and operating conditions, a finite element (FE) model of the printer frame is developed using APDL (R) scripting, enabling automated evaluation of printhead deflection and natural frequencies. Using the FE-generated dataset, a multilayer feed-forward neural network (MLFFNN) is trained as a surrogate model to predict the structural responses of the frame. Parametric investigations demonstrate that the artificial neural network (ANN) surrogate model substantially reduces computational time while maintaining high prediction accuracy, with errors ranging from 1 % to 4 % compared to direct FE analysis. A mass-minimization optimization model is then formulated with ten design variables and four constraints related to printhead deflection and natural frequencies. Genetic algorithms are employed to solve the optimization problem using two different approaches: direct optimization coupled with FE analysis and surrogate-based optimization using the ANN model. Notably, the optimization is conducted in a discrete design domain consistent with the standard dimensions of commercially available steel box sections. The optimal solutions obtained from different optimization strategies, including continuous and discrete FE-based models, the ANN surrogate model, and an experience-based design, are systematically compared. The optimization results demonstrate that the proposed framework achieves a structural weight reduction of 27 % to 38 % compared to the initial experience-based design. Furthermore, the ANN-based surrogate optimization reduces the total computational time from approximately 38 hours to about 200 seconds, clearly demonstrating the efficiency and practical applicability of the proposed approach for real-world large-scale machine design.
Supersonic wind tunnel testing is critical for aircraft aerodynamic configuration validation, where test chamber flow uniformity and turbulent pressure pulsations are core determinants of test data reliability. Existing back-pressure matching studies only optimize flow uniformity as a single objective, ignoring its coupling effect on pressure pulsations. This study proposes a rapid Reynolds-averaged Navier-Stokes (RANS)-based back-pressure matching method with the shear stress transport (SST) k-omega model to determine the optimal back-pressure P-m (ideal expansion at <5 % jet centerline Mach number deviation). Multiscale Large Eddy Simulations (LES) show ideal expansion extends the uniform core by 23 % with 3.4 % velocity pulsation standard deviation, while back-pressure mismatch causes up to 18 % total pressure loss via periodic shocks. Critically, ideal expansion yields the strongest pressure pulsations as unimpeded shear layer turbulence develops fully, uncovering a key trade-off: shock-based turbulence suppression reduces pulsations but sacrifices 7 % to 15 % flow uniformity. This work fills the research gap of coupled uniformity-pulsation analysis and proposes scenario-specific back-pressure strategies for supersonic wind tunnel tests.
Mechanized onion planting is crucial for improving efficiency and reducing labor costs. However, traditional elliptical gear-driven planting mechanisms often exhibit issues such as unstable trajectories and excessively high velocities and accelerations. To address this, this paper proposes a parallelogram planting mechanism based on a denatured Pascal limacon gear drive. By analyzing the mechanism's operating principle and the transmission characteristics of the denatured Pascal limacon gear, a kinematic model was established. The effects of parameters such as gear denaturation coefficient, drive speed, and link dimensions on the planting point trajectory and motion velocity were investigated. Results indicate that the denaturation coefficient, crank length, and initial mounting angle significantly influence the mechanism's performance. Based on these findings, multi-objective optimization using a genetic algorithm was conducted to meet agronomic requirements. The optimized mechanism achieves a planting depth of 27 mm at a forward speed of 0.3 m/s and a gear angular velocity of 2 pi rad/s. Horizontal velocities at soil entry and exit approach zero, acceleration changes gradually during the planting phase, and operational stability is significantly enhanced. Compared to the elliptical gear drive configuration, it demonstrates superior overall performance.
To prolong the service life of the rolling bearings and improve the reliability of the associated mechanical systems, inspired by the leaves of Monstera deliciosa, eleven biomimetic texture patterns, featuring various leaf-geometry characteristics - such as leaf-veins, elliptical holes, and their combinations - were designed and prepared on the raceway of the shaft washer of cylindrical rollers thrust bearings using a laser surface texturing method. The effects of these leaf-inspired patterns on the tribological and friction-induced vibration performance of rolling bearings were systematically investigated under starved lubrication conditions. The results show that a significant "superposition effect" on vibration signals was observed in the earlier stages of testing, but this effect diminished over time. Larger aspect ratios of elliptical holes did not improve the friction-wear performance of the biomimetic textured groups. When the elliptic area was larger, the bearing experienced relatively lower wear losses, higher average coefficients of friction, and greater fluctuations in time-domain vibration signals. The influence of different elliptic areas on the frequency-domain vibration signals was minimal. This work would provide a valuable insight into the raceway optimization of rolling bearings.
Alumina ceramic gears exhibit excellent mechanical properties as well as resistance to high temperatures and corrosion, making them suitable for extreme working conditions that traditional metal gears cannot accommodate. However, their inherent high hardness and brittleness present significant challenges in ensuring high-quality surfaces during molding and manufacturing. In this study, alumina ceramic gears were polished using a picosecond pulsed laser. By proposing a novel alternating superimposed scanning strategy, processing errors were effectively reduced, and surface integrity was enhanced. A univariate experimental approach was used to optimize the key laser processing parameters, including laser power, scanning speed, number of scans, and line spacing. The optimal combination of parameters (7 W power, 220 mm/s scanning speed, 4 scans, and 0.005 mm line spacing) was finally determined to obtain a tooth surface with a surface roughness (S-a) of 1.091 mu m (+/- 0.025 mu m). Comparative analysis showed that the surface roughness was significantly reduced by 41.93 % to 44.53 % compared with the conventional machining (1.922 mu m). In addition, the microhardness of the laser-treated tooth surface increased by 6.36 % and showed improved resistance to tooth chipping under localized high-load conditions. The enhanced surface flatness and mechanical properties significantly improve the meshing performance required for mechanical transmission systems. Notably, the laser surface treatment method significantly reduces the processing cost compared with the traditional mechanical polishing process, providing a cost-effective alternative for ceramic gear molding surface treatment process. This paper innovatively applies laser polishing directly to the tooth surfaces of actual ceramic gears featuring complex curved surfaces, thereby providing crucial process support for their practical application in high-precision transmission systems.
This paper presents a novel double-arc geometric analytical model to analyze the static characteristics of convoluted air springs (CAS). The model assumes that the profile of the bellows is composed of double circular arcs with different radii of curvature and considers the effects of bellows stretching deformation. Based on this model, this paper proposes a hybrid analysis method that uses the CAS geometric analytical method instead of the computationally expensive fluid-structure interaction (FSI) static analysis. Using the geometric parameters and internal pressure of the CAS under loaded equilibrium, as derived from the proposed double-arc model, an FSI simulation model of the CAS is established in Fluent for subsequent static and dynamic characteristic analysis. The feasibility of the double-arc model and the proposed analysis method is validated through static and dynamic characteristic experiments of the CAS. Furthermore, the hybrid analysis method is applied to analyze the dynamic response of an air spring vibration isolation system. A comparison with the results of the traditional full-process FSI simulation demonstrates that the proposed method improves the average computation efficiency by approximately 10.8 % while maintaining computational accuracy.
This study investigates the deformation coordination of an engine crankshaft-bearing system and presents a tolerance-based method to improve stiffness matching. First, a continuous-beam analytical model is developed to derive bearing support reactions, with its accuracy being validated through a threedimensional finite element model under worst-case loading conditions. Then the Reynolds equation is combined with the Gumbel boundary condition to explain hydrodynamic effects. Based on the journal eccentricity, the load-carrying coefficient is described by fitting an exponential function. Finally, using the relative bearing clearance as the design variable, a clearance-optimization model is formulated to minimize the variance in support displacements. The results show that the sequential quadratic programming method after particle swarm optimization can improve the deformation coordination by 31 %. This method can provide a practical and computationally efficient guideline for improving structural stiffness matching and lubrication performance in crankshaft-bearing system design.
As a critical component of helicopter variable-speed transmission systems, the wet clutch directly influences transmission stability; consequently, clutch plate warping is a primary cause of operational malfunctions. This study investigates the engagement characteristics of warped friction pairs by developing an inter-plate load-carrying capacity model. This model integrates geometric, flow field, and microscopic contact characteristics, alongside the force-displacement properties of the separation spring and warped plates. Through the coupling of axial and circumferential motions, key parameters, including inter-plate bearing capacity and transmitted torque, are quantitatively determined. The results indicate that the engagement process of a warped friction pair consists exclusively of squeeze and mixed friction phases, which correspond to the non-contact, deformation, and plastic stages of the steel plate. During the squeeze phase, the piston pressure is balanced solely by the hydrodynamic pressure of the oil film, whereas in the mixed friction phase, it is supported by a combination of oil film pressure and micro-asperity contact forces. Furthermore, friction pair type 2 exhibits slightly lower torque transmission due to spline frictional resistance. Engagement tests conducted using an MM6000 tester validate the reliability of the proposed model, demonstrating engagement time errors of less than 8.5 %.
To address the issues related to the elastic deformation of non-circular profiles during high-speed grinding, this study proposes a novel mathematical model for predicting the deviation between preset and actual grinding depths in multi-pass operations. The model establishes a correlation between the feed displacement of the high-speed grinding wheel frame and the rotational angle of the camshaft's non-circular contour. A series of experiments were conducted on a dedicated high-speed grinding platform to examine the influence of grinding depth, number of grinding passes, and grinding wheel speed on the elastic deformation and the dynamic stiffness of the grinding system. The results show that the discrepancy between the theoretical and measured displacements remains within 5.56 %, confirming the accuracy and robustness of the proposed model. Increasing the number of grinding passes significantly reduces feed errors induced by the elastic concession of non-circular profile, with the maximum elastic deformation displacement decreasing markedly from 68.9 % to approximately 1 % of the preset depth after five passes. This study pioneers the incorporation of the elastic concession characteristics of non-circular profiles into grinding deformation analysis, providing both a theoretical basis and practical guidance for compensating elastic deformation in camshaft grinding, thereby effectively improving machining accuracy and process stability.
Modern numerical models use time-dependent material parameters as input data to simulate the viscoelastic response of polymers. Reliable numerical predictions therefore depend on the accurate determination of these parameters. Understanding the measurement uncertainty associated with their identification is essential for assessing the expected range and reliability of the simulation results. Although creep-based uncertainty analyses have been reported for other materials, uncertainty evaluations for polymers that require the determination of multiple viscoelastic material functions remain scarce, with existing polymer studies primarily relying on relaxation tests. This study experimentally analyzes the viscoelastic behavior of polypropylene at 60 degrees C through tensile and shear creep tests based on extensional and rotational rheometry. The tensile, shear, and bulk compliance functions were determined together with their corresponding standard and expanded measurement uncertainties in accordance with the JCGM 100:2008 guideline. Type A uncertainties were found to dominate the overall uncertainty, with relative expanded uncertainties of approximately 3 percent for shear compliance and up to 25 percent for bulk compliance. The study identifies the main sources of uncertainty and proposes strategies for their reduction, including increasing the number of measurement repetitions and improving environmental control. Overall, a comprehensive uncertainty evaluation of the creep-based determination of viscoelastic material functions is presented, leading to more reliable input data for numerical simulations.
The five-degrees-of-freedom (5-DOF) hybrid polishing robot is utilized for machining large optical mirrors. Since the existing kinematics control strategy does not meet high-precision control requirements, it is necessary to develop a dynamics controller to improve the operational performance of the robot. To enhance the trajectory control accuracy of the polishing robot's end-effector, a sliding mode control algorithm based on a nonlinear disturbance observer is proposed. First, the dynamic model, which accounts for joint friction effects, is derived using the Newton-Euler method, and a complete explicit dynamic model is established through parameter substitution. Subsequently, considering the influence of the coupling of inertia parameters of each component in the dynamic model on computational efficiency, the model is simplified while compensating for errors caused by neglected terms via the Whale Optimization Algorithm-Elman (WOA-Elman) algorithm to reconstruct the dynamic model with error compensation terms. Finally, based on an analysis of the reaching law and the design of the nonlinear disturbance observer, the end-effector trajectory sliding mode control algorithm is developed. Simulation and experimental results indicate that the inclusion of an improved reaching term effectively reduces system chattering. Furthermore, the nonlinear disturbance observer is employed to estimate system errors and external disturbances, significantly mitigating error fluctuations during the convergence process and thus validating the robustness and high precision of the trajectory tracking control system for the polishing robot.
To address inaccurate external force estimation caused by nonlinear friction in robotic systems, this paper proposes a friction compensation and external force estimation method based on an adaptive neuro-fuzzy inference system (ANFIS). The approach integrates Stribeck friction modeling with a Takagi- Sugeno fuzzy inference structure to identify joint friction parameters from measured data. Experimental results show that ANFIS yields lower identification errors and better generalization performance than baseline methods including fuzzy neural networks, particle swarm optimization, and least squares. The implemented feedforward compensation strategy achieves maximum torque errors of 0.263 Nm and 0.184 Nm for the two joints, lower than those obtained by the compared approaches. By incorporating the identified friction model into a generalized momentum observer with median and Butterworth filtering, the proposed method reduces the root mean square error and maximum absolute error by 18.3 % and 27.9 %, respectively, and achieves a coefficient of determination (R2) of 0.994. In collision detection tests, the method identifies impact events with reduced false alarm rates under the same experimental settings, supporting its applicability to high-precision force control in robotic applications.
To address the challenges of low calibration efficiency and limited accuracy in the discrete element modeling of wet concrete within high-dimensional parameter spaces, this study developed a parameter calibration scheme that integrates experimental design and intelligent algorithms. It achieved efficient and high-precision inverse function optimization for determing contact parameters, thereby providing a robust foundation for related engineering simulations. Specifically, the repose angle of wet concrete was determined to be 32.07 degrees based on the heap experiment. Through the Plackett-Burman (PB) experiment and steepest ascent experiment, the three parameters with the greatest influence on the repose angle of wet concrete and their optimal value ranges were identified. These parameters are static friction (X1), the coefficient of rolling friction (X2), and surface energy (X3). Subsequently, using the Box-Behnken (BB) test, the optimal 17 sets of combined data for these three significant factors were determined. To establish the objective function between the repose angle of wet concrete and its influencing parameters, and to obtain optimal parameter values, the particle swarm optimization (PSO) - back propagation (BP) - genetic algorithm (GA) method (PSO-BP-GA) is adopted. First, 80 % of the 17 sets obtained from the BB test were used as the training samples for the BP neural network (BPNN), while the remaining 20 % served as test samples. Then, the PSO is used to optimize the weights and thresholds within the BPNN. After deriving the objective function, GA was utilized to perform inverse function optimization, targeting repose angle of 32.07 degrees. Finally, the static friction coefficient (X1) between wet concrete particles was determined to be 0.158, the rolling friction coefficient (X2) 0.187, and the surface energy (X3) 1.580 J/m2. With these parameters, five simulations were conducted, yielding an average repose angle of 32.31 degrees. Compared with the actual repose angle, the relative error was 0.748 %.
This editorial introduces the Special Issue of the Strojniški vestnik - Journal of Mechanical Engineering dedicated to the 30th anniversary of the Faculty of Mechanical Engineering as an independent member of the University of Maribor, and the 50th anniversary of the University of Maribor. The Faculty of Mechanical Engineering is one of the most successful members at the University of Maribor and is recognised for its excellence in education, research and collaboration with industry. Its history of development, from its early beginnings in 1959 to becoming an internationally active and research-driven institution, reflects a continuous commitment to technological progress and societal impact. The Special Issue presents a selection of articles covering applied fluid mechanics, advanced materials and metamaterials, manufacturing science, and biomedical modelling. The collected works combine experimental, numerical, and review-based approaches to address contemporary challenges in mechanical engineering. This publication not only highlights the scientific excellence achieved at the Faculty of Mechanical Engineering, University of Maribor, but also celebrates its enduring mission to connect knowledge, innovation and human creativity in shaping a sustainable and technologically advanced future.
To investigate the influence of typical mode shapes and wheel polygons on the dynamic characteristics of railway freight car body, this study takes railway freight wagon C80 as the research object. Vehicle-level and system-level finite element models of the C80 railway wagon were developed, revealing that the lateral and vertical stiffness of railway freight cars significantly affects system mode. Furthermore, co-simulation using NASTRAN and SIMPACK was used to establish a fully flexible dynamic model of the C80 wagon. The influence of typical modal frequencies on the dynamic performance of the wagon was analyzed considering the wheel polygon and wear parameters. The results show that suppressing the torsional mode shape of the railway wagon reduces its impact on the derailment coefficient, wheel load reduction rate, axle transverse force and overturning coefficient by over 40 %. When the train speed corresponds to the polygon order, the wheel load reduction rate increases with the polygon wear depth under both the 10th order and 18th order conditions, by as much as 14.4 % when wear depth increases from 0.01 mm to the 0.05 mm for the 18th order condition. In addition, under the 6th order, 10th order, 16th order and 18th order polygon conditions, suppressing the torsional mode notably increased the wheel load reduction rate, especially under the 6th order polygon condition. This research provides valuable guidance for optimizing suspension parameters and controlling polygonal wear in railway wagons, offering a guiding basis for enhancing their dynamic performance of railway wagons.
The environmental hazards associated with coal mining operations are extremely high, making the use of robotic arms to replace manual labor crucial for improving both the safety and cost-effectiveness of the work process. To address the various environmental constraints, such as spatial limitations, obstacles, and internal and external disturbances, this study proposes a kinematic-based tracking and control method for mining robotic arm. The objective of this numerical study is to mitigate the impact of environmental constraints on the stability of robotic arms, ensuring that they can maintain high precision and stability in complex operating conditions. Simulation results showed that the proposed method enabled the robotic arm to achieve operational thrust peaks exceeding 13,968 N and screw torque peaks greater than 0.06 Nm. The system reached steady state in an average of 0.24 s, with an error reduction of 2.3 %. Compared to other methods, the disturbance tracker reduced the average error by 2 %, and the feedback controller decreased the prediction lag by 5 %. Overall, this method significantly enhances the accuracy and stability of robotic arms in coal mining operations, making it a promising approach for real-world applications.
The design of the metro body structure must balance both safety and cost indicators. The underframe is not only the main load-bearing component of the metro body but also accounts a significant portion of its overall mass. To reduce operational costs and enhance the safety performance of the metro body, this paper focuses on optimizing the design of the underframe. A two-stage optimization approach was proposed, addressing the limitation of existing methods and the challenges in balancing realistic operating conditions with manufacturability. First, manufacturing constraints were incorporated using the variable density method, and topology optimization of the underframe sub-model was carried out with the objective of minimizing flexibility-weighted strain energy. Next, the rough topology was refined through parametric optimization after determining the approximate shape of the cross section, resulting in a more precise model. The results show that the proposed optimization method reduces underframe mass by about 4.7 % while lowering the maximum deflection of the metro car body under the maximum vertical load case by 0.601 mm. This demonstrates that the proposed framework efficiently combines optimization capabilities with simplicity.
