Mechanika (ISSN: 1392-1207, eISSN: 2029-6983) journal publishes theoretical and practice oriented papers, dealing with problems of Mechanics of Solid Bodies, Mechanics of Fluids and Gases, Dynamics of Mechanical Systems, Design and Optimization of Mechanical Systems, Mechanical Technologies. Impact Factor: 0.8 (2025); 5 Year Impact Factor - 0.8 (2025) according to Journal Citation Reports (Clarivate, 2026).
The copyright for the articles in this journal is retained by the author(s) with the first publication right granted to the journal. The journal is licensed under the Creative Commons Attribution License 4.0 (CC BY 4.0).
Design of the Complex Aluminum Bridge Extrusion Die Through Finite Element Analysis
In this study, the aluminium bridge die extrusion process for AA6061 alloy was investigated using a coupled thermomechanical finite element analysis (FEA) combined with industrial extrusion experiments. The process parameters, including die temperature, ram speed, billet diameter, and billet length, were defined according to industrial production conditions. Unlike conventional approaches that evaluate die parameters separately, this study presents an integrated optimization of a complex bridge die by simultaneously modifying baffle plates, pocket geometry, and bearing lengths. The numerical model was used to analyze the temperature and velocity distributions at the die exit and to evaluate stress levels within the bridge die structure. The initial die design exhibited severe flow imbalance, characterized by a high velocity relative difference (VRD ≈ 25.3%) and excessive exit temperatures, which led to profile bending, dimensional deviations, and insufficient welding pressure. Through systematic design modifications, the optimized die configuration significantly improved the flow balance, reducing the VRD to approximately 4.6% while maintaining exit temperatures within the desired range. The improved metal flow also increased the welding pressure and enhanced the dimensional stability of the extruded profile. Mechanical tests confirmed the effectiveness of the optimized design, showing increases in tensile strength and yield strength, as well as a more uniform hardness distribution across profile regions with varying thickness. The results demonstrate that a coordinated optimization of baffle plates, pocket geometry, and bearing lengths is essential for achieving balanced metal flow and high weld quality in bridge die extrusion. The combined numerical–experimental approach provides a reliable methodology for improving the performance and structural reliability of complex aluminium extrusion dies used in industrial production.
DESIGN AND OPTIMIZATION OF MECHANICAL SYSTEMS
Copyright (c) 2026 Mechanics https://creativecommons.org/licenses/by/4.0/
Mechanical and Geometric Optimization of Laser Cutting Processes for HDF Components Used in Packaging and Printing Products
The article presents a method for mechanical and geometric optimization of laser cutting processes for HDF (High Density Fiberboard) components used in packaging and printing products. The proposed approach combines polygon-based nesting algorithms with technological constraints of laser processing and mechanical characteristics of sheet materials. The developed model considers irregular geometry of parts, laser kerf width, minimum technological gaps, material anisotropy, and thermal influence during cutting. A multi-criteria optimization function is introduced, which simultaneously minimizes material waste, cutting path length, and mechanical degradation of produced components. The developed algorithm is based on a combination of geometric preprocessing, Bottom-Left placement strategy, inner nesting optimization, and common-line cutting principles. The proposed solution allows increasing material utilization efficiency and improving the functional quality of manufactured HDF components.
Machine Learning for Prediction of Surface Roughness in Stellite 6 Turning: A Comparative Study of ANN, Random Forest and Support Vector Regression Models
In recent years, the landscape of predictive modeling has been significantly transformed by the adoption of sophisticated methodologies, collectively known as soft computing techniques. The surface condition of a machined part plays a crucial role in its overall performance. It can significantly affect properties such as wear behavior, resistance to corrosion, and fatigue strength. Among the various indicators used to evaluate this finish, surface roughness (Ra) remains one of the most important criteria for assessing the quality of a machined surface. Stellite 6 is a cobalt-based alloy widely employed in applications that demand high wear resistance and withstand elevated temperatures. While its mechanical strength makes it highly reliable in harsh environments, it also makes the alloy difficult to machine, particularly when aiming to achieve a good surface finish. The problem is framed using the Design of Experiments (DOE) to measure the Ra of Stellite 6 turning: a total of 27 experiments were performed.To better anticipate surface roughness during turning, this study investigates the performance of several machine learning (ML) models developed in Python. Three algorithms: Artificial Neural Networks (ANN), Random Forests (RF), and Support Vector Regression (SVR) were trained using an experimental dataset that includes tool noise radius, cutting speed, feed rate, and depth of cut. Model performances were assessed using coefficient of determination (R²), root mean squared error (RMSE), and mean absolute error (MAE) metrics. Among the evaluated models, the RF achieved the highest prediction accuracy, followed by ANN and then SVR. The results highlight the capability of Python-based machine learning approaches to capture the nonlinear relationships between cutting parameters and surface roughness. The Random Forest Regressor (RFR) demonstrates the best ability to predict surface roughness (Ra), closely followed by the Epsilon-SVR using the RBF kernel. RFR is less sensitive to data scaling and effectively fits the complex non-linear relationships in the synthetic dataset. This work provides a foundation for future strategies that integrate both advanced prediction to enhance the machinability of hard-to-cut materials such as Stellite 6.
Sensitivity of Flutter Modelling Using the Non-Linear Vortex Lattice Method to Changes in Viscous Airfoil Data
This paper presents an analysis of how sensitive non-linear vortex lattice method based aeroelasticity models are to variations in viscous airfoil data. These models are a useful low-fidelity tool for fast aeroelastic analysis, often used in early aeroelastic aircraft design and flight control law development. However, they are reliant on pre-existing availability of viscous airfoil data, which can be difficult to obtain without expensive wind tunnel testing or high-fidelity CFD computations. To determine how sensitive the aeroelastic results are to the variations in this airfoil data, a flutter simulation is run for the same wing test case, but using several different airfoil performance curves. The results are analysed to determine how much influence on the final flutter results these changes have.
Modeling and Simulation of Screw-in Cartridge Three-Way Flow Control Valve Based on AMESim
Due to their compact structure and high flow capacity, screw-in cartridge three-way flow control valves are widely used in high-end equipment. Therefore, by analyzing the functional characteristics of such valves, this paper establishes its mathematical model during operation. AMESim software is adopted to investigate the influences of structural parameters and load conditions on its performance. Simulation results show that the flow rate through the priority port remains constant when the load at the priority port or the bypass port changes. Increasing the opening area of the flow sensing port, the spring preload, and the spring stiffness leads to an increase in the flow rate through the priority port. The simulation results are consistent with the theoretical analysis, which provides a theoretical basis for the design of screw-in cartridge three-way flow control valves.
screw-in cartridge three-way flow control valve
Process Optimization of Ultrasonic Vibration-Assisted Electrical Discharge Machining for Brass Nozzles Based on Orthogonal Experiments and IPSO-BP Model
Ultrasonic vibration assisted electrical discharge machining (EDM) is a novel precision machining method capable of further enhancing the efficiency and quality of EDM. Brass is frequently utilized as the material for precision agricultural spray nozzles due to its corrosion resistance and superior wear resistance compared to plastics. To obtain the optimal process parameters for ultrasonic vibration assisted EDM of brass, an orthogonal experiment scheme with five factors and four levels was designed. The material removal rate (MRR) and electrode wear rate (EWR) were selected as the primary evaluation indicators, while machining voltage, spindle speed, ultrasonic power, pulse frequency, and feed rate served as the main process parameters. Simultaneously, an improve particle swarm optimization (IPSO)-BP neural network model is train based on the results of the orthogonal experiments. By combining the aforementioned methods, the optimal process parameters for MRR were determined as follows: machining voltage of 95V, spindle speed of 2000 r/min, ultrasonic power of 45%, pulse frequency of 25 kHz, and electrode feed rate of 0.20 mm/s. The optimal process parameters for EWR were identified as: machining voltage of 65V, spindle speed of 3000 r/min, ultrasonic power of 75%, pulse frequency of 30 kHz, and feed rate of 0.16 mm/s. In the verification of above two sets of optimal parameters, the IPSO-BP model achieved R2 accuracies of 87% and 94%, respectively. The overall prediction performance of the model is satisfactory, indicating that the model is capable of assisting and guiding the formulation of process plans for the subsequent ultrasonic vibration assisted EDM of brass nozzles.
Effect of Suction and Injection on the Time-Varying Flow Produced by an Oscillating Porous Disk and a Newtonian Fluid at Infinity Rotating about Parallel Axes
In this paper, the time-dependent flow of a Newtonian fluid generated by a porous disk performing oscillation without torsion and the fluid at infinity rotating about two different axes is considered. As the disk and the fluid at infinity are rotating in an eccentric form at the beginning of the oscillation motion, the disk initiates oscillations without torsion within its own plane. The exact solutions for the velocity and shear stresses within the fluid are presented and the influences of the suction and injection are analysed. It is observed that the application of suction leads to a decrease in the boundary layer thickness whereas it increases with the implementation of injection. Furthermore, a periodic solution is presented and it is shown that the elapsed time until the flow becomes periodic occurs shorter for both suction and injection cases.
Experimental Research on Thermal Deformation Error of CNC Machine Tools Based on Neural Network
During precision machining of CNC machine tools, machining accuracy is affected by multiple error factors, among which thermal deformation error accounts for 40% to 70%, becoming a key bottleneck restricting accuracy improvement. Existing thermal error prediction methods are mostly based on static mapping relationships, directly predicting thermal error at a given moment using temperature sensor data from the same moment, thereby neglecting the dynamic, time-dependent evolution characteristic of thermal error itself. In actual machining, inevitable time delays exist between temperature data acquisition, model computation, and the execution of error compensation, leading to predicted values lagging behind the actual compensation values required. This significantly reduces compensation effectiveness and machining accuracy. To address this prominent issue, this paper proposes a novel thermal error ahead-of-time prediction method based on Long Short-Term Memory (LSTM) neural network. The core of this method lies in leveraging the advantages of LSTM networks in processing time-series data. It utilizes historical and current time-series data from key temperature sensors as training samples to construct a temperature-ahead prediction model, thereby achieving early and accurate estimation of future thermal error. This study elaborates on the fundamental principles of LSTM neural networks, their gating mechanisms, and the specific application process in ahead-of-time prediction modeling. Simulation machining experiments were conducted on a self-developed aspheric surface CNC grinding machine, collecting multiple sets of temperature and thermal error time-series data for model training and validation. Experimental results show that the LSTM-based ahead-of-time prediction model can effectively predict thermal error changes within the next 10 minutes. The maximum residual, mean residual, and mean square error between the predicted and actual values are maintained at low levels, demonstrating significantly improved prediction accuracy. Compared with traditional thermal error modeling methods based on multiple linear regression, the LSTM model not only achieves timeliness in prediction but also exhibits clear advantages in prediction precision, with smaller mean residuals and variance, validating the model's effectiveness and advancement in solving the thermal error compensation lag problem and improving the machining accuracy of CNC machine tools.
Long Short-Term Memory (LSTM) neural network
CFD-Predicted on Rotordynamic Characteristics for Labyrinth Seal Under Gas-Liquid Two-Phase Conditions
In this study, computational fluid dynamics (CFD) was integrated with dynamic mesh technology and user-defined functions (UDFs), and the whirling motion equation was derived in detail. A numerical simulation investigation was conducted on the rotordynamic characteristics of labyrinth seals under gas-liquid two-phase conditions. The reliability of the numerical model was verified by experimental measurements of leakage flow rate, and the results showed that the variation trends of the two were consistent with a maximum relative error of 22.3%, indicating a good applicability of the model. The effects of three pressure drops, three rotor speeds and three different gas volume fractions (GVF) on the frequency-independent and frequency-dependent rotordynamic coefficients (including direct stiffness, cross-coupling stiffness, damping coefficients and effective coefficients) were analyzed systematically. The results demonstrated that an increase in pressure drop enhanced the direct stiffness and direct damping, while a rise in GVF led to a reduction in these coefficients; an increase in rotor speed improved the direct stiffness but decreased the direct damping, and exacerbated the negative effect of cross-coupling damping. The effective stiffness (Keff) exhibited a parabolic variation trend with the whirling frequency, whereas the effective damping (Ceff ) remained positive over the entire frequency range, which ensures the operational stability. This study provides a theoretical basis for the design and optimization of labyrinth seals in two-phase flow environments, and is conducive to improving the operational stability of industrial rotor systems.
Study on the Rehabilitation Performance of 2-SPS-RRR Adjustable Hip Exoskeleton
Based on computer-aided ergonomics the paper proposes a hip joint rehabilitation exoskeleton utilizing a adjustable spherical parallel mechanism, specifically designed for rehabilitation training of individuals with impaired hip joint movement function. Initially, the screw theory is employed to determine the degrees of freedom of the parallel mechanism, revealing that it possesses three rotational degrees of freedom, enabling sagittal plane movements, frontal plane movements and transverse plane movements at the hip joint. The closed vector method is utilized to derive the inverse position solution, and the velocity Jacobian matrix is solved. Leveraging the inverse position solution and the constraint relationships between mechanisms, the reachable workspace of the mechanism is obtained using MATLAB software programming. Utilizing screw theory and velocity Jacobian matrix, the dexterity of the mechanism is analyzed. Ultimately, a human-robot integrated system is established in AnyBody software to verify its capability to achieve certain rehabilitation effects on human muscle groups. Research demonstrated that the hip joint parallel mechanism exhibits good stability and can effectively assist individuals with hip joint dysfunction in rehabilitation training.
Automated Temperature Control System for a Multi-Point Airflow Channel Using PID and MPC Controllers
This study compares MPC with PI and PID controllers for temperature control. While PI and PID are effective for simple and stable systems, MPC shows better performance in complex and dynamic conditions. Laboratory experiments confirm that MPC handles disturbances, airflow variations, and setpoint changes more accurately, with improved stability and reduced overshoot. The results highlight MPC as a more efficient and reliable solution for advanced temperature control applications, with strong potential for use in larger industrial systems.
Research on Error Compensation of CNC Machine Tool Based on Multibody System Kinematics
This paper focuses on the error compensation of CNC machine tools based on the theory of multibody system kinematics. By abstracting the structure CNC machine tools into a multibody system composed of motion pairs and components, a complete spatial error model from driving input to tool execution is constructed using homogeneous coordinate transformation matrix which deeply reveals the generation, transmission, accumulation and coupling mechanism of various geometric errors. The article describes in detail the establishment of coordinate system in multibody system, the homogeneous coordinate method of points and vectors, and the process of constructing the zero-order kinematic equation, and on this basis, the general geometric error model with dual branches of "workpiece- tool" and "tool-machine tool" is established, and the mathematical expressions of position error and attitude error are derived, and the error compensation condition equation for realizing precision machining proposed. In the experimental verification, Renishaw ML10 dual-frequency laser interferometer is used to measure and compensate the error of Z-axis of VMC-500 CNC machine, the results show that the variance of error after compensation is significantly reduced from 6.4534 and 5.8352 before compensation to 3.61, the error fluctuation range is significantly narrowed, and the effectiveness and engineering applicability of the proposed model and compensation method are verified. The research provides systematic theoretical support and path for the precision improvement of CNC machine tools.
Optimization Design Method of High-Speed Angular Contact Ball Bearings
With the rapid development of high-speed rotating equipment technology, angular contact ball bearings (ACBBs) have been increasingly widely applied in industry. However, there is still a lack of systematic design theory support for their service performance under high-speed operating conditions. In existing engineering practices, the design of ACBBs often focuses only on a single performance index, such as rated dynamic load, spin-roll ratio, or static stiffness, with little consideration of the dynamic behavior changes caused by the ball ‘outward-thrown’ under high-speed operation. To fill the above research gaps, this paper innovatively introduces dynamic stiffness as a key design index and proposes a comprehensive design method integrating spin-roll ratio and rated dynamic load. By establishing the kinematic model of balls in the raceway, the analytical relationship of the spin-roll ratio under high-speed steady-state conditions is derived; the applicability of different dynamic load rating calculation models is systematically compared, and a mathematical model of dynamic stiffness is constructed along with a numerical solution strategy. On this basis, the intrinsic laws governing the evolution of rated dynamic load, spin-roll ratio, and dynamic (static) stiffness with key design parameters are revealed. This study clarifies the coupling mechanism of key design parameters under high-speed conditions and establishes a parameter design strategy based on collaborative optimization. It provides a theoretical basis and methodological support for solving problems in engineering applications of high-speed ACBBs, such as inaccurate parameter selection and difficulty in ensuring dynamic performance. The research results help to further improve the design system of high-speed ACBBs and have important guiding significance for enhancing the design quality and service reliability of bearing products.
Copyright (c) 2025 Mechanics https://creativecommons.org/licenses/by/4.0/
Investigation of The Use of Biodiesel Produced from Waste Cooking Oil as Fuel for a CI Engine Under Different Operating Parameters
The environmentally responsible disposal of waste cooking oil is a significant concern for numerous nations. One method to repurpose waste cooking oil is to employ it as diesel fuel. This approach diminishes both the expense of fuel feedstocks and the ecological repercussions of waste cooking oil. In this study, the use of waste cooking oil as fuel was achieved by converting it into biodiesel thru the transesterification method. The negative thermos-physical properties of biodiesel, due to its high density and viscosity, have been improved by increasing the injector injection pressure. Furthermore, the optimal injection advance value for engine torque was established, and combustion properties were enhanced. The impact of varying injection pressures and timings of biodiesel on engine performance, combustion characteristics, and exhaust emission parameters was examined in a three-cylinder, naturally aspirated direct injection diesel engine. Consequently, increasing both injection pressure and advance favorably affected combustion characteristics such as ignition delay, combustion duration, and the crank angle at which maximum pressure was achieved. Thanks to an increase in injection pressure, the specific fuel consumption improved by 6.74% and thermal efficiency by 10.14% compared to using biodiesel into regular engine parameters. The impact of injection advance was more significant in the exhaust emission parameters. Extra reductions of 7.92%, 5.79%, and 3.14% were established for CO, HC, and smoke opacity emissions, respectively. Enhanced combustion efficiency, leading to elevated end of combustion temperatures, resulted in a rise of up to 15.66% in NOX emissions.
Composite Nanofiltration Membranes for Water Treatment: Implementation and Educational Issues
This paper implements a Case Study Analysis method within the Mechanical engineering masters’ course Module T210M109 Microelectromechanical Systems Design at Kaunas University of Technology. This method guides students through experimental planning, fabrication, characterization, and interpretation, leading to deeper understanding of membrane composition and its structure, and relationship between properties and real-world engineering application areas. The paper examines the implementation and educational issues in design of composite nanofiltration membranes used in removal of microplastics (MPs) and nanoplastics (NPs) from water. It highlights both, the scientific challenges of membrane fabrication and the pedagogical value of integrating Case Study Analysis into engineering. Thus, students demonstrated improved ability to make technical decisions, analyze the cases of different microfabrication techniques and interpret the obtained results with the help of Case Study method.
Study on the Creep Deformation Characteristics of the Cylindrical Tubes under External Pressure
Cylindrical tubes are fundamental structural components across many engineering fields, particularly for bearing external loads. In pressurized water reactors (PWR), the metal cladding tube acts as the first safety barrier, containing radioactive materials under extreme conditions of external pressure, high temperature, and intense fast neutron flux. Accurate prediction of its creep deformation remains a critical challenge in nuclear engineering. This study analyzes the coupled thermal and irradiation creep behavior of cladding tubes using the commercial finite element code ABAQUS and its user subroutine CREEP. The results demonstrate that creep deformation mode and magnitude are highly sensitive to geometric and mechanical factors. Specifically, longer axial lengths correlate positively with greater creep deformation, while increased wall thickness reduces it. Cladding tubes with length-to-diameter ratios exceeding 21 can be treated as infinitely long structures. Furthermore, relaxed boundary conditions have an effect equivalent to increasing the tube’s effective length, thereby amplifying creep deformation. These insights are important for evaluating cladding performance and structural safety under long-term creep deformation.
Application of Worm Optimization Algorithm to Design Challenges: A Focus on Pressure Springs
Meta-heuristic algorithms are approximate algorithms that are used in situations where traditional optimization techniques cannot achieve acceptable results. Specifically, they are widely used to achieve optimum design of machine elements. Designing springs according to minimum weight or volume is one of the most basic problems in this field. In this study, a bio-inspired meta-heuristic Worm Optimization Algorithm (WOA) inspired by the contribution of worms to nature was used to solve the problem. The results obtained were compared with those obtained by other algorithms and it was seen that similar data was obtained recently. As a result, it was determined that this algorithm can be used for optimum design of mechanical structures.
The Research on Horizontal Motion Axis Control System Error Prediction of the Gantry-Type Vertical Machining Center
As the cornerstone of high-end equipment manufacturing industry, the GVM Center breaks through the limitations of traditional processing through intelligent control technology, and has become a core equipment to promote the upgrading of precision manufacturing, which plays an irreplaceable role in enhancing the competitiveness of national high-end manufacturing. However, in the actual machining process of the GVM Center, due to the superposition of various factors, it is inevitable to produce complex machining errors. In order further improve the machining quality of the GVM Center, it is necessary to carry out error compensation technology research. HMM, as an important content in the field pattern recognition, has further developed its application scope with the rapid change of information data in recent years. Based on the data characteristics of HMM, the research on error compensation of the GVM Center is an innovative attempt. This research combines the theory of HMM dual stochastic process, deeply analyzes the motion error form of the horizontal moving axis X Y axis of the GVM Center, integrates the error lookahead prediction idea, describes the basic theory of HMM in detail, and embeds it into the g vertical machining center. It constructs a control error compensation model based on HMM, and analyzes the specific steps of HMM to implement error compensation. Based on the actualizing content, the error compensation effect of HMM with different training data is compared and analyzed, and the effectiveness of the research content is proved through data analysis before and after error. The research results provide theoretical support and engineering examples for the intelligent error control of high-precision CNC machine tools, and have important application value in the fields of aerospace-wall parts and precision mold processing.
Influence of Pressure Angle on the Dynamic Characteristics of Asymmetric Helical Gear Transmissions
To address the lack of research on the dynamic characteristics of asymmetric helical gears, this study systematically investigates their time-varying meshing stiffness and meshing impact characteristics based on the lumped-parameter method and nonlinear dynamic theory. An eight-degree-of-freedom dimensionless dynamic model is developed. Using the pressure angle on the driving side as the main variable, the dynamic characteristics of asymmetric and symmetric helical gears are compared, and the effects of different pressure angles on time-varying meshing stiffness, time histories, phase portraits, and Poincaré maps are analyzed. The results show that the asymmetric helical gear exhibits higher average meshing stiffness, smaller vibration amplitudes, and smoother responses than the symmetric gear. Within a driving-side pressure angle range of 30 to 35 degrees, the system achieves the best stability and periodicity, and the meshing impact amplitude is effectively suppressed. However, excessively small or large pressure angles lead to reduced system stability. This work clarifies the dynamic behavior of asymmetric helical gears and provides a theoretical basis for parameter design and high-performance transmission optimization.
Fail-Safe Mass Reduction of a Heavy-Duty Front Axle Using Topology Optimisation
A Topology Optimization and Design of Experiments (DoE) based mass reduction process for the rigid front axle of an in-use heavy commercial vehicle is summarized. Finite Element (FE) analyses were performed to determine the stress concentration regions of the axle beam for different loading conditions. Then, topology optimization was used to determine the regions where mass reduction is possible under critical loading condition. According to the results of the analyses, design parameters for the form and location of the holes were determined. A parametric study was carried out on the axle beam using the DoE-Response Surface Methodology (RSM) approach. The failure potential of these holes was evaluated with the help of Goodman-Haigh diagram. The results showed that the total mass can be reduced by 14.2% without creating a new failure mode. Considering the annual production capacity of the axle studied, it is seen that approximately 8,000 additional axle beams can be produced using the same amount of material.
Research on Many-Objective Scheduling Optimization Method for the Large-Scale Hybrid System
For the many-objective collaborative optimization problem of high performance alloy casting manufacturing process, how to achieve high efficiency, low energy consumption and intelligent scheduling process is an urgent problem to be solved. Firstly, a multi-objective pro-duction scheduling model of the whole process was established according to the production characteristics, process flow and production constraints. Meanwhile, a new high-dimensional multi-objective strong dominant optimization algorithm was proposed to solve the production scheduling model of high-performance alloy melting casting production line. The proposed algorithm is designed by combining strong domination algorithm and large neighborhood search algorithm. The proposed algorithm method implements deep exploration of solution space through cyclic destruction-repair operation. Its dynamic neighborhood characteristics can effectively escape the local optimal. Based on the industrial data set of alloy casting production line, the performance of the model and the proposed algorithm are tested. Experimental results substantiate the effectiveness and comparative advantages of the many-objective scheduling model and the proposed algorithm method.
Research on Dynamic Load Identification Based on the Newmark-β Method and Novel Iterative Regularization Strategy
This work focuses on the dynamic load identification motivated by Newmark-β Method and regularization strategy. Given the ill-posed nature of load identification, an improved hybrid LSQR regularization strategy is proposed, which incorporates an adaptive correction mechanism by iteratively comparing the computed response with the measured response. For load identification problems with prior conditions, an augmented hybrid LSQR regularization strategy is further developed, which expands the Krylov subspace with prior information and combines the improved hybrid LSQR regularization strategies to ensure stable iteration. Simulation examples using a simply supported beam as a representative structure of continuous systems, involving different load types and noise levels, validate the stability and accuracy of the two methods. The results show that the improved hybrid LSQR algorithm exhibits superior stability and accuracy compared to the conventional LSQR algorithm and yields relatively favorable identification results. Moreover, the superposition load identification simulation indicates that the augmented hybrid LSQR algorithm further improves the accuracy of load identification when prior knowledge of the load is available.
Design and Parametric Study of Lightweight Aluminium Alloy Bumper Beams for Enhanced Crashworthiness Using the Finite Element Method
The study focuses on improving the performance and efficiency of a vehicle bumper beam by addressing limitations in the baseline design, which uses Steel 350MC material with a thickness of 3 mm. The baseline design, though robust, is bulky, absorbs limited crushing energy during high-speed impacts, and contributes to reduced fuel efficiency due to its heavy weight. To address these challenges, a three-point bending test was employed to evaluate stiffness and energy absorption characteristics at the component level. Based on numerical simulations, three novel bumper beam designs were developed using aluminum alloy materials to achieve weight reduction while maintaining performance. Design of Experiments (DOE) was utilized to validate these designs through iterative three-point bending tests, identifying the optimal configuration using AA6056 material. The optimized bumper beam demonstrated a 61.29% reduction in mass, an 8.8% improvement in energy absorption, and no compromise in stiffness compared to the baseline. Furthermore, the optimized design was validated in a slow-speed RCAR (Research Council for Automobile Repairs) numerical simulation to ensure compliance with crashworthiness standards. Results confirmed the optimized bumper beam meets RCAR requirements, highlighting its suitability for real-world application. This research provides automakers with a lightweight, high-performance bumper beam design that enhances crashworthiness, reduces vehicle mass, and improves fuel efficiency, contributing to sustainable automotive innovation.
Research on Efficient Dynamics Simulation Technology for Artillery Equipment
In response to the efficiency bottleneck in the overall design phase of artillery, this paper proposes an agile design tool for artillery based on the RecurDyn solver, IAOD. This tool achieves rapid evaluation and optimization design of artillery dynamics simulation models through parametric modeling and integration of shooting test data. The IAOD system adopts a fully parameterized driven template model, combined with machine learning, parameter identification, and multi-level optimization algorithms, significantly improving the design efficiency of large caliber self-propelled artillery. The system innovatively applied a sparse single hidden layer neural network proxy model and a simulation parameter identification method based on test data, achieving multi-objective collaborative optimization. The effectiveness and practicality of the IAOD system have been verified through the practical application of a vehicle mounted artillery design case, demonstrating its potential for application in the field of artillery design. This study has significant military and defense value in improving the efficiency and quality of artillery equipment development.
A Novel Fault Diagnosis Method for Rolling Bearings Based on ELTSA and CGWO-WGKELM
Fault diagnosis method for rolling bearings by using entropy-weighted distance-based local tangent space alignment and Weighted Gaussian Kernel extreme learning machine with Circle chaotic mapping-based grey wolf optimization (ELTSA-CGWGKELM) is presented in this paper. This study introduces two innovative methodologies for rolling bearing fault diagnosis. First, an entropy-weighted distance-based local tangent space alignment (ELTSA) technique is developed to address feature dimensionality reduction in rolling bearing data. This approach effectively resolves limitations associated with conventional Euclidean distance measurement while significantly enhancing critical data information preservation capabilities. Secondly, a weighted Gaussian kernel extreme learning machine optimized through circle chaotic mapping-enhanced grey wolf optimization (CGWGKELM) is proposed for fault classification. The Gaussian kernel implementation substantially improves nonlinear processing performance and robustness compared to traditional weighted ELM architectures. The circle chaotic mapping strategy integrated into the grey wolf optimization algorithm (CGWO) enables superior optimization of the weighted Gaussian kernel ELM training parameters, ensuring enhanced global search capability and convergence efficiency. The experimental results indicate the following fault diagnosis accuracy rates for rolling bearings: ELTSA-CGWGKELM achieves 99.5%, LTSA-WGKELM attains 95.5%, LTSA-ELM reaches 94%, and PCA-ELM attains 92%.It can be seen that ELTSA-CGWGKELM is the better fault diagnosis ability for rolling bearings than LTSA-WGKELM, LTSA-WGKELM, and PCA-ELM.
Research on Error Characteristics Analysis and Error Compensation Methods for High-Precision Angular Measurement Turntables
To address the issues of low calibration accuracy, complex system structure, and cumbersome detection processes in traditional angular measurement equipment, this study proposes an innovative Opto-Mechanical coupled high-precision angular measurement turntable solution based on the principle of reciprocal angular displacement measurement. By systematically analyzing the structural characteristics and error distribution patterns of the turntable, a comprehensive error mathematical model based on relative pose matrices was established, enabling quantitative characterization of the error characteristics in the high-precision angular measurement turntable system. The three-point reverse decoupling algorithm, combined with a systematic continuous error compensation strategy, effectively suppresses installation errors of the components under test on the turntable. Furthermore, by optimizing BP neural network parameters using an improved particle swarm optimization algorithm, the system's error compensation performance was significantly enhanced. Experimental results demonstrate that the compensated system achieves a calibration accuracy of 0.38″, meeting the sub-arcsecond precision calibration requirement. The post-compensation overall error distribution remains within ±0.5″, and the reliability and accuracy of the turntable system were thoroughly validated through multiple comparative experiments. This study provides a novel technical approach for high-precision angular measurement.
Analysis of Material Optimization and Performance Parameter Difference of Expandable Sand Screen
Sand control technology of expansion sand screen is one of the effective methods to solve the sand problem in oil wells, due to its unique working mode, which has significant benefits in improving production and reducing costs, and has been widely used in the oilfield field. However, the operational conditions that expansion sand screens are subjected to, such as high temperatures, high pressure and high corrosion, exert greater demands on the material performance of the expansion sand screens. The advancement of this technology is predominantly constrained by the intrinsic characteristics of the material in question. In this study, five materials were selected as the base tube materials of the expansion sand screen, namely stainless steel 654SMo, stainless steel Incoloy 27-7Mo, stainless steel 2507, stainless steel Incoloy 625 and stainless steel 316L. These materials were chosen based on their performance requirements in relation to the expansion sand screen. The impact of diverse materials on the expansion performance of base tubes was investigated through a multifaceted approach, integrating tensile experiments, prototype expansion experiments, and finite element analysis. The experimental results demonstrate that stainless steel 316L is a more suitable base tube material under the condition of relatively low requirements for temperature and corrosive environment. Conversely, stainless steel Incoloy 27-7Mo is a more suitable base tube material in more demanding deep well environments. Finally, stainless steel Incoloy 625 is a more suitable base tube material for working conditions with higher requirements for corrosion resistance and mechanical properties. A comparison of the results of the finite element analysis with those of the prototype expansion experiment indicates that the volume percentage of the high stress region is a useful indicator of the expansibility of the base tube to a certain extent. The findings of this study provide a novel evaluation metric for the optimisation of parameters and the selection of materials for expandable sand screen. This has the potential to reduce costs and time during the research process.
Multi-Objective Optimization and Design of Hydraulic and Hemolysis Performance of Micro-Axial Heart Pump
In order to optimize the hydraulic performance of the heart pump and reduce blood damage, the effects of the main structural parameters on efficiency, head and hemolysis values was studied by CFD, outlet impeller diameter, inlet impeller blade angles, outlet impeller blade angles and blade thickness were selected as design variables using OFSD method, the efficiency, head, and hemolytic performance of the pump were taken as objective functions, the response surface method is used to fit the multivariate regression model of design variables and objective functions. Finally, the multi-objective optimization is carried out with NSGA-II genetic algorithm, one of the qualified structural parameter combinations was selected according to actual needs as the final solution of the optimization. The outlet impeller blade angles is β2=30°, the outlet impeller diameter is D2 =20 mm, the outlet impeller blade angles is β1=12.5°, and the blade thickness is δ = 1 mm. Efficiency is 74.5% , head is 96.3 mmHg, the hemolytic value is 0.0172 g/100L. The study results can provide a reference for the structural optimization of heart pumps.
Effect of Dimensions in Laser Surface Texturing on Cutting Tools
A significant part of the costs the companies that produce with the machining method is the cost of the cutting tool. The high cost and the continuous increase in the competition in the sector have pushed the companies operating in this field to work to extend the life of cutting tools. In this study, laser patterning was applied to the surfaces of silicon nitride-based cutting tools, and the most ideal combination was determined by changing the dimensions of the patterning. Before the laser patterning process, an experimental design was created using the Taguchi method and the L9 orthogonal array. The experiments were carried out on a Doosan Puma V550 turning machine, and the chip removal process was carried out dry. Material conforming to the GG25 standard is processed on the turning machine. In the experiments, the widths of the patterns, the spaces between the patterns, and the angles between the lines of the patterns were varied. The wear number of the cutting tools was observed using an Axio Zoom V16 microscope. After all these examinations, the data were collected and the results were compared using the signal-to-noise ratio method. With this comparison, it was seen that the most efficient pattern was 20 μm-300 μm-30°. In addition, it was determined that the most influential factor on cutting tool performance was the angle of the patterns.
The Effect of the Radius of Curvature on the Joint Strength in Butt-Curved Lap Joints
In this research, butt curved lap joints (BCLJs) were considered for the aluminum alloy plates exposed to tensile load, and the effect of the radius of curvature (RC) on the joint strength was investigated. For this purpose, butt-curved v joints were formed upon aluminum plates (AL2024-T3), and they were connected using an adhesive (DP810). Finite element analysis joint models were created three-dimensionally, and Cohesive Zone Materials Models (CHZMs) based on energy principles were used to estimate the strength of the adhesively bonded joint. In order to obtain numerical solutions, the parameters of the materials used in the joint model were determined experimentally. Furthermore, verification tests were performed to verify the numerical solutions. From the results obtained, it was understood that the radius of curvature has a important effect on the failure load and stress distributions in butt curved lap joints.