International Journal of Mechanical Engineering and Robotics Research
IJMERR is a scholarly peer-reviewed international scientific journal published bimonthly, focusing on theories, systems, methods, algorithms and applications in mechanical engineering and robotics.
Dynamic Response and Activation-Timing Effects in Hybrid Shape Memory Alloy Semi-Active Vehicle Suspensions
This paper presents a unified numerical investigation of hybrid semi-active vehicle suspension systems integrating Shape Memory Alloy (SMA) elements with Magnetorheological (MR) dampers and piezoelectric actuators. A two-degree-of-freedom quarter-car model is developed to evaluate three configurations—Passive + SMA, SMA + MR, and SMA + Piezo—under harmonic, stochastic, and transient road excitations. The governing equations are solved using a Python-based numerical framework to analyze time-domain responses, frequency-domain behavior, and standard ride-comfort metrics. Special attention is given to the role of SMA activation timing and control coordination. Both fixed-time and logic-based activation strategies are examined, demonstrating that delayed activation can significantly degrade vibration suppression performance, particularly during transient events. Comparative results indicate that the SMA + Piezo configuration achieves the largest reduction in sprung-mass displacement and acceleration, while the SMA + MR system offers a favorable balance between vibration mitigation and control complexity. Frequency-domain analysis further confirms reduced resonance amplitudes for hybrid configurations compared to the passive baseline. Although the study is limited to numerical simulations, the results highlight the critical importance of activation logic and hybrid actuator coordination in semi-active suspension design. The proposed modelling framework provides a reproducible basis for future experimental validation and supports the development of intelligent suspension systems for automotive and rail applications.
Semi-active suspension, Shape Memory Alloy, hybrid damping mechanisms, vibration control, Piezoelectric actuator
Copyright (c) 2026 International Journal of Mechanical Engineering and Robotics Research
Assessment of the Service Life of TE33A Series Locomotive Wheelset Components Using Statistical Analysis Methods
This paper presents a methodology for assessing wear indicators and the service life of wheelsets of TE33A diesel locomotives operated on the Kazakhstan railway network, based on their dimensional parameters. To evaluate the reliability of wheelsets in current operation, the service life corresponding to a 90% probability of failure-free operation was determined and compared with existing regulatory requirements. The rolling surface thickness and flange profile were selected as the key parameters influencing wheelset reliability and limiting its service life. It should be noted that TE33A locomotives are equipped with monoblock (unbandaged) wheelsets; therefore, once the service life is exhausted, the wheels are subject to complete replacement. The study applies a mathematical evaluation methodology based on reliability theory to assess the operating time of wheelset components. Methods of mathematical statistics were used to analyze time-to-failure data. The distribution of time to failure was approximated using the Pearson distribution, which made it possible to estimate the mean time to failure, variance, and other reliability characteristics. The obtained results allow the identification of key parameters affecting wear and provide a basis for determining maintenance and repair intervals.
Topic: Fracture Mechanics and Fatigue Analysis
A Hybrid RBF_GPR_EWMA Model for Data-driven Fault Detection in Centrifugal Chiller Systems
Fault detection and diagnosis (FDD) are vital for ensuring the energy efficiency and reliability of centrifugal chillers. This study presents a hybrid data-driven approach combining Radial Basis Function (RBF) regression and Gaussian Process Regression (GPR) to form a precise residual-based reference model. Deviations in thermodynamic parameters are continuously tracked using an Exponentially Weighted Moving Average (EWMA) control chart to detect condenser fouling and refrigerant leakage at multiple severity levels. The RBF-GPR-EWMA framework is validated with the ASHRAE RP-1043 dataset and real chiller data from a hospital in Ho Chi Minh City. Results indicate excellent prediction accuracy (R² > 0.99) and reliable identification of early-stage performance degradation. The method requires no fault labels or complex feature design, offering robustness and interpretability. Its simplicity and adaptability make it highly suitable for integration into Building Management Systems to enable condition-based maintenance and energy-efficient HVAC&R operation.
Design and Evaluation of a Device for Reconstitution of Lyophilized Medications in the Hospital Setting
The procedure for reconstituting powdered (lyophilized) medications in hospital settings involves shaking the medication with a diluent, which poses risks of ergonomic injuries to healthcare personnel and incomplete dosing of patients. In this study, a novel device for reconstituting powdered medications for use in hospital settings was designed and evaluated, preserving the occupational safety of healthcare personnel and optimizing patient recovery. To achieve this objective, the requirements of the healthcare personnel at Hospital III Goyeneche were compiled. The components of the device were then designed using 3d modeling software, and an transmission system was implemented that allows the powder vials to be shaken more effectively than with the manual method. The prototype was evaluated with Ceftriaxone and PIPTABAC vials, demonstrating its high effectiveness in diluting powder vials. The results show that the device has solid capabilities for implementation and optimization of procedures in hospital settings.
Artificial Intelligence in Controlling Mechanical Vibrations: An Active Control Systems Perspective
Mechanical vibrations have a big effect on how reliable, accurate, and long-lasting mechanical, robotic, and mechatronic devices. Conventional strategies to control vibrations work well in certain situations, but they are frequently unable to keep working when there are nonlinearities, unknowns, and changing dynamics over time. Artificially intelligent-based control techniques emerged as effective remedies to these issues, facilitating adaptive, information-driven and capable of learning vibration prevention. An extensive simulation-based study of artificial intelligence methods for actively mechanically control of vibration is demonstrated in this article. Neural network training is incorporated into intelligent vibration control frameworks using a focused on control systems paradigm. Lyapunov concept is used to give a mathematical evaluation of stability for machine learning control systems. When compared to traditional PID and LQR control algorithms, computer simulations on an elastic mechanical framework show enhanced vibration reduction. Prospective study guidance, implementation obstacles, and practical implications are highlighted. The findings demonstrate that artificial intelligence-based vibration control is a reliable and expandable solution for the robotics and mechanical devices.
Convolutional Neural Network Classification for Femtosecond Laser Texturing
Given the substantial progress in advanced manufacturing, reaching the micro-scale has become necessary in several industries, like the sensor, semiconductor, and aerospace sectors. With this significant advancement, achieving precise accuracies and new material properties has become possible. Indeed, industries will thus require the means to detect, classify, and determine the topological parameters of the micro-features imprinted on the workpiece in the micro-scale. In this paper, microstructures will be imprinted with the use of the femtosecond laser machine. To ensure the benefits of these microstructures with varied input parameters, artificial intelligence (AI) will be used to detect the structures and classify them accurately. A method of facilitating these measures will be presented with the integration of Convolutional Neural Networks (CNNs), specifically GoogleNet and ResNet-50. An experimental comparison between the two pre-trained neural networks will be conducted to identify the most suitable model for industrial use by evaluating their accuracy, processing speed, and adaptability to variations in microstructures’ geometry. Various microstructures, also known as dimples, were simulated and trained to test the accuracy and precision of these CNNs. ResNet demonstrated greater stability and consistency, achieving higher accuracy in detecting round shapes, while GoogleNet performed better in detecting straight edges. This multi-stage approach provides a comprehensive assessment of CNN-based micro-texture analysis and introduces an innovative framework for quality control in manufacturing.
Efficient and Computationally Lightweight Design for MP6 Piezoelectric Micropump Flow Regulation Using Gain-Scheduled Fuzzy Logic Control
Piezoelectric micropumps such as the MP6 are widely used in portable and implantable microfluidic systems, where stable and accurate flow delivery is required under tight hardware constraints. Their coupled electro-mechanical and fluid dynamics, together with valve nonlinearities and viscosity changes, make fixed-gain linear control difficult to tune across the full operating range. This work develops a gain-scheduled fuzzy logic controller for the MP6 that uses flow error and its derivative as inputs, a compact 25-rule Mamdani structure, and gain factors obtained from open-loop characterisation at multiple flow rates. The controller is implemented on an STM32F4 microcontroller and its timing and memory use are profiled. Closed-loop tests compare the fuzzy controller with a carefully tuned PID controller over six flow setpoints from 13 to 700 μL/min and three working fluids: deionised water, a 10% glycerol mixture and phosphate-buffered saline. Performance is evaluated using settling time, overshoot, steady-state error and integral absolute error, with five repetitions per setpoint. Averaged over the operating range, the fuzzy controller shortens settling time by about 33%, reduces overshoot by about 48% and lowers steady-state error and integral absolute error by roughly 42% and 31%, respectively, relative to PID. The measured average and worst-case execution times of about 85 μs and 142 μs, together with RAM and flash usage of 7.4 kB and 12.8 kB, fit comfortably within a 1 kHz control loop on the target platform. These results indicate that gain-scheduled fuzzy control is a practical option for MP6-based drug-delivery and diagnostic systems within the tested flow and viscosity ranges, and provide quantitative data to support future extensions to other micropump configurations.
Investigation on Improving Performance by Varying Stagger Angle of Cylinder in Returning Position to Overlap Savonius Wind Turbine
Wind energy is an alternative energy driver in reducing the use of fossil energy. Investigations into Savonius wind turbines are crucial for improving turbine performance. The investigation of the performance is conducted by the overlap turbine () of 0.3 using experiments and numerical. Numerical studies were carried out to validate the experimental results using the Realizable k epsilon turbulence model. The Savonius turbine adds a cylinder by varying  of 45°, 55°, 65°, and 75° placed on the upstream return side. The parameters in this study are ds/D of 0.4, and S/d of 1.7 with varying wind celerity of 5 m/s, 6 m/s, and 7 m/s. The experimental study shows that the highest performance occurs at  = 55o at wind celerity of 5 m/s with the highest CP of 0.389 and an increase in turbine performance of 18.70% compared to the original savonius wind turbine. Numerical simulations show valid values and flow phenomena indicate the performance in streamline and pressure contour.
A Numerical Analysis of Different Micro-Textures in Improving Tribological Properties of Surfaces
Laser surface texturing has become an important technology to improve the tribological properties of surfaces under lubricated friction. While several studies have analyzed how different micro-textures affect hydrodynamic pressure generation, further study of how different characteristics of the micro-texture geometry affect pressure generation remains needed. This study aims at conducting a comparative analysis of an optimized and modified chevron micro-texture with other conventional micro-textures used in literature using computational fluid dynamic (CFD) simulations. The study showed that the modified chevron micro-texture with a large leg-width ratio outperformed other conventional micro-texture shapes such as the circle and rectangle by generating an asymmetric pressure distribution. Ongoing research is being done to experimentally verify the results, which can be significant for various applications.
Topic: Tribology: Friction, Wear, and Lubrication
Intelligent Control of Ackermann Autonomous Vehicles via Neural Network-Enhanced Fuzzy-PID: A Simscape Multibody-Based Simulation Study
This paper presents a novel intelligent control framework for Ackermann-type autonomous vehicles that combines neural network optimization with a Fuzzy Logic-based Proportional–Integral–Derivative (Fuzzy-PID) controller to address the increasing need for safe and reliable autonomous driving. It tackles the limitations of traditional PID and Fuzzy-PID controllers, which lack adaptability due to fixed tuning parameters and manually crafted fuzzy rules, by proposing a Neural Network-Optimized Fuzzy-PID (NN-FPID) controller. This controller changes the membership functions and PID gains instantly based on the vehicle's conditions, such as steering angle error, speed error, and side-to-side position. The vehicle's dynamics are modelled using Simscape Multibody to realistically simulate kinematic and dynamic behaviour. Simulation results across straight paths, curved trajectories, and zigzag manoeuvres show that the NN-FPID controller significantly reduces trajectory tracking error Root Mean Squared Error (RMSE), overshoot, and settling times compared to conventional PID and fixed-rule Fuzzy-PID controllers while maintaining computational efficiency suitable for real-time implementation. This work makes a significant contribution by applying neural network-enhanced Fuzzy-PID control to Ackermann vehicles, offering a promising approach for robust, adaptive, and intelligent steering and speed control in autonomous driving.
Heterogeneous Robots Cooperation via Multi-Agent Reinforcement Learning
Efficient collaboration among heterogeneous robots in dynamic environments remains challenging due to differences in functionality, communication, and task complexity. Multi-Agent Reinforcement Learning (MARL) offers a promising paradigm by enabling decentralized robots to learn coordinated behaviors through interaction with each other and the environment. However, most existing MARL methods focus on homogeneous robots and single-task settings, overlooking heterogeneous robots, multi-step sequential tasks, and collaborative coordination. In this study, we propose a cooperative MARL framework that allows heterogeneous robots to learn individual, sequential, and collaborative tasks using a single shared policy. Our approach employs explicit type-defining encoding in the observation space to distinguish robot types while enabling knowledge sharing across a unified policy. Through curriculum learning, pretrained heterogeneous-task policies are efficiently transferred to sequential and collaborative tasks without retraining from scratch. We evaluate our framework in Unity ML-Agents scenarios involving 2-3 robot types performing various task structures. Experimental results demonstrate a significant reduction in training time compared to training separate models, while maintaining stable task completion and coordinated behavior across heterogeneous, sequential, and collaborative task settings.
Multi-objective Optimization of the 7075 Aluminum Alloy Milling Process: A Comparative Study of MOPSO, NSGA-II, SPEA2, and MOACO
This study investigates the effect of machining parameters and optimization of surface roughness (Ra) and material removal rate (MRR) in milling 7075 aluminum alloy based on a comparative analysis of the MOPSO, NSGA-II, SPEA2, and MOACO algorithms. Three machining parameters, including spindle speed (S), feed rate (f), and depth of cut (d), were considered. The results of the analysis of variance (ANOVA) showed that S and f had significant effects on Ra (p < 0.05), contributing 20.06% and 79.68%, respectively, while f and d were significant for MRR (p < 0.05), accounting for 45.01% and 40.11% of contribution, respectively. The predictive model for Ra using the Group Method of Data Handling (GMDH) indicates that the Double (2 variables) function exhibits exceptional predictive performance, with an R² value of 0.9990 on the training set and 0.9962 on the validation set. Multi-objective optimization results indicate that NSGA-II provides the most stable solutions, while SPEA2 and MOACO show less effectiveness in providing optimally balanced solutions based on the observed distribution. The MOPSO algorithm has a fast convergence speed; however, the optimal values are highly dispersed and unstable for both objectives. The performance of these optimization algorithms was validated by experiments. The results show that the mean deviation of Ra between predicted and actual values is 6.5%, while that of MRR is 0.37%. These findings indicate that all four algorithms in this study have certain effectiveness in multi-objective optimization of the trade-off between Ra and MRR in machining.
Analysis of Robotic Manipulation Technologies Applied to the Cleaning/Extraction of FOD from Fighter Aircraft Air
In the aerospace industry, maintenance protocols are in place to ensure the safety of personnel and the integrity of aircraft. Air intakes are difficult to access, making it challenging to clean them and perform Foreign Object Debris (FOD) extraction. This paper presents a technical-economic analysis of robotic handling technologies, aimed at identifying the most suitable technology for cleaning and FOD removal operations in fighter aircraft air intakes. First, a research team from the Universidad Nacional de San Agustin de Arequipa (UNSA) in collaboration with maintenance personnel from the Fuerza Aerea del Peru (FAP), conducted a review of recent technologies in manipulation robotics focused on cleaning and removing FOD from fighter aircraft air intakes, classifying them into 12 types of manipulators according to the end effector, to rate their performance in FOD cleaning and/or extraction activities, while also meeting the needs of FAP personnel. Nine indicators aligned with the requirements were determined, each manipulation technology was rated, and these values were operated with relevance factors calculated for each indicator by the Mudge diagram. Micro-gripper handling technology obtained the highest score for both technical analysis and technical-economic analysis, with weights of 25.7 and 30.1, respectively. This result makes it the technology best suited for cleaning and FOD extraction in air intakes. These results were based on an assessment by FAP expert staff, and no experimental validations or tests with actual prototypes were performed. The results of this study will be corroborated in future work through advanced simulations and experimental laboratory tests.
Topic: Autonomous Robots in Industry 4.0
A Multi-Metric Benchmarking Study of Hybrid Path-Planning Algorithms Under Different Grid Resolutions
This study systematically evaluates the performance of four hybrid path-planning algorithm combinations formed in two global planners (A* and Dijkstra) with two local planners, the Dynamic Window Approach (DWA) and the Time Elastic Band (TEB). Four combinations of the hybrid planner were conducted by autonomous differential-drive robots navigating in simulated environments. The proposed study tested across three grid map resolutions (0.025 m, 0.10 m, and 0.15 m) to understand the impact of environmental granularity on various environmental settings with variation of the velocity range (0.1m/s ~ 1m/s). A comprehensive quantitative evaluation framework was implemented to justify the performance of the planner incorporating some key metrics such as the Spatial Coefficient (SPC), Temporal Coefficient (TEC), Smoothness Coefficient (SMC), Success Rate (SR), Failure Rate (FR), Path Length, Execution Time and Goal Convergence (GC). The collected experimental data were evaluated through a normalization process with the execution of some statistical analyses, such as ANOVA, T-test, and Bonferroni correction, which revealed differences between planner performance and the impact of grid resolutions. Results revealed that Dijkstra+TEB consistently outperformed the other combinations and demonstrated superior SPC, TEC, and SMC values, particularly in complex environments. Conversely, the DWA-based planners suffered from low success rates and parameter sensitivities. This work offers a systematic benchmarking framework for evaluating path-planning strategies and serves as a valuable guide for deploying reliable autonomous navigation in real-world scenarios.
Topic: Recent Advances in Mobile Robotics Navigation
Autonomous Navigation, Global-Local Planner Integration, Grid Resolution, A*+TEB, Dijkstra+TEB
Optimized Maze-Solving Algorithm for Mobile Robots
This article addresses the development of an optimized algorithm for maze solving using image processing techniques and resistive grids. Since the diagonal square geometric cell is used as basic resistive cell, the proposed algorithm optimizes the process of finding the collision-free shortest path between the start- and end-node into an irregular maze plagued with irregular obstacles. Depending on the dimensions of the robot, a node matrix with the same size as the maze-image is generated. The distance between each node is equal to the largest dimension of the robot. Therefore, the maze-image size can be scaled into the interval [1mX1m, 66mX66m]. This scaling interval can be moved down or up which depends on the size of the robot to be used. In addition, the formulation of the system of equations is also improved, since it is built from the collision-free node matrix instead the full resistive grid. In this sense, the admittance matrix is filled by nodal analysis and by applying Kirchhoff's current law, the shortest path is found within the reduced resistive network due to the elimination of those nodes and branches absorbed by irregular obstacles. Furthermore, using an undirected graph decision algorithm, the generation of singular matrices during the formulation of the system of equations is also avoided. Hence, the omnidirectional robot follows the collision-free shortest path from the start-node until the end-node in the main sub-network. As a result, travel time, distance traveled and battery consumption of an omnidirectional mobile robot used as test vehicle are minimized. Three studies cases are used for validating the proposed algorithm. Comparing with the results of using the square resistive cell directly, experimental results demonstrate that the optimized algorithm works better and more efficiently. Finally, the optimized algorithm can be used in some applications such as rescuing robots, agriculture, entertainment, intelligent traffic control, among others.
Development and Balancing Control of a Unicycle Robot Using Omnidirectional Wheel
Mobile robots are being studied by many researchers in fields such as automotive, logistics, and robotics. Moreover, they are used in areas like transportation, healthcare, and personal assistance because they can move flexibly, fast, and accurately. Unicycle robots are one of the mobile robots that have unique characteristics, including designing, constructing, moving, and controlling. The unicycle robot uses one wheel for moving forward and backward and needs one reaction wheel to make itself stay upright while it is moving. In this paper, researchers design new mechanisms for the unicycle robot wheel to be appropriate for them to move in any direction that combines the pitch wheel and roller wheel into one wheel; the wheel is called an omnidirectional wheel. The control system uses an STM32 as the main controller that operates to work with the driver motor, IMU sensor, and encoder sensor. The LQR controller is applied to control the unicycle robot. Experimental results compare simulation and actual experiment to show time, angle, speed, and torque.
Development of a Single-Seater Electric Vehicle for Electrathon America Competition
A Electraton Americas is an organization that promotes the development of electric vehicles for competition. The present work is focused on the design of the America single-seater structure which was based mainly on the protection of the driver, space for various accessories, improve range and aerodynamics. The design was carried out in strict adherence to the Electrathon America 2021 manual. The 3D CAD modeling was done in SolidWorks software. Aluminum and steel profiles with diameters of ½ in and ¾ in with a thickness of 2.7 mm were used. The regulations specify that tests must be performed on the structure of the single-seater, mainly torsion and bending tests to ensure its correct operation with a minimum safety factor of 2; the structural analyses were carried out in Ansys software. Additionally, a fluid analysis was carried out. In addition, with strain gauges, measurements of the structural deformations were made to validate the stress analyses performed. A functional single-seater vehicle was obtained, with a safe structure, aerodynamic, suspension and adequate steering.
Design, single-seater Americas, Ansys, validation
Application of the DfT Methodology for the Design and Validation of Exoskeletons Used in Hand Rehabilitation for Patients with Cerebral Vascular Accident
This study investigates the application of the Design for Testability (DfT) methodology to define mechanical and functional evaluation criteria in the design of a hand exoskeleton for use in the rehabilitation of cerebral vascular accidents (CVA). To this end, recent studies presenting methodologies for evaluating mechanical and/or functional aspects in the development of hand exoskeletons for use in CVA rehabilitation were first reviewed. Based on this and taking into account DfX concepts, a DfT methodology for CVA hand exoskeletons was developed and verified with a case study for patients with a 50th percentile. The prototype was designed in 3D modeling software, manufactured, and verified using the proposed methodology. The results show that the application of the DfT methodology allowed for the comprehensive development and evaluation of hand exoskeletons for CVA, covering everything from the design stage to manufacturing, resulting in better mechanical and functional characteristics and low cost in line with the needs of patients CVA.
Recent Advances in Welding Processes for High-Entropy Alloys: A Comprehensive Review
High-entropy alloys (HEAs) have gained attention for their exceptional mechanical properties, corrosion resistance, and unique microstructure. However, welding HEAs remains challenging, attributed to intricate microstructures, inadequate thermal conductivity, and elevated melting points. This review systematically summarizes recent advances in metal welding of HEAs with both similar and dissimilar alloys, including stainless steel, titanium alloys, aluminum alloys, and nickel-based alloys. Welding techniques, including gas metal arc welding (GMAW), laser welding (LW), electron beam welding (EBW), and gas tungsten arc welding (GTAW) are critically compared in terms of joint microstructure, mechanical performance, and corrosion behavior. The influence of filler composition, heat treatment, and process parameters on weld quality are discussed. Key challenges, including microstructure evolution under complex service conditions and controlling brittle intermetallic phases, are identified. Finally, potential research directions and application prospects of HEA welding in advanced manufacturing are outlined.
Diagnosis of Multiple Bearing Faults in BLDC Motors Using ReliefF Feature Selection and Random Forest Classifier
Bearing faults remain a leading cause of failure in Brushless DC (BLDC) motors, making early and accurate diagnosis critical for industrial reliability. This study proposes an intelligent multi-class fault diagnosis system that integrates ReliefF-based feature selection with a Random Forest classifier. Vibration data were collected from seven bearing conditions using a BLDC motor testbed. From each signal, 18 statistical features were extracted across time and frequency domains. The ReliefF algorithm was employed to identify the most discriminative features, significantly reducing dimensionality while preserving diagnostic relevance. The Random Forest classifier was then trained on the selected features to distinguish between normal and six fault classes, including compound fault types. Experimental results show that the model achieved perfect classification accuracy on the training set and 92.2% on the testing set. Confusion matrices and scatter plots validate the model’s effectiveness in fault classification, with strong generalization and minimal misclassification. This framework offers a robust, efficient solution for real-world bearing fault diagnosis in BLDC motors.
Topic: Mechanical Fault Diagnosis Based on Transfer Learning
Development and Measurement of the Jumping with Pneumatic Cylinder
This study presents the design and testing of a lightweight jumping robot powered by a pneumatic cylinder and elastic elements. Experiments varying the number of rubber bands revealed that four bands provided the best balance of rebound force and air consumption, achieving a maximum vertical jump height of 270 mm and a rebound cycle of 0.8 s. The results confirm that pneumatic–elastic actuation enables efficient, repeatable jumps without complex control. Beyond competitions, the robot’s use of low-cost, readily available materials such as rubber bands and air tanks highlights its potential for rapid assembly and deployment in disaster zones, where conventional equipment is limited. The proposed approach offers a practical framework for developing agile, resource-efficient robots capable of overcoming obstacles in emergency conditions.
The Nonlinear Study of Composite Plates Natural Frequency Using RNN-LSTM combined with Wavelet Transform Features
In this research, we discuss how we extract features related to natural frequency of nonlinear composite plates (NCP) using a combination of Wavelet Transform (WT) and artificial intelligence (AI) algorithms. The nonlinearity is represented in the plate geometry and boundary conditions (BCs). Our findings, which build on previous works by the authors, indicate that WT can effectively reflect the natural frequency features of NCP. However, we also noted that this approach can be quite complex, involving numerous calculations and iterations, which means it might not be ideal for quickly and accurately extracting natural frequency. To tackle this issue, we’ve developed a new AI model designed to learn from and test these results by extracting key natural frequency features that reveal crucial information about the NCP behavior. The AI model is based on a recurrent neural network with long short-term memory blocks (RNN-LSTM) since the natural frequency datasets have a time-dependent and memory-dependent behavior. Our results strongly suggest that the proposed technique is a promising approach, especially for complex structures in varying environmental conditions.
A New Adaptive Neural Fuzzy Sliding Control Method for Dynamic Nonlinear Plants
This paper proposes a New Adaptive Neural Fuzzy Sliding Mode Controller (NANFSMC) for the regulation of a Coupled Tank System (CTS), targeting the adaptive control of unknown dynamic nonlinear systems subject to external disturbances, sensor noise, and other uncertainties in experimental environments. The CTS exhibits strong nonlinearities and uncertainties arising from sensor noise, parameter variations, variations in output valve characteristics, and significant time delays. The proposed control architecture integrates two synergistic components. The first is an adaptive control component that employs a Radial Basis Function Neural Network (RBFNN) to approximate the adaptive control law, with an adaptive updating mechanism in compensating for RBFNN approximation errors. The second is a sliding mode control component whose parameters are updated in real time via a fuzzy inference mechanism to enhance robustness. Both control laws are derived within the framework of Lyapunov stability theory, ensuring closed-loop stability under all operating conditions. The proposed approach is experimentally validated and benchmark in comparison with an adaptive neural sliding mode ANSMC and an optimal PID controller. Experimental results demonstrate that the proposed NANFSMC achieves superior tracking performance, robust stability, and adaptability to diverse uncertainties, including sensor noise, external disturbances, and time-varying reference signals.
novel adaptive neural fuzzy sliding mode (NANFSMC) algorithm, coupled tank system (CTS)
Particle Swarm Optimization-Based Hazardous Configuration Identification and Dynamic Analysis for Industrial Robots
This paper takes the KUKA KR3 R540 six-degree-of-freedom (6-DOF) industrial robot as the research object. Based on the kinematic model established using the standard D-H method, the Newton-Euler dynamic theory is introduced to construct the multi-body dynamic model of the robot. To identify the dangerous working postures that cause the maximum joint torque, a search strategy based on the Particle Swarm Optimization (PSO) algorithm is proposed, and Adams-MATLAB co-simulation is adopted for solution. With the maximization of the sum of absolute values of each joint driving torque as the optimization objective and joint angles as decision variables; MATLAB drives the PSO algorithm to generate candidate postures and calls Adams for high-precision dynamic solution, automatically traversing the high-dimensional joint space and iteratively searching for the global optimal solution. Simulation results show that this method can effectively locate the global most hazardous configuration and obtain the corresponding maximum total torque for each joint, providing crucial load boundary conditions for the robot's structural strength verification and lightweight design, while also offering references for the selection of key components for structural optimization.
KUKA robot; Dynamic analysis; Hazardous configuration; PSO; Adams-MATLAB co-simulation
Pose Estimation-Driven Control of Humanoid Upper Arms for Human Motion Mimicry
This paper presents a real-time teleoperation framework that maps human upper-body motion, captured by a single RGB-D camera, to a dual-arm upper humanoid robot designed with low-cost servos. The system employs MediaPipe-based pose estimation and a torso-anchored coordinate transformation to achieve operator-centric retargeting that is robust to variations in camera placement and subject geometry. To suppress tremor and sensor noise, a constant-velocity Kalman filter combined with an adaptive dead-zone is applied to wrist trajectories, ensuring smooth motion while maintaining responsiveness. A min–max scaling function with saturation enforces safe workspace mapping, while joint commands are computed using damped-least-squares inverse kinematics with joint-limit and self-collision checks. The execution layer incorporates incremental speed-aware stepping to emulate continuous trajectories on servo actuators. Experimental results demonstrate accurate static pose reproduction, robust dynamic path following, and zero joint-limit violations, achieving an average wrist-tracking RMSE of 12.4 mm and median end-to-end latency of 86 ms. The platform is reproducible, cost-effective, and adaptable for applications in education, rehabilitation, and human–robot collaboration.
Humanoid Robotics; Teleoperation; Human-Robot Interaction; Robot Kinematics; Motion Capture System; Image Processing; Signal Processing; Kalman Filtering.
Dynamics-Driven Policy Transfer to Heavy Wheel-Legged Robots via Imitation and Optimization
Policy transfer is an efficient approach for developing specific robots. Its effectiveness depends on high-quality imitation datasets and a stable learning process. However, substantial differences in geometry and dynamics between source and target robots pose challenges. Purely kinematics-driven mapping methods and manual parameter tuning often fail to maintain kinematic-dynamic consistency. In this study, we transfer control policies from the quadruped robot Unitree Go1 to our self-developed heavy wheel-legged robot Tiangou. We propose a Consistency-Aware Retargeting (CAR) method. This extends conventional inverse kinematics by adding dynamic consistency constraints. Using motion data from Go1’s MPC controller, CAR generates a reference dataset for Tiangou. We then integrate Bayesian Optimization (BO) into the imitation learning framework. This enables autonomous tuning of policy model structures and optimization hyperparameters. Experiments show that CAR reduces foot-end position errors, mitigates joint angular velocity fluctuations, and decreases foot-end slippage. Moreover, Bayesian optimization improves sample efficiency and training stability. These contributions establish a practical foundation for policy transfer across heterogeneous robotic platforms.
Prediction of Wear Behavior in Surface Contact using CDM and ANN models
The objective of this study is to investigate and predict wear behavior in contacting surfaces through an integrated approach that combines Continuum Damage Mechanics (CDM) and Artificial Neural Networks (ANN). Pin on disk wear experiments were performed under dry conditions using three engineering materials ST37, C45E4, and Al 7075 with variations in load, sliding speed, and hardness systematically designed using a full factorial Design of Experiments (DOE). The CDM model quantified material degradation and estimated wear coefficients, which were then used as training data for a feed-forward back-propagation ANN. Both models were validated against independent experimental data. Results indicate that the ANN model achieved high prediction accuracy (average error <5%), outperforming the CDM model (average error ≤12%). Scanning Electron Microscopy (SEM) revealed adhesive wear as dominant in the steels, while Al 7075 exhibited reduced wear due to higher hardness. The interaction effects showed that load and sliding speed have significant influences on wear, whereas hardness plays a secondary role. The findings establish a robust framework for wear prediction, process optimization, and potential real-time monitoring in engineering applications, demonstrating the effective integration of physics based and data-driven modeling in predictive tribology.
Tribology; Artificial Intelligence (AI); Surface Wear; Continuum Damage Mechanics (CDM); Steady-State; Artificial Neural Network (ANN).
A Hybrid Bayesian ICA-LSTM Framework for Unsupervised-Like Anomaly Detection in Rolling Element Bearings
This study proposes a hybrid anomaly detection framework based on Independent Component Analysis (ICA) and Bayesian Long Short-Term Memory (LSTM) networks for early fault diagnosis in rolling element bearings. The model is trained solely on normal condition data from the CWRU Bearing Dataset, enabling unsupervised-like deployment suitable for real-world industrial environments. Vibration signals are preprocessed using ICA to extract statistically independent features, followed by a Bayesian LSTM that captures temporal dynamics and provides uncertainty-aware predictions. A dynamic thresholding mechanism based on Interquartile Range (IQR) is implemented to autonomously distinguish between normal and anomalous behavior without manual calibration. Experimental results demonstrate exceptional performance with 99.1% accuracy, perfect recall (100%) on fault conditions, and zero false negatives, ensuring no fault is missed. The composite anomaly score effectively tracks degradation progression, making the system highly reliable for predictive maintenance applications. This approach combines statistical signal processing with probabilistic deep learning to deliver a robust, interpretable, and adaptive solution for bearing health monitoring.
Topic: Recent Innovations in Mechanical Vibrations
Bearing fault diagnosis, anomaly detection,
The Effect of Preheating on The Quality of Spot Welding Between Dissimilar Mild Steel and Stainless Steel 304
Welding dissimilar materials often presents specific challenges due to differences in thermal properties, expansion, and chemical reactions between the joined metals. This study assesses the effects of preheating mild steel and AISI 304 stainless steel plates on tensile strength, elongation, and material hardness. The variations in preheat temperature used are 100°C, 120°C, 150°C, and 180°C. Welding mild steel and stainless steel AISI 304 while preheating shows that 180°C is the best preheating temperature for getting an average tensile strength of 12.41 kgf/mm² and an average strain of -0.655. The hardness test results show that a preheat of 120°C provides optimal hardness in the weld area (106.1 HR), HAZ (96.7 HR), and base metal (103.9 HR). More than that, spot welding between mild steel that has not been treated in any way and stainless steel AISI 304 that has been heated up shows that a temperature of 180°C for the heating up process gives the best improvement in plasticity, with a tensile strength of 15.94 kgf/mm² and an average elongation of 1.7970. Hardness test data show that preheating at 180°C provides the highest hardness in the weld area (109.30 HR), and 120°C provides the highest hardness in the HAZ (99.03 HR). In contrast, the base metal decreases hardness with increasing preheat temperature, with the lowest value at 180°C (77.63 HR). The selection of the correct preheat temperature is crucial to ensure optimal weld joint quality.
Modal Analysis-Informed Topology Optimization for Dynamic Performance Enhancement of Horizontal Canned Motor Pumps
This study proposes an integrated framework for ship applications, combining high-fidelity finite element model simulation, experimental modal analysis, and topology-based structural optimization to enhance the dynamic performance of the horizontal shielded pump under actual installation constraints. A high-fidelity finite element model was developed that accurately considers material properties and boundary conditions. This model was validated through impact hammer tests, with the maximum modal frequency deviation found to be less than 2%. Modal analysis revealed that the shielded pump exhibits a "low-frequency global-high-frequency local" vibration mode, with key deformation regions concentrated at the base transition section, flange, and outlet section. Based on topology optimization and modal energy distribution, the research team implemented a targeted "reinforcement-lightweight-augmentation" strategy, which included strengthening the base support, thinning the non-load-bearing hull, and reinforcing the outlet section. After optimization, the first-order natural frequency of the shielded pump was significantly increased from 42.31 Hz to 76.86 Hz, a 81.7% improvement. The average enhancement of higher-order modes was approximately 18%, with the vibration modes becoming more uniform, effectively suppressing local vibrations. The method proposed in this study not only significantly improves the modal stability of the shielded pump and reduces resonance risk but also provides valuable practical guidance for vibration control and structural lightweight design of high-speed rotating machinery in the shipbuilding industry.