Search Results (186)

Existing quadruped robots typically place their center of mass (CoM) near the geometric center of the body to achieve structural symmetry and simplify control design. In contrast, many quadrupedal animals capable of agile running exhibit a pronounced anteriorly biased mass distribution, with the CoM located closer to the front of the body. This biological characteristic motivates a re-examination of whether a geometrically centered CoM necessarily corresponds to dynamically balanced loading between the fore- and hindlimbs during high-speed locomotion. To address this question, this study investigates the influence of longitudinal CoM position on load distribution during high-speed straight-line locomotion of quadruped robots. A unified analytical framework is established by combining whole-body force and pitch moment equilibrium, sagittal-plane kinematics, and Jacobian-based force-to-torque mapping, thereby linking longitudinal CoM position, foot-end support forces, and joint loads. Simulation validation is conducted on the Black Panther 2 quadruped robot using four central-body CoM configurations, denoted as ×0, ×5, ×10, and ×15. In the primary evaluation at 5 m/s, shifting the CoM forward from ×0 to ×15 reduces the absolute median fore–hindlimb differences in support force and joint torque by approximately 86.6% and 93.4%, respectively, indicating a transition from hindlimb-dominated loading toward cooperative load sharing between the fore and hindlimbs. Independent training runs with multiple random seeds further confirm the robustness of this load-redistribution trend to reinforcement learning variability. Consistent behavior is also observed at 8 m/s, while no evident degradation in turning response or locomotion stability is found under the tested turning and randomly generated rough-terrain conditions. These results demonstrate that a moderate forward shift of the longitudinal CoM can alleviate hindlimb load concentration and promote a more balanced fore–hindlimb load distribution, providing a theoretical basis for the morphological design and control optimization of high-speed quadruped robots. Full article

Ferrofluid seals provide non-contact operation, low friction, and strong sealing performance in high-speed rotating equipment and liquid-sealing systems. However, high speed, liquid contact, thermal loading, vibration, and eccentricity can cause ferrofluid loss, performance degradation, leakage, and premature failure. Here, intelligent reliability denotes a reliability-centered framework that integrates condition monitoring, physically interpretable health assessment, fault diagnosis, uncertainty-aware prognosis, condition-based maintenance, and feedback-driven active control. This structured review synthesizes centrifugal, thermal, liquid-medium, eccentricity-related, and material-degradation mechanisms and evaluates pressure, leakage, temperature, torque, vibration, and acoustic-emission signals. It also assesses health indicators, diagnostic methods, remaining useful life (RUL) prediction, maintenance decisions, and health-management strategies. Its principal contribution is a framework linking degradation physics with observable evidence, health assessment, diagnosis, prognosis, and maintenance and design feedback. The framework also distinguishes direct ferrofluid-seal evidence from transferable methods and supports sensor selection, indicator design, validation planning, and intervention development. Key limitations include scarce public degradation datasets, inconsistent evaluation protocols, weak physical consistency of health indicators, and limited closed-loop implementation. Future work should prioritize public run-to-failure data, physics-informed multisource assessment, uncertainty-aware RUL prediction, and experimentally validated condition-based interventions and active-control strategies. Full article

Downhole vibrations are a primary cause of premature wear and failure of rock-cutting elements (RCEs) during well drilling. This study develops an integrated finite element (FEM) model that couples axial and torsional vibrations with the evolution of the wear flat, friction, and temperature. The model is validated against laboratory experiments on a drilling stand using MEMS accelerometers. Two types of tungsten-cobalt (WC-Co) inserts were compared: uncoated and coated with a 3–5 nm titanium nitride (TiN) layer. Thirty tests were performed on granite and sandstone under varying single-RCE weight on bit (WOB = 1.0–2.2 kN) and rotation speed (RPM = 80–120). The TiN coating, by providing a low-friction running-in surface, reduced axial RMS acceleration by 18%, torsional amplitude by 24%, and the steady-state wear rate by 27% (from 0.154 to 0.112 mm/h) in granite. Frequency spectra revealed a resonant torsional peak at 55 Hz when RPM exceeded 120, with torque fluctuations increasing by 240%. A safe operating chart was constructed, defining green (WOB 1.0–1.6 kN, RPM 80–110), yellow, and red zones. The recommended regime (WOB = 1.7 kN, RPM = 105) gives 94% of maximum rate of penetration while reducing predicted wear by 35% compared to the red zone. The model prediction errors are 8–12% for axial and 10–15% for torsional vibrations. This work demonstrates that the proposed laboratory framework, combining nanoscale TiN-coated inserts with low-cost MEMS sensors, enables improved characterization of drilling vibrations and wear and supports the development of practical operating charts for drilling optimization. Full article

Rack railway systems operating on steep-gradient routes rely on rack-and-pinion propulsion mechanisms that generate substantial vibrational excitation through cyclic gear mesh contact, adversely affecting passenger comfort and long-term mechanical reliability. Conventional integrated steel gears transmit propulsive forces without inherent vibration attenuation, and a systematic design methodology for optimising the internal rubber spring configuration of elastic gears for such applications has not been established. This study develops a kinematic spring-mass model for both conventional steel and elastic rubber gear configurations in a Korean rack railway propulsion system and validates it through controlled experimental testing. A high-speed rail–wheel contact simulator was employed to measure vertical vibrational accelerations under rigid–rigid (steel–steel) and rigid–resilient (steel–rubber elastic gear) contact conditions, with a load simulating steep-gradient operational forces applied to the gear assembly. The elastic gear achieved a 25.1-fold reduction in vertical vibrational acceleration relative to the steel gear baseline (6.4 m/s² vs. 160.7 m/s²). Time-domain statistics (mean, RMS, standard deviation and peak envelope) are reported for both configurations from repeated runs. Analysis of the normalised effective stiffness as a function of the number of rubber springs predicts that four springs represent a practical optimum, beyond which the incremental stiffness change falls below 0.5%; experimental validation of intermediate spring counts is identified as future work. A spring-number optimisation framework is proposed that returns both a spring count and a rubber compound specification, balancing vibration attenuation against load distribution, torque-transmission capacity and component fatigue life. Full article

Field-oriented control and direct torque control of induction-motor (IM) drives, and conventional finite-control-set model predictive control (FCS-MPC), all depend explicitly on machine parameters that drift with temperature and saturation, degrading current quality and, for the predictive case, even threatening closed-loop stability. This paper develops a parameter-robust, weighting-factor-less model-free predictive current control (MFPCC) for IM drives, in which the lumped disturbance of an ultra-local model is reconstructed online by a linear extended-state observer (ESO) and the inverter state is chosen by a current-only cost function. A two-step ($k+2$) prediction horizon is utilized to explicitly compensate for the one-sample microprocessor computation-and-actuation delay inherent to digital predictive control. The speed loop is governed by a model-free intelligent-proportional controller; thus, no proportional-integral current regulator and no machine parameter appear in the control path, with the single exception of the ultra-local model design gain $\mathsf{\alpha }$, which is fixed at the nominal input gain $1/\left(\mathsf{\sigma } {\mathrm{L}}_{\mathrm{s}}\right)$ to establish parameter robustness. A comparative disturbance-observer study shows that the finite-difference estimator amplifies measurement noise and that an adaptive super-twisting observer cannot track the fast back electromotive force (back-EMF)-dominated lumped term of the IM at practical sampling rates, whereas the ESO tracks it faithfully (correlation 0.98). Under full inverter non-idealities (3 µs dead-time, 12-bit quantization, 50 mA offset, and 3% DC-link ripple), the proposed scheme attains 3.2% stator-current THD, matching an accurately tuned model-based FCS-MPC (3.9%) and significantly outperforming the finite-difference baseline (11.3%) while using none of the five machine parameters. Closed-loop stability and robust tracking are confirmed via a 50-run Monte-Carlo study, while a DSP timing estimate confirms real-time feasibility. Full article

Natural gas hydrate is an important emerging strategic resource, but low permeability makes the horizontal well length a key factor limiting productivity. A prediction model for friction torque of deepwater riserless drilling strings was established, and the segmented friction coefficient of hydrate horizontal wells was inverted and applied to the Shenhu hydrate reservoir. The results show that the main limiting factor for the extreme extension length of natural gas hydrate horizontal wells is the mechanical extreme extension length. The main affecting factor of the mechanical extreme extension length is the running limit of the screen pipe. The friction coefficient is the most significant factor affecting the mechanical extreme extension of horizontal wells, with the friction coefficient inside the casing in the high-build-rate section being the largest. The research identifies the primary factors governing the limit extension during horizontal well construction. The findings provide theoretical guidance for reservoir selection, well site determination, and wellbore configuration optimization in hydrate development. Ultimately, this contributes to maximizing single-well productivity and advancing the commercialization of hydrate resources. Full article

The compliant rolling-contact element (CORE) bearing is a compliant mechanism similar to a planetary gear that provides customizable rotational torque while maintaining high radial stiffness, enabling it to simultaneously function as a parallel elastic element and a bearing replacement. This work reexamines the CORE bearing as a combined spring and bearing element for parallel elastic actuator systems. It introduces alternative CORE bearing designs, evaluates the accuracy of a previous constant-torque model proposed in the literature, describes a finite element analysis to corroborate run-up behavior, presents an optimization tool for generating bearing geometry, and includes radial stiffness experiments to assess the consequences of different fabrication methods. Together, these results provide design guidance for determining the suitability of CORE bearings for parallel elastic systems and for selecting appropriate parameters. Full article

Reliable identification of the tribologically relevant braking phase in torque signals recorded on flywheel dynamometers is a prerequisite for quantitative friction analysis and data-driven modeling of dry-running friction brakes. We define brake torque plateaus as intervals with quasi-constant surface pressure and appreciable sliding velocity in which fading or drift of the coefficient of friction is explicitly admissible, while rise and decay ramps dominated by actuator dynamics are excluded. To automate this extraction across large industrial data sets, we propose a two-stage detection algorithm that sequentially narrows the search space using physics-based amplitude, gradient, and stability criteria, complemented by a Pruned Exact Linear Time (PELT)-based fallback for difficult cycles. Evaluation on 10,386 brake cycles, including 275 expert-annotated ground-truth cycles validated by a second independent expert, shows that the proposed method reaches 95% of the inter-annotator agreement ceiling on 75 held-out cycles, achieves a median Intersection-over-Union of 0.893 (11 percentage points above the strongest baseline), and a mean quality score of 9.18/10 across all cycles at under 1 ms per cycle (signals averaging 951 samples), outperforming six baseline configurations in both detection quality and runtime. Full article

Three-phase induction motors account for nearly two-thirds of industrial electricity consumption, making accurate parameter identification essential for efficiency optimization, predictive maintenance, and digital twin calibration. This paper introduces the stochastic spheric navigator algorithm (SSNA) for estimating the equivalent circuit parameters (stator and rotor resistances, leakage reactances, and magnetizing reactance) of induction motors by minimizing the normalized squared error between manufacturer-provided torque characteristics (starting, peak, and full-load) and their analytical counterparts derived from the steady-state Thévenin model. The SSNA employs an adaptive spherical search mechanism with a decaying radius schedule that progressively narrows the exploration neighborhood, enabling a balanced transition from global exploration to local refinement. Validated on 5 hp and 25 hp motors against the genetic algorithm (GA), particle swarm optimizer (PSO), hybrid GA-PSO, and sine–cosine algorithm (SCA), the SSNA demonstrates distinct advantages. For the 5 hp motor, it achieves the lowest errors in maximum torque ($1.34\times {10}^{-4}\%$) and full-load torque ($5.08\times {10}^{-4}\%$). For the previously unreported 25 hp motor, the SSNA yields an objective function value of $4.68\times {10}^{-12}$—six orders of magnitude lower than the SCA—and reduces magnetizing reactance estimation error from 46.55% (SCA) to 16.18%. Statistical analysis over 100 independent runs reveals that the SSNA uniquely combines the lowest minimum (best) value, the lowest maximum (worst) value, and the lowest standard deviation, demonstrating superior accuracy, reliability, and consistency. These results position the SSNA as a highly competitive optimization framework for induction motor parameter identification, with particular suitability for applications demanding high precision and robust performance. Full article

The in-service assessment of marine propulsion engines requires more than nominal rating comparison because operating severity is shaped by propeller demand, resistance growth, air-path response, and thermal state. This study develops a quantitative benchmarking method for the regime-dependent performance assessment of a low-speed two-stroke Wärtsilä 6RT-flex58T-D engine installed on a 31,000 DWT multi-purpose container vessel. The method integrates certified sea-trial measurements, endurance-test records, manufacturer load-diagram constraints, and a 15% service-margin projection within one reference framework. Three representative regimes are evaluated: a measured light-running baseline (SR1), a measured thermally stabilised sustained regime (SR2), and a projected heavy-running regime derived from the baseline using a 15% sea-margin assumption (R2). Comparison is performed using indicators of operating-point position, shaft torque, propeller-law consistency, selected air-path and thermal variables, load-diagram proximity, and corrected specific fuel oil consumption where available. The SR1 baseline followed the fitted propeller law with deviations not exceeding 1.18%, confirming a coherent light-running reference. In SR2, corrected SFOC decreased from 174.4 to 172.0 g/kWh, while the exhaust temperature before turbine increased from 359 °C to 435 °C, and the corresponding thermal margin decreased from 156 °C to 80 °C. Under the +15% service-margin projection, the required shaft power at the 100% trial point increased from 12,046.0 to 13,852.9 kW, exceeding the 13,560 kW installation MCR by 2.2%, with corresponding 15% increases in torque and BMEP. These results demonstrate that measured baseline operation, sustained-load severity, and projected heavy-running demand can be distinguished quantitatively within one installation-specific load-diagram-based benchmarking framework. Full article

Legged robots are widely used for walking, running, jumping, and landing on the ground. As mission terrains become increasingly complex, legged robots with greater adaptability are required. However, limited research attention has been paid to enhancing their impact resistance and obstacle-surmounting capabilities. Due to the limitations of motor manufacturing and material, it is more difficult to improve the impact resistance of the motor than to design proper leg lengths. Considering rigid multi-link medium- and large-sized legged robots, we optimize leg lengths to minimize the impact torque on leg joints. An optimal leg-length combination that maximizes obstacle-surmounting capability for medium- and large-size multi-link legged robots is conducted. This research provides a concrete design basis for leg-length optimization in medium- and large-sized multi-link legged robots with the aim of improving impact resistance and obstacle surmounting. Full article

High-temperature, high-pressure (HTHP) hard-rock drilling frequently causes chamfer spalling of polycrystalline diamond compact (PDC) cutters, leading to ~20% loss in the rate of penetration (ROP) and large torque oscillations. We propose a surface-gradient chamfer comprising a thin SiC interlayer (t_SiC ≈ 0.7 μm) and a nanocrystalline diamond topcoat (t_D ≈ 5 μm, d_D ~100 nm), combined with shallow cobalt leaching (L_deCo ≈ 100 μm). The structure was verified by microscopy/spectroscopy and evaluated by scratch adhesion, SEVNB toughness, instrumented impact, thermal shock, 400 °C pin-on-disc wear, and bench-scale granite drilling with vibration/torque monitoring. A coupled thermo-mechanical finite-element model, calibrated with Raman stress maps and thermal measurements, was used to interpret failure trends. Relative to untreated cutters, the gradient design reduced peak tensile residual stress by ~45% and lowered high-temperature wear volume by ~40%. In the present impact dataset (limited cutters per condition), the observed spall incidence at 1.0 J decreased from 2/3 (baseline) to 1/5 (gradient-treated). Short bench drilling runs suggested improved signal separability between healthy and pre-spall states (ROC-AUC ≈ 0.85 vs. ~0.65 for baseline, evaluated using a leave-one-cutter-out protocol); these drilling results should be interpreted as trend-level evidence given the limited number of cutters. These gains arise from mitigated thermal mismatch and residual stresses at the chamfer. Full article

Industrial robots are widely used in critical tasks such as assembly, welding, and material handling as core components of modern manufacturing systems. For the reliable operation of these systems, early and accurate detection of execution failures is crucial. In this study, a comprehensive comparison of machine learning and deep learning methods is conducted for the classification of robot execution failures using data acquired from force–torque sensors. Three different feature engineering approaches are proposed. The first is a Baseline approach that includes 90 raw time-series features. The second is the Domain-6 approach, which consists of 6 basic statistical features per sensor (36 in total). The third is the Domain-12 approach, which comprises 12 comprehensive statistical features per sensor (72 in total). The domain features include the mean, standard deviation, minimum, maximum, range, slope, median, skewness, kurtosis, RMS, energy, and IQR. In total, ten classification algorithms are evaluated, including eight machine learning methods and two deep learning models: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Naive Bayes (NB), Decision Trees (DT), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM-LGBM), as well as a One-Dimensional Convolutional Neural Network (CNN-1D) and Long Short-Term Memory (LSTM). For traditional machine learning algorithms, 5 × 5 nested cross-validation is used, whereas for deep learning models, 5-fold cross-validation with a 20% validation split is employed. To ensure statistical reliability, all experiments are repeated over 30 independent runs. The experimental results demonstrate that feature engineering has a decisive impact on classification performance. In addition, regardless of the feature set, the highest accuracy (93.85% ± 0.90) is achieved by the Naive Bayes classifier using the Baseline features. The Domain-12 feature set provides consistent improvements across many algorithms, with substantial performance gains. The results are reported using accuracy, precision, recall, and F1-score metrics and are supported by confusion matrices. Finally, permutation feature importance analysis indicates that the skewness features of the Fx and Fy sensors are the most critical variables for failure detection. Overall, these findings show that time-domain statistical features offer an effective approach for robot failure classification. Full article

Biarticular actuators can enhance efficiency and stability in legged locomotion by transferring energy between joints. Their effectiveness depends strongly on the lever arm ratio—the ratio of the actuator’s moment arm at one joint to its moment arm at another—which governs how torque is distributed across joints during movement. Inspired by biomechanics, early robotic studies implemented biarticular actuators to improve energy efficiency, joint coordination, and positional control, primarily in planar or single-joint systems, leaving a gap in fully 3D robotic legs. Here, we present a geometry optimization framework for a robotic leg incorporating both biarticular and monoarticular actuators. Using human motion capture and joint torque data, we optimized the linkage mechanisms so that the system can maintain the required joint torques while keeping biarticular actuator moment arm ratios near their optimal values during walking and running. The optimized leg achieved a minimum achievable cost of transport of approximately $0.41 \mathrm{J}/(\mathrm{kg}·\mathrm{m})$ for walking and $0.62 \mathrm{J}/(\mathrm{kg}·\mathrm{m})$ for running. Full article

This study investigates the deposition of silicon nitride (SiN) thin films for advanced semiconductor applications, with a specific focus on overcoming thermal challenges in plasma-enhanced atomic layer deposition (PE-ALD) at an elevated temperature of 550 °C. At such high temperatures, a critical obstacle is wafer warpage induced by thermal and mechanical stress, which increases localized thermal contact resistance and degrades film uniformity. To address this, a wafer chucking function was integrated into a monopolar electrostatic chuck (ESC) heater. The ESC secures the wafer to the heater surface, effectively mitigating warpage and ensuring a uniform temperature distribution. Chucking performance was verified by monitoring lift-up motor torque variations and plasma parameters, such as self-bias voltage (Vdc) and peak-to-peak voltage (Vpp), confirming the formation of stable electrostatic coupling. A comparative analysis was conducted between SiN films deposited with and without a chucking voltage of +1000 V. Statistical evaluation across repeated experimental runs (n = 3) confirmed that ESC chucking significantly enhanced spatial uniformity without altering the fundamental PE-ALD growth mechanism. Notably, the application of ESC chucking suppressed the localized temperature drop at the wafer periphery, reducing the in-wafer temperature gradient from 7~8 °C to 2~3 °C. This thermal stability resulted in improved thickness uniformity (variation < 1 Å) and an increase in film density from 2.83 to 2.94 g/cm³. Furthermore, the physical contact between the wafer and the heater effectively eliminated backside deposition to near-zero levels. Pattern evaluation revealed an exceptional step coverage of 99% in high-aspect-ratio (20:1) structures. These results suggest that ESC-assisted PE-ALD provides a robust and reproducible method for high-quality SiN deposition by minimizing thermally induced film variations. Full article