Search Results (3,591)

Heterogeneous fault information and a lack of real-time synchronization in CNC machine tools hinder effective Prognostics and Health Management (PHM). This paper designs and implements a digital twin-driven PHM framework for machine tools that integrates a unified machine-tool fault information dictionary and a mechanism-data dual-driven diagnostic model (ResNet-TCN). A cyber–physical platform was developed using OPC UA and RESTful APIs to ensure real-time data synchronization. Experiments on the PHM 2010 dataset demonstrate that the proposed ResNet-TCN model achieves a root mean square error (RMSE) of 5.46 μm for tool wear prediction. Its performance surpasses that of traditional LSTM models, and the proposed framework effectively eliminates information silos, providing a responsive, scalable and accurate PHM solution for smart manufacturing. Full article

With the continuous advancement of drilling technologies for deep and ultra-deep well operations, drillpipes are subjected to increasingly severe wear and corrosion conditions. To enhance the wear and corrosion resistance of drillpipe surfaces, this study developed a novel Ni60 alloy hardbanding via laser cladding technology. To solve the problem of crack sensitivity, the cracking mechanism of Ni60 coatings directly deposited on 4137H steel substrates was systematically investigated and a crack suppression strategy was proposed. By employing a 316L translation layer between the 4137H substrate and the Ni60 alloy coating, the interfacial thermal stress induced by the mismatch of thermal expansion coefficients between dissimilar materials was relieved. Therefore, crack-free 316L-Ni60 gradient coatings were obtained. The microstructure, phase composition, and mechanical properties of the coatings were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), and microhardness testing. The experimental results demonstrate that the 316L-Ni60 gradient coating exhibits a homogeneous microstructure and forms a dense metallurgical bond with the 4137H steel. The microhardness of the coating is 2.2 times that of the 4137H steel, while its wear rate is reduced by nearly half. Furthermore, the Ni60 coating possesses higher corrosion resistance compared with 4137H steel. This study promotes the potential application of the Ni60 alloy coating as a new type of hardbanding on drillpipes. Full article

Orthosis compliance monitoring provides insights into effective orthosis design and user wear time. Frequently, patient reports of orthosis use are subjective and often result in overestimation of compliance. Therefore, a tool to objectively observe whether patients wear their orthoses as instructed is vital. This study assessed the real-world practicality of using an objective compliance monitoring device with a hand orthosis. A device consisting of a pressure sensor and accelerometer was tested by ten healthy volunteers who wore a hand orthosis daily and completed a diary of their wear time and activities for a week. Sensor data obtained from the compliance monitoring device were analysed to discern each user’s orthosis wear time. Differences between estimated wear time and actual wear time were insignificant. Pressure-based wear time estimations had a specificity of 99.3 ± 0.7% and a sensitivity of 80.3 ± 19.2%, whilst acceleration-derived estimations had a specificity of 94.5 ± 6.4% and a sensitivity of 73.2 ± 15.8%. This study demonstrated that orthosis compliance can be monitored outside the laboratory, and, furthermore, this device offers insights into the intensity and frequency of a user’s activities and has the future potential to monitor orthosis fit and forces applied to affected joints using pressure. Full article

Current deep learning methods for tool wear monitoring suffer from poor interpretability and struggle to reveal the intrinsic relationship between signals and wear states. To address this issue, this paper presents an interpretable tool wear monitoring method based on Sparse Identification of Nonlinear Dynamics (SINDy). Multi-domain features are extracted from cutting force and acoustic emission signals to construct a time series. The SINDy algorithm is used to identify ordinary differential equations that describe the evolution of tool wear. An iterative “predict-validate-correct” mechanism is applied to optimize model parameters. Experimental results show that the mean absolute percentage error (MAPE) between the predicted and actual values is below 6%. Moreover, the optimal model demonstrates an average MAPE as low as 0.067% in cross-condition tests. This study provides an effective solution for online tool wear monitoring that achieves high precision, strong generalization, and physical interpretability. Full article

Signals generated during tool wear are nonlinear, non-stationary, and easily affected by machining noise, which makes reliable tool condition monitoring difficult in intelligent manufacturing. To address this issue, this study proposes a tool wear degree classification framework, FCTrans-CA, that fuses time-domain and frequency-domain information through a lightweight cross-attention (CA) bridge. Fast Fourier transform (FFT) is first used to obtain frequency-domain representations. The raw time-domain signals are processed by a multi-scale one-dimensional convolutional neural network (MS-CNN) to extract temporal wear features, while the FFT-derived representations provide complementary spectral cues. These two feature streams are fused by an asymmetric CA module in which frequency-domain features guide the selection of wear-sensitive temporal features. K-means clustering is used to divide the measured flank wear (VB) trajectory of each tool into initial-, normal-, and severe-wear stages, thereby reducing subjectivity in label generation. Experiments on the PHM2010 milling dataset show that FCTrans-CA achieves 99.43% classification accuracy on 40,648 test samples. The results indicate that cross-domain feature interaction improves the separability of wear states and provides a reproducible data-driven route for tool wear monitoring. Full article

In the existing tool wear status monitoring process, the difference in the distribution of tool wear signal characteristics under different processing conditions leads to insufficient generalization of the model and poor accuracy of wear status recognition. Aiming at the problem, a method for monitoring the wear status of milling cutters under variable working conditions based on transfer learning and a lightweight SqueezeNet model is proposed. Firstly, the continuous wavelet transform (CWT) is employed to realize the conversion of the raw vibration signal to a time–frequency energy diagram to completely preserve the joint feature distribution of the vibration signal in the time and frequency dimensions. Secondly, based on the phased transfer learning strategy and the lightweight SqueezeNet, a monitoring model of the wear status of the milling cutter under variable working conditions is established, which realizes the adaptive and accurate identification of the wear status of the milling cutter under different milling conditions. Finally, comparative experiments were performed using three groups of vibration signals under different milling condition as the model inputs. As demonstrated by the experimental results, the recognition accuracy of the test set of the proposed monitoring model under variable working conditions can reach 94.583%, which is higher than the 91.133% of the LSTM-DBO-SVM model, which proves the accuracy and feasibility of the presented approach under variable working conditions. Full article

Multi-UAV formation control in low-altitude urban environments faces critical challenges from atmospheric boundary layer (ABL) disturbances, including turbulence, wind gusts, and communication inefficiency in resource-constrained swarms. This paper proposes an adaptive dynamic event-triggered formation control (ADETFC) strategy integrated with a finite-time disturbance observer (FTDO) for multi-agent hexarotor UAV formations operating under ABL conditions. The novelty of the proposed ADETFC lies in employing dual adaptive parameters to simultaneously account for tracking error magnitude and inter-agent formation geometry, dynamically adjusting communication frequency. A nonsingular terminal sliding mode manifold ensures rapid transient convergence and robustness against nonlinearities and inter-agent coupling. The FTDO estimates lumped disturbances with finite-time convergence to a bounded residual neighborhood, enabling reduced control gains that mitigate chattering and actuator wear. Lyapunov-based analysis establishes finite-time reachability of the sliding manifold and guarantees that the tracking error converges to a bounded residual set in finite time. The Zeno-free operation is guaranteed by a strictly positive minimum inter-event time analytically derived from system dynamics. Simulations under three ABL scenarios, including Dryden turbulence, wind gusts, and sinusoidal disturbances, demonstrate formation tracking RMSE reductions of up to 29.4%, disturbance estimation RMSE reductions of up to 54.3%, and communication-event reductions of 46.4–63.2% compared with benchmark schemes. These results confirm accurate formation tracking, efficient communication, and robust multi-agent networking under challenging wind conditions, making the framework suitable for networked UAV applications in complex environments. Full article

The microstructural characteristics and precipitate features of titanium matrix composites (TMCs) are critical to tribological performance. In this study, TiCp/TA15 composites were fabricated via laser powder bed fusion (LPBF). The as-built composite was then heat-treated at 750 °C for 2 h to obtain a uniform duplex (α + β) microstructure with enhanced TiC precipitation, which was labeled as HT-750. The influence of the microstructural evolution on the tribological performance was systematically investigated. Compared to the as-built composite, the HT-750 composite exhibited a microhardness increase from 360.2 ± 6.4 HV to 459.2 ± 3.1 HV, a reduction in the friction coefficient from 0.649 ± 0.167 to 0.581 ± 0.111, and a decrease in the wear rate from 8.24 ± 0.44 × 10⁻⁴ mm³/(N·m) to 4.81 ± 0.39 × 10⁻⁴ mm³/(N·m), indicating a significant enhancement in wear resistance. This improvement is primarily attributed to the synergistic strengthening effect of the duplex matrix and TiC particles, which enhanced the load-bearing capability and suppressed surface plastic deformation. During the friction process, the dominant wear mechanisms of as-built and HT-750 composites evolved over time but exhibited distinct differences. The as-built composites were prone to continuous plastic deformation and damage accumulation, resulting in severe delamination, oxidative, and abrasive wear. Conversely, the HT-750 composites demonstrated higher resistance to plastic deformation and crack propagation, effectively mitigating interfacial shear and inhibiting damage evolution, with the wear mechanism being dominated by oxidative wear accompanied by abrasive wear and minor delamination. This work provides deep insights into the wear mechanisms of additively manufactured TMCs. Full article

Introduction: The goal of this study was to evaluate the influence of combined artificial aging protocols on the surface roughness and Vickers microhardness of bulk-fill resin composites, compared with a nanofilled composite used as a reference. Materials and Methods: A total of 120 cylindrical specimens were prepared from three bulk-fill composites (Tetric EvoCeram Bulk Fill, Filtek One Bulk Fill, Venus Bulk Fill) and one nanofilled composite (Filtek Supreme Ultra). Specimens were allocated into three aging conditions: mechanical wear (A), mechanical wear combined with pH-cycling (B), and mechanical wear combined with thermocycling (C). Surface roughness (Ra) and Vickers microhardness (VHN) were evaluated at two time points (T1: 120,000 cycles; T2: 240,000 cycles). Non-parametric statistical tests were applied (α = 0.05). Results: Aging protocols significantly influenced both Ra and VHN (p < 0.05). Overall, higher surface roughness and lower Vickers microhardness values were observed after cumulative aging, with material-dependent variations between T1 and T2. The greatest post-aging differences were observed under combined mechanical wear and pH-cycling (subgroup B), whereas mechanical wear alone showed the lowest changes. Filtek One Bulk Fill and Filtek Supreme Ultra showed more favorable post-aging Ra and VHN values, whereas Venus Bulk Fill showed less favorable post-aging surface properties. No significant correlation was found between Ra and VHN (rho = −0.009; p = 0.958). Conclusions: Combined aging conditions significantly affected the surface roughness and Vickers microhardness of resin composites, with the greatest post-aging differences observed under acidic challenges. Bulk-fill materials exhibit variable resistance depending on composition, emphasizing the importance of material selection for long-term clinical performance. Clinical relevance: Composite restorations exposed to combined mechanical and acidic challenges may show altered surface roughness and microhardness, highlighting the need for materials with enhanced resistance in high-risk oral environments. Full article

This study proposes a canopy-adaptive TAD-IRRT* (target-biased sampling, artificial potential field, and dynamic step-size informed rapidly-exploring random tree star) algorithm to solve the collision-free 3D path-planning problem for a 6-DOF apple-harvesting robotic arm. To improve computational speed and search directionality, the method integrates target-biased sampling and a distance-regulated artificial potential field (APF) into the Informed-RRT* framework. Furthermore, an obstacle-distance-based dynamic step-size mechanism is introduced to optimize spatial exploration. The generated routes undergo greedy path pruning and cubic B-spline smoothing to ensure kinematic executability. The simulation results in complicated ROS-based scenarios demonstrate that the TAD-IRRT* algorithm achieves a 100% planning success rate, reducing the average computational time and joint-space path length by approximately 60.1% and 15.6%, respectively, compared to the standard Informed-RRT*. Kinematic analysis via Fourier curve fitting ($R^2=0.9849$) confirms continuous angular velocity and acceleration without high-frequency chattering. Physical prototype experiments in the dense-obstacle scenarios show that the proposed method increases the path execution success rate by 36.7% and reduces the average execution time by 41% compared to the standard Informed-RRT* algorithm. The proposed approach effectively balances high-quality path generation with low computational overhead, providing a reliable and safe solution that significantly reduces mechanical wear. Full article

The contact performance of automotive airbag electrical connectors directly affects the stable conduction of the initiator circuit, yet sufficient failure data are difficult to obtain for such long-life safety-critical components. This study develops a degradation model for connectors with stainless-steel pins, beryllium-bronze sockets, and Ni/Au composite coatings, using the contact resistance increment as the degradation measure. Considering the accumulation of oxidation corrosion products under thermal stress, as well as the local film rupture and re-oxidation induced by fretting wear under combined temperature-vibration stress, a nonlinear time scale t^α is introduced to describe the nonlinear growth of contact resistance. A random-drift nonlinear Wiener process is then constructed: the diffusion term represents local fluctuations within each sample trajectory, while the random drift rate captures growth-rate differences among samples. Parameter estimation was performed using degradation data obtained from 160 °C high-temperature and 160 °C temperature-vibration accelerated degradation tests. The estimation results show that the stress-class-specific time-scale model better reflects the different degradation mechanisms than a common time-scale model, and that the temperature-vibration group exhibits higher resistance growth and stronger trajectory fluctuations. Model diagnostics support the description of the main increment distribution and sample-to-sample differences, while EDS and XPS results provide supplementary evidence for oxidation-related surface composition changes and coating-state evolution. Full article

Assessing motor symptoms in Parkinson’s disease (PD) is challenging due to the progressive evolution of the condition and the variability of symptoms, which are not fully captured by periodic clinical visits. In this context, wearable sensors and machine learning (ML) have emerged as a viable path toward objective and continuous monitoring, although achieving robust generalization to free-living conditions remains a challenge. This work explores the egg-beating task, a simple everyday activity, as a digital approach for PD motor assessment using smartwatch-based inertial measurements and ML techniques. Twenty-two individuals with PD and sixteen healthy controls (HC) completed a one-minute egg-beating task while wearing a smartwatch equipped with tri-axial accelerometer and gyroscope sensors. Data were recorded both under supervised clinical conditions and during unsupervised home sessions. Time- and frequency-domain features were extracted from the inertial signals, and models trained exclusively on supervised recordings were then tested on supervised, unsupervised, and combined data. PD participants showed systematically lower movement amplitude, slower oscillation frequency, and a progressive drop in signal energy over the course of the task, all of which align with the characteristic features of bradykinesia. The support vector machine achieved the best overall performance, reaching 90% accuracy in distinguishing PD from healthy controls under supervised conditions, with a reduction of less than 4% when applied to unsupervised data. These results support the egg-beating task as a practical and ecologically valid method for real-world motor assessment, with potential for future use in remote monitoring and longitudinal assessment. Full article

Background/Objectives: Long-term clinical data on direct posterior composite restorations are scarce, particularly beyond simple survival outcomes. This study aimed to characterize the long-term functional, esthetic, and biological behavior of posterior composite restorations after nearly three decades of service using selected FDI criteria and to assess changes across available follow-up examinations, including within a predefined sub-cohort. Methods: This observational follow-up involved 21 patients with 57 posterior composite restorations placed in 1995–1996 at the Department of Operative Dentistry, Goethe University Frankfurt, by undergraduate dental students under supervision. The 2025 follow-up used FDI criteria to assess functional, aesthetic, and biological properties, classifying outcomes as clinically acceptable, intervention needed, or failure. Descriptive analyses were applied to the entire cohort. Longitudinal analyses were conducted on a sub-cohort of 14 patients with 27 restorations at three time points. Exploratory analyses assessed associations with restoration factors, caries experience, and gingival health. Results: In 2025, 54.4% of restorations were clinically acceptable, 28.1% required intervention, and 17.5% were failures. Functional criteria remained mostly acceptable, though form and contour showed the highest mean values. In the longitudinal sub-cohort, significant changes over time were observed in anatomical form and occlusal wear. Retention, marginal adaptation, proximal contact, and surface luster did not change significantly. Biologically, restorations available for direct assessment had low incidences of secondary caries, hard-tissue defects, and postoperative sensitivity or pulpal issues. Conclusions: Posterior composite restorations can function for nearly three decades but gradually deteriorate in certain aspects. Long-term changes mainly involve cumulative functional aging of the anatomical form and occlusal wear, rather than widespread biological failure. These findings underline the importance of differentiated long-term assessment and support conservative management approaches where clinically feasible before replacement is undertaken. Full article

Hydrate formation and erosion-related pipe wear are critical operational challenges in natural gas pipelines. This study evaluates a pyrocondensate-based liquid composition modified with fine clay particles as a dual-function formulation for apparent hydrate-onset control and erosion-wear mitigation. The liquid phase contains pyrocondensate solvent, heavy gasoline fraction, and white oil. Laboratory screening was performed for composition ranges including 60/20/20 and 65/15/20 with formulations at reported dosages of 20–25 g/1000 m³. Under the applied procedure, visible hydrate formation for model gas samples was suppressed down to −24 °C with the 60/20/20 formulation. For a field gas sample from well No. 422, the 65/15/20 formulation shifted the observed apparent hydrate-onset temperature from +6 °C to approximately −20 °C at a dosage of 20 g/1000 m³. To mitigate erosion-wear, fine clay particles were added at 10 wt.% of the liquid composition. Laboratory tests demonstrated increased visual erosion-onset time in gas–liquid–solid flows. A preliminary four-month field application on well No. 422 recorded no hydrate formation or visible erosion-related complications. The results demonstrate the empirical potential of this dual-function composition under the investigated conditions. Full article

Based on outer raceway control theory and a five-degree-of-freedom quasi-static model of angular contact ball bearings, a raceway wear model considering curvature radius variation is proposed, which couples the quasi-static model with a modified Archard wear formulation and a dynamic curvature radius update mechanism. As wear accumulates, the worn curvature radii are fed back into the quasi-static model to recalculate the raceway contact dynamic parameters. Taking the SKF 7012ACE/HCP4A spindle bearing as an example, the wear depth evolution and the variations of contact ellipse area, contact stress, sliding velocity, and wear coefficient with wear time are investigated under combined loads. The results indicate that as wear progresses, the raceway curvature radii increase, leading to a decrease in contact ellipse area but an increase in contact stress and sliding velocity, which in turn accelerates the wear process. The findings demonstrate that the degradation of raceway curvature radius has a cumulative and non-negligible influence on wear evolution and should be incorporated into bearing wear calculations for more accurate life prediction. Full article