Search Results (1,175)

Wire electrical discharge machining (WEDM) is widely used for the precision cutting of difficult-to-machine materials, including nickel-based superalloys. Wire electrode wear, however, remains a practical limitation, because it affects process stability, wire consumption, and machining cost. This work examines the wear behaviour of a gamma-phase Cu₅Zn₈-coated copper-core wire electrode (Elecut X, ø 0.25 mm) during the WEDM of Inconel 718 using direct gravimetric measurement. A 3³ full factorial experiment was carried out with three electrical parameters: pulse-on time (A), pulse-off time (B), and servo reference voltage (Aj). The discharge process was monitored with an oscilloscope so that measurements only started after the programmed pulse-off time had been reached. Electrode wear was evaluated as the mass loss Δm of 4 m wire segments after 5 min cutting intervals on a Charmilles Robofil 310 machine, and factor significance was assessed by analysis of variance (ANOVA). Pulse-on time was the dominant factor, accounting for 88.45% of the total variation in Δm, followed by servo reference voltage and pulse-off time. SEM/EDS examination showed material transfer from the Inconel 718 workpiece to the worn electrode surface, with local nickel content reaching 16.84 wt.% on the frontal face of the most worn sample. The results provide a quantitative basis for reducing wire consumption during the WEDM of Inconel 718 while recognising the trade-off with cutting productivity. Full article

This study addresses the critical challenge of sediment-driven degradation in aging hydropower infrastructure by implementing a novel Digital Operational Technology modernization framework at the AES Chivor Hydropower Plant in Colombia. While conventional sediment monitoring relies on sporadic manual campaigns, this research introduces a continuous, real-time sensing architecture that integrates hybrid acoustic–optical sensors, covering a range of 10 to 6000 mg/L, directly into the plant’s SCADA (Supervisory Control and Data Acquisition) system. The novelty of this approach lies in the seamless coupling of high-frequency physical data (15 min sampling) with an Operational Decision Support Module, enabling adaptive turbine management. Statistical validation against laboratory gravimetric standards yielded a robust correlation of 0.93, confirming the system’s precision in quantifying suspended sediment concentrations. By identifying critical fine particle fractions in real time, the proposed model enables a precision-based maintenance strategy that significantly reduces unscheduled production downtime and mitigates accelerated wear in Pelton turbines. These findings provide a scalable benchmark for extending the operational life of large-scale hydropower facilities facing advanced sedimentation risks through digital transformation. Full article

Transitioning predictive maintenance from expensive, high-frequency piezoelectric sensors to affordable, edge-deployed MEMS sensors poses a significant challenge in industrial tool condition monitoring (TCM). Both technologies differ in signal quality, frequency capability, robustness, and reliability, which would affect how accurately machine faults can be detected. This work presents a systematic framework to bridge this gap, enabling real-time tool wear prediction and cross-sensor transferability. The methodology employs unsupervised Wavelet Packet Decomposition (WPD) and dynamic programming on high-resolution vibration signals to establish ground-truth wear phases: initial, steady-state, and accelerated. Multi-resolution time-frequency features are extracted and globally ranked using a multi-metric scoring system. A multi-task Bidirectional Long Short-Term Memory (Bi-LSTM) network is then trained to simultaneously predict a continuous wear index and classify discrete wear zones. To ensure model portability, Canonical Correlation Analysis (CCA) is utilised to align the high-fidelity piezoelectric feature space with the lower-frequency MEMS domain. The optimised multi-task Bi-LSTM architecture achieved up to 97.9% zone classification accuracy and a mean absolute error of 0.042 for wear index regression. Furthermore, CCA-based domain adaptation successfully transferred a model trained on piezoelectric data to classify unseen low-cost MEMS sensor data, maintaining a robust 87% accuracy. Combining optimised WPD features with CCA effectively overcomes hardware and sampling rate discrepancies, proving the viability of using low-cost sensors for reliable industrial retrofitting and real-time degradation tracking. Full article

Ground engaging tool (GET) wear monitoring is important for mining excavator maintenance, but progressive multi-tooth wear estimation remains insufficiently explored. This study presents a vibration-based framework for GET wear estimation during operations using modal analysis, finite element (FE) modelling, and machine learning as a supporting evaluation tool. A laboratory-scale mining bucket surrogate with detachable attached masses was used to represent progressive tooth wear through controlled mass-loss conditions. Experimental impact hammer tests under approximately free-free boundary conditions were conducted to validate the FE modal model through natural-frequency comparison and qualitative mode correspondence. The validated FE model was then used to generate a broader dataset of multi-tooth wear scenarios, from which the first ten natural frequencies were extracted as modal features. Linear Regression (LR) was adopted as a simple and interpretable baseline to evaluate both overall wear estimation and individual tooth wear estimation. High accuracy was obtained for overall wear estimation for both the non-symmetric and symmetry-augmented datasets, with R² values of 0.9983 and 0.9976, respectively. In contrast, individual tooth prediction was more challenging, and the symmetry-augmented results showed that mirrored tooth locations can produce non-unique frequency-based signatures. An additional asymmetric FE sensitivity study further confirmed that structural symmetry can limit local wear identifiability when only global natural frequencies are used. These findings demonstrate the potential of FE-derived modal frequency features for laboratory-scale GET wear assessment, while also highlighting the limitations of frequency-only features for unique local wear localisation in symmetric structures. This is a promising approach for wear estimation during mining operations. Full article

The increasing demand for personalized conservative treatment of idiopathic scoliosis (IS) highlights the need for objective and continuous monitoring of corrective forces during brace therapy. This study aims to evaluate the feasibility and clinical relevance of a smart orthopedic brace equipped with integrated force sensors for long-term biomechanical assessment. Three female patients with different types of idiopathic scoliosis were treated using a custom-designed thoracolumbosacral orthosis incorporating four flexible pressure sensors, enabling real-time and long-term recording of corrective forces at key anatomical locations. Sensor data were analyzed in relation to brace-wearing adherence, patient activity, and radiological outcomes assessed using Cobb angle measurements. The results demonstrated substantial variability in force distribution and wearing patterns among patients, which was associated with differences in treatment effectiveness. Higher and more stable corrective forces near curve apices were generally accompanied by improved radiological outcomes, whereas irregular brace use and uneven pressure distribution limited therapeutic effects. Long-term monitoring enabled identification of insufficient correction zones and adherence issues. In conclusion, the proposed sensor-based orthotic system provides clinically relevant information on force distribution and brace use, supporting individualized therapy optimization. These findings indicate that smart braces can enhance clinical decision-making and contribute to more effective and personalized scoliosis management. Full article

Background: Youth with type 1 diabetes (T1D) often show low physical activity levels and a long time spent in sedentary and poor sleep, which may worsen their health. This study aimed to describe baseline movement and sleep patterns in children and adolescents with T1D and compare them with behaviors recorded during a structured summer camp. Methods: Twenty-three participants (13.33 ± 2.13 years) completed physical fitness tests, self-report questionnaires, and 7–8 days of wearable monitoring before camp. During a 10-day diabetes summer camp, participants continued wearing the devices to track physical activity, sedentary time, and sleep. Comparisons between pre- and during-camp periods were performed using paired statistics, and linear regressions examined associations between activity and sleep awakenings. Results: At baseline, device-based monitoring showed low physical activity levels, long sedentary time and poor sleep. Self-reported data confirmed low activity levels and long time spent in sedentary activities, especially screen time. During camp, daily steps increased significantly (p < 0.001), as well as all the physical activity intensities (p < 0.01). Sedentary time decreased significantly (p < 0.001), and sleep duration declined (p < 0.001), but awakenings were shorter (p = 0.005). Baseline sedentary time predicted longer nocturnal awakenings, while greater increases in steps during camp correlated with longer awakenings. Conclusions: Children and adolescents with T1D showed low baseline activity, high sedentary time, and poor sleep. Participation in the structured summer camp appears to be associated with changes in physical activity, sedentary behavior, and sleep patterns. Full article

This paper proposes an online wear monitoring and lifetime assessment method for textile needle hooks, based on yarn tension sensing, closed-loop tension control, and the capstan model. The yarn tensions on both sides of the yarn–needle wrap interface are measured in real time and used to estimate an equivalent friction coefficient, which serves as the monitoring index for wear evolution. Closed-loop average-tension control was employed to reduce variability in operating conditions and enhance the consistency of friction coefficient estimation. To improve robustness, the signal-processing pipeline includes tension-floor gating, ratio clipping, missing-data handling, outlier rejection, pre-filtering, and post-filtered differentiation. Wear-life determination is achieved through a baseline-referenced criterion that combines a relative threshold with persistence time, defining the life endpoint as the earliest sustained deviation from the steady-stage baseline, rather than isolated spikes. Experiments conducted on needle hooks of different quality grades demonstrate that the proposed method yields stable yarn-tension measurements, enables clear discrimination among wear states, and produces wear-life assessments consistent with offline microscopy observations. The aforementioned method is computationally lightweight and suitable for practical online wear monitoring, thereby enabling data-driven timing of needle replacement in looms. Full article

Background/Objectives: Global climate change has increased occupational heat exposure, posing significant risks to outdoor workers. Automotive maintenance workers face high temperatures, radiant heat from machinery, and physically demanding tasks; however, their awareness and preventive behaviors regarding heat-related illness remain insufficiently understood. This study examined heatstroke awareness and preventive behaviors among automotive maintenance workers in Japan. Methods: A cross-sectional web-based survey was conducted among 371 automotive maintenance workers. Self-reported heat-related illness experience was assessed based on subjective judgment without formal medical diagnosis. Associations between heat-related illness experience and behavioral, physical, and health-related factors were analyzed using chi-square tests with Bonferroni correction and multivariable logistic regression. Results: Approximately 39.6% of participants reported experiencing heat-related illness during summer work. In multivariable analysis, headache (OR: 2.66, 95% CI: 1.25–5.64), dizziness (OR: 2.06, 95% CI: 1.12–3.80), obesity (OR: 1.86, 95% CI: 1.06–3.27), and lower self-perceived health (OR: 2.19, 95% CI: 1.36–3.55) were independently associated with heat-related illness experience. Some preventive behaviors, including wearing cooling garments and frequent hydration, showed associations in the multivariable analysis; however, these findings should be interpreted with caution due to possible reverse causation, small cell sizes, and residual confounding. Conclusions: Behavioral and individual health-related factors, particularly symptom recognition and self-perceived health, are associated with heat-related illness experience among automotive maintenance workers. Interventions focusing on early symptom awareness, risk perception, and self-monitoring may be important components of workplace-based heat illness prevention. Future studies incorporating objective environmental and physiological measurements are needed to clarify causal relationships. Full article

Background: The majority of Class II malocclusions stem from mandibular deficiency, leading to chin retrusion. In growing patients, the ideal correction—aiming for a skeletal mandibular response—should avoid common pitfalls such as “Point B” dropping postero-inferiorly, excessive labial proclination of mandibular incisors, or the lingual tipping and extrusion of maxillary incisors. When planning mandibular advancement (MA) using clear aligners with integrated advancement features, biomechanical forces are not the only consideration; precise management of the lower incisor position is critical for success. Current literature highlights not a good control in digital planning software: these platforms are primarily dentoalveolar-based and lack integrated cephalometric analysis. Consequently, mandibular advancement is often defined by standard linear parameters (typically 2 mm per step), while incisor position is managed through virtual alignment without correlation to cephalometric landmarks like the Pogonion, NB line, or IMPA. The software cannot monitor real-time sagittal or vertical skeletal relationships, the software will elaborate the treatment planning after doctor’s prescription, the clinician must manually adjust incisor positioning based on external cephalometric analysis to prevent dental compensation or excessive proclination. Aim: This clinical case demonstrates a specific arch preparation protocol designed to optimize mandibular advancement in a growing patient with mandibular retrusion. Methods: A 12-year-old female presented with a skeletal and dental Class II malocclusion, characterized by increased overjet and a normal overbite. Treatment was conducted using Invisalign^® clear aligners (22 h/day wear, weekly changes). The treatment objectives were: transverse: Correct upper dentoalveolar contraction and coordinate arch form while restoring midline alignment; sagittal: establish Class I molar and canine relationships by correcting the overjet and reducing the labial inclination of the lower incisors; vertical: level the curve of Spee. A key clinical condition of our protocol was the pre-advancement phase: the lower arch was reshaped by reducing the buccolingual inclination (retroclination) and intruding the lower incisors. This was specifically intended to increase the available overjet space, creating the necessary room for subsequent mandibular advancement. Results Treatment was completed in 24 months with high patient compliance. Objectives were successfully met, including the correction of skeletal and dental discrepancies, the establishment of harmonious arch forms, and precise overjet reduction through enhanced control of the mandibular incisors. Conclusions: This case report outlines an optimized clinical strategy for Class II correction. Cephalometric Integration: Perform an initial analysis outside the digital planning software to define the ideal IMPA and NB angles. Anatomic Verification: Utilize radiographic overlays to ensure tooth movement remains within alveolar bone limits. Pre-MA Optimization: Prioritize a “pre-advancement” phase to maximize the sagittal inter-arch space (overjet). A larger overjet allows for a more significant orthopedic effect from the MA features. Stepwise Advancement: Implement mandibular advancement in increments (≥2 mm) with periodic clinical reassessment to facilitate the adaptation of the muscular sling and functional occlusion. Full article

Integrated die-cast components reduce machining/assembly steps and improve mechanical dynamic characteristics, eliminating joint loosening/fracture risks after long-term use. However, the highly variable geometries and random spatial distributions of burrs, flash, parting lines, and risers in castings invalidate pre-programmed or teach-in robotic grinding methods. This paper reviews recent progress and future trends in robotic grinding, analyzing four core aspects: force control stability/adaptability (e.g., adaptive impedance control can reduce average force-tracking error to 0.38 N), trajectory planning/path generation (e.g., error-driven compensation can lower contour error by 34.2–55.1%), process parameter optimization, and challenges of sensing latency/quality evaluation (e.g., deep learning models achieve 97.64% accuracy in identifying abrasive belt wear states). The key enabling technologies are summarized, including active/passive compliant force control, model-/data-driven adaptive trajectory planning, intelligent process parameter optimization integrating physical mechanisms and data-driven approaches, and multi-modal state monitoring with online quality assessment. Representative applications (metal castings, aero-engine blades, thin-walled components, weld seams) are presented, and prospective research directions are proposed. This paper provides a comprehensive reference for theoretical research and engineering practice in this field. Full article

Train wheel wear is a critical factor affecting train operational safety, making the accurate and objective evaluation of wheel wear condition essential. However, current approaches are still constrained by inadequate measurement accuracy and incomplete evaluation methods. To address this issue, this study proposes an integrated method for the high-precision measurement and wear condition evaluation of train wheels. A multi-sensor data fusion-based measurement method is developed to synchronously acquire key wear-related parameters, including wheel diameter, flange height, and flange thickness. Based on the measured data, a matter-element model combined with game-theoretic weighting is established to evaluate wheel wear condition. Experimental results show that the proposed online measurement method for in-service wheels achieves standard deviations below 0.15 mm, and the measurement errors satisfy the requirements of Chinese railway industry standards. The evaluation results derived from the high-precision measurement data indicate that wheel wear condition gradually deteriorates with increasing service mileage, and that flange height wear is the dominant factor affecting the wear grade. These findings are consistent with actual operating conditions. The proposed method integrates high-precision multi-parameter measurements with wear condition evaluation, providing a reliable technical basis for wheel condition monitoring and predictive maintenance in rail transit. Full article

Accurate tool wear identification is essential for ensuring the continuity of intelligent machining and workpiece quality. To address the challenges of multi-source fusion inefficiency and inadequate feature extraction, this study proposes a novel identification architecture combining physics-guided multi-channel Gramian angular field (PG-MGAF) with a minimalist 14-layer CSA-Kansformer network. Multi-source signals are preprocessed via PG-MGAF to convert 1D time-series into 2D RGB images, effectively characterizing spatial coupling and interactive energy across three channels. Subsequently, the minimalist network maps these composite features to tool states, significantly reducing computational overhead. Experimental results demonstrate that the proposed model achieves an average accuracy of 93.6% with a single-step inference latency of only 5.90 ms, significantly outperforming mainstream methods such as MobileNet-V2 and ConvNeXt. This architecture provides a high-efficiency, low-latency solution for real-time tool condition monitoring under complex industrial conditions. Full article

A homemade NiCr/NiSi thin-film thermocouple integrated with a PCBN turning tool was developed for real-time temperature monitoring during dry turning of AISI 1045 steel. The study addresses a practical limitation of existing cutting-temperature methods, namely the difficulty of combining local in situ sensing near the cutting edge with a transient thermal analysis framework that can interpret the measured signal under repeatable cutting conditions. The sensor was fabricated on an Al₂O₃ substrate by magnetron sputtering, protected by a SiO₂ layer, and tested at cutting speeds corresponding to spindle speeds of 1000, 1500 and 2000 rpm, with a cutting depth of 0.5 mm, a feed rate of 0.1 mm/rev and cutting times of 30–90 s. A three-dimensional transient heat-conduction model and inverse heat-flux reconstruction were then used to interpret the temperature history. The maximum measured temperature increased from 342 °C to 488 °C, and VB increased from 0.082 mm to 0.295 mm, showing a strong temperature–wear association within the investigated parameter window. Full article

Concrete vibration quality has an outsized effect on structural durability, but construction sites have no reliable way to monitor it in real time. Compounding this, vibration machinery has no self-awareness of its own operating state, so failures and degradation tend to go unnoticed until something goes wrong. The proposed system integrates a Raspberry Pi controller and a hybrid neural network model within the vibrator apparatus itself. The model pairs a 1D CNN with a Kolmogorov–Arnold Network (KAN). The CNN initially conducts the majority of the computational workload: it systematically reduces dimensionality and extracts salient features from extensive time-series data, thereby circumventing the convergence challenges that a KAN encounters when processing unrefined high-dimensional sequences independently. Subsequently, a B-spline-based classification module supersedes the conventional fully connected layer. This innovation is noteworthy; the module is capable of identifying minute damping variations and frequency alterations during the process of concrete liquefaction, accurately distinguishing between states such as “adequate compaction” and “over-vibration,” which may appear nearly indistinguishable in their dynamic responses. The achieved accuracy in vibration state classification was 97.55%, while recognition of no-load conditions reached 98.17%. The system provides millisecond-level active protection against hazardous impacts, effectively reducing equipment wear. With a low implementation cost of approximately 800 RMB and a projected 20% improvement in construction compliance, this work provides reliable technical support for ensuring controllable construction quality and extending equipment service life, offering an efficient solution for the intelligent upgrade of building equipment. Full article

Train door actuation systems are critical safety components in railway vehicles, where early fault detection is essential for safe operation and reduced service disruptions. Conventional monitoring approaches often rely on additional sensors such as infrared detectors or vision systems, which increase system complexity and cost. To overcome these limitations, this study proposes a wavelet-based health monitoring structure for detecting electrical and mechanical faults using motor current signal analysis. A dynamic model of the train door actuation mechanism, including a DC motor, gearbox, and lead screw, was developed in MATLAB/Simulink to simulate conditions such as armature electrical faults, brush wear, increased friction, and lead screw misalignment. Motor current signals were analyzed using the Discrete Wavelet Transform with a Daubechies (db10) mother wavelet to extract diagnostic features based on the L1-norms of wavelet coefficients at levels W8 and W9 along with the motor starting current peak. Experimental validation using a LabVIEW-based test platform demonstrated fault detection accuracy above 96% with a response time below 0.3 s, confirming the effectiveness of the proposed approach for predictive maintenance of railway door systems. Full article