Search Results (415)

Long-wavelength flank waviness plays a critical role in the excitation behavior of geared transmissions. While coordinate measuring machine (CMM) exports provide detailed geometric information, conventional evaluations typically focus on individual tooth curves and do not quantify circumferential inhomogeneity across teeth. This study introduces a tooth-to-tooth long-wavelength waviness inhomogeneity indicator (ΔW1) derived directly from Klingelnberg-style MKA plot files and demonstrates its behavior on a large industrial dataset comprising 3375 measured gear parts. Each flank curve was detrended using a second-order polynomial fit, and lobe-based waviness amplitudes (W1–W3) were extracted via sine–cosine projection. The proposed ΔW1 metric was defined as the difference between the maximum and minimum W1 values across measured teeth within the same part. To eliminate measurement edge effects, a mid-section evaluation (10–90% of the face width) was additionally performed. Population-level analysis revealed consistent separation between geometrically homogeneous and inhomogeneous parts, with ΔW1 values in the most critical components exceeding 7–9 µm after mid-section filtering. Unsupervised clustering based on ΔW1 and maximum W1 further distinguished a high-variability subset of parts exhibiting systematic long-wavelength modulation patterns. The results demonstrate that circumferential waviness variability can be quantified using standard CMM outputs without additional hardware or specialized measurement procedures. The proposed indicator provides a practical geometric screening tool for large production batches and establishes a reproducible framework for linking detailed flank geometry to manufacturing consistency assessment. Although acoustic validation is outside the scope of the present work, the metric is intended as an NVH-relevant geometric risk indicator for future vibroacoustic correlation studies. Full article

Thin-walled laminated cooling plates integrate internal channels and pin-fin cores, producing reduced and spatially non-uniform stiffness that changes welding restraint and distortion. This study investigates stiffness-controlled plastic-strain evolution in laser butt welding of GH3230 laminated plates, with geometrically identical solid plates as reference. A coupled heat-transfer and thermo-mechanical finite element model was developed in MSC Marc using a composite Gaussian surface–volumetric moving heat source and temperature-dependent properties. The thermal solution was validated against near-weld thermal cycles and fusion geometry; mechanical predictions were evaluated by CMM distortion and residual-stress measurements. Both structures show comparable residual-stress magnitudes and spatial trends, indicating that residual stress is governed mainly by the local weld thermal gradient. In contrast, the laminated plate exhibits larger angular/bending distortion. Simulations show that, although the plastic-strain pattern is similar, the laminated plate develops higher peak plastic strain confined to a narrower band near the weld, with the transverse plastic strain dominating. Plastic strain–temperature paths reveal continued transverse plastic-strain accumulation during cooling with limited recovery, consistent with restraint redistribution induced by stiffness non-uniformity. An equivalent restraint–stiffness spring model explains this “narrower-but-stronger” plastic zone and links stiffness to yielding and residual plastic-strain magnitude, supporting distortion prediction and stiffness-informed control of welded laminated cooling plates. Full article

Background/Objectives: Cardiometabolic multimorbidity (CMM) significantly reduces healthy life expectancy in older adults. The specific role of adiposity indices derived from the triglyceride-glucose (TyG) index, body mass index (BMI), and waist-to-height ratio (WHtR) in predicting incident CMM has not been fully elucidated in longitudinal settings. We investigated these associations and the mediating role of the atherogenic index of plasma (AIP). Methods: We analyzed 304,586 community-dwelling adults aged ≥65 years from the prospective Guangzhou Older Longitudinal Dynamic Health (GOLD-Health) cohort (2018–2019), who were free of CMM at baseline. Multivariable Cox proportional hazards models evaluated the risk of incident CMM (coexistence of ≥2 cardiometabolic diseases) across quartiles of six TyG-derived indices. Mediation analysis quantified the contribution of atherogenic dyslipidemia via AIP. Results: Following a median observation time of 4.3 years, the study recorded 7816 participants who developed CMM. All six indices showed significant positive associations with CMM risk. TyG-WHtR demonstrated the strongest association (Hazard Ratio [HR] comparing highest vs. lowest quartile = 2.150; 95% Confidence Interval [CI] 1.998–2.314), closely followed by TyG-BMI (HR = 2.146). AIP significantly mediated the associations, explaining 7.5–33.0% of the effect, with the highest proportion observed for TyG using the Chinese visceral adiposity index (CVAI). Conclusions: TyG-derived adiposity indices, particularly TyG-WHtR and TyG-BMI, are robust independent risk markers for incident CMM in older adults. The substantial mediating role of AIP suggests that targeting atherogenic dyslipidemia may be a key strategy to interrupt the progression from insulin resistance to multimorbidity. These accessible metrics hold promise for large-scale risk stratification and early intervention in primary care settings. Full article

Background/Objectives: This study aimed to clinically investigate how variations in foot morphology influence spinal and lower limb alignment, based on the concept of an ascending kinetic chain. Methods: We analyzed the medical records of 100 patients who met the inclusion criteria. The X-ray image data used in the analysis included weight-bearing lateral views of both feet, whole-spine anteroposterior (AP) and lateral views, and full-length standing AP scanograms of the lower legs. In the obtained X-ray images, Calcaneal Inclination Angle (CIA), Tibiotalar Tilt Angle (TTA), Tibiotalar Angle (TA), Quadriceps Angle (Q-angle), Pelvic Incidence (PI), Pelvic Tilt (PT), Sacral Slope (SS), and L1–S1 Lordosis (LL) were measured. Participants were categorized into subgroups based on their CIA values: Pes Planus, Normal, and Pes Cavus. These subgroups were analyzed by foot orientation (right and left) using one-way analysis of variance (ANOVA) and Pearson correlation coefficient analysis. Results: The one-way ANOVA identified significant differences in mean right foot PT values among subgroups. Correlation analysis shows moderate associations between foot CIA and Q-angle of the knee, as well as pelvic parameters including PI, PT, SS, and LL. Conclusions: Analysis of the correlation between foot parameters and body alignment, in the context of diagnostic and evaluative aspects of Chuna manual medicine (CMM), revealed moderate correlations among the foot, ankle, knee, pelvis, and lumbosacral regions. These findings suggest that foot morphology may play a clinically relevant role in posture-related disorders and could contribute to preventive and corrective strategies for musculoskeletal alignment. Full article

The increasing deployment of collaborative and industrial robots in manufacturing systems places high demands on equipment reliability, availability, and maintenance efficiency. Robotic workcells, in which multiple automated subsystems operate in tightly coordinated cycles, are particularly sensitive to unplanned downtime, as failures of individual components can disrupt the entire production process. Traditional time-based preventive maintenance is often insufficient under such conditions, as it does not adequately reflect actual operating loads or component degradation. This paper proposes a structured framework for the design of an integrated maintenance concept for a multi-robot packaging workcell. The framework systematically combines component identification, criticality assessment, and the selection of appropriate maintenance strategies, including preventive, predictive, corrective, proactive, and reactive approaches. Preventive maintenance is complemented by condition-based monitoring and trend analysis of selected diagnostic parameters, enabling predictive decision-making for critical components. The proposed methodology further integrates maintenance planning and performance evaluation through a computerized maintenance management system (CMMS), supporting the coordination of maintenance activities and the assessment of key performance indicators. The novelty of the proposed framework lies primarily in the dynamic allocation of maintenance strategies based on semi-quantified component criticality and in the structured integration of predictive diagnostic information with CMMS-supported maintenance planning. Unlike traditional RCM-based or single-strategy maintenance approaches, the framework enables coordinated preventive, predictive, corrective, proactive, and reactive actions within a unified decision-making architecture, supporting proactive continuous improvement of maintenance performance through a closed-loop feedback mechanism that updates component criticality based on real-time operational data. The framework is demonstrated on a robotic workcell comprising a collaborative robot, an industrial robot, pneumatic subsystems, and a centralized control architecture. The results suggest that the integrated approach may provide a coherent basis for reducing reactive maintenance actions, improving system availability, and supporting data-driven maintenance planning. As a conceptual framework with partial (pilot) practical implementation within the context of this paper, the proposed approach establishes a foundation for future broader implementation, experimental validation and the integration of advanced diagnostic and prognostic methods, mainly in the context of multi-Robot workcell and production process maintenance. Full article

Augmented Reality (AR) and Large Language Models (LLMs) have made significant advances across many fields, opening new possibilities, particularly in complex machine operations. In complex operations, non-expert users often struggle to perform high-precision tasks and require constant supervision to execute tasks correctly. This paper proposes a novel AR-MLLM-based training system that integrates AR, multimodal large language models (MLLMs), and prompt engineering to interpret real-time machine feedback and user activity. It converts extensive technical text into structured, step-by-step commands. The system uses a prompt structure developed through an iterative design method and refined across multiple machine operation scenarios, enabling ChatGPT to generate task-specific contextual digital overlays directly on the physical machines. A case study with participants was conducted to assess the effectiveness and usability of the AR-MLLM system in Coordinate Measuring Machine (CMM) operation training. The experimental results demonstrate high accuracy in task recognition and feature measurement activity. The data further show reduced time and user workload during task execution with the proposed AR-MLLM system. The proposed system not only provides real-time guidance and enhances efficiency in CMM operation training but also demonstrates the potential of the AR-MLLM design framework for broader industrial applications. Full article

Background: Cutaneous melanoma metastases (CMM) represent a clinically relevant manifestation of advanced melanoma and may constitute the first sign of disseminated disease. Their diagnosis is challenging because CMM shows highly variable clinical and dermoscopic presentations and frequently mimic other benign or malignant skin lesions. Although dermoscopy is routinely used to improve skin lesion assessment, dermoscopic criteria specific to CMM remain poorly defined and still non-standardized. Methods: We performed a narrative review of the literature to summarize dermoscopic features reported in CMM. MedLine (via PubMed) and Web of Science were searched up to 3 December 2025 using the keywords “dermoscopy” and “melanoma metastasis,” complemented by manual reference screening. Eligible studies were English-language full-text articles in peer-reviewed journals providing a complete dermoscopic description. Extracted data included patient demographics and major dermoscopic criteria, categorized as global patterns and focal dermoscopic and vascular structures. Due to heterogeneity, results were synthesized descriptively. Results: Twenty studies were included, comprising 774 patients. Dermoscopic findings were markedly heterogeneous. Globally, lesions frequently showed homogeneous pigmentation with variable colors and included amelanotic presentations. Commonly evaluated focal features included irregular dots and globules, crystalline structures, peripheral gray dots, and lacuna-like areas. Vascular patterns were prominent, particularly serpentine and corkscrew-like vessels. Conclusions: CMM dermoscopy is characterized by substantial heterogeneity and a lack of standardized criteria. Systematic classification of recurring dermoscopic features may improve diagnostic consistency and provide an interpretable framework for future artificial intelligence-based approaches supporting non-invasive recognition of melanoma metastases. Full article

Background: Lupus nephritis (LN) is a severe manifestation of systemic lupus erythematosus (SLE), characterised by unpredictable outcomes due to the absence of reliable biomarkers. This hypothesis-generating study aimed to evaluate whether changes in the N-glycosylation of IgG, C3, AGP, and the serum proteins over one year of treatment correlate with clinical and histological features of LN and predict renal outcomes. Methods: Serum samples from 19 treatment-naïve patients with LN were collected at baseline and 12 months post-treatment, in conjunction with per-protocol repeat kidney biopsy. IgG (Fc, Fab, and total), C3, AGP, and total serum glycoproteins were isolated and analysed as either released N-glycans or N-glycopeptides using high-throughput glycomic approaches. Clinical and histological data were obtained at both time points, along with assessments of clinical and histological response at 12 months and long-term renal function. Results: In total, we identified 24/243 increased N-glycosylation traits (2 total IgG, 5 IgG Fc, 7 IgG Fab, 5 serum glycoproteins, 4 AGP, and 1 C3) and 10/243 decreased N-glycosylation traits (7 total IgG, 2 IgG Fc, 1 IgG Fab) following treatment. Baseline AGP IORMIF1N5H6S2F1 showed a positive correlation with eGFR both at baseline (r = 0.64, p = 0.005) and at 12 months (r = 0.51, p = 0.032). Among AGP N-glycosylation traits, IVORMI1N7H8S3 (r = 0.66, p = 0.002; r = 0.48, p = 0.041, respectively), VORMI1N8H9S4 (r = 0.51, p = 0.029; r = 0.49, p = 0.038, respectively), and VORMI1N8H9S4F1 (r = 0.48, p = 0.039; r = 0.49, p = 0.034, respectively) significantly correlated with activity index (AI) at baseline and at 12 months. Presence of cellular crescents at baseline positively correlated with three AGP N-glycosylation traits: IORMISORMIIA1N4H5S2 (r = 0.49, p = 0.036), VORMII1N5H6S3F1 (r = 0.63, p = 0.006), and VORMII1N4H5S2 (r = 0.48, p = 0.046). Total serum N-glycan (structure) N5H4F1 at 12 months was associated with both clinical and histological response to treatment. Delta of total serum N-glycan structure N5H5S1 was independently associated with poor long-term outcome. Conclusions: This study suggests that glycosylation changes over one year of treatment are associated with specific clinical and histological features and both short- and long-term renal outcomes in LN. Given the small cohort size, results should be considered hypothesis-generating warranting further investigation in independent cohorts. Full article

Hospitals are energy- and waste-intensive systems. Inpatient buildings dominate the sector’s electricity and gas consumption, and healthcare waste streams—especially device-associated disposables—increase environmental burdens. AI-enabled predictive maintenance (PdM) offers a dual lever: (1) reducing energy use by keeping assets operating at efficient points, and (2) preventing avoidable waste by extending component life, reducing emergency spares, and avoiding device-induced clinical workflow disruptions. In this study, an end-to-end architecture is developed by integrating multi-modal sensing (electrical, thermal, acoustic, vibration), computerized maintenance management systems (CMMS), risk-based maintenance under International Electrotechnical Commission (IEC)/International Organization for Standardization standards (ISO 60601, 62353/62304, 81001-5-1), and learning pipelines (self-supervised anomaly detection, remaining useful life estimators, and carbon-aware work order scheduling). Using representative hospital archetypes and equipment classes (imaging, patient monitoring, laboratory analyzers, sterilizers, and pumps), energy, downtime, and waste avoidance are simulated under baseline preventive maintenance (PM) versus PdM with alternate equipment management. Results showed that 10–22% site electricity reduction was achieved, attributable to equipment efficiency and optimized duty-cycling, 18–35% fewer unplanned failures, and a 12–28% reduction in associated consumable waste and emergency part scrappage across scenarios, while maintaining compliance with Joint Commission/Centers for Medicare & Medicaid Services and IEC safety testing intervals. We discuss cybersecurity (IEC 81001-5-1) and the trustworthiness of AI, present a governance model linking CMMS events to carbon telemetry, and provide an implementation roadmap. Full article

Dimensional verification of turbomachinery rotors requires traceable accuracy on functional data and dense coverage of freeform blades. This study quantifies the expanded measurement uncertainty (U95) for a centrifugal rotor inspected with a bridge-type CMM (Nikon Altera 10.10.8) and a structured-light scanner (ATOS Compact Scan 5M Rev.1), using repeated measurements in accordance with ISO 10360 and ISO 15530-3. The CMM achieved U95 ≈ 4–6 µm on bores, whereas optical scanning yielded U95 ≈ 12–18 µm on freeform blade regions. Cross-system results exhibited systematic offsets, indicating that the two methods are not directly interchangeable in absolute terms. Nevertheless, they are complementary: CMM ensures datum traceability, while optical scanning enables rapid full-field blade assessment, supporting uncertainty-aware hybrid inspection. Full article

Reverse engineering (RE) is essential in the automotive and aerospace industries for reconstructing high-precision components, such as exhaust valves, when design documentation is unavailable. However, different measurement methods introduce varied errors that can affect engine performance and safety. This study presents a comparative analysis of contact and optical measurement systems—specifically the CMM Accura II (ZEISS Group, Oberkochen, Germany), Mahr MarSurf XC 20 (Esslingen am Neckar, Germany), GOM Scan 1 (ZEISS/GOM, Braunschweig/Oberkochen, Germany) and MCA-II with an MMD×100 laser head (Nikon Metrology, Leuven, Belgium)—to assess their accuracy in reconstructing exhaust valve geometry. The research procedure involved measuring global surface deviations and critical functional parameters, including stem diameter, straightness, and seat angle. The results indicate that tactile methods (CMM and Mahr) provide significantly higher accuracy and lower dispersion than optical methods. The Mahr system was the most effective for stem precision, while the CMM was the only system to pass the seat angle tolerance requirement unambiguously. In contrast, the MCA-II laser system failed to meet the required precision–mechanical tolerances. The findings suggest that an optimal industrial strategy should adopt a hybrid methodology: utilizing rapid optical scanning (GOM) for general geometry and high-precision tactile systems (CMM, Mahr) for critical functional features. This approach can reduce total inspection time by 30–40% while ensuring technical safety and preventing catastrophic engine failures. Full article

Additive manufacturing (AM) is a significant contributor to Industry 4.0. However, one considerable challenge is usually encountered by AM due to the bed size limitations of 3D printers, which prevent them from being adopted. An appropriate post-joining technique should be employed to address this issue properly. This study investigates the influence of key friction stir butt welding (FSBW) factors (FSBWFs), such as tool rotational speed (TRS), tool traverse speed (TTS), and pin profile (PP), on the weldability of 3D-printed PLA–Chromium (PC) composites (3PPCC). A filament containing 10% by weight of chromium reinforced in PLA was used to prepare samples. The material extrusion additive manufacturing process (MEX) was employed to prepare the 3D-printed PCC. A Taguchi-based design of experiments (DOE) (L9 orthogonal array) was employed to systematically assess weld hardness (WH), weld temperature (WT), weld strength (WS), and weld efficiency. As far as the 3D-printed samples were concerned, two distinct infill patterns (linear and tri-hexagonal) were also examined to evaluate their effect on joint performance; however, all other 3D printing factors were kept constant. Experimentally validated findings revealed that weld efficiency varied significantly with PP and infill pattern, with the square PP and tri-hexagonal infill pattern yielding the highest weld efficiency, i.e., 108%, with the corresponding highest WS of 30 MPa. The conical PP resulted in reduced WS. Hardness analysis demonstrated that tri-hexagonal infill patterns exhibited superior hardness retention, i.e., 46.1%, as compared to that of linear infill patterns, i.e., 34%. The highest WTs observed with conical PP were 132 °C and 118 °C for both linear and tri-hexagonal infill patterns, which were far below the melting point of PLA. The lowest WT was evaluated to be 65 °C with a tri-hexagonal infill, which is approximately equal to the glass transition temperature of PLA. Microscopic analysis using a coordinate measuring machine (CMM) indicated that optimal weld zones featured minimal void formation, directly contributing to improved weld performance. In addition, scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) were also performed on four deliberately selected samples to examine the microstructural features and elemental distribution in the weld zones, providing deeper insight into the correlation between morphology, chemical composition, and weld performance. Full article

The progressive stamping process includes blanking, piercing, bending, and drawing operations on press machines with a single die set for high production runs. The processing conditions at individual progressive stamping stations are intricately coupled, posing a challenge for maintaining part quality at high production rates and dimensional precision. This study investigated the effects of the die bottom dead center (and later, BDC) depth, punch-die clearance, tool wear condition, and lubrication performance on the precision of stamped parts and bending angles. Quality characteristics were measured using a coordinate measuring machine (CMM) by employing a thin-sheet steel progressive die in a factorial experimental design. Using Pareto effect plots and the MINITAB platform, it was observed that for part bending angles, the first greatest factor of importance is BDC, followed by clearance as the second greatest, and then tool condition. The results reveal that although it affects part quality through interactions, the lubrication effect is not as significant as the main factors. However, SEM analyses show that worn tools and inadequate lubrication induce grain boundary separation, microcracking, and dislocations, while proper lubrication and sharp tooling maintain more homogeneous grain structures. Research indicates that achieving the full control of part quality in the progressive stamping process requires more than bottom dead center (BDC) adjustment; factors such as component clearances, punch condition, and lubrication level must also be considered. Process-based knowledge of the relationships among process parameters in multi-stage stamping processes can be used to develop adaptive monitoring systems that stabilize part geometry and minimize production variation. Full article

Managing risk in drifting complex systems is hindered by the weak integration of unstructured incident narratives into quantitative, decision-ready models. We present a phenomena-centric semantic factor framework that closes the data–model–decision gap by transforming free-text incident reports into transparent, traceable drivers of risk and actionable interventions. The pipeline normalizes and encodes narratives, extracts domain-invariant phenomena, couples them to risk outcomes through calibrated partial least squares factors, and applies scenario optimization to recommend portfolios of measures aligned with EAM/CMMS taxonomies. Applied to a large corpus of incident notifications, the method yields stable, interpretable phenomena, improves out-of-sample risk estimation against strong text-only baselines, and delivers prescriptive recommendations whose composition and cost–risk trade-offs remain robust under concept drift. Sensitivity and ablation analyses identify semantic factorization and PLS coupling as the principal contributors to performance and explainability. The resulting end-to-end process is traceable—from tokens through phenomena and factors to actions—supporting auditability and operational adoption in critical infrastructure. Overall, the study demonstrates that phenomenological semantic factorization combined with scenario optimization provides an effective and transferable solution for integrating incident text into the proactive risk management of complex, drifting systems. Full article

Background/Objectives: The cardiometabolic index (CMI) is a simple anthropometric–metabolic indicator that has recently gained attention as a marker of cardiometabolic risk. This study compared the associations and predictive utility of CMI, body mass index (BMI), and waist circumference (WC) for cardiometabolic multimorbidity (CMM). Methods: Data were drawn from 3348 adults (mean age 63.5 years; 45.1% male) in the English Longitudinal Study of Ageing who were free of hypertension, coronary heart disease, diabetes, and stroke at wave 4 (2008–2009). CMI was calculated using the triglyceride-to-HDL-cholesterol ratio and the waist-to-height ratio. Incident CMM at wave 10 (2021–2023) was defined as the presence of ≥2 of these conditions: hypertension, cardiovascular disease, diabetes, or stroke. Odds ratios (ORs) with 95% confidence intervals (CIs) and measures of discrimination were estimated. Results: During 12–15 years of follow-up, 197 CMM cases were recorded. CMI, BMI, and WC were each linearly related to CMM. Higher CMI was associated with increased CMM risk (per 1-SD increase: OR 1.25, 95% CI 1.08–1.44; highest vs. lowest tertile: OR 1.88, 95% CI 1.09–3.25), with similar effect sizes for BMI. WC showed stronger associations (per 1-SD increase: OR 1.46, 95% CI 1.25–1.71; highest vs. lowest tertile: OR 2.16, 95% CI 1.35–3.44). Adding CMI to a base model resulted in a small, non-significant improvement in discrimination (ΔC-index = 0.0032; p = 0.55) but significantly improved model fit (−2 log-likelihood p = 0.004), with comparable effects for BMI and greater improvements for WC. Conclusions: In this older UK cohort, higher CMI levels were associated with increased long-term risk of CMM but did not outperform traditional adiposity measures such as BMI and WC. Full article