Search Results (4,235)

The application of digital twin (DT) technology to intelligent machining shows promise, but its effectiveness in predicting vibration and assessing surface quality has not been thoroughly validated for widespread industrial use. This study presents a physics-informed predictive digital twin framework operating in an offline or near-real-time predictive configuration for vibration prediction and surface roughness modeling in milling processes. Impact hammer testing was conducted to extract the dominant modal properties of the spindle–tool assembly, which were embedded into a Simulink-based dynamic framework to predict tool vibration under varying cutting conditions. Full-immersion slot milling experiments on AL6061 were performed for validation. Within all datasets, including training phase and validation phase, the predicted vibration amplitudes exhibit a coefficient of determination $R^2=0.94$ with measured values. The overall MAPE and RMSE are about 10.39% and 0.234, respectively. Power-law regression-based surface roughness prediction models were subsequently established using cutting parameters and both measured and DT-predicted vibration features through logarithmic transformation and least-squares fitting. The results show that the roughness prediction model using vibration features predicted by the digital twin model achieved a correlation coefficient of approximately $R^2=0.84$, with MAPE = 9.57% and RMSE = 0.16 μm, which is comparable to the predictive model based on experimentally measured vibration. These results indicate that, within the investigated machining conditions, the digital twin can provide vibration features suitable for surface roughness prediction, demonstrating its potential as a virtual sensing approach. This work advances digital twin applications from process monitoring toward predictive, quality-oriented machining systems and provides a foundation for adaptive parameter updating in intelligent manufacturing environments. Full article

The management of fine bauxite tailings, rich in clay minerals, represents an environmental and operational challenge for the aluminum industry. This study (Part 1) presents a pilot-scale investigation into the dewatering of these ultrafine tailings using a decanter centrifuge, 0.62 m in diameter, as an alternative to conventional wet storage. Tests were conducted at three bowl speeds, 1600 rpm, 1700 rpm, and 1800 rpm, corresponding to G-forces of 888, 1003, and 1124 G. The feed slurry behaved as a non-Newtonian, yield-pseudoplastic fluid, as confirmed by rheology tests. A comprehensive mass balance and performance analysis were conducted. The results demonstrated a monotonic improvement in key performance metrics with increasing bowl speed. Accordingly, increasing the G-force from 888 G to 1124 G improved the final cake solid content from 66.3% to 71.5% (by weight), together with an increase in the average solid recovery from 40.0% to 56.2%. Partition curve analysis revealed the primary limitation: while recovery of particles coarser than 20 µm was very high (>98%), recovery of particles finer than 20 µm remained low, ranging from 22.0% to 35.1%. Partition curve analysis using the Whiten model identified a mechanical cut size (d_50c) ranging from 9.72 µm to 12.0 µm. Hydraulic bypass increased from 8.35% to 14.9% with increasing bowl speed, indicating a significant non-size-selective component of separation. Rheological analysis further showed that the apparent viscosity at 100 s⁻¹ decreased from 0.332 to 0.111 Pa·s across the tested conditions, confirming enhanced slurry mobility and its contribution to increased ultrafine bypass. While overall solid recovery reached 56.2% at 1124 G, the mechanical capture of the ultrafine fraction (<5 µm) remains the primary bottleneck for industrial viability. It is concluded that while the decanter centrifuge is mechanically viable for producing a high-solid cake, the limited recovery of fines would create an unsustainable circulating load in an industrial plant. These results demonstrate that G-force alone, within the tested range, is insufficient to manage these tailings and provide the basis for the mathematical modeling required to design the process, as described in Part 2 of this investigation. Full article

Carbon fiber-reinforced polymer (CFRP) composites are widely employed in the aerospace industry due to their excellent properties such as high specific strength and corrosion resistance. However, the delamination and tearing of composites are prone to occur in the machining of CFRP, which significantly affect its performance. The existing laser-assisted cutting model generally simplifies the machining process into high-temperature conventional cutting, and only reflects the thermal effect by modifying the material parameters. The core selective ablation characteristics of laser–CFRP interaction are completely ignored, and the unique mechanical behavior of bare fiber under a large cutting angle is not modeled, and the quantitative correlation between cutting force evolution and machining damage is lacking. In this study, an innovative method of partially exposing fibers is proposed to simulate laser-assisted machining. A micromechanical model is developed to analyze the removal mechanisms of different phases during CFRP processing, and a cutting force prediction model from the micro to macro scale is also established. At the micro-scale, a micromechanical model for fiber cutting in orthogonal machining of CFRP is constructed based on the elastic foundation beam theory. The results show that the proposed cutting force prediction model has high reliability, and the relative error between the predicted value and the experimental measured value is only 7.81%~8.99%. All experiments were repeated three times. Statistical analysis showed that the repeatability of the results was excellent. Compared with conventional cutting, laser-assisted cutting fundamentally changed the failure mode of the fiber from matrix-constrained crushing fracture to controllable free-end large-deflection bending fracture. This transformation leads to a smoother and more regular fiber fracture surface, which effectively inhibits fiber breakage, matrix tearing, and fiber–matrix interface debonding. Quantitative analysis confirms that under laser-assisted processing conditions, the matrix tearing length is positively linearly correlated with the cutting depth, cutting speed, and bare fiber length. Full article

This paper presents a field case study of a mechanical failure that occurred in the bottom-hole assembly (BHA) during composite plug milling after hydraulic fracturing operations. The failure sequence was reconstructed using field hook load and torque records, operational documentation, and inspection of the damaged components recovered from the borehole. The results indicate that the critical condition developed progressively and was associated with increasing resistance to drill string movement, insufficient hole cleaning, and repeated attempts to continue milling and release the partially immobilized assembly. The observed damage pattern, together with the presence of residual cuttings and metallic debris in the borehole, supports the conclusion that the loss of the BHA section at the hydraulic safety sub resulted from the interaction of several adverse operational factors acting simultaneously, particularly the combined action of pull-up force and rotation under deteriorating borehole conditions. A supporting strength assessment of the hydraulic safety sub was used to relate characteristic operating points to the admissible working range of the connector. The study shows that hook load and torque data provide the greatest practical value when interpreted jointly and in their operational context rather than as isolated peak values. The findings support safer planning and execution of plug-milling and stuck-pipe remediation operations in highly deviated wells. Full article

HT250 grey cast iron is a material of significance in the manufacture of precision machine tool guideways. The performance of guideways is significantly affected by the wear resistance of the machined surface. The present paper studies comparative grinding experiments conducted on HT250 using CBN and SiC wheels. The aim was to investigate the potential benefits of CBN grinding in enhancing surface wear resistance and to illuminate the underlying mechanisms. The results of these experiments demonstrate that, compared with SiC grinding, CBN grinding produces guideway specimens’ subsurface layer with finer grains (refined by approximately 15%) and notably higher microhardness (peak value of 382 HV). These microstructural improvements directly enhance the wear resistance of the ground surface. Within the tested parameter range, the optimal wear-resistant surfaces were obtained at a grinding speed of v_s = 30 m/s, a feed rate of v_f = 2000 mm/min, and a depth of cut of a_p = 6 μm. Under these conditions, surface roughness is better than R_a 0.4 μm, and surface microhardness achieves its maximum value. The wear tests were conducted using a ball-on-disk configuration under room temperature, oil lubrication, and applied loads ranging from 20 N to 80 N. The results show that, under the same loading and wear testing conditions, the wear depth of specimens machined with CBN wheels is reduced to 80–50% of that of specimens processed with conventional SiC wheels. Full article

This study aimed to express the canine parvovirus (CPV) VP2 protein prokaryotically and develop an indirect ELISA for detecting CPV-specific antibodies in canine serum. The VP2 gene from a laboratory-isolated CPV strain was amplified and cloned into the pET-28a vector. Following prokaryotic expression optimization, the recombinant protein was purified via Ni-NTA affinity chromatography and validated using Western blotting. An indirect ELISA was established utilizing the purified VP2 as the coating antigen, with optimal parameters determined by checkerboard titration. A 1773 bp VP2 fragment was amplified. Optimal expression of the 64.8 kDa recombinant VP2 was achieved with 2 mmol/L isopropyl β-D-thiogalactoside (IPTG) at 32 °C for 8 h. For the indirect ELISA, the optimal antigen coating concentration was 2 μg/mL, alongside primary (canine serum) and secondary antibody dilutions of 1:320 and 1:4000, respectively. The diagnostic cut-off optical density at 450 nm (OD₄₅₀) threshold was established at ≥0.2066, and the analytical sensitivity reached a serum dilution of 1:5120. Compared with the hemagglutination inhibition (HI) assay using 192 clinical serum samples, the ELISA showed a diagnostic sensitivity of 85.94%, a diagnostic specificity of 88.28%, and an overall agreement rate of 87.50%. The mean intra-assay and inter-assay coefficients of variation were 4.39% and 3.02%, respectively. These findings indicate that the recombinant VP2-based indirect ELISA showed good analytical sensitivity, reproducibility, and diagnostic agreement with the HI assay for detecting CPV-specific antibodies in canine serum under the tested conditions, although broader cross-reactivity validation is still required. Full article

Control-barrier-function-based safety filters are promising for autonomous driving, but most existing formulations treat obstacle perception as deterministic or account only for bounded ego state-estimation errors. This becomes limiting when obstacle existence, position, motion, and sensing quality vary online. We present a sensor-health- and belief-aware risk-adaptive high-order control barrier function (HOCBF) safety filter for dynamic obstacle avoidance. The method uses obstacle belief from a perception/tracking module, inflates residual obstacle uncertainty according to an object-wise sensor-health score, and converts upper-tail risk into adaptive HOCBF tightening through conditional value-at-risk (CVaR). Sensor health enters the controller through both covariance inflation and online CVaR confidence scheduling. The resulting quadratic program combines deterministic ego-error robustness with probabilistic perception uncertainty while minimally modifying the nominal control input. The zero-slack solution guarantees forward invariance of the risk-tightened safe set under the stated assumptions, whereas the slack-activated mode provides a quantified least-violation fallback rather than a strict safety guarantee. Simulations on a nonlinear 3-DOF bicycle model evaluate critical cut-in, sudden perception degradation, merge-bottleneck, fixed-CVaR, sensitivity, runtime-scaling, heterogeneous multi-obstacle, and heavy-tailed uncertainty cases. Full article

Cyclone separators remain widely used for gas–solid separation, yet analytical prediction of cut size and pressure drop remains challenging. This study presents an explicit semi-empirical model for the cut size (d₅₀) of reverse-flow cyclones based on the radial particle equation of motion in cylindrical coordinates, with d₅₀ obtained by equating radial migration time and residence time. A closed-form solution is derived in the Stokes regime, whereas non-Stokes behavior is handled numerically through the Schiller–Naumann drag correction. Turbulence is incorporated through a phenomenological correction, and the grade–efficiency curve is represented by a logistic relation. The model was implemented in MATLAB R2025a and applied in a parametric study covering inlet velocity, particle density, cyclone diameter, and gas viscosity. A Euler-type pressure drop relation was included to examine the separation–energy trade-off. Validation on the Kim et al. benchmark using one calibration point per cyclone family and six independent verification cases yielded a mean absolute percentage error of 13.5% and a root mean square error of 0.22 μm for d₅₀; the paired pressure drop check gave a 2.8% mean absolute percentage error. A complementary benchmark based on Wang et al. using 15 cm 1D3D and 2D2D cyclones under actual-air and standard-air conditions further supported the family-calibrated use of the model. A separate scale-up test showed that constant swirl intensity similarity is not transferable across large diameter changes. The formulation provides a transparent reduced-order tool for preliminary design and sensitivity analysis. Full article

This study evaluated a post-cut treatment combining sanitization, carboxymethylcellulose (CMC) coating, and chokeberry pomace extract for preserving fresh-cut sweet peppers during 7 days of refrigerated storage. Sliced peppers of two cultivars, Sunny F₁ (yellow) and Yecla F₁ (red), were assigned to five treatments: water washing (control), BioActiW 2000 Food sanitizer (BAW), BAW followed by CMC coating (BAW + CMC), CMC coating with 3.5% chokeberry extract (CMC + AE), and 3.5% aqueous chokeberry extract (AAE). Samples were stored at 5 ± 1 °C and assessed for physicochemical, microbiological, sensory, and postharvest quality attributes. The response was cultivar-dependent. Coating-based treatments reduced polyphenol and L-ascorbic acid contents, although chokeberry-containing formulations mitigated these losses relative to BAW + CMC. Total sugars and carotenoids were not significantly affected. In both cultivars, BAW and BAW + CMC best limited mesophilic bacteria and yeast growth, reduced softening, and decreased weight loss. AAE applied without prior sanitization increased microbial counts in Sunny F₁. Sensory analysis showed cultivar-specific acceptance: Sunny F₁ tolerated CMC + AE and BAW + CMC better, whereas Yecla F₁ was more sensitive to off-flavors linked to the extract. These results indicate that sanitization is essential for microbiological stability, while CMC can provide an additional barrier effect. Chokeberry pomace extract showed mixed effects and appears to be a formulation component whose usefulness depends on cultivar and treatment conditions. Full article

Miniature vapor compression refrigeration systems are gaining increasing relevance in cutting-edge applications such as drone docking station cooling, electric vehicle battery thermal management, portable medical and diagnostic devices, compact beverage dispensers, field-mounted telecom cabinet cooling, as well as the already established fields of electronics and personal cooling. These systems offer a promising pathway to localized and mobile cooling solutions. When coupled with the implementation of alternative low-GWP refrigerants that match or even enhance system performance, the result is a more efficient, environmentally responsible, and potentially sustainable refrigeration technology. Therefore, this study experimentally evaluates the performance of R515B as a low-GWP drop-in replacement for R134a in a miniature vapor compression refrigeration system. Key parameters were analyzed to determine the most suitable operating conditions, resulting in a capillary length of 1.25 m, refrigerant charge of 110 g, compressor speed of 4500 rpm, and high condenser fan speed, under which R515B achieved a COP of 5.16 and a cooling capacity of 252.20 W, representing improvements of 38% and 6.5%, respectively, compared to R134a. These results confirm the viability of R515B as an efficient, environmentally friendly alternative for miniature small-scale vapor compression systems. Full article

This study addresses asymmetric large surrounding rock deformation induced by narrow coal pillar instability in a gentle-dipping coal seam gob-side coal roadway (GSCR) under water-immersed and high-humidity conditions. The corresponding instability mechanism and control technology are systematically studied via integrated laboratory, theoretical, numerical and field methods. From constant temperature–humidity rock deterioration tests, SEM and XRD analysis, it is revealed that hydration of hydrophilic minerals (kaolinite, chlorite) in immediate roof mudstone intrinsically drives its macro–micro structural disintegration and mechanical degradation, and the catastrophic chain mechanism of water-induced mudstone weakening–force transmission medium failure of coal pillars and overlying strata–sliding instability of key voussoir beam blocks–linked large surrounding rock deformation is clarified. A mechanical model of the overlying voussoir beam structure for the target roadway is established considering both mudstone weakening and excavation-induced load transfer effects. The sliding criterion of key overlying blocks is derived, which quantitatively confirms that higher mudstone weakening and excavation-induced stress concentration elevate the sliding instability risk of the voussoir beam structure. Based on the findings and field conditions, a combined near-field and low-position field support scheme is proposed, including near-field reinforcement (shotcreting sealing, bolt–cable cascade reinforcement, deep grouting modification) and low-position field pressure relief via liquid CO₂ phase transition long–short boreholes roof cutting. Field application verifies that the maximum roadway deformation is controlled within 172 mm, with excellent surrounding rock control performance. Full article

Hard ceramic coatings are essential for extending the performance of metal parts under the extreme heat and stress found in aerospace and defense environments. There is a major knowledge gap regarding this topic in the current literature. While there has been significant research on individual fabrication methods or specific coating materials separately, no previous review has combined experimental lifecycle data with a broad computational design approach that covers the entire design-to-deployment process. This review fills that gap by offering a unified roadmap from integrated computational materials engineering (ICME) to machine learning (ML). This roadmap speeds up the rational design of coatings for next-generation aerospace systems. The practical importance of this framework is its clear use in gas turbine engine qualification, hypersonic vehicle thermal protection, and landing gear surface engineering. It can cut down on experimental trial-and-error cycles by allowing ML-guided composition screening and condition-based maintenance through digital twin integration. The main ceramic material systems, tungsten carbide (WC), boron nitride (BN), boron carbide (B₄C), silicon carbide (SiC), alumina (Al₂O₃), and zirconia (ZrO₂), are examined for their protective roles in aerospace-grade alloys. A key contribution is the multiscale computational framework that includes density functional theory, molecular dynamics, finite element analysis, and ML-driven inverse design. Together, these methods improve predictions for thermal breakdown, multi-axial stress responses, and coating lifetime. Future research should focus on ultra-high-temperature ceramics, multifunctional self-healing coatings, and surface engineering methods driven by data. Full article

Smart building services are increasingly realised through combinations of sensors, actuators, communication infrastructures, software platforms, analytics, and artificial intelligence-based functions. These configurations enable adaptive control, real-time monitoring, contextual automation, predictive support, user interaction, and cross-domain coordination across heating, ventilation, air conditioning, lighting, energy management, security and access control, water management, and user-centric comfort services. At the same time, the European Union Smart Readiness Indicator provides a formal basis for assessing building smartness through technical domains, service functionalities, and multidimensional impact criteria. A systematic basis for translating real-world descriptions of smart building services and their enabling technology stacks into Smart Readiness Indicator-aligned assessment inputs remains underdeveloped. A PRISMA ScR informed review was conducted to identify principal smart building service domains, synthesise their core functionalities, and reconstruct the digital technologies through which these functionalities are realised. The synthesis shows that heating, ventilation, and air conditioning and lighting provide comparatively direct translation pathways to formal Smart Readiness Indicator domains, while energy management operates mainly as a supervisory and cross-domain layer. Security and access control, water management, and several user-centric services contribute meaningfully to building smartness but often show partial or extended formal correspondence. Monitoring and control emerge as a central cross-cutting layer because many higher-order smart building capabilities are expressed through visibility, supervision, orchestration, and digital representation. Building on this review, a methodological framework is established for translating smart building services into Smart Readiness Indicator-aligned assessments. The procedure uses the smart building service instance as the unit of analysis and links service identification, functionality formulation, technology stack reconstruction, formal domain correspondence, impact profiling, maturity classification, and building-level aggregation. This enables heterogeneous service descriptions to be converted into structured readiness profiles while preserving the distinction between operational functionality, enabling technology, formal assessment correspondence, and multidimensional impact contribution. Application of the framework to the IoT Building Cloud platform shows that a substantial share of smart building capability may derive from supervisory digital infrastructure rather than from isolated end-use control alone. The resulting readiness profile is characterised by strong representation in monitoring and control, information to occupants and operators, and maintenance awareness, together with more selective contributions to indoor environmental control and limited flexibility-related capability. The proposed framework supports Smart Readiness Indicator-aligned pre-assessment, comparative analysis, design stage reasoning, and digital tool development by providing a transparent bridge between smart building service descriptions and formal assessment-oriented interpretation. Full article

The study investigates position-dependent variability in the slitting and rewinding process of filtration paper rolls under industrial conditions. Although individual cutting positions operate under identical machine settings, systematic differences between them lead to quality deviations and reduced process performance. Spatial variability was analyzed using descriptive statistics, control charts, and process performance indices (P_p, P_pk), complemented by non-parametric statistical testing. The results revealed a significant spatial effect, with one slitting position responsible for most nonconforming products, highlighting the limitations of global capability indices, which may mask local systematic deviations in a multi-stream process. Potential root causes were identified using the 5 Whys method within the Quick Response Quality Control (QRQC) methodology. Following the implementation of corrective actions, including parameter adjustments, position-dependent control, and revised operating procedures, the observed proportion of nonconforming products reduced from 14.7% to 6.0%. Furthermore, after excluding the first rolls from the start-up phase, process performance improved to P_p = 1.36 and P_pk = 1.21. The study suggests that integrating global and position-level analysis in multi-stream manufacturing systems enables more targeted identification and mitigation of quality deviations. Full article

This paper presents a study on evaluating the effectiveness of nanofluid Minimum Quantity Lubrication (NF MQL) in machining Hastelloy C276 alloy—a difficult-to-cut material. The study compares NF MQL using different types of nanoparticles (Al₂O₃, MoS₂, SiC, and GrP) with dry and pure MQL conditions in terms of surface roughness, cutting force components, and especially the variation of cutting forces over time. Experimental results indicate that the graphene-containing nanofluid MQL showed the most superior performance in terms of surface roughness R_a with 54.3% and 34% reduction, followed by MoS₂ and Al₂O₃ nanofluid MQL conditions. Regarding the active cutting force F_a, Al₂O₃ nanofluid MQL achieves the largest reduction of about 18.4% and 22.1% when compared to dry and pure MQL, followed by GrP nanofluid MQL, MoS₂ nanofluid MQL, and then SiC nanofluid MQL. Meanwhile, GrP nanofluid MQL shows the highest percentage of F_z reduction at about 13.4% and 26% when compared to the dry and pure MQL conditions, followed by MoS₂ nanofluid MQL. Furthermore, the application of NF MQL also significantly improves tool life and extends about 36.4 ÷ 61.1% and 18.2 ÷ 50% compared to dry and pure MQL, respectively. Notably, through in-depth analysis of the variation of cutting forces, the study has elucidated the superior lubrication and cooling mechanism of the NF MQL method, confirming its potential application in machining advanced materials. Full article