Search Results (1,543)

Al-Cu-Fe quasicrystalline coatings were deposited on AISI 321 stainless steel substrates by high-velocity oxy-fuel (HVOF) spraying at oxygen pressures of 3.0, 3.5, and 4.0 bar. The influence of oxygen pressure on the phase composition, microstructure, porosity, corrosion behavior, thermal stability, and microhardness of the coatings was investigated using X-ray diffraction (XRD), scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM/EDS), ImageJ porosity analysis, electrochemical corrosion testing in 3.5 wt.% NaCl solution, simultaneous thermal analysis (TGA/DSC), and microhardness measurements. XRD analysis revealed the formation of quasicrystalline-related intermetallic phases together with Al, Fe₃Al₁₃, FeAl, Fe₃O₄, CuFe₂O₄, Cu₂O, and CuO phases. The coating deposited at 3.5 bar exhibited the lowest porosity (5.37%), the most homogeneous microstructure, and the largest residual coating thickness after corrosion testing. SEM and EDS analyses indicated that corrosion preferentially initiated at pores, splat boundaries, and phase interfaces, while the coating produced at 3.5 bar demonstrated the most stable surface condition after exposure to a 3.5 wt.% NaCl solution. Thermal analysis showed that all coatings remained stable up to 900 °C. Sample (a) exhibited the lowest mass loss and the highest thermal stability, whereas sample (b) demonstrated the most favorable combination of structural integrity, phase ordering, coating density, corrosion-related performance, and thermal stability. Microhardness values of the coatings ranged from 754 to 778 HV, significantly exceeding that of the AISI 321 substrate. The results demonstrate that oxygen pressure is a critical parameter controlling the microstructure and functional properties of HVOF-sprayed Al-Cu-Fe coatings, with 3.5 bar providing the most balanced set of properties. Full article

High-temperature structural components are susceptible to creep deformation, which can ultimately lead to failure. In this work, an AI-based framework was developed capable of predicting the creep time of 316 austenitic stainless steel. Here, creep time refers to both the time to reach specific strain levels and the time to rupture. However, the scope of the present work is limited to rupture-time prediction, while the application of the framework to strain-level prediction will be reported in future work. The dataset consisted of creep strain curves from four heats, including both rupture and non-rupture curves. Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Support Vector Regressor (SVR), Gaussian Process Regressor (GPR), and Neural Network (NN) were employed. The effects of square-root and cube-root transformations on data distribution and model learning behaviour were analysed using model learning curves. An Optuna (version 4.3.0)-based hyperparameter tuning strategy was employed. The cube-root transformation improved the learning performance of SVR, GPR, and NN, whereas RF, GB, and XGB remained unaffected. Learning curves revealed mild overfitting for RF, GB, and XGB, and very minimal overfitting for SVR, GPR, and NN. NN achieved the best predictive performance (${\mathrm{R}}^2=0.92,\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}=0.195$, deviation factor of 1.57). The findings demonstrated that the combined useof creep strain curves, data transformation, learning curve guided model selection, and rigorous hyperparameter tuning can improve the prediction accuracy under a limited dataset. Full article

Existing AISI S100 provisions for cold-formed steel (CFS) screw connections lack codified strength equations for screw shear and net section fracture, and traditional machine learning (ML) models struggle to predict these minority failure modes due to imbalanced experimental datasets. This study proposes a cascaded ML framework that first classifies the failure mode and then predicts strength using mode-specific regressors. Two cascade strategies are evaluated: a Hard Classification Cascade (HC-C) and a novel Probability-Weighted Cascade (PW-C) that weights predictions by class probabilities to mitigate error propagation from misclassification. The predictive performance of the two cascaded models is benchmarked against a single regressor without classification. The superior PW-C model is then compared with AISI S100, and its resistance factor ϕ is subsequently calibrated in accordance with LRFD. Results show that the proposed cascaded models outperform the direct regression model, with PW-C improving the R² for minority-class screw shear from 0.765 to 0.933 and for net section fracture from 0.784 to 0.912. Compared with AISI S100 provisions, PW-C extends coverage to the currently unaddressed failure modes and effectively captures screw group effects on shear strength based on a database of 564 tests. Reliability analysis yields an overall ϕ_c of 0.64 for the PW-C model, with a recommended divisor of 1.15 for direct application within the AISI design framework. This work provides a practical, data-driven pathway for updating design codes to cover failure modes beyond current specification limits. Full article

Background. Hematological inflammatory indices from the complete blood count have been proposed as inexpensive prognostic markers in sepsis. The systemic immune-inflammation index (SII) and neutrophil-to-lymphocyte ratio (NLR) are the most studied, but the performance of monocyte-containing alternatives (SIRI, AISI) in the elderly, in whom immunosenescence may alter the leukocyte phenotype, remains poorly characterized. Methods. In a single-center retrospective cohort of patients aged ≥65 years admitted to a tertiary ICU with Sepsis-3-defined sepsis (n = 127, 33 deaths), we compared the discrimination of six indices (NLR, PLR, MLR, SII, SIRI, AISI) for 30-day all-cause mortality using AUROC with bootstrap confidence intervals and pairwise DeLong tests. Independent associations were assessed by logistic regression adjusted for APACHE II and age; incremental value over APACHE II was explored using IDI, cNRI, calibration and decision curve analysis, with bootstrap optimism correction. Results. Thirty-day mortality was 26.0%. The monocyte-containing indices (AISI, SIRI, MLR) discriminated better than SII and NLR, and AISI was significantly superior to SII, NLR and PLR on DeLong testing, though not to SIRI, MLR or APACHE II. After adjustment for APACHE II and age, AISI, SIRI and MLR remained independently associated with mortality, whereas SII and PLR did not. Adding AISI to APACHE II improved reclassification and calibration and yielded higher net clinical benefit across clinically relevant thresholds. Conclusions. In this exploratory, single-center analysis, monocyte-containing indices, particularly AISI, were more strongly associated with 30-day mortality in elderly ICU sepsis than SII or NLR. AISI, SIRI and MLR were strongly intercorrelated and near-equivalent, and AISI did not significantly exceed APACHE II in discrimination. These hypothesis-generating findings require prospective external validation before clinical use. Full article

The (3aS, 6aR)-lactone serves as the key chiral intermediate for the synthesis of d-biotin. A promising approach involves the asymmetric hydrolysis of meso-dimethyl ester catalyzed by an esterase to yield the (4S, 5R)-monomethyl ester, which is subsequently reduced and cyclized to afford (3aS, 6aR)-lactone. This study first optimized the fermentation medium and culture conditions for the recombinant E. coli pET21a-EstSIT01 harboring the Microbacterium esterase gene, which exhibits high selectivity for the asymmetric synthesis of (4S, 5R)-monomethyl ester. Under optimal conditions (fermentation medium: glycerol 25 g/L, yeast extract 15 g/L, NaCl 10 g/L, MgSO₄•7H₂O 5 g/L; induction was initiated 2 h post-inoculation at 30 °C and pH 7.2), the enzyme activity increased 5.1-fold compared to the initial level, reaching 1072.7 U/L. Secondly, the reaction conditions for the whole-cell synthesis of (4S, 5R)-monomethyl ester catalyzed by EstSIT01 were optimized. The results indicated that organic solvents adversely affected enzyme stability, while high buffer salt concentration negatively impacted enzyme activity at elevated substrate concentrations. The optimal reaction strategy involved maintaining the pH of the aqueous reaction system at 7.5 by the controlled addition of aqueous ammonia to neutralize the (4S, 5R)-monomethyl ester produced during the reaction. Using 17.5 g/L cells and 200 mM substrate meso-dimethyl ester in deionized water, with the reaction pH mentioned at 7.5, complete conversion (100%) was achieved within 4 h at 30 °C. The space–time yield reached 441.6 g/L/d, exceeding the typical requirement for industrial biotransformation (>100 g/L/d), with 99.1% enantiomeric excess (ee) of (4S, 5R)-monomethyl ester. Finally, (4S, 5R)-monomethyl ester was reduced using sodium borohydride to synthesize (3aS, 6aR)-lactone with an ee value of 98.7%. The overall yield from meso-dimethyl ester to (3aS, 6aR)-lactone was 86.2%. These results demonstrate that this integrated chemo-enzymatic approach constitutes a greener method with promising potential for industrial application. Full article

Ultra-High Molecular Weight Polyethylene (UHMWPE), the standard base material in ski manufacturing, offers excellent gliding performance but exhibits limited mechanical and scratch resistance on hard and icy snow conditions. In this work, stainless steel is proposed as a mechanically robust alternative, and its inherently higher friction against snow is addressed through surface engineering. The snow friction behavior of 301H stainless steel surfaces decorated with fishbone-like microstructures combined with Laser-Induced Periodic Surface Structures (LIPSSs) was investigated using a custom-built snow tribometer. Several pattern designs, with different pitch distances and depths, were engraved using femtosecond laser pulse irradiation. We conducted morphological, physical, and chemical investigations through microscopy, static contact angle measurements, and X-ray Photoelectron Spectroscopy analyses. Results indicate that the gliding performance is not directly related to the modifications in surface chemistry and wetting behavior of the samples but is affected by the geometry and orientation with respect to the sliding direction of the specific micro- and nano-features. Overall, we achieved friction coefficient values comparable to those found in UHMWPE with a fast and economically sustainable single-step laser-texturing process. This approach allows the industrial up-scaling of the fishbone-texture design to real-size alpine ski prototypes. Full article

Drilling is often used to create holes in CFRP/AISI 316L hybrid stacks to facilitate the assembly process. Due to the non-uniform properties and difficult machinability of Cr-Ni-Mo AISI 316L steel, drilling CFRP/AISI 316L stacks poses significant challenges in manufacturing processes. This paper aims to evaluate the tool–workpiece interaction and the effect of the drilling strategy on the technological aspects of drilling CFRP/AISI 316L stacks. The experimental results show that cutting parameters have a significant impact on the drilling performance of CFRP/AISI 316L stacks. The AISI 316L → CFRP drilling strategy provides lower hole surface roughness with less burr formation in the AISI 316L layer, while the CFRP → AISI 316L drilling strategy is preferred in terms of minimizing delamination damage. The high temperature generated during drilling of the AISI 316L layer directly affects the hole surface quality in the CFRP layer and the phenomena occurring in the interlayer of the stack materials. The experimental results presented in this work allowed us to formulate several recommendations regarding the selection of cutting strategy and cutting parameters when drilling CFRP/AISI 316L hybrid stacks. Full article

The complex geometries feasible with Laser Powder Bed Fusion (PBF-LB/M) lead to varying sizes of scanned cross sections within the layers and hence differing cooling rates. Since PBF-LB/M results in intragranular cell structures, which cause relatively high strengths in the austenitic steel AISI 316L, the influence of changes in the specimen size on the cell structure was investigated. The results obtained from the geometries realized in this work showed no significant influence of the specimen size on the cell sizes. To analyze the relation between the cell structure and the mechanical properties, cyclic indentation tests (CIT) were performed accordingly, revealing no clear influence of the specimen size on the mechanical properties and no correlation between the cell size and the mechanical properties. Additionally, the impact of the cell size on the well-known anisotropy in mechanical properties of AISI 316L produced via PBF-LB/M was investigated. While the cell size was observed to be independent of the specimen orientation on the build plate, the orientation between the direction of loading and the building direction reveals a slight influence on the mechanical properties obtained from CIT. In comparison to the properties determined using CIT, a stronger influence of the orientation between the load and the building direction was observed in tensile tests, which was not caused by the intragranular cells. It was concluded that the anisotropy in the tensile properties is mainly affected by the texture, the elongated grains, and the layer orientation. Full article

The influence of internal substrate geometry on thermal stability during Laser Directed Energy Deposition Repair (DED-R) remains insufficiently understood, particularly for components containing internal cavities and cooling channels. This study investigates the thermal response of solid (Alpha), blind-hole (Bravo), and channeled (Charlie) AISI 316L substrates using dual infrared thermography, transient finite element modeling, and Short-Time Fourier Transform (STFT)-frequency-domain analysis. Despite substantial differences in internal heat-dissipation pathways, all substrate configurations exhibited similar peak surface temperatures (~1700–2100 °C), indicating that conventional temperature monitoring alone is insufficient to distinguish geometry-dependent melt-pool behavior. To address this limitation, a Spectral Entropy Index (SEI) derived from STFT analysis was proposed to quantify thermal stability. The channeled substrate exhibited the lowest entropy value (H_s = 0.172), compared with the solid (H_s = 0.181) and blind-hole (H_s = 0.183) configurations, indicating a more ordered and predictable thermal response. Furthermore, distinct variations in the spectral stability shadow revealed geometry-dependent oscillatory behavior that was not observable from thermal histories. Finite element simulations showed good agreement with experimental measurements in conduction-dominated regions (RMSE ≈ 46 °C), whereas deviations were observed within the melt-pool region (~250–310 °C), highlighting the increasing influence of fluid-flow phenomena not captured by the conduction-based model. The results demonstrate that internal substrate architecture primarily influences melt-pool stability through frequency-domain thermodynamics rather than significant changes in peak temperature. The proposed STFT method provides a quantitative approach for monitoring thermal stability and assessing the feasibility of L-DED repair over complex internal geometries. Full article

This manuscript evaluates the electrochemical corrosion resistance of diamond-like carbon (DLC) coatings deposited via High-Power Impulse Magnetron Sputtering (HiPIMS) on AISI 52100 steel in synthetic seawater. While AISI 52100 steel is valued for its hardness, it is highly susceptible to localized and uniform corrosion in chloride-rich marine environments. In this study, samples were characterized using Raman spectroscopy to analyze sp²/sp³ bonding, and their corrosion behavior was assessed through potentiodynamic polarization, linear polarization resistance (LPR), and electrochemical impedance spectroscopy (EIS) over 24 h of immersion. Results demonstrated that the DLC coatings significantly enhanced electrochemical stability, shifting corrosion potentials toward more noble values and reducing the corrosion current density from (1.81 ± 0.12) × 10⁻⁷ to (1.03 ± 0.09) × 10⁻⁹ mA·cm⁻². EIS data revealed high polarization resistance and effective barrier properties, despite a calculated total porosity of 3.06% resulting from intrinsic micro-defects. Although localized subsurface degradation and minor flaking were observed at defect sites, the HiPIMS-deposited DLC coatings effectively mitigated the corrosive impact of synthetic seawater, providing a significant contribution to the electrochemical barrier despite the persistence of electrolyte accessibility mediated by localized defects. Full article

Accurate measurement of the temperature in the cutting zone is essential for closed-loop machining. However, it remains difficult due to the small size of the tool–chip contact area, its partial concealment by chips and the steep thermal gradients present. This study presents an integrated framework that combines a thin-film thermocouple (TFTC) on the rake face of a polycrystalline cubic boron nitride (PCBN) tool with a thermo-mechanical wear-coupled simulation in order to monitor cutting temperature and predict tool wear. The three-dimensional finite-element turning model includes a moving heat source that represents plastic and frictional heat at the tool–chip interface, as well as an Archard-type wear law that is enhanced by a temperature correction factor. The TFTC is fabricated by magnetron sputtering NiCr and NiSi films onto an insulating layer, after which it is embedded in the tool as a minimally intrusive in situ sensor. Turning experiments on AISI 1045 steel were performed at spindle speeds of 1000–3000 rpm, feeds of 0.05–0.20 mm/rev and depths of cut ranging from 0.3 to 1.0 mm under dry, wet (emulsion) and cryogenic (liquid nitrogen) cooling conditions. Simulated temperature fields reveal strong localisation at the tool–chip contact and a nonlinear increase in peak rake-face temperature with spindle speed, which fits a quadratic regression with R² = 0.99. The TFTC shows a response time of around 0.3 s with less than 5% overshoot, and its thermoelectric voltage is almost perfectly linear with temperature (R² = 1), with a sensitivity of approximately 12 µV/°C. During cutting, TFTC readings agree with infrared measurements within ±3 °C and demonstrate improved robustness in occluded zones. The coupled wear model replicates the observed wear growth trend with the compact expression VB = 0.0001·t^0.8. Sensitivity tests indicate that thermo-mechanical coupling increases wear rates compared to single-factor models, and that cooling reduces thermal loads by approximately 15% (wet) and 25% (cryogenic). Full article

Railway screw couplings are safety-critical, yet service failures show fatigue cracking at geometric discontinuities. This work assesses the response of two UIC screw-coupling components—the shackle and trunnion—under longitudinal forces from Traction–Emergency Braking (TEB) manoeuvres. A linear-elastic 3D finite element model was built for 42CrMo4/AISI 4140 steel, idealising the threaded load transfer with an RBE2 condensation and the hook–shackle interface with a tied contact to provide a repeatable baseline. Longitudinal force histories were generated in TrainDy for a freight consist and mapped to Regions of Interest; fatigue was evaluated in Altair HyperLife using rainflow counting, Goodman mean-stress correction, and Palmgren–Miner accumulation on a uniaxial S-N curve. For the 636 kN envelope case, the model predicts an axial displacement of 0.985 mm and von Mises stresses in several relevant regions near the nominal yield strength. Fatigue results rank the trunnion pin fillet as the governing hotspot: representative TEB sequences yield damage indices greater than 1 (often of order 20), while a lower-amplitude braking block shows negligible damage. Overall, the analysed spectra leave little endurance margin for the current geometry and support redesign of critical radii and more realistic contact/boundary modelling. Full article

Silver-doped diamond-like carbon (Ag–DLC) coatings were investigated with respect to their tribological behavior under ambient and physiologically relevant conditions. Gradient Ag–DLC coatings deposited on AISI 316L stainless steel were tested in air, simulated body fluid (SBF), and an albumin-containing solution using a pin-on-disk configuration. Increasing silver content resulted in a systematic reduction in the H³/E² ratio, leading to increased coating wear irrespective of the environment. In contrast, friction behavior was strongly controlled by the surrounding medium. Under dry sliding in air, all coatings exhibited similar steady-state friction governed by the DLC matrix. The lowest steady-state friction coefficients were obtained in SBF, indicating that the aqueous ionic environment provided the most favorable friction conditions among the tested media. In the albumin-containing medium, friction also remained low, indicating that protein adsorption and interfacial layer formation modified the sliding conditions, although the CoF did not fall below that observed in SBF. Wear was highest in air and generally lowest in SBF, while tests in albumin promoted surface layer formation. Surface analyses indicated silver redistribution, transfer-layer formation, and the presence of protein-related surface agglomerates, with higher apparent surface coverage on coatings containing more Ag. Overall, the results show that Ag-doped DLC coatings exhibit environment-dependent tribological behavior under physiologically relevant conditions. The present work should be regarded as a tribological study rather than a direct validation of antibacterial performance. Future studies should combine tribological assessment with dedicated antibacterial and cytocompatibility experiments. Full article

Tool wear in CNC milling increases friction and torque demand at the tool-workpiece interface, which is reflected in spindle motor current. This study develops a non-intrusive tool wear condition classification method using spindle motor current monitoring during practical CNC milling of commercial medium-carbon steel workpieces (JIS S50C/AISI SAE 1050-equivalent; as-received and non-heat-treated; nominal laboratory hardness approximately 4.3 HRC). Experiments were performed on a Tongtai MDV-508 vertical machining center at fixed cutting conditions (3000 rpm spindle speed, 2 mm axial depth of cut, 5 mm cutting width, and 300 mm/min feed rate) using eight TiAlN-coated fine-grain WC–Co solid carbide end mills (10 mm diameter, four flutes; nominal Co binder approximately 10 wt%). An oil-based HS Highstart/HS-SSHS-BH10 cutting fluid was applied through the machine external coolant nozzle in flood mode at an estimated nominal flow rate of approximately 3 L/min and near-room coolant temperature (25 ± 2 °C), and was used as supplied without dilution. A clamp-type AC current sensor was installed on one phase line supplying the spindle motor, and current was acquired using an NI-9221 module at 20 kHz. Cutting intervals were isolated by envelope-based segmentation, concatenated, and divided into 1 s windows (0.5 s overlap) for feature extraction. Three feature sets were evaluated: time-domain statistics, frequency-domain statistics, and an FFT→PCA hybrid representation. Tool states (New, Mid-life, Old) were labeled using post-process surface roughness Ra thresholds supported by microscope observation. The PCA transformation was fitted only on training data and then applied to the held-out test data. A logistic regression classifier achieved 97.44% test accuracy (152/156 windows; 95% Wilson CI: 93.59–99.00%) with the PCA-hybrid features, outperforming time-domain (89.74%) and frequency-domain (94.87%) models. The results support spindle current monitoring as a low-cost approach for quality-aligned tool condition monitoring, while the external validity remains limited to the tested machine, material, tool, coolant, and cutting-parameter combination. Full article

AISI 1040 steel is extensively used in structural and automotive applications, where surface integrity plays a significant role in service performance and coating adhesion. Furthermore, the selected cutting fluids are expected to effectively reduce surface roughness and tool wear by improving lubrication at the tool and workpiece interface. This study investigates the influence of SiO₂ nanoparticle-assisted Pongamia pinnata oil on surface roughness and tool wear during the machining of AISI 1040 steel using an uncoated tungsten carbide tool by varying nanoparticle concentration (Vol.%), cutting speed (m/min), depth of cut (mm), and feed rate (mm/rev). The incorporation of 0.5 (Vol.%) SiO₂ nanoparticles significantly enhances machining performance by improving surface finish and reducing tool wear. Further, a minimum surface roughness value of 1.95 microns and tool wear value of 0.047 mm were achieved at a cutting speed of 101 m/min, feed rate of 0.11 mm/rev, depth of cut of 0.25 mm and 0.5 (Vol.%) SiO₂ nanoparticle concentration. ANOVA results indicate that nanoparticle concentration is the most dominant parameter affecting both surface roughness and tool wear, contributing 85.35% to the variation in surface roughness and 82.2% to the total variation in tool wear. Cutting speed is the second most influential factor, accounting for 11.63% of surface roughness variation and 11.07% of tool wear variation, while feed rate and depth of cut exhibit minimal influence in both cases. A second-order RSM model was developed to predict surface roughness and tool wear, showing excellent agreement with experimental results. The model predicted surface roughness with an average error below 2.43%, while the second-order model for tool wear exhibited an average prediction error of 4.95%, confirming its statistical significance and predictive reliability. Desirability Function Method (DFM) analysis yielded a desirability value of 1.000, confirming the optimal combination of machining parameters at 0.5354 (Vol.%) nanoparticle concentration, a cutting speed of 45 m/min, a depth of cut of 0.50 mm, and a feed rate of 0.1298 mm/rev. Overall, this study demonstrates that 0.5 (Vol.%) SiO₂ nanoparticle-incorporated Pongamia pinnata oil is an effective and sustainable cutting fluid, significantly improving surface integrity and machining performance of AISI 1040 steel during machining. Under these settings, the predicted tool wear was 0.0614 mm, accompanied by a high composite desirability value of 0.92786, indicating excellent overall performance. Moreover, the close agreement between experimental, response surface model and BP-ANN-predicted tool wear and surface roughness confirms that the ANN model reliably and robustly captures the complex, nonlinear effects of machining parameters with minimal systematic error. Full article