Search Results (1,405)

Laser cladding is an essential method for strengthening and restoring component surfaces. To increase its efficacy and provide a reliable surface treatment technique, it is necessary to optimize process parameters, enhance material adhesion, and guarantee high-quality, reliable coatings. These measures help to extend the lifespan of components. In this study, the surfaces of AISI 904L stainless steel samples were cladded to prepare various Co-based composite coatings with single and multiple layers reinforced with WC–CoCr–Ni powder. The phases within the newly developed layers were investigated using X-ray Diffraction (XRD), while the microstructure was examined using Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDX). Further tests were performed to assess the hardness, wear resistance and corrosion performance of the deposited coatings. Analyzing and comparing the coatings, it was observed that the coating performance increased with increasing thickness and generally due to a lower amount of Fe present within the microstructure. Full article

This study presents an experimental methodology for characterizing the crack-tip region using high-resolution Digital Image Correlation (DIC). The approach utilizes a stereoscopic microscope setup combined with 3D-DIC analysis to enable precise measurements within the small-scale region surrounding the crack tip. Two nonlinear parameters are evaluated: the plastic component of the crack-tip opening displacement (CTOD_p) and the cyclic plastic zone size. The investigation was conducted on disk-shaped compact tension specimens made of AISI 1020 steel under constant-ΔK fatigue testing. The results demonstrate a strong correlation between these nonlinear parameters and fatigue crack propagation, which was maintained stable, validating the proposed methodology. Furthermore, the relevance of crack-tip plasticity in fatigue crack propagation is verified under the tested conditions, highlighting its utility for fatigue life assessment under complex loading scenarios. Full article

This article investigates the anticorrosive properties of Zr-ZrN coatings, including Zr-(Zr,Hf)N, Zr-(Zr,Ti)N, Zr,Hf-(Zr,Hf,Nb)N, and Zr,Nb-(Zr,Nb)N, deposited on AISI 321 stainless steel substrates. The hardness and elasticity modulus of these coatings, as well as their scratch test strength, were measured. Corrosion current densities were calculated using the polarisation resistance method and by extrapolating the linear sections of the cathodic and anodic curves under electrode polarisation. The structure and composition of the sample surfaces were analysed by transmission electron microscopy. Notably, the nitride coatings reduced the corrosion current density in a 3% aqueous NaCl solution at 25 °C by more than 10 times, from 6.96 for the uncoated substrate to 0.17 μA/cm² for the Zr-(Zr,Ti)N-coated sample. The addition of Ti nitride to Zr-ZrN led to the most significant decrease in the corrosion current density. However, the introduction of Nb caused an increase in the corrosion rate and a decrease in the polarisation resistance, and Hf did not affect the corrosion-protective properties of the studied nitride coatings. Full article

The accurate detection and quantification of martensitic transformation in steel during quenching are essential for controlling the resulting material properties. Numerous studies have investigated this phenomenon using Acoustic Emission (AE) techniques, owing to the significant energy release associated with the transformation. However, no model based on acoustic emission currently exists that can estimate the martensite volume formed during induction hardening. In this work, a novel model is proposed to estimate the transformed martensite volume in induction hardening treatment, focused on the material, geometry, and AE settings used. By integrating acoustic emission data with conventional Vickers hardness measurements, the model parameters can be calibrated. Induction quenching experiments were carried out on cylindrical 42CrMo4 (AISI 4140) steel bars equipped with acoustic emission sensors to capture transformation-related events during heat treatment. The martensite volume after quenching was estimated from hardness values. Model calibration using the experimental acoustic emission data and martensite volume demonstrated strong agreement between predictions and experimental observations. The proposed model offers the potential for in-process monitoring of induction quenching, thereby reducing reliance on conventional characterization techniques. Full article

Yttrium oxide (Y₂O₃) is a crucial protective material for the inner walls of semiconductor etching chambers. This study employed Suspension Plasma Spray (SPS) technology to deposit Y₂O₃ coatings on AISI 304 stainless steel substrates. A water ring guide cover, which injects deionized water toward the center of the plasma flame at the torch outlet, was installed. The critical parameter ratio between the water ring flow rate and the suspension feed rate was investigated, with a specific focus on its influence on the coating’s microstructure and mechanical properties. The findings reveal that this parameter exhibits a significant positive correlation with porosity, with the coefficient of determination R² for their linear fit reaching 0.91236. When the water ring flow rate ratio was reduced to 79.66%, the porosity decreased to 0.946%, while the primary composition of the coating remained unchanged. Bond strength tests demonstrated that the adhesion strength of the coating exhibits an upward trend with increasing proportion of water ring flow. The adhesion strength reached its maximum value of 27.02 MPa when the water ring flow rate proportion was increased to 85.45%. Roughness exhibits a non-monotonic variation trend within the ratio range, attaining its optimal minimum value at the lower end of the ratio, indicating complex interrelationships among process characteristics. This work concludes that a low water ring flow rate ratio is essential for fabricating dense, well-adhered, and smooth Y₂O₃ coatings via SPS, providing a critical guideline for process optimization for applications such as semiconductor protection. Full article

Background: Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest cancers, largely due to late diagnosis and the lack of reliable non-invasive biomarkers. Altered trace element homeostasis has been implicated in tumor biology and systemic inflammation, but comprehensive metallomic profiling in PDAC is still limited. Methods: Using inductively coupled plasma mass spectrometry (ICP-MS), we quantified 20 serum and 15 urinary metals in 71 PDAC patients and 69 matched controls. Statistical analyses included univariate Wilcoxon testing, correlation with systemic inflammatory indices (NLR, MLR, SIRI, AISI, HGB/RDW, PCT), and multivariate chemometric modeling (PCA-LDA). K-means clustering was applied to identify patient subgroups with distinct biochemical signatures. Results: PDAC patients showed significantly elevated urinary antimony, chromium, cadmium, and vanadium, whereas controls exhibited higher serum selenium, zinc, barium, vanadium, and cobalt (all p < 10⁻⁵). The PCA-LDA model achieved 99% classification accuracy (Monte Carlo cross-validation, 1000 iterations), highlighting complementary diagnostic contributions of serum and urinary profiles. Serum selenium was inversely associated with SIRI and NLR, while urinary cobalt correlated positively with NLR. Clustering revealed three PDAC subgroups with different inflammatory and metallomic patterns, suggesting underlying biological heterogeneity. Conclusions: PDAC is characterized by opposite serum-urine metal signatures, indicating altered absorption-excretion dynamics. Selenium depletion may represent a protective biomarker, whereas urinary cobalt excretion reflects systemic inflammation. This integrative ICP-MS–chemometric approach provides a promising diagnostic tool for improving early detection and patient stratification in clinical practice. Full article

Boriding is a widely used thermochemical treatment to improve surface hardness and wear resistance in steels used in demanding mechanical applications. However, boronizing processes using new boron paste increase costs and generate waste, creating a need for more sustainable alternatives. In this context, the reuse of dehydrated boron paste has proven effective in the formation of Fe₂B layers on AISI 9254 steel. In this study, AISI 9254 steel was boronized using reused dehydrated boron paste at 1173 K, 1223 K, and 1273 K for 3600, 7200, 10,800, and 14,400 s. Optical microscopy revealed layer thicknesses ranging from 16.07 $\mathsf{\mu }\mathrm{m}$ to 69.35 $\mathsf{\mu }\mathrm{m}$ X-ray diffraction confirmed the formation of single-phase Fe₂B, while EDS indicated elemental redistribution within the layer. The Vickers microhardness profile characterized the mechanical behavior, and the adhesion force showed HF1-HF2 ratings. The activation energy for boron diffusion in Fe₂B was calculated at 106.567 kJ ${\mathrm{mol}}^{-1}$. Auto-affine analysis verified the fractal nature of interface growth, with a scale $\omega \left(d\right)$ according to $\omega \left(\delta \right)\sim {\delta }^H$. These results confirm that reused paste allows the formation of Fe₂B layers, supporting sustainable boronization strategies with controlled interfacial evolution. Full article

This study investigates the influence of laser processing parameters on the surface roughness and color formation of AISI 304 stainless steel. Experiments were conducted to explore how raster step, scanning speed, frequency, linear energy density, and overlap coefficient affect the surface characteristics of laser-marked zones. It was found that increasing the raster step from 20 µm to 80 µm led to a consistent increase in surface roughness (from 1.23 µm to 1.47 µm at 20 kHz and 25 mm/s), accompanied by a shift in color from dark brown to lighter yellow hues. In contrast, increasing scanning speed (from 25 mm/s to 125 mm/s) caused a nonlinear reduction in roughness (e.g., from 1.23 µm to 0.76 µm at 20 kHz and Δx = 20 µm), resulting in a lighter surface color. Frequency was identified as a critical factor; increasing it from 20 kHz to 100 kHz resulted in a threefold decrease in roughness (from 1.23 µm to 0.25 µm at 20 µm raster step and 125 mm/s), which correlated with a shift to brighter yellow tones. Higher linear energy density values (1.60–8.00 J/cm) increased roughness and darkened the surface color, while higher overlap coefficients produced the opposite trend. The study highlights the relationship between surface nanostructuring and the formation of stable interference colors, providing quantitative parameters for achieving desired chromatic effects. These findings establish a basis for the industrial application of laser color marking, where both aesthetic differentiation and functional enhancements—such as corrosion resistance, hydrophobicity, and antibacterial properties—are essential. Future research will focus on quantitatively evaluating the functional properties, including corrosion resistance, hydrophobicity, and durability, of the colored surfaces produced under optimized parameters. This research aims to further develop laser marking as a foundational tool for both aesthetic and functional surface engineering. Full article

Metal binder jetting (MBJ) is an additive manufacturing technology of increasing interest due to its potential competitiveness in medium- and large-scale production, especially from a sustainability perspective. However, challenges in controlling the product accuracy and precision significantly limit the widespread adoption of this technology. This work investigates the achievable accuracy, precision, and spatial repeatability of parts produced using the MBJ process. Additionally, the paper aims to identify the causes of inaccuracy and suggest countermeasures to improve the product quality. The study was conducted experimentally by designing a benchmark geometry with various basic features. This geometry was scaled to three sizes—10–20 mm (small), 20–30 mm (intermediate), and 30–50 mm (large)—and produced using two different stainless-steel powders: AISI 316L and 17-4PH. In the green state, the dimensional tolerances ranged from IT8 to IT12 for features parallel to the build direction (heights) and from IT9 to IT13 for features parallel to the build plane (lengths). In the sintered state, the tolerances ranged from IT10 to IT16. This study reveals the challenges in scaling geometries to compensate for accuracy loss originating from the printing and sintering stages. In the green state, accuracy issues are likely due to non-uniform binder application and drying operations. In the sintered state, the accuracy loss is related to variable shrinkage based on the feature size, anisotropic shrinkage depending on the print direction, and differing densification mechanisms influenced by the material type. This study offers novel insights for improving MBJ process precision, supporting wider adoption in the manufacturing industry. Full article

The effect of tempering temperature and tempering time on the density of hydrogen traps, hydrogen diffusivity, and microhardness in a vanadium-modified AISI 4140 martensitic steel was determined. Tempering parameters were selected to activate the second, third, and fourth tempering stages. These conditions were intended to promote specific microstructural transformations. Permeability tests were performed using the electrochemical method developed by Devanathan and Stachurski, and microhardness was measured before and after these tests. It was observed that hydrogen diffusivity is inversely proportional to microhardness, while the density of hydrogen traps is directly proportional to microhardness. The lowest hydrogen diffusivity, the highest trap density, and the highest microhardness were obtained in the as-quenched condition and the tempering at 286 °C for 0.25 h. In contrast, tempering at a temperature corresponding to the fourth tempering stage increases hydrogen diffusivity and decreases the density of hydrogen traps and microhardness. However, as the tempering time or temperature increases, the opposite occurs, which is attributed to the formation of alloy carbides. Finally, hydrogen has a softening effect for tempering temperatures corresponding to the fourth tempering stage, tempering times of 0.25 h, and in the as-quenched condition. However, with increasing tempering time, hydrogen has a hardening effect. Full article

Crystal grain size control in steel is critical for achieving mechanical properties. This study investigates the effectiveness of microalloying with titanium, niobium, zirconium, and yttrium to inhibit grain growth with the pinning effect. The comparison of selected microalloying elements in the exact same conditions is crucial for understanding their effect and is novel. Hot-rolled samples were annealed across a wide range of temperatures (1050 to 1200 °C) for up to eight hours. Microstructural analysis confirmed the presence of stable precipitates and non-metallic inclusions such as Nb(C,N), Ti(C,N), ZrO₂, and Y₂O₃ acting as obstacles to grain boundary migration. All microalloying elements significantly outperformed the reference steel, but their effectiveness was highly dependent on the annealing temperature. Titanium was the most effective inhibitor at lower temperatures (1050 °C), while zirconium maintained control up to 1150 °C. Critically, at the highest temperature of 1200 °C, only the yttrium-alloyed steel retained a fine-grain structure, demonstrating superior thermal stability. Niobium, conversely, only showed a minimal effect at 1050 °C, though this grade also exhibited the highest hardness (up to 165 HB) due to precipitation hardening. The kinetics of grain growth were successfully modeled using the Arrhenius-type Sellars–Whiteman equation, accurately describing the behavior for up to four hours of annealing. The findings provide critical insight for selecting optimal microalloying strategies based on maximum operating temperature. Full article

Background/Objectives: This study aimed to evaluate the relationship between maternal systemic inflammatory indices, hematological parameters, and fetal thymus size, as measured by the thymus–thoracic ratio (TTR), among diabetic pregnancies, and to establish predictive cut-off values for reduced thymus size. Methods: This prospective cohort study enrolled 532 pregnant women, divided into four groups: pregestational diabetes mellitus (PGDM, n = 44), diet-controlled gestational diabetes mellitus (GDM, n = 73), insulin-treated GDM (n = 49), and normoglycemic controls (n = 366). Fetal thymus size, alongside serum levels of neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), aggregate index of systemic inflammation (AISI), fibrinogen-to-albumin ratio (FAR), and C-reactive protein (CRP)-to-albumin ratio, were assessed in the third trimester. Results: All maternal diabetes subgroups demonstrated significantly reduced fetal thymus size compared with controls, with the most pronounced reduction observed in the PGDM group (p < 0.001). NLR, PLR, MLR, SIRI, AISI, and MPV were significantly elevated in the PGDM cohort, whereas CAR, FAR, and fibrinogen levels were markedly increased in the insulin-treated GDM group. Albumin levels were significantly decreased in both the PGDM and the insulin-treated GDM groups (p < 0.001). Among the evaluated biomarkers, AISI and FAR exhibited the highest diagnostic accuracy for predicting reduced fetal thymus size, with optimal cut-off values of 640.3 (sensitivity 82.3%, specificity 86.7%) and 0.114 (sensitivity 74.3%, specificity 88.7%), respectively. Conclusions: Maternal systemic inflammatory burden, as indicated by hematological biomarkers, is significantly associated with reduced fetal thymic size in diabetic pregnancies. These findings suggest that readily accessible blood-derived biomarkers, particularly AISI and FAR, may complement ultrasonographic evaluation, offering a cost-effective, non-invasive approach to predict compromised fetal immune development, especially in settings where direct thymic imaging is impractical. Full article

Resistance spot welding is a process used to join overlapping metals using pressure and electric current, commonly applied in the automotive industry for joining car bodies. This study aimed to understand the mechanical performance of spot welds under dynamic impact conditions. Various welding schedules were tested to observe the effects of different welding currents and times on the impact energy absorbed by spot welds. The results showed that the impact energy absorbed ranged from 26 J to 98 J, with higher welding currents and times generally increasing the impact energy due to more heat input. However, excessive welding parameters led to decreased impact energy. Statistical analysis and modeling revealed that optimal impact energy is achieved with a welding current of 5 kA and welding time of 6.728 cycles. Full article

This study presents a gradient boosting-based machine learning (ML) approach developed to predict cutting speed and feed rate in band sawing operations. The model was built using a dataset of 1701 experimental samples from three industrially common material types: AISI 304, CK45, and AISI 4140. Each sample was defined by key process parameters, namely, material type, a hardness range of 15–44 HRC, and a diameter range of 100–500 mm, with cutting speed and feed rate as target variables. Five ML models were examined and compared in this study, including linear regression (LR), support vector regression (SVR), random forest regression (RFR), least squares boosting (LSBoost), and extreme gradient boosting (XGBoost). Model training and validation were carried out using five-fold cross-validation. The results show that the XGBoost model offers the highest accuracy. For cutting speed estimation, the performance values of XGBoost are an RMSE of 0.213, an MAE of 0.140, an R² of 0.999, and an MAPE of 0.407%; and for feed rate estimation, an RMSE of 0.259, an MAE of 0.169, an R² of 0.999, and a MAPE of 1.14%. These results indicate that gradient-based ensemble methods capture the nonlinear behavior of cutting parameters more effectively than linear or kernel-driven techniques, providing a practical and robust approach for data-driven optimization in intelligent manufacturing. Full article

Achieving optimal lubrication during machining processes, particularly turning of stainless steel, remains a significant challenge due to dynamic variations in cutting conditions that affect tool life, surface quality, and environmental impact. Conventional Minimum Quantity Lubrication (MQL) systems provide fixed flow rates and often fail to adapt to changing process parameters, limiting their effectiveness under fluctuating thermal and mechanical loads. To address these limitations, this study proposes an ambient-aware adaptive Auto-Tuned MQL (ATM) system that intelligently controls both nanofluid concentration and lubricant flow rate in real time. The system employs embedded sensors to monitor cutting zone temperature, surface roughness, and ambient conditions, linked through a feedback-driven control algorithm designed to optimize lubrication delivery dynamically. A Taguchi L9 design was used for experimental validation on AISI 304 stainless steel turning, investigating feed rate, cutting speed, and nanofluid concentration. Results demonstrate that the ATM system substantially improves machining outcomes, reducing surface roughness by more than 50% and cutting force by approximately 20% compared to conventional MQL. Regression models achieved high predictive accuracy, with R-squared values exceeding 99%, and surface analyses confirmed reduced adhesion and wear under adaptive lubrication. The proposed system offers a robust approach to enhancing machining performance and sustainability through intelligent, real-time lubrication control. Full article