Search Results (138)

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

The reuse of WC–Co cutting inserts is a relevant strategy to reduce tooling costs and the consumption of critical raw materials, such as W and Co. Still, the effect of stripping and re-coating cycles on tool performance remains largely unexplored. This work investigates the wear behavior of carbide inserts coated with four PVD nitride systems—CrN, TiAlN, TiAlCrN, and TiAlCrSiN—during CNC turning of AISI 4340 steel. A single cutting edge was subjected to two complete reuse cycles consisting of machining six workpieces, electrolytic stripping of the worn coating, and PVD re-deposition. Tool wear and surface integrity were evaluated by 3D optical profilometry, roughness measurements, and SEM/EDS analysis. CrN exhibited progressive crater and flank wear with large material-loss volumes and increasing roughness. TiAlN exhibited pronounced built-up edge/layer formation, resulting in mixed adhesion–spallation behavior and degradation of roughness in the second cycle. TiAlCrN developed stable adhesive layers with limited coating loss, and after re-coating, its roughness decreased from ~2.7 µm to ~1.0 µm. The most complex coating, TiAlCrSiN, provided the lowest roughness (~1.3–1.6 µm) and the smallest wear volumes in both cycles, associated with a fine Al–Si-induced nanostructure and improved oxidation resistance. The results demonstrate that multicomponent nanostructured coatings, particularly TiAlCrN and TiAlCrSiN, can withstand at least one stripping and re-coating cycle without performance loss, supporting the feasibility of controlled insert reuse in turning AISI 4340 steel. Full article

Corrosion fatigue is an accelerated failure mechanism in metallic components and coated systems, where the effectiveness of the polymer coating is determined by the structural integrity and adhesion at the coating/substrate interface. This study investigated the corrosion fatigue interaction in AISI 410 steel with and without a poly(3-hexylthiophene)/poly (methyl methacrylate) (P3HT/PMMA) coating exposed to a 3 wt.% NaCl solution under four stress levels $\left(∆\sigma \right)$ at room temperature. Electrochemical noise (EN) was recorded during the test, the surface and interface were characterized using scanning electron microscopy (SEM), and the mechanical behavior was quantified using $da/dN$ vs. $∆K$ and $\sigma$ vs. N curves. The coated samples exhibited a wider potential range ($\approx \pm$400 mV) than the uncoated steel ($\approx \pm$200 mV), indicating localized electrochemical activity under the coating. SEM observations revealed microblisters at low stress levels and coating cracking at high stress levels, with localized substrate exposure, slip bands, and microcracks. Overall, the results showed that the corrosion fatigue is governed by electrochemical activity under the coating and cathodic delamination, which reduces adhesion, locally exposes the steel, and causes the initiation and propagation of cracks. Full article

This study presents a comprehensive numerical and experimental investigation into the influence of punch shaft geometry on punching force and tool durability in the cold forming of S500MC steel sheets using an AISI D2 punch. Finite element analyses were conducted to evaluate the effects of varying punch shaft diameters on stress distribution, deformation behavior, and resultant punching forces. Experimental validation was performed through controlled punching tests, measuring force responses and assessing tool wear. The results demonstrate that optimizing the punch shaft diameter reduces the maximum punching force and minimizes stress concentrations, thereby enhancing tool life. Specifically, larger punch shaft diameters contribute to more uniform stress distribution and decreased risk of premature tool failure. These findings provide valuable insights for tooling design in high-strength steel sheet forming processes, enabling improved efficiency and cost-effectiveness in manufacturing operations. Full article

This research investigates the optimization of surface integrity in powder-mixed electrical discharge machining (PMEDM) through the innovative use of Jatropha biodielectric fluid enhanced with titanium dioxide (TiO₂) nanoparticles. A comprehensive experimental framework was developed using design expert software (DOE) with Response Surface Methodology (RSM) to systematically analyze the machining of AISI D2 tool steel using copper electrodes. The study examined five critical process parameters, gap current (Ip), pulse-on duration (Ton), pulse-off time (Toff), gap voltage (V), and powder concentration, evaluating their combined effects on surface roughness (SR), surface crack density (SCD), and residual stress characteristics. Advanced characterization techniques including scanning electron microscopy (SEM) were employed to analyze surface topography and subsurface microstructural changes. The optimization process successfully identified optimal machining conditions of current = 9 A, Ton = 100 µs, Toff = 10 µs, and gap voltage = 65 V, achieving exceptional surface quality with a minimum surface roughness of 3.22 µm. Remarkably, these optimized parameters resulted in crack-free surfaces with zero surface crack density and minimal residual stress values across the 2θ range of 90° to 180°. To enhance predictive capabilities, supervised machine learning algorithms were implemented to model surface roughness behavior. Comparative analysis of classification algorithms demonstrated that Support Vector Machine (SVM), k-Nearest Neighbors (kNNs), and Gaussian Naïve Bayes achieved superior performance with F1-scores of 0.88 and prediction accuracies of 90%. The integration of sustainable Jatropha biodielectric with TiO₂ nanoparticles represents a significant advancement in environmentally conscious precision machining, while the machine learning approach establishes a robust framework for intelligent process optimization and quality prediction in advanced manufacturing applications. Full article

Electrical Discharge Machining (EDM) enables precision post-weld finishing of AISI 304 stainless steel, but stochastic spark overlaps make the fatigue-critical maximum peak-to-valley height (R_max) difficult to predict. This study develops a validated physics-based framework quantifying how crater overlap governs R_max evolution. Experiments on unwelded AISI 304 cylinders—proxying weld metal while excluding heat-affected zone (HAZ) effects—used Central Composite Design (20 trials, 900–9380 μJ discharge energies). Profilometry and scanning electron microscopy (SEM) correlated the crater size, overlap intensity, micro-cracking, and R_max escalation from 18 to 85 μm. Primary and secondary crater formation under minimum and maximum overlap configurations were simulated using a 2D axisymmetric finite element model with Gaussian heat flux and temperature-dependent thermophysical properties. The predictive metric R_max,num = (d_initial + d_secondary)/2 achieved 11–19% average error against the experimental R_max,exp, with complementary valley depth (R_v) validation at 13% error. The Specimen 7 outlier (~50% error) reveals the limitations of deterministic modelling under stochastic debris accumulation and plasma instability at intermediate energies. Crater overlap generates secondary dimples, sharp inter-crater peaks, and rim micro-crack networks, driving the 4.7-fold R_max increase—approaching International Institute of Welding (IIW) fatigue thresholds (<25 μm for high-cycle categories). The framework explicitly links the discharge energy, plasma channel radius (R_pc), and overlap geometry to surface topography, enabling process optimization (I·t_on < 60 A·s maintains R_max < 25 μm). Mesh independence (<2.5% convergence) and six centre-point replicates (CV = 4.2%) confirm robustness. This validated upper-bound R_max predictor supports the digital co-optimization of welding and EDM parameters for aerospace/energy applications, with planned extensions to stochastic 3D models incorporating adaptive remeshing and real weld topographies. Full article

In the production of clad rolled plates from asymmetric sandwich-type slab for pipeline applications, achieving both target mechanical properties and high geometric flatness remains a critical challenge due to differential thermal stresses between the dissimilar steel layers during accelerated cooling. This study aims to develop an optimal cooling schedule for a 25 mm thick clad plate, comprising a X70-grade steel base layer and an AISI 316L cladding, to ensure required strength and minimal bending. A comprehensive approach was employed, integrating a 3D finite element model (Ansys) for simulating thermoelastic stresses with a CatBoost machine learning model trained on industrial data to predict heat transfer coefficients accurately. A parametric analysis of cooling strategies was conducted. Results showed that a standard cooling strategy caused unacceptable bending of plate after cooling exceeding 130 mm. An optimized strategy featuring delayed activation of the lower cooling headers (on the cladding side) created a compensating thermoelastic moment, successfully reducing bending to approximately 20 mm while maintaining the base layer’s requisite mechanical properties. The findings validate the efficacy of the combined FEM-machine learning methodology and propose a viable, industrially implementable cooling strategy for high-quality clad plate production. Full article

The powder-mixed electric-discharge machining (PMEDM) process has been the focus of researchers for quite some time. This method overcomes the constraints of conventional machining, viz., low material removal rate (MRR) and high surface roughness (SR) in hard-cut materials, tool failure, and a high tool wear ratio (TWR). However, to determine the optimal machining parameter levels for improving MRR, surface finish must be measured during actual experimentation using various parameter levels across different materials. It is a very costly and time-consuming process for industries. However, in the age of Industry 4.0 and artificial intelligence machine learning (AI-ML), it provides an efficient solution to real manufacturing problems when big data is available. In this study, experimentation was conducted on AISI D2 steel using the PMEDM process for SR analysis with different parameters, viz. current, voltage, cycle time (T_On), powder concentration (PC), and duty factor (DF). Moreover, machine learning models were used to predict SR values for selected parameter levels in the PMEDM process. In this research, Gaussian process regression (GPR) with a squared exponential kernel, support vector machines, and ensemble regression models were used for computational analysis. The results of this work showed that Gaussian regression, support vector machine, and ensemble regression achieved 95%, 92%, and 83% accuracy, respectively. The GPR model achieved the best predictive performance among these three models. Full article

Our team has initiated work to determine residual stresses by means of monitoring magnetic properties, namely differential permeability, magnetoacoustic emission, and surface field components. Concerning surface field measurements, Hall, AMR, and TMR sensors have been used, with AMR and TMR sensors enabling 3D field determination. In this paper, we compare the surface magnetic field components with residual stresses in 2 mm thick AISI 4130 steel coupons. The steel samples were in a dog-bone structure with residual stresses induced by localized RF induction heating to create a temperature gradient, followed by quenching to transform the temperature gradient into a residual stress one. GMI and AMR sensors were used to determine the localized magnetic field component distribution on the surface of the steel coupons and at the same areas where the residual stresses were determined. The GMI sensor was able to monitor the field component perpendicular to the surface of the steel coupon, while the AMR sensor was able to monitor the three field components at the same points. The results illustrated that both sensors were able to monitor residual stresses, with the GMI sensor illustrating better sensitivity at a higher cost, while the AMR sensor had a lower sensitivity with a significantly lower cost as an integrated sensor. Full article

The objective of this study is to evaluate the impact of thermal and thermochemical treatment, specifically gas nitriding, on the wear properties of AISI D2 cold work tool steel. The steel was austenitized at 1050 °C, then subjected to two annealing cycles at 560 °C for two hours each. It was then gas-nitrided for 16 and 36 h. The Vickers microhardness measurements of AISI D2 steel for the three distinct conditions, non-nitrided (NN), nitride at 16 h (N16) and nitride at 36 h (N36), are 560 HV0.1, 1050 HV0.1 and 1350 HV0.1, respectively. Wear tests were conducted utilizing a ball device, under dry friction conditions at ambient temperature, with loads of 5, 10 and 15 N, over 5000, 10,000 and 15,000 cycles at a constant sliding velocity of 30 mm/s and a sliding distance of 10 mm. Furthermore, the utilization of ANFIS modeling of experimental data facilitated the prediction of the variation in the coefficient of friction as a function of nitriding conditions and specific test parameters. The results show a significant effect of nitriding, leading to a marked reduction in the coefficient of friction. In the non-nitrided condition, the average value reaches 0.80, while extended nitriding to 36 h reduces this value to around 0.49, confirming a substantial tribological improvement. This enhancement is ascribed to the formation of hard, resilient nitride layers on the steel surface, thereby increasing wear resistance and cur-tailing in industrial applications. Full article

The effects of multiple austenitizing and quenching (AQ) thermal cycles on the microstructure and hardness of AISI O2 (90MnCrV8), D2 (X153CrMoV12), and D3 (X210Cr13) tool steels were systematically investigated. Up to four consecutive AQ treatments were applied to assess the influence of repeated austenitization on grain refinement, carbide dissolution, martensitic transformation, and retained austenite. The microstructure was investigated by optical and SEM observations, supported with XRD analyses. The results were correlated with Rockwell and Vickers hardness measurements. In AISI O2, the mean austenitic grain size decreased from (6.5 ± 0.8) μm to (4.3 ± 0.4) μm, accompanied by an increase in hardness from ~800 HV1 to ~950 HV1 (63 HRC), mainly due to the progressive carbide dissolution and a reduction in retained austenite. In AISI D2 and D3, repeated AQ cycles led to a marked reduction in carbide size and volume fraction (up to 25%), with D2 showing partial coarsening beyond the third cycle and D3 exhibiting continuous dissolution owing to higher carbide stability. A linear correlation between the carbide volume fraction and Rockwell hardness was established. Compared with conventional single-step treatments, the multi-cycle AQ approach also promote spheroidization of small carbides. Full article

In this paper, the effects of 3D printing parameters on the metallurgical and mechanical properties of 3D-printed 25CrMo4 steel are presented. Using laser-based powder bed fusion of metals (PBF-LB/M), samples were fabricated under varying conditions of laser power, scan speed, and layer thickness. The study examined how variations in volumetric energy density (VED) and linear energy density (LED) influence the material’s performance. The results show a strong correlation between the printing parameters and key properties such as hardness, porosity, bending strength, compressive strength, and tensile strength. Appropriate VED and LED improved density, reduced defects, and enhanced mechanical performance, whereas excessive energy inputs introduced brittleness. These findings support the advancement of additive manufacturing technologies for high-strength steels and broaden their potential applications in the aerospace, automotive, and construction sectors. Full article

In this work, the effect of preliminary mechanical surface treatment on the kinetics of formation, phase composition, and functional properties of the nitrided layer during electrolytic-plasma nitriding (EPN) of austenitic stainless steel 12Kh18N10T (AISI 321) was investigated. In contrast to traditional approaches, for the first time, this work establishes a direct correlation between the degree of surface deformation induced by shot peening and the formation of the expanded austenite (γN) phase under low-temperature plasma conditions. Quantitative X-ray phase analysis revealed a lattice parameter expansion of Δa/a₀ ≈ 1.4–1.8% and a gradual transformation of γ-Fe → γN without the formation of CrN nitrides at moderate intensity of preliminary treatment. According to SEM/EDS data and microhardness profiles, a multilayer structure was formed, consisting of a thin surface film of CrN/Fe₄N, a developed γN zone with a thickness of 12–15 µm, and a stable austenitic γ-Fe matrix. The surface microhardness increases to 880 ± 20 HV, while the friction coefficient decreases to 0.35–0.40, corresponding to a wear reduction of approximately 55% compared to the initial steel. The results provide a mechanistic understanding of nitrogen diffusion through defect-enriched subsurface layers and show that optimal preliminary deformation (d = 6 mm, v = 40 Hz, t = 20 min) promotes controlled formation of the γN phase with minimal lattice instability. The proposed combined approach—shot peening + EPN—is an effective method for producing wear- and corrosion-resistant surfaces of austenitic steels under atmospheric plasma conditions. 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

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