Search Results (181)

Introduction: In modern vehicles, new enhancements are being sought for engine components, break disks, and valves, using light-weight materials which are applicable not only in conventional cars but also in electric vehicles. The utilization of novel additive manufacturing aims to reduce energy loss and mechanical stress, improving component longevity at a lower cost. Method: The effective hardfacing of silicon carbide (SiC) over stainless steel was achieved using a Rofin Sinar Nd:YAG 2 kW robotized laser system with powder feedstock. Different Metal Matrix Composites (MMCs) were manufactured using Laser Directed Energy Deposition (DED-LB), making it possible to additively manufacture near-net-shaped parts while having the freedom to obtain variable geometries for the surface layout. Samples were prepared from the following flat products of EN 10088: X2CrTi12(1.4512, AISI 409), X15CrNi25-20 (1.4840, AISI 310), X5CrNi18-10 (1.4301, AISI 304), and X1CrNiMoCuN20-18-7 (1.4547, UNS: S31254). The reinforcement comprised a fine carbide powder of SiC, and the MMCs were produced after the solidification of the molten mixture. The study included an assessment of the interface zones and measurement of their microhardness, as well as microstructural analysis with visual defect evaluation focused on porosity and microcracking detection. Results: In order to increase durability and heat and wear resistance, advanced MMC sample components for various vehicles applications were explored. In one layer with a thickness of 1.5 mm, composed of X1NiCrMoCuN20-18-7 and SiC, the hardness characteristic was observed to be about 25 MPA higher than that experimentally obtained for the base material. For the same MMC, the carbide–metal interface zones were investigated. No cracks were observed, and it displayed a porosity of ≈1.57%. DED-LB MMCs possess excellent thermal stability and resistance to abrasion. Conclusion: Laser application allows various geometric applications on the surfaces of car parts. The appropriate selection of component phases can enable the design of parts with specific functionality, where the interaction between the microstructure and properties is complex. Full article

The development of bioactive hybrid coatings for biomedical implants requires materials exhibiting suitable thermal stability, bioactivity, and biocompatibility. In this study, hybrid organic–inorganic sol–gel coatings based on silica (SiO₂) and polyethylene glycol (PEG, 24 wt%) were functionalized with different concentrations of caffeic acid (CafA 5, 10, and 15 wt%) and deposited onto AISI 304 and AISI 316 stainless steel substrates by dip-coating without surface pre-treatment. The proposed approach enabled the formation of homogeneous hybrid coatings on untreated stainless steel substrates through a simple and scalable deposition process. A thermal analysis demonstrated the stability of the hybrid network and the effective integration of the organic and inorganic phases. Bioactivity was evaluated by in vitro immersion in simulated body fluid (SBF), while SEM observations revealed the formation of mineral deposits on the coating surface, and an EDX analysis confirmed the presence of calcium and phosphorus within the deposited layer. The formation of crystalline hydroxyapatite (HA) was subsequently confirmed by X-ray diffraction (XRD), confirming that all investigated formulations retained their ability to induce apatite formation after SBF immersion. In addition, in vitro biocompatibility assays confirmed that the developed materials exhibited concentration-dependent cytocompatibility, with the cellular response being influenced by the amount of incorporated CafA. Overall, the results demonstrate that the proposed hybrid materials combine thermal stability, bioactivity, and cytocompatibility, highlighting their potential as bioactive coatings for biomedical applications. Full article

Laser beam welding (LBW) offers low thermal distortion for thin austenitic stainless steel sheets but is sensitive to butt-joint fit-up and edge quality. This study compared machined and sheared joint edges under selected autogenous and ER308L filler-wire LBW conditions. Bead geometry, defects, microstructure, microhardness, tensile properties, and fracture behavior were characterized using conventional microscopy, EBSD-KAM, and three-dimensional digital image correlation (3D-DIC). The machined-edge autogenous condition (LBW-A-M) produced a stable bead with 0.04 mm top underfill and only a small number of internal discontinuities, while its tensile properties remained close to those of the base metal and fracture occurred in the base metal. In contrast, the sheared-edge autogenous condition (LBW-A-S) exhibited 0.24 mm top underfill, 0.18 mm misalignment, multiple pores, and localized strain near the weld, with tensile strength and elongation of 682.31 MPa and 44.04%, respectively. Under the selected filler-wire conditions, no measurable top underfill was observed, although pores remained. Because the autogenous and filler-wire modes used different process parameters and heat inputs, cross-mode differences are condition-specific rather than isolated filler-wire effects. Overall, joint-edge preparation and fit-up control remained important for thin-sheet LBW. Full article

This study presents a short-term outdoor comparison of mirror-glass and AISI 304 stainless-steel reflectors in parabolic trough collectors equipped with identical helical copper absorbers. Both configurations were operated simultaneously using the same collector geometry, fixed inclination angle, water-supply arrangement, measurement schedule, and instrumentation. Solar irradiance, inlet and outlet water temperatures, absorber temperature, and reflector temperature were recorded over three consecutive experimental days, namely 24–26 October 2025. The results were evaluated using temperature rise and time-dependent temperature output because the gravity-assisted system was not equipped with a flow meter or active flow-control device, preventing reliable calculation of useful heat gain and thermal efficiency. The descriptive results showed that the mirror-glass configuration produced a modestly higher overall temperature response and lower variation among the three daily mean values, although it did not outperform stainless steel at every measurement time or in every daily average. The observed difference is interpreted primarily in terms of the expected higher specular reflectivity and lower optical scattering of mirror glass, which can increase the solar radiation intercepted by the absorber. However, the conclusions are limited by the three-day testing period, absence of verified mass-flow data, lack of direct reflectivity measurements, and unquantified cosine losses associated with fixed operation without automatic tracking. The findings therefore provide configuration-specific guidance for reflector selection rather than a generalized ranking of collector performance. Full article

This study investigates the effect of low-temperature pulsed DC plasma nitriding on the surface properties of AISI 304 stainless steel for homogenizer valve-seat components. Plasma nitriding was performed in an ammonia atmosphere at 400, 440 and 480 °C for 8 h. The treatment led to the formation of expanded austenite at 400 °C, while higher temperatures promoted the formation of Fe-N and CrN-containing phases. The thickness of the modified layer increased from approximately 36 μm at 400 °C to 65 μm at 480 °C. Surface microhardness increased from 203 HV0.1 for the untreated steel to 652.6, 806.0 and 961.8 HV0.1 after nitriding at 400, 440 and 480 °C, respectively. The wear rate decreased markedly, reaching 1.92 × 10⁻⁵ mm³/(N·m) for DCPN480 compared with 30.65 × 10⁻⁵ mm³/(N·m) for the untreated sample. Among the nitrided samples, DCPN440 showed the most favorable corrosion behavior in 3.5 wt.% NaCl solution, indicating a balance between surface hardening and preservation of corrosion resistance. Numerical modeling confirmed that the strengthened surface layer can withstand equivalent homogenizer valve-seat loading without local plastic deformation. The results demonstrate that pulsed DC plasma nitriding can significantly improve the hardness and wear resistance of AISI 304 stainless steel while maintaining acceptable corrosion performance under optimized treatment conditions. Full article

Controlling deposition parameters is fundamental to obtaining the desired properties of cathodic arc physical vapor deposition (PVD) coatings. Achieving uniform coatings on tools with complex, sharp geometries remains a significant challenge due to localized ion flux concentration. Pulsing the substrate bias is an effective way of controlling deposition energy. However, while widely used in cathodic arc PVD, the relationship between the actual bias waveform, coating integrity on sharp tool geometries, and resulting machining performance has not been systematically established. This study investigates the effect of pulsed bias duty cycle (20% to 90%) and frequency (1 to 20 kHz) on the microstructural evolution, residual stress state, and machining performance of AlTiN coated tools. Real-time oscilloscope measurements demonstrated that system inductance and capacitance significantly distort the ideal bias waveform. Microstructural analysis via Focused Ion Beam/Scanning Electron Microscopy (FIB/SEM) cross-sectioning confirmed that all bias parameters generated a dense microstructure. While pulse frequency had no significant influence on micromechanical properties or residual stress states, the duty cycle was the dominant variable. High-energy deposition (90% duty cycle) increased hardness to 33.9 GPa but generated severe compressive residual stresses (−5.2 GPa). This extreme compressive stress led to catastrophic edge delamination on sharp solid carbide endmills. Conversely, a low-energy 20% duty cycle generated a coating with lower hardness (29.4 GPa) and a near-neutral stress state (0.5 GPa), effectively preserving the edge integrity. Unlike the endmills, the turning inserts maintained their edge integrity across all deposition conditions. During the high-speed (350 m/min) dry turning of AISI 304 stainless steel, all evaluated coatings exhibited comparable tool life and cutting forces. Wear progression was characterized by rake cratering, combined with abrasion and adhesion-induced attrition on the flank. The results indicate that tool life in this extreme environment is governed primarily by high-temperature thermo-chemical stability rather than initial room-temperature hardness. Lower-energy pulsed bias deposition therefore represents a robust strategy for coating a wide range of tool geometries, delivering equivalent high-speed machining performance while preventing stress-induced delamination on sharp features. Full article

Wire laser cladding (WLC) of bronze on stainless steel offers a promising approach for combining the structural strength of steel with the superior tribological and corrosion properties of copper alloys. In this study, the influence of key process parameters, including wire preheating current, deposition speed, laser power, and wire feed speed on melt pool temperature and clad geometry was investigated using response surface methodology (RSM). Experiments were performed using a robot-assisted coaxial wire feeding laser cladding system, and real-time thermal monitoring was conducted using an infrared camera. The results showed that defect-free bronze clads with good metallurgical bonding and limited dilution were achieved across the investigated parameter range. Statistical analysis revealed that melt pool temperature is primarily governed by laser power and deposition speed, with a significant interaction between these parameters. Clad height was mainly influenced by wire feed speed and deposition speed, whereas clad width was controlled by laser power and deposition speed. The side angle was affected by deposition speed, laser power, and wire feed speed, reflecting the balance between vertical buildup and lateral spreading. Overall, the study demonstrates that stable and high-quality clads can be achieved by properly balancing energy input and material supply. The developed models provide valuable insight for optimizing process parameters in wire laser cladding of bronze on stainless steel. Full article

While cost-effective austenitic stainless steels like AISI 304 are utilised in intermediate-temperature concentrated solar power (CSP) components, autogenous welding can compromise their structural integrity. This work investigates the corrosion behaviour of autogenous TIG-welded AISI 304 joints exposed to commercial molten solar salt at 550 °C for up to 1350 h under static conditions. Gravimetric and microstructural analyses revealed a stochastic bimodal breakaway oxidation mechanism. After an initial transient passivation regime (0–650 h) attributed to the formation of a protective Fe₃O₄/FeCr₂O₄ bi-layer, a sharp kinetic acceleration occurred. This localized breakdown was synergistically catalysed by trace chloride impurities, which triggered deep pitting along the microsegregated dendritic networks of the weld metal. Furthermore, due to severe X-ray attenuation under massive late-stage oxides, definitive proof of sensitisation was established using the standardised ASTM A262 Practice A topographic evaluation. The appearance of continuous ditch structures only in the heat-affected zone (HAZ) suggests severe intergranular anodic dissolution. This failure is thermodynamically driven by unmitigated residual tensile stresses, highlighting that the long-term reliability of these components is interpreted to be dictated by the localised, asymmetric breakdown of the weldment rather than uniform global oxidation. Full article

The present study investigates the surface integrity and flank wear of uncoated cermet inserts during dry turning of AISI 304 stainless steel. Three-dimensional metrology techniques were employed to assess both surface roughness and cutting-tool flank wear. Cutting speed and feed rate were the process parameters varied in the experiments. Both parameters exhibited a significant influence on the final surface quality. Specifically, increasing the cutting speed resulted in a deterioration of the surface finish under the evaluated conditions. Considering an average flank wear (VBB) of 0.1 mm as the tool life criterion, tool lives of 15 min and 9 min were achieved at cutting speeds of 120 m/min (lowest level) and 150 m/min (highest level), respectively. At lower cutting speeds, abrasive wear and adhesion were the predominant wear mechanisms, whereas chipping and diffusion became more pronounced at the higher cutting speed. The dry turning of AISI 304 stainless steel with uncoated cermet inserts proved viable in terms of sustainability and surface integrity; however, effective chip evacuation remains a critical concern. The use of compressed air or minimum quantity lubrication (MQL) may help mitigate this issue. Full article

High-speed turning of AISI 304 stainless steel is limited by rapid tool wear driven by thermal accumulation and tribological instability. This study compares five cooling/lubrication strategies (dry, flood cooling, MQL, internally cooled tools—ICT, and ICT + MQL) under a fixed severe cutting regime (Vc = 400 m/min, f = 0.1 mm/rev, ap = 0.2 mm) and develops a low-complexity tool end-of-life predictor using cutting power as the sole monitoring signal. Dry machining produced the highest cutting forces 26.7 N), whereas lubricated/cooled conditions showed statistically similar force levels (≈11 6 – 118 N). Cutting force and derived power increased monotonically with wear, supporting power as an indirect tool-state indicator. A binary XGBoost classifier trained on statistical and trend descriptors of one-second power windows achieved accuracies of 96.5% (training), 95.9% (test), and 93.3% (validation) with AUC–ROC values of 0.988, 0.993, and 0.959, respectively, despite moderate class imbalance (≈85 % healthy/15% worn). SHAP analysis identified average power and distributional descriptors (skewness and amplitude ratios) as dominant predictors, providing interpretable links between signal statistics and wear progression. The results demonstrate that reliable end-of-life detection can be achieved using a single energetic signal across heterogeneous cooling environments, supporting scalable monitoring compatible with low-fluid and closed-loop cooling strategies. Full article

Heat treatment critically controls microstructure and mechanical properties in engineering alloys, but experimental optimization is costly and time-intensive. Machine learning (ML) offers a data-driven alternative, though data scarcity and feature leakage often limit predictive reliability. A comprehensive ML framework was developed and validated using a physics-informed synthetic dataset of 332 heat-treated alloy samples covering carbon steels (AISI 4140, 1080, 4340, 5130), aluminum alloys (AlSi7Mg, AlSi10Mg, Al6061, Al2618), and stainless steels (304, 316L). Twenty-seven features describing chemical composition, heat-treatment parameters, and microstructural characteristics were initially included. Following strict data-leakage analysis, all six mechanical property features were fully removed, leaving 22 independent predictors. Five regression models—Extra Trees, Random Forest, Gradient Boosting, Ridge, and ElasticNet—were evaluated using a 70/15/15 train–validation–test split with randomized hyperparameter optimization and 3-fold cross-validation. The Random Forest model showed the best test performance for tensile strength prediction (R² = 0.9282, RMSE = 37.24 MPa, MAE = 28.54 MPa, MAPE = 5.39%), with minimal overfitting. Tempering temperature, carbon content, and manganese content were the most influential features, aligning with established metallurgical principles. The proposed framework demonstrates robust, leakage-free prediction of mechanical properties from composition and processing parameters, offering a scalable approach for accelerated alloy design pending experimental validation. This study serves as a methodological framework demonstration; the reported performance metrics are benchmarks against the synthetic dataset, and experimental validation with real alloy data remains essential for industrial deployment. 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

Laser surface texturing (LST) structures or laser-induced periodic surface structures (LIPSS) are typically created using laser pulses with durations ranging from femtoseconds to nanoseconds. However, nanosecond pulsed lasers, as cost-effective and more productive alternatives, can also be used to generate LST structures on stainless steel (SS) surfaces, making these structures more suitable for industrial applications. In this study, pulsed laser processing is employed to create LST structures on SS (AISI 304), with varying pulse and accumulated fluences, effective pulse counts, and scan parameters, such as pulse-to-pulse distance (pitch) and hatch spacing between scanning lines. A methodology for calculating oxidation density on processed AISI 304 surfaces is presented. Oxidation density, defined as the ratio of the oxidized area to the total processed area, is determined as a function of accumulated fluence, laser power, pulse-to-pulse distance, and hatch spacing. Optical images of the surfaces are analyzed, and oxidation regions are identified using machine learning techniques. The images are converted to grayscale, and machine learning algorithms are applied to classify the images into oxidation and non-oxidation regions based on pixel intensity values. This approach identifies the optimal threshold for separating the two regions by maximizing inter-class variance. Experimental modeling using response surface methodology is applied to experimentally generated data. Optimization algorithms are then employed to determine the process parameters that maximize pulsed laser irradiation performance while minimizing surface oxidation and processing time. This paper also presents a novel method for characterizing oxidation density using image segmentation and machine learning. The results provide a comprehensive understanding of the process and offer optimized models, contributing valuable insights for practical applications. Full article

The study concerns the influence of some basic parameters (speed, raster step, scan overlap coefficient, and surface modification at different processing angles) on the process of color laser marking with a fiber laser on AISI 304 stainless steel samples. Different surface morphology was obtained for single-shot marking; double-shot marking at angles 0° and 90°; and triple-shot marking at angles 0°, 60°, and 120°. According to the created methodology, dependencies for surface roughness, resulting color, color difference, and chromatic distance from the parameters raster step and scanning speed were established. The resulting colors and color differences for different values of these parameters for the three resulting morphological surfaces were compared. Trends in color saturation changes were established for single-shot, double-shot, and triple-shot color marking, as well as for changes in technological parameters in the studied intervals. Full article

The purpose of this paper is to examine the application and development of CAD/CAM technologies in the modern metal cutting industry, with a focus on their role in increasing production accuracy, efficiency, and sustainability. The study presents an industrial case of laser cutting of AISI 304 stainless-steel sheets, in which two approaches are compared under identical material and technological parameters: conventional manual nesting and automatic nesting based on algorithms implemented in a CAD/CAM environment. The methodology evaluates both layouts using clear technical and economic indicators, including number of parts per sheet, material utilization, cutting time, weight of scrap, and cost per sheet. For the analyzed batch, automatic nesting increases the number of parts per sheet from 44 to 76 (≈73%), reduces the unused sheet area from 61% to 39%, and shortens the cutting time from 12 to 9 min (≈25%), which leads to a reduction in material waste by about 36% and cost savings of approximately 314 EUR per sheet. As a result, the process becomes more efficient and reliable, supporting sustainable and digital manufacturing goals. The findings confirm the importance of algorithmic optimization in CAD/CAM systems for enhancing industrial competitiveness, enabling effective resource management, and facilitating the transition towards Industry 5.0. Full article