Search Results (2,793)

Metal additive manufacturing techniques have seen technological advancements in recent years, fueled by their ability to provide industrial use parts with excellent mechanical properties. Wire Arc Additive Manufacturing is a technology that is being widely used in critical industries, and much research is conducted in this field due to the multiple factors involved in the overall process. Within WAAM, gas metal arc welding stands out for its low cost, high production volume, high quality and capability for automation. In this study, a CNC router was retrofitted with a gas metal arc welding setup to facilitate precise metal printing. The flexibility in this process allows for rapid repairs on site without the need to replace the entire part. The literature predominantly focuses on the macro-mechanical properties of GMAW parts, and very few studies try to study the interaction and influence of different process parameters on the mechanical properties. Thus, this study focused on the GMAW WAAM of stainless-steel parts by studying the influence of the wire feed rate, arc voltage and strain rate on the UTS, yield strength, toughness and percentage elongation. ANOVA and interaction plots were analyzed to study the interaction between the input parameters on each output parameter. Results showed that printing stainless steel through the gas metal arc welding process with an arc voltage of 18.7 V and a wire feed rate of 6 m/min resulted in poor mechanical properties. The input parameter that influenced the mechanical properties the highest was the wire feed rate, followed by the arc voltage and strain rate. Printing with an arc voltage of 18.7 V and a wire feed rate of 5 m/min, tested at a crosshead speed of 1 mm/min, gave the best mechanical properties. Full article

The objective of this study was to investigate the influence of additive manufacturing parameters, specifically using laser powder bed fusion (LPBF), and surface finishing methods on the corrosion rate and behavior of maraging steel M350 components. Samples were fabricated via LPBF employing varying laser powers (80 W, 100 W, and 120 W) and subsequently subjected to mechanical polishing. Corrosion performance was evaluated through 450 h immersion tests in a 3.5% aqueous NaCl solution and potentiodynamic polarization measurements. Microstructural characterization and surface topography assessments were performed using optical microscopy, scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM-EDS), and profilometry. The results demonstrate a strong influence of temperature, manufacturing parameters, and polishing on corrosion processes. At room temperature, higher laser power reduced corrosion rates due to better powder consolidation and lower porosity, whereas at 45 °C, the trend reversed, with the highest corrosion rates observed for samples produced at 120 W. Mechanical polishing significantly reduced surface roughness (Ra from ~7–10 μm to ~0.6–1 μm) but did not improve corrosion resistance; in some cases, it increased corrosion rates, likely due to stress redistribution and exposure of subsurface defects. Potentiodynamic tests confirmed that higher laser power reduced corrosion current density for unpolished surfaces, but polishing increased current density at 80 W more than twofold. The findings indicate that optimizing LPBF process parameters is crucial for improving the corrosion resistance of M350 steel. High laser power (≥120 W) is beneficial at ambient conditions, while lower powers (80–100 W) perform better at elevated temperatures. Mechanical polishing alone is insufficient for enhancing resistance and should be combined with stress-relief and porosity-reduction treatments. These results provide guidelines for tailoring additive manufacturing strategies to ensure reliable performance of M350 steel in chloride-rich environments. Full article

GHz-burst laser polishing is as a promising technique for improving the surface quality of metallic materials, offering key advantages over conventional methods. In this study, two distinct approaches are investigated: a single-step polishing process, and a double-step process consisting of an initial laser milling step followed by a finishing/polishing pass. This distinction is critical in evaluating the performance of GHz-burst regimes under different surface conditions and roughness levels. Initial proof-of-concept trials confirm that GHz-burst irradiation can significantly reduce the surface roughness with minimal thermal damage, provided that process parameters are carefully optimized. Further analysis of spot-to-spot overlap reveals that the deposited energy density plays a crucial role in achieving uniform surface quality without inducing surface defects. The number of passes is also studied, showing that while multiple passes can improve surface finish, the benefit strongly depends on the initial roughness state of the substrate. Scalability is demonstrated by increasing both the repetition rate and scan speed proportionally while maintaining processing quality across larger areas. These results support the viability of GHz-burst laser polishing for high-throughput manufacturing. Applications in aerospace, biomedical implants, and precision optics highlight the technique’s potential for industrial adoption in demanding surface finishing contexts. Full article

The aim of this study was to investigate microstructure and the mechanical and operational characteristics of thick and thin walls 3D-built by electron beam additive manufacturing (EBAM). In addition, the milling parameters (rotation speed, feed, and cutting width) were optimized based on simultaneous assessments of Ra roughness on the machined surfaces and material removing rate values. The wall dimensions did not exert a noticeable effect on their chemical compositions, as compared with the original wires used for 3D printing. In comparison, the strength characteristics of the wrought steel (cold-rolled plate) were higher due to finer grains, with both ferrite content and dislocation density being greater as well. In the 3D building process, multiple thermal cycles gave rise to the formation of elongated columnar grains, reducing the strength characteristics. The corrosion rate of the wrought steel was almost twice those of the 3D-printed blanks because of the higher content of both ferrite and twins. By assessing the machinability of the EBAM-built blanks using the stationary milling machine, the cutting forces were comparable due to similar mechanical properties (including microhardness). To improve the removing rate values and reduce the cutting forces, it is recommended to enhance the cutting speeds while not increasing the feeds. For the semi-industrial milling machine, both linear multiple regression and nonlinear neural network models were applied. An integrated approach was proposed that rationally determined both additive manufacturing and post-processing parameters based on a combination of express assessment and analysis of the mechanical, operational, and technological characteristics of built products within a single laboratory complex. Full article

Additive manufacturing (AM) via selective laser melting (SLM) is increasingly deployed in aerospace, biomedical, and tooling applications where complex geometries and high performance are required. Yet, process-induced anisotropy and microstructural heterogeneity can strongly affect mechanical and tribological behavior. This study systematically evaluates the combined effects of build orientation (0°, 45°, and 90° relative to the build plate) and post-build heat treatment (as-built, 600 °C, and 860 °C) on the phase constitution, microstructure, hardness, tensile response, and dry sliding wear of SLM-fabricated 316L stainless steel. X-ray diffraction indicated a fully austenitic (γ-fcc) structure without detectable secondary phases across all conditions. Orientation-dependent substructures were observed: ~1 µm equiaxed cellular features at 0°, finer 0.3–0.5 µm cells at 45°, and 1–2 µm elongated features at 90°. Microhardness varied with orientation; relative to 0°, 45° specimens were ~15 HV higher, whereas 90° specimens were ~10 HV lower. Heat treatment at 600 °C promoted refinement and recovery of the cellular network, most pronounced in the 45° orientation, while treatment at 860 °C largely erased melt pool boundary contrast, producing a more homogeneous particle-like microstructure. Tensile fractography revealed dimpled rupture in all cases; the 90° orientation showed finer dimples and lower hardness, consistent with a ductile failure mode under reduced constraint. Dry sliding wear tests identified adhesive wear, intensified by the build-up of transferred fragments, as the dominant mechanism in both as-built and 600 °C conditions. Changes to melt pool morphology after 860 °C heat treatment correlated with altered wear track widths, with the 0° condition showing a notable narrowing relative to the 600 °C state. These results highlight processing pathways for tailoring anisotropy, strength–ductility balance, and wear resistance in SLM 316L. Full article

This study investigates the microstructure evolution and mechanical properties of DSS2209 duplex stainless steel fabricated via cold metal transfer wire arc additive manufacturing (CMT-WAAM). The as-deposited thin-wall components exhibit significant microstructural heterogeneity along the build height due to thermal history variations. Optical microscopy, SEM-EDS, and EBSD analyses reveal distinct phase distributions: the bottom region features elongated blocky austenite with Widmanstätten austenite (WA) due to rapid substrate-induced cooling; the middle region shows equiaxed blocky austenite with reduced grain boundary austenite (GBA) and WA, attributed to interlayer thermal cycling promoting recrystallization and grain refinement (average austenite grain size: 4.16 μm); and the top region displays coarse blocky austenite from slower cooling. Secondary austenite (γ₂) forms in interlayer remelted zones with Cr depletion, impacting pitting resistance. Mechanical testing demonstrates anisotropy; horizontal specimens exhibit higher strength (UTS: 610 MPa, YS: 408 MPa) due to layer-uniform microstructures, while vertical specimens show greater ductility (elongation) facilitated by columnar grains aligned with the build direction. Hardness ranges uniformly between 225–239 HV. The study correlates process-induced thermal gradients (e.g., cooling rates, interlayer cycling) with microstructural features (recrystallization fraction, grain size, phase morphology) and performance, providing insights for optimizing WAAM of large-scale duplex stainless steel components like marine propellers. Full article

The aim of this study is to evaluate the effect of various lubrication systems (dry cutting, MQL, and nano-MQL) on the machinability of AISI 1040 medium-carbon steel. By dispersing titanium carbide (TiC) nanoparticles into environmentally friendly sunflower oil, a new type of nano-MQL fluid was developed. Machinability parameters such as surface finish, cutting force, energy consumption, chip structure, and tool degradation were examined through scanning electron microscopy (SEM). Based on experimental observations, the use of the nano-MQL technique led to a notable enhancement in machining performance when compared to both dry and traditional MQL machining. In addition, surface roughness was substantially reduced with the nano-MQL, suggesting more effective lubrication and cooling. Reductions in cutting forces and energy consumption were also observed, indicating more efficient material removal and lower mechanical resistance. The SEM examination of the cutting tools proved the low wear rate of the nano-MQL, which exhibited less adhesion and more abrasion wear, and of dry cutting, which showed the most serious wear. Furthermore, chip morphology illustrations indicated that the chips of nano-MQL were relatively uniform and segmented, indicating superior chip breaking quality and cutting stability. The results suggest that employing TiC nanoparticles in MQL offers a clear enhancement of cutting performance in terms of process efficiency, surface quality, and tool wear. These results validate the capability of nano-MQL as an environmentally friendly and high-performance lubrication method for turning medium-carbon steels, supporting more sustainable and efficient manufacturing operations. Full article

The Directed Energy Deposition (DED) process has demonstrated high efficiency in manufacturing steel parts with complex geometries and superior capabilities. Understanding the complex interplays of alloy compositions, cooling rates, grain sizes, thermal histories, and mechanical properties remains a significant challenge during DED processing. Interpretable and data-driven modeling has proven effective in tackling this challenge, as machine learning (ML) algorithms continue to advance in capturing complex property structural relationships. However, accurately predicting the prime mechanical properties, including ultimate tensile strength (UTS), yield strength (YS), and hardness value (HV), remains a challenging task due to the complex and non-linear relationships among process parameters, material constituents, grain size, cooling rates, and thermal history. This study introduces an ML model capable of accurately predicting the UTS, YS, and HV of a material dataset comprising 4900 simulation analyses generated using the “JMatPro” software, with input parameters including material compositions, grain size, cooling rates, and temperature, all of which are relevant to DED-processed low-alloy steels. Subsequently, an ML model is developed using the generated dataset. The proposed framework incorporates a physics-based DED-specific feature that leverages “JMatPro” simulations to extract key input parameters such as material composition, grain size, cooling rate, and thermal properties relevant to mechanical behavior. This approach integrates a suite of flexible ML algorithms along with customized evaluation metrics to form a robust foundation to predict mechanical properties. In parallel, explicit data-driven models are constructed using Multivariable Linear Regression (MVLR), Polynomial Regression (PR), Multi-Layer Perceptron Regressor (MLPR), XGBoost, and classification models to provide transparent and analytical insight into the mechanical property predictions of DED-processed low-alloy steels. Full article

Modeling the structure not only of whole metal products, but also of the protective coatings with which they are coated, brings a number of economic benefits through more resistant coatings and coatings that can be produced by simplifying manufacturing technology or reducing material consumption in the process. This paper presents the results of a study of dip metallization in zinc baths with Ti additions. Both steel and cast iron substrates were coated and similar results were obtained. The obtained coatings were subjected to SEM analysis with chemical composition studies, TEM characterization with selected area electron diffraction (SAED), and corrosion studies. Particle models of the elementary phases present in the zinc coating made with CaRine 3.0 software were presented and used for phase analysis. It emerged that coatings obtained in zinc baths with the addition of Ti are characterized by a more varied microstructure, the occurrence of phase separations to which Ti segregates, and higher corrosion resistance than classical zinc coatings. The higher corrosion resistance is prompted not only by the Ti content in the intermetallic phases, but also by the observed nanostructure favorably located in the alloy layer. Full article

316L stainless steel offers attractive characteristics for hydrogen applications, including low hydrogen diffusivity and high hydrogen solubility. However, its use is limited by relatively low strength and resistance to hydrogen embrittlement (HE) under prolonged hydrogen exposure. Laser-directed energy deposition (L-DED) can not only increase the strength of 316L, but also induce significant tensile residual stresses that promote HE. In this study, 316L stainless steel samples produced by L-DED were post-processed by laser shock peening (LSP) to release the tensile residual stresses and refine the near-surface microstructure. LSP-treated samples showed refined grains, higher hardness, and the introduction of compressive residual stress, which led to improved tensile performance in hydrogen. Notably, after seven passes of LSP, the HE index (reduction in elongation due to hydrogen) was 12.5%, compared with 36.1% for the unpeened material. These results demonstrate that LSP is an effective approach to simultaneously increase strength and significantly improve HE resistance in additively manufactured 316L stainless steel. Full article

The affordability, ease of manufacturing, and assembly efficiency of cold-formed steel profiles have contributed to their widespread use in structural applications. However, the presence of holes in these profile webs is likely to reduce their mechanical resistance. This study explores the bending behavior of a built-up box section constructed using lipped and unlipped C-profiles, which are commonly utilized in the construction industry. The investigation focuses on the influence of self-drilling screw layout density and hole distribution within the section. A total of 30 different models were analyzed, considering three primary variables: the spacing of self-drilling screws, hole diameter, and the number of holes. The steel profiles were connected using self-drilling screws with spacing intervals of 100, 200, and 400 mm. Key parameters, such as moment capacity, effects on elastic zones, shear forces on screws, and ductility, were examined in relation to these variables. The findings indicate that reducing screw spacing and increasing the number of holes are crucial design factors for improving joint strength. However, while greater screw spacing enhances ductility, it leads to lower plastic deformation rates. Additionally, optimizing the number of holes in the section proved to be an effective strategy for improving ductility in the analyzed models. Mathematical evaluation confirmed that hole number and screw spacing significantly affect moment capacity and estimation stability, highlighting the need for their joint optimization in structural design. Full article

Over the past several decades, old industrialized regions worldwide have faced immense pressure to adapt to global economic shifts. Using one of China’s major industrial provinces, Hebei, as a representative case study, this study examines how the evolution of one of China’s old industrial provinces, Hebei’s industrial structure has influenced its economic growth from 1990 to 2023. Drawing on theories of structural transformation and endogenous growth, we argue that the reallocation of resources from lower-productivity sectors (e.g., agriculture) to higher-productivity sectors (manufacturing and services) can act as an engine of growth. We employ a shift-share analysis (SSA) to decompose Hebei’s economic growth into components attributable to national trends, industrial structure, and regional competitive performance. The results reveal a globally relevant pattern of stagnation: while Hebei’s growth largely benefited from nationwide economic expansion (national effect), its heavy industrial structure initially posed a drag on growth (negative structural effect) and its regional competitive advantage in steel and energy sectors has eroded over time (weakening competitive effect). Our regression analysis further shows that growth was overwhelmingly dependent on capital accumulation while the contribution of labor was statistically insignificant, pointing to a low-productivity trap common in such regions. By integrating these methods, this study provides a robust diagnostic framework for identifying the root causes of economic distress in legacy industrial regions both within and outside China. These findings underscore the importance of structural upgrading for sustainable growth and offer critical lessons for policymakers globally, highlighting the necessity of moving beyond extensive, capital-driven growth toward an intensive model focused on industrial diversification, innovation, and human capital to ensure the sustainable revitalization of post-industrial economies. Full article

In modern industrial manufacturing, the mechanical properties of large bearing housing castings are critical to equipment reliability and lifespan. Traditional prediction methods relying on experimental testing and empirical formulas face challenges such as high costs, limited samples, and inadequate generalization capabilities. This study presents a machine learning approach for predicting mechanical properties of ZG270-500 cast steel, integrating multivariate data (chemical composition, process parameters) to establish an efficient predictive model. Utilizing real-world production data from a certain foundry and forging plant, the research implemented preprocessing steps including outlier handling, data balancing, and normalization. A systematic comparison was conducted on the performance of four algorithms: Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The results indicate that under small-sample conditions, the SVR model outperforms other models, achieving a coefficient of determination (R²) between 0.85 and 0.95 on the test set for mechanical properties. The root mean square errors (RMSE) for yield strength, tensile strength, elongation, reduction in area, and impact energy are 7.59 MPa, 7.52 MPa, 0.68%, 1.47%, and 5.51 J, respectively. Experimental validation confirmed relative errors between predicted and measured values below 4%. SHAP value analysis elucidated the influence mechanisms of key process parameters (e.g., pouring speed, normalization holding time) and elemental composition on mechanical properties. This research establishes an efficient data-driven approach for large casting performance prediction and provides a theoretical foundation for guiding process optimization, thereby addressing the research gap in performance prediction for large bearing housing castings. Full article

The geometry and quality of a weld seam are critical factors in laser beam welding, influencing mechanical performance and structural integrity. Dynamically modulated laser beams provide a precise means of tailoring energy input in high-power laser welding processes. This study investigates the influence of beam shape and modulated frequency on weld seam geometry, penetration depth, and capillary behaviour using a coherent beam combining (CBC) laser system from Civan Lasers. Three beam intensity distributions—single point, line–point–line (LPL), and boomerang—were applied across a modulation frequency range of 1, 10, and 100 kHz during the welding of duplex and austenitic stainless steels. High-speed imaging captured real-time capillary dynamics, and the data were analysed to assess capillary stability, measure capillary diameter, and determine the capillary front angle as a function of frequency and beam shape. Transverse cross-sections of the welds were prepared to evaluate seam geometry and microstructure. The results show that beam shape significantly affects energy distribution and weld profile, while modulation frequency critically influences capillary behaviour and penetration characteristics. These findings highlight the critical role of dynamic beam shaping and frequency modulation in optimizing laser welding processes for material-specific performance, offering a versatile platform for advancing precision manufacturing using CBC technology. Full article

The mutual twisting of steel wires is widely used in construction, engineering, and everyday applications, as it is relatively easy to perform and imparts new and useful properties to the wires. Since the process involves large deformations and high stress levels, understanding the mechanical behavior of twisted pairs is essential for both their manufacturing and in-service performance. This study provides a detailed analysis of the stresses and deformations that arise during the twisting of two galvanized steel wires with a diameter of 4 mm. Comprehensive information is presented on the development and validation of a suitable finite element model, with emphasis on geometry definition, the selection of appropriate initial and boundary conditions, and the meshing strategy. Special attention is devoted to the material properties, which are obtained and processed based on original tensile and torsion tests. Both the maximum and residual stresses are investigated. It is found that, for small twist pitches, the equivalent stresses during twisting can exceed the material’s yield strength by a factor of two or more, posing a risk of failure. The residual equivalent stresses show complex spatial distributions that vary with pitch, yet their average magnitudes remain within a narrow range, indicating a consistent residual stress level across different twisting configurations. Full article