Brass Alloy Research Articles

Factors and mechanisms influencing the composition and structure: anodization of brass (Cu0.64Zn0.36) alloy in alkaline solutions

Factors and mechanisms influencing the composition and structure: anodization of brass (Cu0.64Zn0.36) alloy in alkaline solutions

ZnO Nanowire Cold Cathode Hemispherical X‐Ray Sources

AbstractCurved or spherical X‐ray sources are significant for use in intraoperative radiotherapy, adaptive static medical imaging, and high‐throughput industrial inspection, but they are hard to achieve using traditional thermionic cathode point electron sources. In this study, copper (Cu)‐doped zinc oxide (ZnO) nanowires grown on a brass substrate with a designed shape are proposed to achieve cold cathode hemispherical X‐ray sources. The strain‐driven solid–liquid growth model of Cu‐doped ZnO nanowires is proposed, and the oxidation temperature‐dependent and time‐dependent growth characteristics are investigated to optimize the morphologies of ZnO nanowire cold cathodes with a typical turn‐on field of 7.36 MV m−1, a maximum current of 12.54 mA (4.93 mA cm−2) and a uniform field emission image with an area of 2.54 cm2. Hemispherical X‐ray sources formed by Cu‐doped ZnO nanowire field emitters grown on spherical brass alloy and an Al thin film transmission anode target deposited on a hemispherical quartz glass are successfully fabricated, achieving an operating voltage of 39 kV, a dose rate of 240 µGyair s−1 and a projection X‐ray imaging resolution of 2.8 lp mm−1, demonstrating their promising use in a variety of applications.

Microstructural Insights and Composition Analysis of a Fractured Brass Alloy of Unknown Composition Using Advanced Characterization Techniques

This study aims to investigate the microstructural and compositional characteristics of a fractured brass alloy using energy-dispersive X-ray spectroscopy (EDS), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The brass alloy, initially of unknown composition, was determined to consist of 63.65 at% Cu, and 36.35 at% Zn through EDS analysis. Microstructural analysis via SEM revealed a typical ductile fracture pattern with necking and dimple formation, indicative of significant plastic deformation prior to failure. The alloy exhibited a grain size ranging from 10 µm to 80 µm and displayed both α-phase (FCC structure) and possible traces of β-phase, though XRD detected only the α-phase due to the low concentration of β-phase (<5%). A comparison between EDS and XRD results for analyzing chemical composition showed that good assumptions could be made from the lattice parameters of the binary alloy following Nelson-Relay function to predict an approximate composition in binary alloy systems, which was found to be 34 at% Zn in our analysis. Furthermore, the sample’s crystal structure was confirmed as FCC, which supported its observed ductility and plastic deformation prior to fracture. The SEM analysis also confirmed that the material went through an annealing process. The fracture morphology further showed that the FCC structure and the annealing process both contributed to the material’s increased ductility. The analysis further confirms that EDS is more reliable for chemical composition determination in an alloy system. Evidence of annealing twinning in the SEM images suggests the sample underwent an annealing process, contributing to its enhanced ductility.

Investigation of fracture mechanical properties of a brass alloy with microstructural variations

This study investigates the effect of microstructural variations on the mechanical properties of CuZn35Mn2Al1Fe1-C-GS brass alloy. Specimens taken from positions with different cooling rates in a large cast component exhibit coarse-grained (approximately 5mm) and fine-grained (approximately 1mm) microstructures. Fine-grained samples demonstrate at least a 7% increase in Ultimate Tensile Strength (UTS) and up to a 33% higher long crack threshold ΔKth,lc. Hardness measurements are similar between microstructures. The NASGRO model and cyclic R-curve are applied to fit crack propagation data, and fractographic analysis reveals distinct fracture mechanisms. The results indicate that a fine-grained microstructure enhances tensile strength and crack resistance, providing valuable insights for the design and maintenance of heavy machinery components made from cast brass.

Enhancing machinability in free-cutting duplex brass alloys: Isotropic blocky α phase formation via optimized hot extrusion processing

Despite the active utilization of duplex brass alloys in the electronics and automotive industries, optimizing thermomechanical treatment conditions for achieving excellent machinability remains a subject of ongoing debate. In this study, to establish a strategy to achieve enhanced machinability via industrial-scale direct hot extrusion processing, we systematically investigated the effect of hot extrusion processing conditions on microstructural evolution, mechanical properties, and machinability of duplex brass alloys. In particular, the yield strength was effectively interpreted through the extended Hall-Petch relationship, and the machinability was further elucidated through the evaluation of rotational torque during the drilling test as well as the precise analysis of segmented chips produced in the drilling test. As a result, our microstructural analysis of the as-cast Cu58Zn39Pb3 alloy revealed that the cooling process during industrial-scale casting inevitably results in the formation of the Widmanstätten structured α phase and the heterogeneous dispersion of Pb particles. In contrast, 670 ℃ extruded lower part and 730 ℃ extrudates, which were extruded in the single β region that caused complete dynamic recrystallization behavior, exhibited a uniform and isotropic blocky α structure and uniformly redistributed Pb particles, ultimately leading to superior mechanical properties and machinability. Notably, our findings suggest that achieving an isotropic blocky α structure can be attained at temperatures up to 100 ℃ below the β-transus temperature by optimizing extrusion pressure conditions. This approach offers an efficient methodology for minimizing defects caused by high-temperature extrusion, such as surface oxidation and hot shortness cracking, while simultaneously minimizing processing costs.

Assessment of imaging performance for metal alloys of the first Italian neutron imaging beamline NICHE (Pavia, Italy)

In this paper, the imaging capabilities and performance of the new neutron imaging beamline developed at the LENA facility of Pavia (Italy) in the framework of the NICHE project (Neutron Imaging in Cultural HEritage) are presented. For this purpose, the estimation of bronze and brass alloys attenuation coefficient has been obtained through imaging analysis of a set of Cu-based alloy reference samples produced ad-hoc with chemical composition similar to ancient artefacts. In addition, some samples were realised using a different cooling ramp in order to test the influence of the alloy production procedures in neutron attenuation. Moreover, the tomographic capabilities of the beamline were tested for different metallic and plastic materials.

Deep learning assisted femtosecond laser-ablation spark-induced breakdown spectroscopy employed for rapid and accurate identification of bismuth brass

BackgroundOwing to its excellent machinability and less toxicity, bismuth brass has been widely used in manufacturing various industrial products. Thus, it is of significance to perform rapid and accurate identification of bismuth brass to reveal the alloying properties. However, the analytical lines of various elements in bismuth brass alloy products based on conventional laser-induced breakdown spectroscopy (LIBS) are usually weak. Moreover, the analytical lines of various elements are often overlaped, seriously interfering with the identification of bismuth brass alloys. To address these challenges, developing an advanced strategy enabling to achieve ultra-high accuracy identification of bismuth brass alloys is highly desirable. ResultsThis work proposed a novel method for rapidly and accurately identifying bismuth brass samples using deep learning assisted femtosecond laser-ablation spark-induced breakdown spectroscopy (fs-LA-SIBS). With the help of fs-LA-SIBS, a spectral database containing high quality LIBS spectra on element components were constructed. Then, one-dimensional convolutional neural network (CNN) was introduced to distinguish five species of bismuth brass alloy. Amazingly, the optimal CNN model can provide an identification accuracy of 100 % for specie identification. To figure out the spectral features, we proposed a novel approach named “segmented fs-LA-SIBS wavelength”. The identification contribution from various wavelength intervals were extracted by optimal CNN model. It clearly showed that, the differences of spectra feature in the wavelength interval from 336.05 to 364.66 nm can produce the largest identification contribution for an identification accuracy of 100 %. More importantly, the feature differences in the four elements such as Ni, Cu, Sn, and Zn, were verified to mostly contribute to identification accuracy of 100 %. SignificanceTo the best of our knowledge, it is the first study on one-dimensional CNN configuration assisted with fs-LA-SIBS successfully employed for performing identification of bismuth brass. Compared with conventional machine learning methods, CNN has shown significant more superiority. To reveal the tiny spectra differences, the classification contribution from spectra features were accurately defined by our proposed “segmented fs-LA-SIBS wavelength” method. It can be expected that, CNN assisted with fs-LA-SIBS has great promising for identifying the differences from various element components in metallurgical field.

Effect of the brass waste recycling process on mechanical properties with investment casting for gear materials

This study is an experimental research to analyze the characteristics and mechanical properties of the casting process of recycled brass alloy, which will be used as a material for gears. Currently, the remaining production and waste from brass alloys continue to increase. Another thing is the increase in the need for brass gears, so environmentally friendly material engineering is needed to provide good quality and efficient energy use, especially during the casting process. The casting process uses an electric furnace that melts brass alloys at 526 up to 900 °C within 1 hour and molds to make test specimens. The results of microstructural testing for grain size in recycled brass alloys range from 74.63 μm to 84.57 μm. The maximum tensile strength produced is up to 225.2 MPa, the maximum yield strength is up to 179.8 MPa, and the maximum elongation is up to 7.3 %. The roughness of recycled brass alloys has a maximum Ra value (average roughness) of up to 0.836 μm. Validation was carried out by comparing the mechanical properties of CAC 302 brass products and the study results. The data shows that recycled brass's maximum yield strength value is 179.8 MPa and CAC 302 brass material is 175 MPa, but for the ultimate tensile strength value, the value of recycled brass is far below CAC 302 products. These results can be a consideration for the industry to be able to use recycled brass alloys for gear products because the yield strength value is not far from CAC 302 brass products. The impact result of this study produces recycled brass alloys that are environmentally friendly, efficient in smelting energy consumption, and have good yield strength. The results of this research can benefit the manufacture of gear components made of brass alloys.

Microstructure Evolution and Strengthening Mechanism of Regenerated Brass Alloy under Fe-Mn Control during Cold Drawing

Microstructure Evolution and Strengthening Mechanism of Regenerated Brass Alloy under Fe-Mn Control during Cold Drawing

The effect of molecular structure of imidazole-based compounds on corrosion inhibition of Cu, Zn, and Cu-Zn alloys

Three imidazole-based compounds (imidazole, 2-mercaptobenzimidazole, and 2-mercapto-1-methylbenzimidazole) were studied as corrosion inhibitors of Cu, Zn, and a family of Cu-xZn brass alloys (x = 10−40 wt%) in 3 wt% NaCl solution. The composition of inhibitor layers on the surface was studied using X-ray photoelectron and infrared spectroscopies. The molecular adsorption modes on Cu and Zn were modelled using density-functional theory (DFT) calculations. Mercapto-based inhibitors act as efficient inhibitors on all materials studied due to the formation of inhibitor layer through bonding with nitrogen and sulphur atoms. In contrast, imidazole is a moderate inhibitor for Zn, while it is much less efficient for Cu.

Experimental and numerical investigation on temperature analysis and process optimization in friction stir welding of CuZn40 brass alloys

Experimental and numerical investigation on temperature analysis and process optimization in friction stir welding of CuZn40 brass alloys

Antibacterial Activity of Brass against Antibiotic-Resistant Bacteria following Repeated Exposure to Hydrogen Peroxide/Peracetic Acid and Quaternary Ammonium Compounds.

Copper-containing materials are attracting attention as self-disinfecting surfaces, suitable for helping healthcare settings in reducing healthcare-associated infections. However, the impact of repeated exposure to disinfectants frequently used in biocleaning protocols on their antibacterial activity remains insufficiently characterized. This study aimed at evaluating the antibacterial efficiency of copper (positive control), a brass alloy (AB+®) and stainless steel (negative control) after repeated exposure to a quaternary ammonium compound and/or a mix of peracetic acid/hydrogen peroxide routinely used in healthcare settings. A panel of six antibiotic-resistant strains (clinical isolates) was selected for this assessment. After a short (5 min) exposure time, the copper and brass materials retained significantly better antibacterial efficiencies than stainless steel, regardless of the bacterial strain or disinfectant treatment considered. Moreover, post treatment with both disinfectant products, copper-containing materials still reached similar levels of antibacterial efficiency to those obtained before treatment. Antibiotic resistance mechanisms such as efflux pump overexpression did not impair the antibacterial efficiency of copper-containing materials, nor did the presence of one or several genes related to copper homeostasis/resistance. In light of these results, surfaces made out of copper and brass remain interesting tools in the fight against the dissemination of antibiotic-resistant strains that might cause healthcare-associated infections.

Compositional Design and Thermal Processing of a Novel Lead-Free Cu–Zn–Al–Sn Medium Entropy Brass Alloy

In the current work, a novel medium entropy copper alloy was designed with the aim of avoiding the use of expensive, hazardous or scarce alloying elements and instead employing widely available and cost-effective alternatives. In order to investigate this unknown region of multicomponent alloy compositions, the thermo-physical parameters were calculated and the CALPHAD method was utilized. This led to the design of the Cu50Zn25Al20Sn5 at. % (Cu53.45Zn27.49Al9.08Sn9.98 wt. %) alloy with a relatively low density of 6.86 g/cm3 compared with conventional brasses. The designed alloy was manufactured through vacuum induction melting, producing two ingots weighing 1.2 kg each, which were subjected to a series of heat treatments. The microstructural evolution of the alloy in the as-cast and heat-treated conditions was assessed through optical and scanning electron microscopy. The hardness of the as-cast and heat-treated alloy at room temperature was also studied. The alloy was characterized by a multiphase microstructure containing a major Cu-rich (Cu–Zn–Al) matrix reinforced with a secondary Zn-rich (Zn–Cu) phase and pure Sn. In terms of mechanical properties, the developed alloy exhibited high hardness values of roughly 378 HV0.2 and 499 HV0.2 in the as-cast and heat-treated conditions, respectively.

DEVELOPMENT OF MICROSTRUCTURE, MECHANICAL AND WEAR CHARACTERISTICS OF THE BRASS ALLOY PROCESSED BY ECAP

In the present study, the microstructure, mechanical and wear characteristics of commercial Cu-30Zn brass alloy were developed by an equal channel-angular process (ECAP) using a particular die in constant dimensions. The ECAP process was experimentally conducted at room temperature using (1-4) passes in route C with lubricating conditions. Also, the post-annealing treatment at 350 oC has been done for some brass samples, which were deformed with four passes. Findings revealed that by conducting the ECAP, a significant reduction in the grain size of the deformed brass samples is achieved compared to the as-received alloy. The grain refinement increased with the increasing number of ECAP passes. However, the post-annealing treatment increased the grain size of the deformed brass alloy, but still it was lower than the as-received alloy. Moreover, the mechanical performance, i.e. micro-hardness and strength, was significantly enhanced after the ECAP. The samples processed with three passes presented the highest hardness value (237 HV) and mechanical strength (UTS= 692 MPa, and YS= 542 MPa) due to the homogeneous strain hardening and substantial grain refinement throughout the ECAP process. However, the micro-hardness and mechanical strength of brass alloy decreased after post-annealing treatment compared to those of the ECAP deformed samples. The elongation to failure also decreased greatly with increasing the number of passes of ECAP. Additionally, the wear resistance of the investigated samples increased significantly after increasing the number of ECAP passes compared to the as-received alloy. The highest wear resistance has been achieved for samples deformed by three and four passes of ECAP due to the considerable grain size refinement and higher hardness. However, a slight increase in the wear rate occurred after post-annealing treatment on a brass alloy sample processed with four passes due to the increase in grain size.

Optimization of electrolyte composition for enhanced photocatalytic performance of the ceramic coating produced on brass by plasma electrolytic oxidation

PEO coatings are formed by subjecting a metal substrate to an electrolytic plasma discharge, resulting in the formation of a porous and rough oxide layer. Herein, the significant role of the chemical composition of the electrolyte system and additives in the formation of an oxide coating on brass alloy in the PEO process was investigated. It was observed that the coating created with 8 g/l aluminate, 1 g/l NaOH, and 1 g/l KF electrolyte had higher homogeneity than other coatings. This is because a composite oxide coating of Cu2O, ZnO, and AlCuO2 was formed. Methylene Blue (MB), a widely used dye in various industries, is known to be persistent and environmentally harmful. The photocatalytic activity of the PEO coating on brass for the removal of MB was investigated under visible light exposure. According to the results, the presence of the sample produced in an electrolyte containing 8 g/l Aluminate, 1 g/l NaOH, and 1 g/l KF resulted in a degradation rate of approximately 65 % for MB photodegradation after 6 h of irradiation. The PEO-coated brass shows promising photocatalytic efficiency compared to our research group's PEO-coated TiO2 coatings, indicating its potential as a photocatalyst. The results highlight that PEO coating on brass could be a potential candidate for photocatalytic and photoelectrochemical applications.

Characterization of 17th century archaeological metallic statuettes using combined neutron tomography and neutron diffraction

A combination of neutron computed tomography and neutron diffraction analysis has been studied to demonstrate the efficacy of employing neutron-based methodologies to investigate brass metallic statuettes, thereby shedding light on their structural and phase characteristics, all the while obviating the need to dissect the sample physically. In order to corroborate the findings obtained from the neutron-based techniques, supplementary methodologies such as Energy Dispersive X-ray Fluorescence were employed to extract elemental information and validate the results. These preliminary findings draw attention to the potential of neutron imaging to unveil intricate information regarding the techniques and components employed by artisans of antiquity in the production of Indian brass artifacts. We were also able to discern concealed structural attributes within metallic statuettes, as well as ascertain the precise phase composition of the brass alloy, thus further enriching our understanding of manufacturing practices of brass statuettes in the 17th-century era.

Deep Learning Neural Network (DNN) Coupled Laser Induced Breakdown Spectroscopy for Quantitative Analysis of Copper Alloys

Conventional Laser Induced Breakdown Spectroscopy (LIBS) quantitative analysis, employing both calibration and calibration-free techniques, is challenged by spectral overlapping, self-absorption, and spectral broadening effects, leading to decreased accuracy. Recently, integration of machine learning (ML) algorithms with LIBS has been increasingly employed to tackle these challenges. This article explores the augmentation of LIBS with deep learning neural networks (DNN) to enhance accuracy of quantitative analysis of multi-elemental copper alloys. Sufficient training data was acquired by simulating optical emission spectra for bronze (Cu-Sn) and admiralty brass (Cu-Zn-Sn) alloys under standard laser produced plasmas conditions, encompassing different alloy concentrations, electron temperatures, and densities. We designed a regularized DNN structure, fine-tuned using a validation dataset to optimize quantitative results. The model’s accuracy was assessed with test dataset. The quantitative results demonstrated reduced loss as training spectra increased from 500 to 5000 for both alloys. The decline in mean squared error, from 2.793×10−3 to 4.283×10−5 for bronze and from 3.245×10−2 to 5.598×10−4 for admiralty brass alloys, as training data increased from 500 to 5000, underscores the proposed DNN model’s potential for metallurgical alloy quantification.

Achieving exceptional combination of strength and ductility in α+β diphase brass with harmonic structure

Heterogeneous structures are an effective way to give materials excellent combined strength-ductility properties. In this study, Cu–Zn–Al–Fe alloys were prepared by homogenizing and hot rolling-annealing, and an α+β diphase brass with harmonic structure was obtained by adjusting the content of Zn. The microstructure and mechanical properties of Cu-xZn-4Al-2.8Fe (x = 14, 20, 22.5, 24, wt%) were systematically investigated. The Cu–Zn–Al–Fe alloy has an α single-phase matrix at 14 wt% Zn and an α+β diphasic matrix at 20, 22.5, and 24 wt% Zn, all uniformly distributed with nano β(FeAl). A large amounts of twins are produced after rolling in α phase that has a low stacking fault energy. The Cu–20Zn–4Al-2.8Fe alloy exhibits a unique harmonic structure of the α-phase soft cores embedded in α+β biphasic hard shells. This alloy exhibits an excellent strength-ductility combination at room temperature, possessing an ultimate tensile strength of 473.7 MPa while maintaining a total elongation of 50.7%. Increases total elongation by 30.3% while maintaining high strength compared to homogeneous alloys (22.5 wt% Zn). While α single-phase alloy (14 wt% Zn) exhibits excellent plasticity (total elongation of 58.0%), their strength is not satisfactory (ultimate tensile strength of 419.7 MPa). In contrast, too much Zn (24 wt% Zn) produces a high proportion of β phase, which exhibits high strength (ultimate tensile strength of 507.9 MPa) and relatively poor plasticity (total elongation of 22.8%). Overall, the Cu–20Zn–4Al-2.8Fe alloy with a harmonic structure achieves excellent overall mechanical properties due to twinning, precipitation strengthening, and hetero-deformation induced (HDI) strengthening. This study provides an essential reference for designing heterogeneous structures of brass alloys.

Effect of Choice of Drilling Kinematic System on Cylindricity Deviation, Roundness Deviation, Diameter Error and Surface Roughness of Holes in Brass Alloy

This article presents the results of an experimental study on the effect of the selection of kinematic system for the drilling process on the cylindricity deviation, roundness deviation, diameter error and surface roughness of holes in brass alloy. Three different kinematic systems based on the dependence of the direction of rotation of the workpiece and the drill bit were used. The drill bit was mounted in an axially driven holder that allowed it to be put into motion. Cutting tests were conducted at three different spindle speeds and three different feed rates per revolution (27 tests in total). A static ANOVA analysis was used to evaluate the effect of each input parameter on each output parameter. The results of this work have practical applications in machining. The following input parameters of the drilling process should be used to obtain the smallest values of each output parameter: for CYL, n = 4775 rpm, fn = 0.14 mm/rev and KIN III; for RON, n = 4775 rpm, fn = 0.1 or 0.12 mm/rev and KIN II; for DE, n = 3979 rpm, fn = 0.1 mm/rev and KIN I; and for Rz, n = 4775 rpm, fn = 0.1 mm/rev and KIN II. This research work also used Grey Relational Analysis with which input parameter optimization was derived. The optimal drilling parameters are spindle speeds of 4775 rpm, a feed per revolution of 0.1 mm/rev and the use of the first kinematic system. This paper also includes equations for predicting each parameter that describes the dimensional and shape accuracy and roughness of the hole surface. Using the first kinematic system reduced the roughness of the hole surface by as much as 58%. The correct selection of kinematic system improved its dimensional accuracy by 15%. On the other hand, the roundness deviation of the hole improved by 33% and the cylindricity deviation of the hole by 6%.

Prediction of contact resistance of electrical contact wear using different machine learning algorithms

H62 brass material is one of the important materials in the process of electrical energy transmission and signal transmission, and has excellent performance in all aspects. Since the wear behavior of electrical contact pairs is particularly complex when they are in service, we evaluated the effects of load, sliding velocity, displacement amplitude, current intensity, and surface roughness on the changes in contact resistance. Machine learning (ML) algorithms were used to predict the electrical contact performance of different factors after wear to determine the correlation between different factors and contact resistance. Random forest (RF), support vector regression (SVR) and BP neural network (BPNN) algorithms were used to establish RF, SVR and BPNN models, respectively, and the experimental data were trained and tested. It was proved that BP neural network model could better predict the stable mean resistance of H62 brass alloy after wear. Characteristic analysis shows that the load and current have great influence on the predicted electrical contact properties. The wear behavior of electrical contacts is influenced by factors such as load, sliding speed, displacement amplitude, current intensity, and surface roughness during operation. Machine learning algorithms can predict the electrical contact performance after wear caused by these factors. Experimental results indicate that an increase in load, current, and surface roughness leads to a decrease in stable mean resistance, while an increase in displacement amplitude and frequency results in an increase in stable mean resistance, leading to a decline in electrical contact performance. To reduce testing time and costs and quickly obtain the electrical contact performance of H62 brass alloy after wear caused by different factors, three algorithms (random forest (RF), support vector regression (SVR), and BP neural network (BPNN)) were used to train and test experimental results, resulting in a machine learning model suitable for predicting the stable mean resistance of H62 brass alloy after wear. The prediction results showed that the BPNN model performed better in predicting the electrical contact performance compared to the RF and SVR models.