Search Results (10)

Although powder metallurgy (PM) is known as a near-net-shape fabrication process, a large number of PM parts need to be machined for dimensional conformance or to produce complex geometrical features that cannot be achieved through compaction. However, due mainly to the presence of porosity, the machinability of PM steels is difficult compared to that of wrought steels and can add 20% or more to the overall fabrication cost of PM parts. Among the various measures known to improve the machinability of PM steels, the addition of machining aids, either as admixed or pre-alloyed constituents, is the most popular. Manganese sulfide (MnS) is by far the most common machinability-enhancing additive used in the PM steel industry. Although it is extremely efficient in improving the machining response of PM steels, MnS is known to have detrimental effects on mechanical properties and corrosion resistance. Thus, the use of MnS involves a compromise between obtaining good machinability at the expense of lower mechanical properties and corrosion resistance. In this study, free graphite particles are introduced as a new additive that not only noticeably improves the machinability of PM steel components but also does not affect their mechanical properties or corrosion resistance. It was found that it is possible to obtain free graphite particles in press-and-sintered PM steel components by coating graphite particles with a metallic layer. This coating prevents graphite from diffusing into the iron matrix while creating metallurgical bonds with the surrounding steel matrix during sintering. In this research, graphite particles were coated with nickel and copper through a cementation process. A heat treatment was then performed on this newly developed material to obtain a more uniform single-layer coating and achieve dimensional changes during sintering that are similar to those measured when MnS is used as a machinability enhancer. The results showed that the tensile properties as well as the fatigue resistance of components made of FC-0208-type PM steel containing admixed copper/nickel-coated graphite particles are not affected by the presence of the latter. Moreover, the corrosion resistance of the samples containing copper/nickel-coated graphite was found to be the same as that of samples without the additive, which is a significant improvement on the case where MnS is used. The performance of the newly developed additive in terms of machinability was also characterized in drilling. It was found that this new additive has an identical machinability-enhancing performance to admixed MnS. Full article

With the development of industrialization, the demand for high-performance metal materials has increased, and copper and its alloys have been widely used. The microstructure of these materials significantly affects their performance. To address the issues of subjectivity, low efficiency, and limited quantitative capability in traditional metallographic analysis methods, this paper proposes a deep learning-based approach for segmenting the second phase in Cu-Fe alloys. The method is built upon the YOLO11 framework and incorporates a series of structural enhancements tailored to the characteristics of the secondary-phase microstructure, aiming to improve the model’s detection accuracy and segmentation performance. Specifically, the EIEM module enhances the C3K2 structure to improve edge perception; the CSPSA module is optimized into C2CGA to strengthen multi-scale feature representation; and the RepGFPN and DySample techniques are integrated to construct the GDFPN neck network. Experimental results on the Cu-Fe alloy metallographic image dataset demonstrate that YOLO11 outperforms mainstream semantic segmentation models such as U-Net and DeepLabV3+ in terms of mAP (85.5%), inference speed (208 FPS), and model complexity (10.2 GFLOPs). The improved YOLO11 model achieves an mAP of 89.0%, a precision of 84.6%, and a recall of 81.0% on this dataset, showing significant performance improvements while effectively balancing inference speed and model complexity. Additionally, a quantitative analysis software system for secondary phase uniformity based on this model provides strong technical support for automated metallographic image analysis and demonstrates broad application prospects in materials science research and industrial quality control. Full article

In this paper, we present the analysis of functional alloy samples containing metals aluminum (Al), copper (Cu), lead (Pb), silicon (Si), tin (Sn), and zinc (Zn) using a Q-switched Nd laser operating at a wavelength of 532 nm with a pulse duration of 5 ns. Nine pelletized alloy samples were prepared, each containing varying chemical concentrations (wt.%) of Al, Cu, Pb, Si, Sn, and Zn—elements commonly used in electrotechnical and thermal functional materials. The laser beam is focused on the target surface, and the resulting emission spectrum is captured within the temperature interval of $9.0\times {10}^3$ to $1.1\times {10}^4$ K using a set of compact Avantes spectrometers. Each spectrometer is equipped with a linear charged-coupled device (CCD) array set at a 2 μs gate delay for spectrum recording. The quantitative analysis was performed using calibration-free laser-induced breakdown spectroscopy (CF-LIBS) under the assumptions of optically thin plasma and self-absorption-free conditions, as well as local thermodynamic equilibrium (LTE). The net normalized integrated intensities of the selected emission lines were utilized for the analysis. The intensities were normalized by dividing the net integrated intensity of each line by that of the aluminum emission line (Al II) at 281.62 nm. The results obtained using CF-LIBS were compared with those from the laser ablation time-of-flight mass spectrometer (LA-TOF-MS), showing good agreement between the two techniques. Furthermore, a random forest technique (RFT) was employed using LIBS spectral data for sample classification. The RFT technique achieves the highest accuracy of ~98.89% using out-of-bag (OOB) estimation for grouping, while a 10-fold cross-validation technique, implemented for comparison, yields a mean accuracy of ~99.12%. The integrated use of LIBS, LA-TOF-MS, and machine learning (e.g., RFT) enables fast, preparation-free analysis and classification of functional metallic materials, highlighting the synergy between quantitative techniques and data-driven methods. Full article

The mechanical and hydrodynamic characteristics of single-piece nets are key to the design and optimization of offshore aquaculture net cages. A numerical approach for offshore submerged aquaculture net materials based on the Morison equations and finite element is proposed, simulating the hydrodynamic characteristics of single-piece nets under varying parameters such as wire diameter, mesh size, and flow velocity, and simulating the impact of marine organism attachment on nets by modifying the drag coefficient. The simulation results of nets made from materials such as Copper–Zinc Alloy (Cu-Zn), Zinc–Aluminum Alloy (Zn-Al), Semi-Rigid Polyethylene Terephthalate (PET), and Ultra-High Molecular Weight Polyethylene (UHMWPE) are compared, which provides a theoretical basis for optimizing design parameters and selecting materials for nets based on force conditions and hydrodynamic characteristics. The simulation results indicate that the current force on the net is positively correlated with flow velocity; the maximum displacement of the net is also positively correlated with the flow rate. Compared to other materials, the Cu-Zn net is subjected to the greatest water flow force, while the UHMWPE net experiences the greatest displacement; the larger the diameter of the netting twine, the greater the current force on the net; the mesh size is inversely related to the current force on the net. With increasing drag coefficient, both the maximum displacement of the net and the current force experiences increase, and UHMWPE material nets are more sensitive to increases in the drag coefficient, which indicates a greater impact from the attachment of marine organisms. The density and elastic modulus of the netting material affect the rate of increase in force on the net. The research results can provide a basis for further research on material selection and design of deep-sea aquaculture nets. Full article

Material-resolving computed tomography is a powerful and well-proven tool for various clinical applications. For industrial scan setups and materials, several problems, such as K-edge absence and beam hardening, prevent the direct transfer of these methods. This work applies dual-energy computed tomography methods for material decomposition to simulated phantoms composed of industry-relevant materials such as magnesium, aluminium and iron, as well as some commonly used alloys like Al–Si and Ti64. Challenges and limitations for multi-material decomposition are discussed in the context of X-ray absorption physics, which provides spectral information that can be ambiguous. A deep learning model, derived from a clinical use case and based on the popular U-Net, was utilised in this study. For various reasons outlined below, the training dataset was simulated, whereby phantom shapes and material properties were sampled arbitrarily. The detector signal is computed by a forward projector followed by Beer–Lambert law integration. Our trained model could predict two-material systems with different elements, achieving a relative error of approximately 1% through simulated data. For the discrimination of the element titanium and its alloy Ti64, which were also simulated, the relative error increased to 5% due to their similar X-ray absorption coefficients. To access authentic CT data, the model underwent testing using a 10c euro coin composed of an alloy known as Nordic gold. The model detected copper as the main constituent correctly, but the relative fraction, which should be 89%, was predicted to be ≈70%. Full article

Laser-directed energy deposition (LDED) is a promising technology for coating, repairing, and building near-net-shape 3D structures. However, the processing of copper alloys, specifically, has presented a significant challenge due to their low laser absorptivity at the 1060 nm laser wavelength and high thermal conductivity. This study undertook a methodical examination by employing a 2 kW disk laser, operating at a wavelength of 1064 nm, and a coaxial nozzle head to comprehensively examine the processability of the highly conductive CuCrZr alloy for expanding the range of materials that can be successfully processed using LDED. The investigation focuses not only on optimizing the input process parameters that are the laser power, scanning speed, powder feed rate, and overlap ratio, but also on planning the toolpath trajectory, as these factors were found to exert a substantial influence on processability, geometrical accuracy, and the occurrence of defects such as lack of fusion. The optimal toolpath trajectory discovered involved implementing a zigzag strategy combined with a 90° rotation of the scanning direction. Additionally, a start point rotation was considered between each layer to even out the deposition of the layers. Moreover, a contour with a radial path at the corners was introduced to enhance the overall trajectory. Based on the hierarchal experimental study, the appropriate ranges for the key process parameters that leads to 99.99% relative density have been identified. They were found to be from 1100 up to 2000 W for the laser power (P), and from 0.003 up to 0.016 g/mm for the amount of powder that is fed to the melt pool distance (F/V). Regarding the influence of process parameters on the microstructure of the samples with equal deposition height, it was observed that varying combinations of process parameters within the optimal processing window resulted in variations in grain size ranging from 105 to 215 µm. Full article

Marine aquaculture facilities have been working in a high salt and humidity marine environment for a long time, which makes them be inevitably affected by seawater corrosion, especially the main structures such as metal mesh and frame. Therefore, studying the corrosion behavior of net and frame steel is of great significance for the selection of materials and corrosion protection of marine aquaculture facilities. The influence of NaCl concentrations and immersion state on self-corrosion behavior and the influence of layer thickness and overlapping area on galvanic corrosion behavior of H62 copper alloy mesh/D36 steel plate was discussed in this study using weight loss and electrochemical measurements. The tensile tests were conducted to observe the influence of different corrosion conditions on maximum force and tensile strength of the net. The corrosion rate of the two materials increased rapidly with the increase of NaCl concentrations; the corrosion rate of both materials showed the decreasing trend with the extension of time, and the corrosion rate of H62 was always lower than D36 steel. When two materials were coupled, the galvanic corrosion rate would decrease with the increase of the layer thickness and overlapping area. The tensile results were consistent with the corrosion results. When these two materials work together, adding layer thickness or increasing the overlapping area is one of the ways to protect the frame steel to a certain extent. Full article

The liquid metal infiltration of carbon preformed with copper and its alloys is already an established and well-known process. It is extensively used by the electronic industry to produce heat sinks of power electronics and electric contacts and sliding electric contacts. The advantage of the process is its ability to produce near net shape components with high volume fractions of carbon at a relatively low price. The process is carried out in a vacuum and with low applied pressure. However, a strong dependence on the temperature of infiltration and its precise control is significant for the sound final product. For certain pair carbon matrix–copper alloys, different results could be obtained according to the infiltration temperature. If the temperature is too low, the solidification may occur prior to complete infiltration (high final porosity). When the temperature is too high, undesirable reactions may occur at the fiber–matrix interface (e.g., corrosive carbides). Therefore, there are still a lot of scientific papers pushing this technology to new directions and over old limits. Publications inside scientific journals within this field deal with composite materials for sliding electrical contact and electrical contact materials, sealing materials, parts of brake disks, pantograph strips for high-speed railways, other electric and mechanical applications and even for wall surface shields in future fusion devices. The present paper reviews used carbon preforms, copper alloys, technological parameters, properties of prepared composites prepared via infiltration during the last 12 years. It can be stated that 1/3 of the papers were published within the last 3 years. Moreover, renewed interest in this low-cost technique could be expected within the next few years due to climate programs and increasing prices of the energy resources. Full article

Titanium investment casting is one of the leading and most efficient near-net-shape manufacturing processes, since complex shape components are possible to obtain with a very low amount of material waste. But melting these reactive alloys implies the usage of specific melting technologies such as the Induction Skull Melting (ISM) method. In this work the ISM was extensively studied with the aim of deepening the characteristics of this specific melting method and improving the too low energy efficiency and overall process performance. A 16 segment copper crucible and 3 turns coil was employed for the melting of 1 kg of Ti-6Al-4V alloy. Through the calorimetric balance, real-time evolution of the process parameters and power losses arising from the crucible and coil sub-assemblies was displayed. Results revealed the impact of coil working conditions in the overall ISM thermal efficiency and titanium melt properties, revealing the use of these conditions as an effective optimization strategy. This unstudied melting control method allowed more heat into charge and 13% efficiency enhancement; leading to a shorter melting process, less energy consumption and increased melt superheat, which reached 49 °C. The experimental data published in this paper represent a valuable empiric reference for the development and validation of current and future induction heating models. Full article

A New Ecological Paradigm scale was used as a measurement tool to determine consumer perception of the environment through the context of red seabream (Pagrus major) aquaculture and the use of copper-alloy nets. To identify the underlying dimension of consumer perception, exploratory factor analysis was conducted, which showed that consumer perception comprised two dimensions—nature and balance, and human dominance—yielding two indicators as independent variables for a contingent valuation method estimation. The estimation results indicate that demographic variables and one consumer perception variable (i.e., the human dominance indicator) are insignificant. However, the economic variable, one consumer perception variable (i.e., nature and balance), and seafood preference are significant. Finally, willingness-to-pay was estimated for sustainable aquaculture products by comparing the mean willingness-to-pay within New Ecological Paradigm-level groups. Full article