Search Results (10)

This study focuses on improving the wear resistance of cutting tools and extending their service life under intense mechanical, thermal, and radiation loads in nuclear power plant environments. This research investigates the potential of electrospark alloying (ESA) using W–Zr–B system electrodes obtained from disks synthesised by spark plasma sintering (SPS). The novelty of this work lies in the use of SPS-synthesised W–Zr–B ceramics, which are promising for nuclear applications due to their high thermal stability, radiation resistance and neutron absorption, as ESA electrodes. This work also establishes the relationship between discharge energy, coating microstructure and performance. The alloying electrode material exhibited a heterogeneous microstructure containing WB₂, ZrB₂, and minor zirconium oxides, with high hardness (26.6 ± 1.8 GPa) and density (8.88 g/cm³, porosity < 10%). ESA coatings formed on HS6-5-2 steel showed a hardened layer up to 30 µm thick and microhardness up to 1492 HV, nearly twice that of the substrate (~850 HV). Elemental analysis revealed enrichment of the surface with W, Zr, and B, which gradually decreased toward the substrate, confirming diffusion bonding. XRD analysis revealed a multiphase structure comprising WB₂, ZrB₂, WB₄, and BCC/FCC solid solutions, indicating the formation of complex boride phases during the ESA process. Tribological tests demonstrated significantly enhanced wear resistance of ESA coatings. The results confirm the efficiency of ESA as a simple, low-cost, and energy-efficient method for local strengthening and restoration of cutting tools. Full article

Characterising and quantifying complex multiphase steels is a challenging and time-consuming process, which is often open to subjectivity when based on image analysis of optical metallographic or SEM images. The properties of multiphase steels are highly sensitive to their individual phase properties and fractions, necessitating the development of robust characterisation tools. This paper presents a method for classifying nanoindentation maps into proportional fractions of up to five distinct microstructural regions in dual-phase and complex-phase steels. The phases/regions considered are ferrite, ferrite containing mobile dislocations, bainite, tempered martensite, and untempered martensite. A range of microstructures with varying fractions of phases were evaluated using both SEM/EBSD and nanoindentation. A machine learning (ML) approach applied to EBSD data showed good consistency in characterising a two-phase system. However, as the microstructural system complexity increased, variations were observed between different analysts and the sensitivity to the ML training data increased when four phases were present (reaching up to ~11% difference in the ferrite phase fraction determined). The proposed nanoindentation mapping technique does not show operator sensitivity and enables the quantification of additional microstructural features, such as identifying and quantifying ferrite regions with a high density of mobile dislocations and the degree of martensite tempering. Full article

The Magnetic Barkhausen Noise (MBN) is a non-destructive testing method, which, due to its high sensitivity to changes in the microstructure of the material, is increasingly being applied with success as a tool for evaluation of magnetic material state and properties. However, it is no less difficult to analyze the measurement signals and their correct interpretation due to the complex, non-deterministic and stochastic nature of the Barkhausen phenomenon. Depending on the material to be examined, a signal with different characteristics can be observed. Frequently, a signal with multi-phase Barkhausen activity characteristics is obtained, like in the case of grain-oriented electrical steels. Due to the increased computational capabilities of computers, more and more advanced signal analysis methods are being used and artificial intelligence is being involved as well. Recently, the time–frequency (TF) approach for MBN signal analysis was introduced and discussed in several papers, where short-time Fourier Transform (STFT) found frequent application with promising results. Due to the automation of the search for diagnostic patterns, the stage of selecting transformation parameters becomes extremely important in the process of preparing training data for evaluation algorithms. This paper investigates the influence of the STFT computational window size on the material state evaluation results obtained using convolutional neural network (CNN). The studies were performed for MBN signals obtained from grain-oriented electrical steel with anisotropic properties. The carried out work made it possible to draw connections on the importance of the choice of the window during the implementation of CNN network training. Full article

This study addresses the characterization of the particular microstructural constituents of multiphase transformation-induced plasticity (TRIP)-aided steels belonging to the first and third generations of Advanced High Strength Steels (AHSS) to explore the possibilities of the EBSD method. Complex microstructures composed of ferrite, bainite, retained austenite and martensite were qualitatively and quantitatively assessed. Microstructural constituents with the same crystal structure were distinguished using characteristic EBSD parameters like confidence index (CI), image quality (IQ), kernel average misorientation (KAM) and specific crystallographic orientation relationships. A detailed linear analysis of the IQ parameter and misorientation angles was also performed. These tools are very helpful in linking different symmetric or asymmetric features of metallic alloys with a type of their structure and morphology details. Two types of samples were investigated: thermomechanically processed and subjected to 10% tensile strain to study the microstructural changes caused by plastic deformation. Full article

In this study, a newly developed high-strength cast Fe81Cr15V3C1 (wt%) steel with a high resistance against dry abrasion and chloride-induced pitting corrosion is presented. The alloy was synthesized through a special casting process that yielded high solidification rates. The resulting fine, multiphase microstructure is composed of martensite, retained austenite and a network of complex carbides. This led to a very high compressive strength (>3800 MPa) and tensile strength (>1200 MPa) in the as-cast state. Furthermore, a significantly higher abrasive wear resistance in comparison to the conventional X90CrMoV18 tool steel was determined for the novel alloy under very harsh wear conditions (SiC, α-Al₂O₃). Regarding the tooling application, corrosion tests were conducted in a 3.5 wt.% NaCl solution. Potentiodynamic polarization curves demonstrated a similar behavior during the long-term testing of Fe81Cr15V3C1 and the X90CrMoV18 reference tool steel, though both steels revealed a different nature of corrosion degradation. The novel steel is less susceptible to local degradation, especially pitting, due to the formation of several phases that led to the development of a less dangerous form of destruction: galvanic corrosion. In conclusion, this novel cast steel offers a cost- and resource-efficient alternative to conventionally wrought cold-work steels, which are usually required for high-performance tools under highly abrasive as well as corrosive conditions. Full article

In this study, microstructural evolution and phase transition of nickel-free Fe-18Cr-18Mn (wt. %) austenitic steel powders, induced by mechanical alloying, were investigated. X-ray diffraction, scanning electron microscopy, and microhardness testing techniques were used to observe the changes in the phase composition and particle size as functions of milling time. The first 30 h of mechanical alloying was performed in an argon atmosphere followed by nitrogen for up to 150 h. X-ray diffraction results revealed that the Fe-fcc phase started to form after 30 h of milling, and its fraction continued to increase with alloying time. However, even after 150 h of milling, weak Fe-bcc phase reflections were still detectable (~3.5 wt. %). Basic microstructure features of the multi-phase alloy were determined by X-ray profile analyses, using the whole powder pattern modeling approach to model anisotropic broadening of line profiles. It was demonstrated that the WPPM algorithm can be regarded as a powerful tool for characterizing microstructures even in more complicated multi-phase cases with overlapping reflections. Prolonging alloying time up to 150 h caused the evolution of the microstructure towards the nanocrystalline state with a mean domain size of 6 nm, accompanied by high densities of dislocations exceeding 10¹⁶/m². Deformation-induced hardening was manifested macroscopically by a corresponding increase in microhardness to 1068 HV_0.2. Additionally, diffraction data were processed by the modified Williamson–Hall method, which revealed similar trends of domain size evolutions, but yielded sizes twice as high compared to the WPPM method. Full article

In this paper, gamma attenuation has been utilised as a veritable tool for non-invasive estimation of the thickness of scale deposits. By simulating flow regimes at six volume percentages and seven scale thicknesses of a two phase-flow in a pipe, our study utilised a dual-energy gamma source with Ba-133 and Cs-137 radioisotopes, a steel pipe, and a 2.54 cm × 2.54 cm sodium iodide (NaI) photon detector to analyse three different flow regimes. We employed Fourier transform and frequency characteristics (specifically, the amplitudes of the first to fourth dominant frequencies) to transform the received signals to the frequency domain, and subsequently to extract the various features of the signal. These features were then used as inputs for the group method for data Hiding (GMDH) neural network framework used to predict the scale thickness inside the pipe. Due to the use of appropriate features, our proposed technique recorded an average root mean square error (RMSE) of 0.22, which is a very good error compared to the detection systems presented in previous studies. Moreover, this performance is indicative of the utility of our GMDH neural network extraction process and its potential applications in determining parameters such as type of flow regime, volume percentage, etc. in multiphase flows and across other areas of the oil and gas industry. Full article

Continuous casting (CC) is one of the most important processes of steel production; it features a high production rate and close to the net shape. The quality improvement of final CC products is an important goal of scientific research. One of the defining issues of this goal is the stability of the casting process. The clogging of submerged entry nozzles (SENs) typically results in asymmetric mold flow, uneven solidification, meniscus fluctuations, and possible slag entrapment. Analyses of retained SENs have evidenced the solidification of entrapped melt inside clog material. The experimental study of these phenomena has significant difficulties that make numerical simulation a perfect investigation tool. In the present study, verified 2D simulations were performed with an advanced multi-material model based on a newly presented single mesh approach for the liquid and solid regions. Implemented as an in-house code using the OpenFOAM finite volume method libraries, it aggregated the liquid melt flow, solidification of the steel, and heat transfer through the refractory SENs, copper mold plates, and the slag layer, including its convection. The introduced novel technique dynamically couples the momentum at the steel/slag interface without complex multi-phase interface tracking. The following scenarios were studied: (i) SEN with proper fiber insulation, (ii) partial damage of SEN insulation, and (iii) complete damage of SEN insulation. A uniform 12 mm clog layer with 45% entrapped liquid steel was additionally considered. The simulations showed that parasitic solidification occurred inside an SEN bore with partially or completely absent insulation. SEN clogging was found to promote the solidification of the entrapped melt; without SEN insulation, it could overgrow the clogged region. The jet flow was shown to be accelerated due to the combined effect of the clogging and parasitic solidification; simultaneously, the superheat transport was impaired inside the mold cavity. Full article

As a detection tool to identify metal or alloy, metallographic quantitative analysis has received increasing attention for its ability to evaluate quality control and reveal mechanical properties. The detection procedure is mainly operated manually to locate and characterize the constitution in metallographic images. The automatic detection is still a challenge even with the emergence of several excellent models. Benefiting from the development of deep learning, with regard to two different metallurgical structural steel image datasets, we propose two attention-aware deep neural networks, Modified Attention U-Net (MAUNet) and Self-adaptive Attention-aware Soft Anchor-Point Detector (SASAPD), to identify structures and evaluate their performance. Specifically, in the case of analyzing single-phase metallographic image, MAUNet investigates the difference between low-frequency and high-frequency and prevents duplication of low-resolution information in skip connection used in an U-Net like structure, and incorporates spatial-channel attention module with the decoder to enhance interpretability of features. In the case of analyzing multi-phase metallographic image, SASAPD explores and ranks the importance of anchor points, forming soft-weighted samples in subsequent loss design, and self-adaptively evaluates the contributions of attention-aware pyramid features to assist in detecting elements in different sizes. Extensive experiments on the above two datasets demonstrate the superiority and effectiveness of our two deep neural networks compared to state-of-the-art models on different metrics. Full article

The third generation of advanced high strength steels shows promising properties for automotive applications. The macroscopic mechanical response of this generation can be further improved by a better understanding of failure mechanisms on the microstructural level and micro-mechanical behavior under various loading conditions. In the current study, the microstructure of a multiphase low silicon bainitic steel is characterized with a scanning electron microscope (SEM) equipped with an electron backscatter diffraction detector. A uniaxial tensile test is carried out on the bainitic steel with martensite and carbides as second phase constituents. An extensive image processing on SEM micrographs is conducted in order to quantify the void evolution during plastic deformation. Later, a new post-mortem electron backscatter diffraction-based method is introduced to address the correlation between crystallographic orientation and damage initiation. In this multiphase steel, particular crystallographic orientation components were observed to be highly susceptible to micro-void formation. It is shown that stress concentration around voids is rather relaxed by void growth than local plasticity. Therefore, this post-mortem method can be used as a validation tool together with a crystal plasticity-based hardening model in order to predict the susceptible crystallographic orientations to damage nucleation. Full article