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The object of the research is thermal spray process for the formation of metal coating from bronze powder in plasma-fuel variant, using direct current (DC) electric arc plasma torch, on steel samples. The aim of the work was to investigate and develop the technology for plasma-fuel spraying of functional coatings (for wear-resistant and antimicrobial applications) on machine-building and medical purpose pieces with increased process capacity and moderate energy consumptions in a comparison with conventional thermal spray technologies with use of inert and oxygen-free gas media. During the study, using experimental and thermodynamic estimation methods, the thermal and chemical parameters of the process under the spraying conditions at ambient pressure were characterized, which made it possible to determine the area of preferred regimes of the developed technology. On the modernized testing unit for plasma spraying of metal powders with power of up to 40 kW, operating using a controlled combination of three types of gases – technical nitrogen and propane-butane (LPG) with compressed air, the measurement and optimization of the operating and constructive/assembling parameters of the system for aluminum bronze coating spraying were established. In this case, the experiments were carried out using the designed fuel intensifier, which is joined with the PP-25 arc plasma torch, as well as additional technological equipment (protective shroud). For samples of the resulting coatings with a thickness of 100 to 450 mm from the bronze material, testing of phase composition and some parameters of the resulting coatings on steel products was carried out. Operating capacity of the proposed process reaches 7–15 kg/h for bronze powder when using a moderate power of the torch – up to 35–40 kW and a limited flow rate of hydrocarbon gas (for example, LPG of the SPBT grade) – 0.1–0.35 kg/h. Analysis of the energy efficiency parameters of the developed technology, as well as its calculated technical characteristics, in a comparison with plasma and combined equipment of a similar purpose, showed that it has an advantage in terms of target indicators, in particular, in terms of energy consumption and total energy efficiency of the spraying unit, not less than 20–30 %. This makes it to proceed later to the stage of application of this technology into production based on a new process for the metal coating formation, in particular with antimicrobial properties, with improved energy efficiency of the process.

High-strength pipeline steel was subjected to friction stir welding (FSW) at rotation rates of 400–700 rpm, and the grain refinement mechanism of the nugget zone (NZ) was determined. The thermo-mechanical process during FSW in the NZ was simulated by multi-pass thermal compression, thereby achieving the austenitic non-recrystallization temperature (Tnr). The austenitic non-recrystallization in the NZ at the lowest rotation rate of 400 rpm caused a significant grain refinement. Furthermore, the reduced rotation rate also resulted in the formation of a high ratio of island-like martensite-austenite (M-A) constituent. The toughness of the NZs was enhanced as the rotation rate decreased, which is attributed to the fine effective grains and homogeneously distributed fine M-A constituents dramatically inhibiting crack initiation and propagation.

We present an electron backscattered diffraction (EBSD)-trained deep learning (DL) method integrating traditional material characterization informatics and artificial intelligence for a more accurate classification and quantification of complex microstructures using only regular scanning electron microscope (SEM) images. In this method, EBSD analysis is applied to produce accurate ground truth data for guiding the DL model training. An U-Net architecture is used to establish the correlation between SEM input images and EBSD ground truth data using only small experimental datasets. The proposed method is successfully applied to two engineering steels with complex microstructures, i.e., a dual-phase (DP) steel and a quenching and partitioning (Q&P) steel, to segment different phases and quantify phase content and grain size. Alternatively, once properly trained the method can also produce quasi-EBSD maps by inputting regular SEM images. The good generality of the trained models is demonstrated by using DP and Q&P steels not associated with the model training. Finally, the method is applied to SEM images with various states, i.e., different imaging modes, image qualities and magnifications, demonstrating its good robustness and strong application ability. Furthermore, the visualization of feature maps during the segmenting process is utilised to explain the mechanism of this method's good performance.

Physical metallurgical (PM) and data-driven approaches can be independently applied to alloy design. Steel technology is a field of physical metallurgy around which some of the most comprehensive understanding has been developed, with vast models on the relationship between composition, processing, microstructure and properties. They have been applied to the design of new steel alloys in the pursuit of grades of improved properties. With the advent of rapid computing and low-cost data storage, a wealth of data has become available to a suite of modelling techniques referred to as machine learning (ML). ML is being emergingly applied in materials discovery while it requires data mining with its adoption being limited by insufficient high-quality datasets, often leading to unrealistic materials design predictions outside the boundaries of the intended properties. It is therefore required to appraise the strength and weaknesses of PM and ML approach, to assess the real design power of each towards designing novel steel grades. This work incorporates models and datasets from well-established literature on marageing steels. Combining genetic algorithm (GA) with PM models to optimise the parameters adopted for each dataset to maximise the prediction accuracy of PM models, and the results were compared with ML models. The results indicate that PM approaches provide a clearer picture of the overall composition-microstructure-properties relationship but are highly sensitive to the alloy system and hence lack on exploration ability of new domains. ML conversely provides little explicit physical insight whilst yielding a stronger prediction accuracy for large-scale data. Hybrid PM/ML approaches provide solutions maximising accuracy, while leading to a clearer physical picture and the desired properties.

At present, optimization of mechanical properties in quenching and partitioning (Q&P) steels relies primarily on the tailoring of microstructural characteristics by manipulating the alloying elements and the Q&P process. However, adjusting the final microstructure by taking advantage of the inheritance effect derived from the initial microstructure has received less attention. Based on a typical Fe–C–Mn–Si system, the present investigation explores the effect of microstructural inheritance in an ultrahigh strength hot-rolled Q&P steel. The typical hot rolling process is followed by three cooling rates, including quenching, air cooling, and coil cooling, to obtain different initial microstructures of full martensite, bainite–austenite, and ferrite–pearlite, respectively. The same Q&P treatment is then performed to elucidate the inheritance mechanism, and the final Q&P products consist of different fractions of tempered martensite, retained austenite, bainite and fresh martensite. With decreasing cooling rates after hot rolling, the ultimate tensile strengths of the steels after the Q&P treatment are identical at ~ 1.5 GPa, while the total elongations increase gradually from 10.9, 12.7 to 14.5 pct. The highest elongation of the coil-cooled Q&P steel can be attributed to the effect of microstructural inheritance on the Q&P process and retained austenite, leading to larger austenite grain sizes and more equiaxed morphologies. The combination of austenite chemistry and morphology results in the optimal mechanical stability for retained austenite to provide continuous and durable strain hardening. Compared to cold-rolled products, hot-rolled Q&P steel is very attractive to the industry as it can potentially eliminate the cold rolling step and reduce the production cost. Utilizing the microstructural inheritance effect provides a simple and feasible route to control the microstructures and mechanical properties of ultrahigh strength hot-rolled Q&P steel.

Variant selection during the martensitic transformation in steels may play an important role in determining the transformation kinetics and the resulting mechanical properties. In this study, the variant selection and crystallographic features of deformation-induced martensite were investigated by quasi in situ electron backscatter diffraction (EBSD) in grade SUS321 during tensile deformation. Significant differences in variant selection between austenite (γ)→hcp-martensite (e)→bcc-martensite (α’) and γ→α’ transformation routes were observed and reported in detail, which demonstrated that e-martensite plays an important role in the variant selection of α’. Variant selection at different deformation stages was also analysed and revealed that α’ variants with the highest priority and variant pairs were preferred at the initial and last deformation stages in the γ→e→α’ sequence, respectively. Meanwhile, the single α’ variant nucleated at the thin slip band keeps its crystallography feature upon further deformation in the γ→α’ sequence. In addition, the strain work of the martensitic transformation for applied loads was quantitatively estimated to explain the variant selection and associated mechanism. When these calculations are compared to the experimental results it is found that they are not able to predict which α’ variant is forming preferentially during either during the γ→α’ or the e→α’ sequences, while only accurate predictions are obtained for the γ→e transformation which indicates that the γ→α’ variant selection is more complex.

The design of high-strength steel has long been discussed in the field of metal structural materials. To further increase the strength of common high-strength steel and further decrease the cost for production, three direct-quench hot rolled steels were designed and fabricated. The rolling and coiling processes were set based on continuous cooling transformation curves. In addition, the effect of the coiling temperature on the tensile properties was discussed to further guide the optimization of the process. It was found that compared with granular bainite, lower bainite probably has more advantages for both the strength and low temperature impact toughness of direct-quench hot rolled steels. Through a process of tailoring the morphology of bainite and controlling the grain boundary precipitation, the newly designed direct-quench hot rolled steels showed greatly improved strength and acceptable ductility/toughness compared with traditional quenched and tempered steels.

The martensite start temperature (Ms) is a significant parameter to describe the stability of retained austenite in steels. This paper provides a prediction model of Ms for lath martensite considering chemical and non-chemical driving forces. The non-chemical driving force includes shearing energy of austenite, dilatation strain energy of martensite and dislocation stored energy, where dilatation strain energy and dislocation stored energy provide the major contribution. Based on the calculation method, the contour lines of transformation driving forces and Ms in Fe-Ni-C system are predicted which agrees with experimental data with calculation error of 4.6%.

A dual band metamaterial absorber composed of dielectric and metallic atoms with high symmetry was numerically designed and experimentally verified. Due to simultaneously generated electric and magnetic resonances of both plasmon and Mie resonators, two absorption peaks with near unity absorptivity were obtained at 9.45 and 9.80 GHz. The loss of the electromagnetic wave at the first resonance frequency was mainly caused by ohmic loss based on plasmon resonance. For the second absorption peak resonance frequency, the incident wave was trapped inside the dielectric cube and the main loss of the electromagnetic wave was caused by dielectric loss based on Mie resonance. Most of the proposed dual band metamaterial absorbers were sensitive to the polarization direction hindering its potential applications in scientific and technological areas. Combing both plasmon and Mie resonances provides a new and simple way to build dual band isotropic metamaterial perfect absorbers eliminating polarization effect.

Thermodynamic theory was used to calculate the formation temperature and site fraction of MC carbides in Fe–C–Ni–V–Ti system. The calculation results showed the theoretical formation conditions of core–shell MC carbides. One-step and two-step heat treatment processes were used in Fe–C–Ni–V–Ti alloy to, respectively, obtain homogeneous and core–shell MC carbides, which was consistent with the thermodynamic calculation results. The transmission electron microscopy observations showed that the core–shell MC carbide obtained by the two-step heat treatment process contained homogeneous (Ti, V)C as the core and basically VC as the shell. The mechanical test results proved that compared with homogeneous MC carbides, core–shell MC carbides could improve the basic mechanical properties of the alloy because VC shell greatly increased the bonding strength and separation work of Fe/MC interface. Thus, the core–shell MC carbide with a VC shell structure can be a better grain refiner and can be used in steels with a high standard of fracture toughness.

Isothermal decomposition of austenite during the partitioning process of quenching and partitioning (Q&P) steels is normally ignored but may play an important role in determining the condition of retained austenite in the final microstructure and hence the mechanical properties of the steel. To provide a theoretical basis and practical guideline for controlling the isothermal bainitic transformation and optimizing the competition with other transformation mechanisms, the isothermal bainitic transformation was systematically investigated and the associated time-temperature-transformation diagrams were constructed for 0.2C-1.60/2.82/3.92Mn-1.6Si steels during one-step and two-step quenching and partitioning processes. The influences of quenching temperatures, partitioning temperatures, and Mn concentrations on the bainitic transformation window were quantitatively analyzed by dilatometry and electron microscopy. The similarities and differences between one-step and two-step quenching and partitioning processes were discussed. The results reveal the conditions of isothermal bainitic transformation when subjected to different Q&P heat treatment scenarios and justify the relative importance of these conditions in determining the final microstructure with respect to the industrial practices of hot rolled and continuous annealing processes. The bainitic transformation process window obtained by this work could provide direct guidelines in optimizing the compositions and heat treatment parameters of Q&P steels.

Understanding mechanisms of the nucleation and transformation sequence is of critical importance to control temperature-induced martensitic transformations and hence retained austenite. In the present work, quasi-in-situ Electron Backscattering Diffraction and Transmission Electron Microscopy were employed to investigate the nucleation and transition in a prototype austenitic stainless steel. Detailed evidence was provided, that nucleation could occur on new defects generated during cryogenic treatment, and not just on pre-existing defects. Moreover, transformation follows both austenite → e-martensite → α′-martensite and austenite → α′-martensite routes, their associated mechanisms, and crystallographic characteristics were analysed. These new insights could provide further guidelines for the optimization and modelling of martensitic transformation.

One of the important requirements for the application of reduced activation ferritic/martensitic steel is to retain proper mechanical properties in irradiation and high temperature conditions. In order to simulate the impact toughness with the effect of temperature and irradiation, a simulation model based on energy balance method consisted of crack initiation, plastic propagation and cleavage propagation stages was established. The effect of temperature on impact toughness was analyzed by the model and the trend of the simulation results was basicly consistent with the previous experimental results of CLAM steels. The load-displacement curve was simulated to express the low temperature ductile-brittle transition. The effect of grain size and inclusion was analyzed by the model, which was consistent with classical experiment results. The transgranular-intergranular transformation in brittle materials was also simulated. Keywords: Impact toughness, Simulation, Energy balance method, Temperature and irradiation

In this research a machine learning model for predicting the rotating bending fatigue strength and the high-throughput design of fatigue resistant steels is proposed. In this transfer prediction framework, machine learning models are first trained to estimate tensile properties (yield strength, tensile strength and elongation) on the basis of composition and critical process conditions. Then, based on the predicted tensile properties, transfer models are trained to estimate fatigue strength. The results are compared with those of a similar model not having such a transfer layer. The transfer prediction framework shows high accuracy for fatigue strength prediction with a remarkably high tolerance to limitations in the amount of calibration data available for training. By combining the transfer prediction framework with evolutionary algorithms, a robust high-throughput alloy design model is achieved requiring only tens of fatigue data points to get a decent reliability. The newly designed steel showed the predicted high fatigue strength. The method as presented here might also be applicable to other alloy design challenges in which only a limited database for the property to be optimized is available.

Evolution of deformation mechanisms and mechanical properties of Ti-3Al-5Mo-4.5 V alloy with different β phase stability have been systematically investigated. β phase stability alteration is achieved through quenching temperature variation from dual α + β field (700 °C) to single β field (880 °C). Tensile tests at ambient temperature show that apparent yield strength of the alloy experiences an abrupt decrease followed by a significant increase from 700 °C to 880 °C. Work hardening behavior is characterized by transition from the initial two-regime feature to the three-stage outlook. Concurrently, the maximum working hardening rate drops from 14000 MPa to 3000 MPa, which is concurrent with the shrinking volume fraction of primary α phase. Detailed discussion about the relationship between deformation mechanisms and β phase stability has been outlined.

The microstructure evolution and phase composition of an α + β titanium alloy, Ti-3Al-5Mo-4.5V (wt.%), have been investigated. Electron probe micro analysis (EPMA) quantitative results manifest that the stability of β phase decreases with increasing quenching temperature, which is influenced by the significant variation of β-stabilizing elements concentration. Detailed microstructure analysis shows that the β → ω phase transformation does occur when quenching at 750 °C and 800 °C. The ω-reflections change from incommensurate ω-spots (750 °C) to ideal ω-spots (800 °C) as the β stability of the alloy decreases. Further the decrease of β phase stability encourages the formation of athermal α′′ martensite, which has the following orientation relationships: [111]β//[110]α′′, [100]β//[100]α′′ and [-110]β//[00-1]α′′ with respect to the β matrix.

Metastable austenite plays a critical role in achieving improved combinations of high strength and high ductility/toughness in the design of advanced high-strength steels (AHSS). The thermal stability of metastable austenite determines the transformation characteristics of AHSS and thus primarily determines the microstructure evolution during complex processes, e.g., the quenching and partitioning process, to achieve the desirable microstructure. This study provides a review of the thermal stability of austenite and its influence on martensitic transformation from both experimental and theoretical modeling perspectives. From the experimental perspective, factors affecting the thermal stability are analyzed, the relative sensitivities are compared, and their corresponding mechanisms are discussed. From the theoretical modeling perspective, the most representative kinetic models that describe athermal and isothermal martensitic transformation are reviewed. The advantages, shortcomings, and applicability of each model are discussed. The systematic review of both experimental and theoretical aspects reveals critical factors in tailoring the stability of metastable austenite and, therefore, provides guidance for the design of advanced steels.