Recycling-oriented alloy design is a crucial part of material sustainability, as it reduces the need for raw material extraction and minimises environmental impact. This requires that scraps be reused or repurposed effectively, even when the scraps are co-mingled and have higher costs for further sorting and separation. In this work, we explore an alloy design concept by creating a compositionally flexible domain that can recycle multiple alloy grades and yet maintain relatively consistent properties across chemical variations. This is demonstrated through the Fe-Cr-Ni-Mn system to identify compositionally flexible austenitic stainless steels (CF-ASS) and accommodate the recycling of mixed austenitic stainless steel scraps. Alloys within the nominal composition spaces exhibit relatively consistent mechanical properties and corrosion resistance despite significant variations in different alloy compositions. We illustrate how we can utilise the compositionally flexible austenitic stainless steels to recycle mixed 200 and 300-series stainless steel and ferronickel scraps, demonstrating its practical viability. While this demonstration focuses on the stainless steel system, the underlying principles can be extended to other systems related to mixed scrap recycling.

A universal numerical model based on the particle size distribution (PSD) approach has been developed for the simulation of precipitation kinetics in multicomponent alloys during isothermal ageing. Nucleation was implemented utilizing the classical nucleation theory (CNT). Growth and coarsening were modeled by a single growth kinetics equation, which is constructed based on the interfacial diffusion flux balance and the capillarity effect. Only partial off-diagonal terms in the diffusion matrix (diffusion of individual components in the matrix) were taken into account in the calculations to minimize the computational cost while coupling with CALPHAD to extract thermodynamics equilibrium around the interface. A new feature of the model is the incorporation of a more realistic spatial site distribution via a Voronoi construction in the characteristic cell, for the purpose of modifying the diffusion distance. Computational predictions of the precipitate dimensions and the precipitation kinetics were compared with the atom probe tomography (APT) measurements on ternary Ni-Al-Cr alloys isothermally aged at 873 K. It is found that the temporal evolution of the dimensions and composition of the precipitates is well captured, as is the dependence on changes in the alloy composition. The new modification with Voronoi construction demonstrates that the overall precipitation kinetics depends on the density and the spatial site distribution of precipitates. The ability to handle sophisticated alloy chemistries by quantitative equations, the compositional sensitivity of microstructural characteristics emerging from the simulation results, and the ability to visualize the spatial distribution of precipitates make the work very promising for multicomponent alloy design and optimization.

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.

Herein, the effect of Nb content on the phase transformation kinetics, microstructure, and mechanical properties of hot-rolled quenching and partitioning (Q&P) steel is investigated. The characteristics of three C–Mn–Si–Ti steels (0.18C, 2.0Si, 2.6Mn, and 0.015Ti) containing 0, 0.027, or 0.061 wt% Nb are compared. Results reveal that grain boundary pinning by precipitates and Nb solute drag effects refine the austenite grain size during the hot-rolling process; the microstructural refinement is carried over to the final microstructure subjected to the Q&P treatment. The remaining supersaturated Nb suppresses the bainite formation and decreases the final bainite fraction formed in the Q&P process. The microstructural evolution leads to an increase in the ultimate tensile strength (UTS) of the steel containing 0.027 wt% Nb from 1169 to 1228 MPa, while keeping the total elongation at 18%. When the Nb content is increased to 0.061 wt%, the UTS of the steel increases to 1313 MPa, but the elongation at break drops to 16%. The effect is due to the carbon consumption by the Nb precipitates, which causes a decrease in the stability of the retained austenite and reduces the strain hardening at high strain levels.

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.

An experimental investigation was conducted to study the fretting wear behavior of low alloyed construction steel in the tempered fully martensitic state. The resulting damage mechanism and the resistance to fretting wear of martensitic steels subjected to different tempering temperature was evaluated and compared with the virgin (un-tempered) martensitic steel under the different loading conditions. The results show that the friction coefficient increases with the increase of the tempering temperature for all the applied loads. The fretting wear resistance mainly depends on the tempering temperature. Compared to the virgin (un-tempered) full martensite, most of the tempered martensites have better fretting wear resistance, in which the tempered martensitic (TM) steel of 200° due to a good balance of strength and ductility has a super fretting wear resistance for all loading conditions. In addition, the correlation of fretting wear resistance with the initial hardness was discussed.

The high performance of Ni single crystal superalloys during high temperature low stress creep service, is intrinsically determined by the combined effects of microstructural evolution and the dislocation behaviour. In the field of the evolution of dislocation network, two main recovery mechanism based on dislocation migration dominate the process. One is superdislocations shearing into γ’ rafts through a two-superpartials-assisted approach. Another is the compact dislocations migrating along γ/γ′ interface. These two mechanisms are similarly climb-rate-controlled process. In this work, a model for the minimum creep rate based on thermodynamic and kinetic calculations and using an existing detailed dislocation dynamics model has been built by taking the dislocation migration behaviours as well as the rafted microstructure into consideration, which can well reproduce the ([100] tensile) creep properties of existing Ni superalloy grades, without the need to make the dislocation parameter values composition dependent.

In this work, we combine a generic alloy-by-design model with a novel concept, the nucleation barrier for the formation of Laves phase to fill the creep cavities, in order to develop multi-component creep resistant steels with kinetically tuned self-healing behaviour. In the model the high-temperature long-term strength is estimated by integrating precipitation strengthening due to M23C6 carbides and solid solution strengthening, while the optimized compositional solutions are determined by employing the coupled thermodynamic and kinetic principles. W-containing Laves phase herein is selected as the self-healing agent to autonomously fill the grain boundary cavities, so as to prolong the creep lifetime. To achieve the effective healing reaction, the nucleation time for Laves precipitates are expected to coincide simultaneously with which creep cavities start to form or reach a healable size. Using experimental data from literature, an empirical relationship to estimate the incubation time for Laves phase formation has been constructed, from which the thermodynamic driving force for onset of precipitation as a function of temperature and intended precipitate nucleation time was derived. Three sample alloys have been selected among the desirable solutions, which are predicted to have the same strength but widely different Laves phase nucleation times. The calculations are also performed for different use temperatures to explore the compatibility between high temperature strength and timely cavity filling behaviour. In its current form the model is not expected to yield the truly optimal composition but to demonstrate how the kinetics of the healing reaction can affect the predicted optimal alloy compositions.

In order to obtain high-quality superalloy castings, the wettability and interactions between superalloy melts with various Y contents and SiO₂-based ceramic cores were investigated at 1823 K. The results indicated that the wettability and interface reactions were affected by the content of Y in the alloy. For the alloys with Y content less than 0.011 wt%, no Y-oxide was found at the interface, but HfO₂, Al₂O₃ and ZrO₂ phases were formed, and the wetting angle dropped slightly. However, different Y-oxides precipitated at the alloy-ceramic interface for the alloys with Y content more than 0.017 wt%, and the wetting angle dropped sharply. When the content of Y was 0.017 and 0.025 wt%, Al₂O₃, Y₃Al₂(AlO₄)₃, HfO₂ and ZrO₂ phases were formed at the interface. When the content of Y was 0.1 wt%, YAlO₃, Y₃Al₅O₁₂, Y₄Al₂O₉, HfO₂ and ZrO₂ phases were formed. The formation of different reaction products was mainly caused by the change of Y activity (a_Y) in the alloy. The reaction between Y and SiO₂ could improve the wettability of the system apparently.

Systematic experimental observations concerning the response of microstructural features on fretting wear of steel grades were conducted. Five steel grades with different typical microstructural features were selected. The effect of various microstructures on fretting wear behaviour and the resulting wear mechanism were evaluated. Results show that microstructural features play a significant effect on fretting wear resistance depending on the applied loads. The DP and Q&P steel having low hardness show comparable or even better fretting wear resistance than the FM steel of the highest hardness regardless of loads, which are all better than TWIP and IF steel. The result suggests that the combination of soft ferrite and hard martensite with/without retained austenite having low hardness displays superior fretting wear resistance.

With the development of the materials genome philosophy and data mining methodologies, machine learning (ML) has been widely applied for discovering new materials in various systems including high-end steels with improved performance. Although recently, some attempts have been made to incorporate physical features in the ML process, its effects have not been demonstrated and systematically analysed nor experimentally validated with prototype alloys. To address this issue, a physical metallurgy (PM) -guided ML model was developed, wherein intermediate parameters were generated based on original inputs and PM principles, e.g., equilibrium volume fraction (V_f) and driving force (D_f) for precipitation, and these were added to the original dataset vectors as extra dimensions to participate in and guide the ML process. As a result, the ML process becomes more robust when dealing with small datasets by improving the data quality and enriching data information. Therefore, a new material design method is proposed combining PM-guided ML regression, ML classifier and a genetic algorithm (GA). The model was successfully applied to the design of advanced ultrahigh-strength stainless steels using only a small database extracted from the literature. The proposed prototype alloy with a leaner chemistry but better mechanical properties has been produced experimentally and an excellent agreement was obtained for the predicted optimal parameter settings and the final properties. In addition, the present work also clearly demonstrated that implementation of PM parameters can improve the design accuracy and efficiency by eliminating intermediate solutions not obeying PM principles in the ML process. Furthermore, various important factors influencing the generalizability of the ML model are discussed in detail.

Controlling the kinetics of austenite decomposition by controlled partitioning of alloying elements, in particular carbon and manganese, is the key factor for optimizing the microstructures of advanced high-strength steels. In this study, a systematic set of computational and experimental cyclic partial phase transformations in low to medium manganese steels revealed a critical manganese concentration range of 1.5–2.5 mass% at which designated manganese partitioning at moving austenite–ferrite interfaces can be used to locally increase the effective Mn concentration and temporarily suspend further transformation during subsequent cooling. Most interestingly, this critical concentration only becomes visible in cases of reversed partial transformations in the intercritical regime and is un-noticeable in continuous cooling or conventional isothermal treatments.

The degradation of creep resistance in Nickel-based single crystal superalloys is essentially ascribed to their microstructure evolution. Yet there is a lack of work that manages to simulate the effect of alloying element concentrations on microstructure degradation. In this research, a computational model is developed to connect the rafting kinetics of Ni superalloys with their chemical composition, by combining thermodynamics calculation and an energy-based microstructure model. The isotropic coarsening rate and γ/γ′ misfit stresses have been selected as composition related parameter, and the effect of service temperature, time and applied stress are also taken into consideration to simulate the evolutions of microstructure parameters during creep process. The different generations of commercial Ni superalloys are selected and their chemical compositions are calculated based on this model. The simulated microstructure parameters are validated by the results from experimental results and the existing analytical model. The capability of the model in predicting the microstructure characteristics may provide instructional thought in developing a novel computational guided design approach in Ni superalloys.

In this study, the long term creep strength behavior of commercial heat resistant martensitic/ferritic steels with Cr levels ranging from 1 to 15 wt% is analyzed by linking their computed equilibrium compositions to their creep properties. At lower Cr levels, the calculated strength due to precipitation hardening agrees well with the experimental results. At high chromium levels and longer exposure times, an accelerated strength loss due to the formation of Z-phase precipitates has been reported. The accelerated strength loss is computationally analyzed and a correlation between accelerated strength loss and Z-phase formation is confirmed. A study is made to explore the option of adjusting the chemical composition of existing high-chromium steels to reduce the driving force for Z-phase formation. However, no proper composition ranges are found which combine a high Cr concentration with a significantly lower driving force for Z-phase formation.

Predicting the effect of alloying elements on the degree of incomplete austenite to bainite transformation in low carbon steels is of great industrial importance. This study introduces an extended Gibbs energy balance model which makes use of an additive approach to calculate the coupled effect of Mn, Si and Mo on the fraction of bainitic ferrite after the incomplete transformation in multicomponent steels. The model predicts significant effects of Mn and Mo and the negligible effect of Si levels on the fraction of bainitic ferrite. This is attributed to the high value of dissipation of Gibbs energy caused by interfacial diffusion of Mn and Mo and low values caused by Si diffusion. The model predictions for quaternary Fe–C–Mn–Si system are comparable with the experimentally measured values of bainite fraction. For the Fe–C–Mn–Mo system, the agreement is less accurate and the accuracy decreases with increasing Mo content, which is attributed a substantial carbide formation but interaction effects between Mn and Mo or a temperature dependent binding energy cannot be ruled out.

In the present work, three different scratch tests are compared on their ability to rank the abrasion resistance of low alloy steels for industrial applications where the abrasion play a key role, e.g., in earthmoving, agricultural and mining equipment. The first test involves single pass scratching of pristine surfaces with a relatively large rigid indenter. The second test involves multi-pass scratching along a fixed track using the same large indenter. The third test involves the creation of a multi-pass scratch track using the same large indenter followed by final scratching of the abrasion track with a sharp indenter, i.e., Multi-Pass Dual Indenter (MPDI) scratch test. The three test protocols activate different abrasion mechanisms. Five low alloy construction steel grades with different strain hardening capabilities, i.e., Interstitial-free Ferritic steel (IF steel), Fully Martensitic steel (FM steel), Dual Phase steel (DP steel), Quench Partitioning steel (Q&P steel) and TWining Induced Plasticity steel (TWIP steel), are used. The results show that for both single (large) indenter scratch test protocols, the scratch depths always increase with indenter load and the failure mechanism is pure ploughing except for the IF steel due to the nature of softness. While for the dual-indenter scratch test, the scratch depth is a more complex function of the load applied during creation of the work hardened surface layer. The conventional scratch test protocols cannot well reveal micro-cracks and defect in (sub) surface and cannot well reflect the real response of strain hardening of steels. However, the MPDI test can distinguish between different steels with different initial hardness and different strain hardening behaviour and reveal the damage and, equally important, can indicate how average loading conditions may affect the relative abrasion resistance of construction steels during steady state conditions.

The degradation of creep resistance in Ni-based single-crystal superalloys is essentially ascribed to their microstructural evolution. Yet there is a lack of work that manages to predict (even qualitatively) the effect of alloying element concentrations on the rate of microstructural degradation. In this research, a computational model is presented to connect the rafting kinetics of Ni superalloys to their chemical composition by combining thermodynamics calculation and a modified microstructural model. To simulate the evolution of key microstructural parameters during creep, the isotropic coarsening rate and γ/γ′ misfit stress are defined as composition-related parameters, and the effect of service temperature, time, and applied stress are taken into consideration. Two commercial superalloys, for which the kinetics of the rafting process are selected as the reference alloys, and the corresponding microstructural parameters are simulated and compared with experimental observations reported in the literature. The results confirm that our physical model not requiring any fitting parameters manages to predict (semiquantitatively) the microstructural parameters for different service conditions, as well as the effects of alloying element concentrations. The model can contribute to the computational design of new Ni-based superalloys.

We report results of a large computational 'alloy by design' study, in which the 'chemical composition-mechanical strength' space is explored for austenitic, ferritic and martensitic creep resistant steels. The approach used allows simultaneously optimization of alloy composition and processing parameters based on the integration of thermodynamic, thermo-kinetics and a genetic algorithm optimization route. The nature of the optimisation depends on both the intended matrix (ferritic, martensitic or austenitic) and the desired precipitation family. The models are validated by analysing reported strengths of existing steels. All newly designed alloys are predicted to outperform existing high end reference grades.

An experimental investigation of the scratch and abrasive wear behaviour of a lean C-Mn construction steel in its tempered fully martensitic (TM) state is presented. The scratch resistance and the corresponding failure mechanisms as a function of the tempering temperature (200-500 °C) were evaluated using a multi-pass dual-indenter (MPDI) scratch test applying different loading conditions. Results show that the scratch resistance depends not only on the tempering temperature, but also on the load applied during scratching. The optimal tempering temperature depends on the applied load. For both low and high loading conditions, the dual phase (ferrite-martensite) variant with an optimised martensite volume fraction and morphology yields an even better combination of scratch/abrasion resistance and hardness. The scratch resistance at different loading conditions is linked to the strength coefficient K in the Hollomon equation (σ=Kεⁿ). The scratch behaviour in the MPDI scratch test at a low load correlates quite well with the standard ASTM G65 multi-particle abrasion test.