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High Co–Ni secondary hardening steels are valuable in the field of aviation materials. With the increasing demand of lightweight, further enhancing its strength has received widespread attention. In this study, instead of using only M2C carbides as the reinforcements in the traditional high Co–Ni secondary hardening steels, nano-size NiAl phases were introduced to provide additional strength. To obtain the collaborative precipitation of these two reinforcements, systematically thermodynamic calculation, including the driving force, coarsening rate, etc., was made to quantitatively tailor the composition and the two-step aging parameters. The calculation results showed that adding 1 % Al into the standard composition of M54 is a plan with high potential and a second aging process at 450 °C should be added to promote NiAl precipitation. Based on the experimental validation, the target microstructure formed as the design. With the dual-phase strengthening of NiAl particles and M2C carbides, the yield and tensile strength are greater than 2.3 and 2.7 GPa, respectively, which increases the strength of M54 steel by >30 %, retaining sufficient elongation and relatively low cost. This new designed alloy increased the strength–ductility balance of high Co–Ni secondary hardening steels and aviation maraging steels from ∼12.5 to 13.5 GPa%.

Abstract Enhancing the interpretability of machine learning methods for predicting material properties is a key, yet complex topic in materials science. This study proposes an interpretable convolutional neural network (CNN) to establish the relationship between the microstructural evolution and mechanical properties of non‐uniform and nonlinear multisystem dual‐phase steel materials and achieve an inverse analysis of the elastic‐plastic mechanism. This study demonstrates that the developed CNN model achieves an accuracy of 94% in predicting the stress‐strain curves of dual‐phase steel microstructures with different compositions and processes, with the mean absolute error not exceeding 50 MPa, representing merely 5.26% of the average tensile strength of dual‐phase steels in the dataset. The reverse visualization results of the CNN model indicate that, during tensile deformation, the grain boundaries maintain deformation coordination within the grains by impeding dislocation slip. This results in a significant stress concentration at the grain boundaries, with stresses at the boundaries being higher than those borne by the martensitic phase and minimal stresses in the ferrite phase. Moreover, compared with traditional crystal plasticity models, the CNN model exhibits a substantial improvement in computational efficiency. This method provides a generic plan for improving the interpretability of machine learning methods for predicting material properties and can be easily applied to other alloy systems.

The Co40Cr20Ni30Al5Ti5 multi-principal element alloy (MPEA) with heterogeneous grain structure (HGS) and nano L12 precipitates is successfully designed by cold rolling and proper heat treatment. The HGS alloy exhibits excellent corrosion resistance and passivation performance, originated from high-density grain boundaries in fine grains and abundant defects in deformed grains, which encourages surface to form a thicker and more efficient passive layer to inhibit the initial pitting corrosion. This finding indicates that the developed MPEA with HGS can possess an excellent combination of corrosion resistance and mechanical properties, providing highly positive factors for future applications.

The effect of solutes, Co, Cu, Cr, Mn, Mo, Nb, Ni, Ta, Ti, V and Zr, in steel on the penetration properties of Zn and stability of surface or matrix were explored theoretically using density functional theory method, and the role of solutes in the Zn-coated surface and Zn-induced transgranular fracture performance was considered. The effect of solution on α-Fe are more obvious than that on γ-Fe, they significantly change surface energy value and the order of stability of low-index surface in α-Fe. The segregation energies of solutes were calculated and concluded that most of selected elements possibly segregate to the surface from the matrix. The study about Zn adsorption properties found that Zn atoms is energetically favorable to absorb at the (001) surface layer in α-Fe, and the segregation of Zr, V, Nb and Ta are helpful to the process of Zn-coating and increase the stability of Zn-coated surface. In addition, the solutes in the matrix increase the difficulty of transgranular fracture. Although the surrounding of solutes is the preference site for cracking occurring and propagation, alloying is found to lead to a general increase in the cleavage energy of α-Fe during Zn penetration, suggesting a reduction in the tendency for transgranular fracture. Therefore, it is feasible that the addition of Zr, V, Nb and Ta as alloy element in steel to improve the properties of Zn-coating and inhibit the occurrence of Zn-inducing transgranular cracking. Our results are helpful to understand and design AHSSs inhibiting LME susceptibility.

Femtosecond laser drilling is extensively used to create film-cooling holes in aero-engine turbine blade processing. Investigating and exploring the impact of laser processing parameters on achieving high-quality holes is crucial. The traditional trial-and-error approach, which relies on experiments, is time-consuming and has limited optimization capabilities for drilling holes. To address this issue, this paper proposes a process design method using machine learning and a genetic algorithm. A dataset of percussion drilling using a femtosecond laser was primarily established to train the models. An optimal method for building a prediction model was determined by comparing and analyzing different machine learning algorithms. Subsequently, the Gaussian support vector regression model and genetic algorithm were combined to optimize the taper and material removal rate within and outside the original data ranges. Ultimately, comprehensive optimization of drilling quality and efficiency was achieved relative to the original data. The proposed framework in this study offers a highly efficient and cost-effective solution for optimizing the femtosecond laser percussion drilling process.

Understanding the process of martensitic transformation and accurately predicting the fraction of retained austenite and martensite after athermal martensitic transformation are of great significance for optimizing the mechanical properties of various advanced high strength steels. This paper proposes an integral transformation model for the description of both the martensitic transformation temperature and its volume fraction considering both chemical driving force and non-chemical free energy. The proposed model was validated in both incomplete and complete martensitic transformation systems with different austenite grain sizes. Compared with the conventional Koistinen and Marburger model, the integral transformation model can predict the Ms and Mf temperatures as well as the transformation fraction in an integrated manner and depict the relationship between martensite fraction and temperature as a more realistic S-shaped curve. This can be attributed to the fact that the integral transformation model is developed from a generic energy viewpoint and takes into account not only the chemical driving force but also the significant non-chemical free energy contribution.

To thoroughly explore the relationship between precipitation and corrosion in a precipitation-strengthened Al-4.47Zn-2.13Mg-1.20Cu (wt%) wrought alloy plate after a nonisothermal aging (NIA) treatment, specifically, a heating-aging treatment (HAT), electrochemical testing and transmission electron microscopy (TEM) were conducted in the present work. The H(5) process exhibited excellent corrosion resistance with a low corrosion current density (icorr) and a high corrosion potential (Ecorr) compared to those for the T6 treatment. After the HAT, the precipitates at the grain boundaries (GBs) gradually became intermittent with a narrow precipitate-free zone (PFZ), effectively preventing GBs precipitates from anodic dissolution. The HAT enabled the rapid diffusion and enrichment of Cu at the GBs and reduced the potential difference between the grain boundary and matrix, thereby decreasing the intergranular corrosion (IGC) susceptibility. Moreover, the time spent for a HAT can be decreased by approximately 70% in comparison with that for a T6 treatment. The current results indicate that NIAs could improve the mechanical properties and production efficiency with a decreased energy consumption.

In order to make reasonable design for the improvement of comprehensive mechanical properties of RAFM steels, the design system with both machine learning and high-throughput optimization algorithm was established. As the basis of the design system, a dataset of RAFM steels was compiled from previous literatures. Then, feature engineering guided random forests regressors were trained by the dataset and NSGA II algorithm were used for the selection of the optimal solutions from the large-scale solution set with nine composition features and two treatment processing features. The selected optimal solutions by this design system showed prospective mechanical properties, which was also consistent with the physical metallurgy theory. This efficiency design mode could give the enlightenment for the design of other metal structural materials with the requirement of multi-properties. Keywords: Machine learning, High-throughput optimization, Mechanical property, RAFM steel

Creep-oriented alloy design is a long-standing interesting topic in the field of metal structural materials. However, the high cost for creep testing limits the development efficiency of new alloys using traditional trial-and-error methods. Additionally, the complex mechanism and influencing factors significantly increase the difficulty of physical modeling and simulation-guided design. In this study, an alloy design framework for creep life improvement is established, including two modules: creep life prediction and high-throughput design. For the first module, based on the creep dataset used in this research, the best machine learning model for creep life prediction was obtained by comparison of various machine learning strategies. By this way, the limitation of complex creep mechanism was partially removed and an accurate and generic model was established. For the second module, a genetic algorithm with a filter was used to obtain promising new alloy plans with optimal composition and processing parameters under specific creep conditions. After proving the rationality of this design framework and related design results, map design was used to guide the new alloy development for different creep conditions, and a new multifunctional alloy plan was proposed. This design system could provide preliminary guidance for high-efficiency alloy designs with complex target properties.

In recent decades, various previous research has established empirical formulae or thermodynamic models for martensite start temperature (Ms) prediction. However, most of this research has mainly considered the effect of composition and ignored complex microstructural factors, such as morphology, that significantly affect Ms. The main limitation is that most microstructures cannot be digitized into numerical data. In order to solve this problem, a convolutional neural network model that can use both composition information and microstructure images as input was established for Ms prediction in a medium-Mn steel system in this research. Firstly, the database was established through experimenting. Then, the model was built and trained with the database. Finally, the performance of the model was systematically evaluated based on comparison with other, traditional AI models. It was proven that the new model provided in this research is more rational and accurate because it considers both composition and microstructural factors. In addition, because of the use of microstructure images for data augmentation, the deep learning had a low risk of overfitting. When the deep-learning strategy is used to deal with data that contains both numerical and image data types, obtaining the value matrix that contains interaction information of both numerical and image data through data preprocessing is probably a better approach than direct linking of the numerical data vector to the fully connected layer.

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

The surface coating properties of turbine blades are highly dependent on the material’s surface roughness, and the femtosecond laser-induced micro-structure can provide a wide range of roughness with periodicity. However, precise control of femtosecond laser-induced micro-structure is difficult. In this paper, we extend the application of the two-temperature model and combine it with experiments to accurately reveal the evolution law of micro-structure depth at different single pulse energies, as well as the influence of two processing parameters on micro-structure, namely, defocusing amount and scanning speed. The findings of this study provide reliable theoretical guidance for fast and accurate control of material surface roughness and open new possibilities for coating properties.

Tailoring martensite transformation is critical for improving the mechanical properties of advanced steels. To provide preliminary guidance for the control of martensite transformation behaviour using external fields by computational simulation method, the phase-field method was used to calculate the morphology evolution, kinetics, and variant selection of the martensite transformation under different loading modes and magnetic field intensities. The incubation, transformation, and stable stages of the three variants based on the Bain strain group were investigated using different kinetic curves. These results clearly indicate that both uniaxial tension and compression can greatly promote the formation of martensite during the transformation stage and cause an obvious preferred variant selection. In contrast, the different variants have relatively balanced forms under shearing conditions. In addition, the magnetic field is a gentler way to form a state with balanced variants than other techniques such as shearing. Additionally, all these simulation results are consistent with classical martensitic transformation theory and thermodynamic mechanism, which proves the rationality of this research. The aim of the present study was to provide qualitative guidance for the selection of external fields for microstructural improvement in advanced steels.

Various models were established for deformation-induced martensite start temperature prediction over decades. However, most of them are empirical or considering limited factors. In this research, a dual mode database for medium Mn steels was established and a convolutional neural network model, which considered all composition, critical processing information and microstructure images as inputs, was built for Msσ prediction. By comprehensively considering composition, processing and microstructure factors, this model was more rational and much more accurate than traditional thermodynamic models. Also, by the full use of images information, this model has stronger ability to overcome overfitting compared with various traditional machine learning models. This framework provides inspiration for the similar data analysis issues with small sample datasets but different data modes in the field of materials science.

Reduced activation ferritic/martensitic (RAFM) steels are widely applied as structural materials in the nuclear industry. To investigate hydrogen's effect on RAFM steels' elastic properties and the mechanism of that effect, a procedure of first principles simulation combined with experiment was designed. Density functional theory models were established to simulate RAFM steels' elastic status before and after hydrogen's insertion. Also, experiment was designed to measure the Young's modulus of RAFM steel samples with and without hydrogen charging. Both simulation and experiment showed that the solubility of hydrogen in RAFM steels would decrease the Young's modulus. The effect of hydrogen on RAFM steels' Young's modulus was more significant in water-quenched steels than it was in tempering steels. This indicated that defects inside martensite, considered to be hydrogen traps, could decrease the cohesive energy of the matrix and lead to a decrease of the Young's modulus after hydrogen insertion.

One of the important requirements for the application of reduced-activation ferritic/martensitic (RAFM) steel is to retain proper mechanical properties under irradiation and high-temperature conditions. To simulate the yield strength and stress-strain curve of steels during high-temperature and irradiation conditions, a multiscale simulation method consisting of both microstructure and strengthening simulations was established. The simulation results of microstructure parameters were added to a superposition strengthening model, which consisted of constitutive models of different strengthening methods. Based on the simulation results, the strength contribution for different strengthening methods at both room temperature and high-temperature conditions was analyzed. The simulation results of the yield strength in irradiation and high-temperature conditions were mainly consistent with the experimental results. The optimal application field of this multiscale model was 9Cr series (7–9 wt.%Cr) RAFM steels in a condition characterized by 0.1–5 dpa (or 0 dpa) and a temperature range of 25–500°C.