Physical Metallurgy Principles Research Articles

Prediction and analysis of mechanical properties of hot-rolled strip steel based on an interpretable machine learning

Establishing a low-cost, high-precision predictive model for mechanical properties is crucial for enhancing the properties of steel. Due to the complexity of the steel production process, traditional mechanistic models struggle to efficiently and accurately describe the relationships among alloy elements, processes, and properties. In response to this, the paper introduces a high-precision framework for predicting and optimizing mechanical properties through feature engineering and machine learning models. Firstly, an effective dimension reduction of features is achieved through a weighted fusion heterogeneous feature selection strategy, thereby identifying the optimal model input features. Subsequently, an improved seagull optimization algorithm is utilized to optimize the hyperparameters of Extreme Gradient Boosting, further enhancing the predictive accuracy of the model. Moreover, based on the Shapley Additive Explanation method, a quantitative analysis of the model's predictive outcomes is conducted, elucidating the impacts of alloy elements and processes on mechanical properties, consistent with established principles of physical metallurgy. The proposed framework not only enables precise prediction of mechanical properties in steel but also provides theoretical guidance and technical support for process optimization design and the development of new steel grades.

Research on the Influence of Cold Drawing and Aging Heat Treatment on the Structure and Mechanical Properties of GH3625 Alloy.

With the development of the petroleum industry, the demand for materials for oilfield equipment is becoming increasingly stringent. The strength increase brought about by time strengthening is limited in meeting the needs of equipment development. The GH3625 alloy with different strength levels can be obtained through cold deformation and heat treatment processes. A study should be carried out to further develop the potential mechanical properties of GH3625. In this study, the GH3625 alloy was cold drawn with different reductions in area (0-30%) and heat treated, and its mechanical properties were tested. The microstructure of the alloy during deformation and heat treatment was characterized by methods such as optical microscopy (OM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) based on the principles of physical metallurgy. The strength increase caused by dislocation strengthening was calculated from the dislocation density, tested by X-ray diffraction (XRD). The calculated value was compared to the measured value, elucidating the strengthening effect of cold deformation and heat treatment. The results showed that the yield strength and yield ratio of the cold-drawn alloy significantly reduced after aging at 650 °C and 760 °C. Heat treatment can make a cold-deformed material recover, ablate dislocations, and greatly reduce the dislocation density in the microstructure of the GH3625 alloy, which was the main factor in the decrease in yield strength. The work-hardening gradient of the cold-drawn material varied greatly with different reductions in area. When the reduction in area was small (10%), the hardness gradient was obvious. When it increased to 30%, the alloy was uniformly strengthened as the deformation was transmitted to the axis. This study can provide more mechanical performance options for GH3625 alloy structural components in the petrochemical industry.

Scandium Alloying of Aluminum Alloys

Basic physical metallurgy principles of alloying of aluminum alloys with scandium are considered and the fundamentals of the production of deformed semiproducts from these alloys are discussed. The discussion is based on the expected widening of the range of application of deformed semiproducts from scandium-alloyed aluminum alloys.

Compositional and microstructural optimization and mechanical-property enhancement of cast Ti alloys based on Ti-6Al-4V alloy

The effects of alloying additions on the microstructure and mechanical properties of cast Ti alloys based on Ti-6Al-4V were systematically investigated through a combination of thermodynamic calculations and experimental verifications. New cast titanium alloys with significantly improved properties were developed through combining thermodynamic calculations and physical metallurgy principles, which achieve an optimum combination of high strength and high ductility at both room and elevated temperatures. Our analysis indicates that the improved strength is due mainly to the α colony and lath size refinement induced by both macro- and micro-alloying of Cr, Fe, B and C elements, whereas the mechanisms for such property optimization include: (1) an increased volume fraction of ductile β phase in the cast state, (2) a refined α colony and lath size by the Cr, Fe, B and C alloying additions, and (3) an enhanced grain-boundary cohesion and refined grain size of prior-β grains induced by proper amount of B additions. The newly developed cast Ti alloys with substantially enhanced mechanical properties for engineering applications.

Design of Light-Weight High-Entropy Alloys

High-entropy alloys (HEAs) are a new class of solid-solution alloys that have attracted worldwide attention for their outstanding properties. Owing to the demand from transportation and defense industries, light-weight HEAs have also garnered widespread interest from scientists for use as potential structural materials. Great efforts have been made to study the phase-formation rules of HEAs to accelerate and refine the discovery process. In this paper, many proposed solid-solution phase-formation rules are assessed, based on a series of known and newly-designed light-weight HEAs. The results indicate that these empirical rules work for most compositions but also fail for several alloys. Light-weight HEAs often involve the additions of Al and/or Ti in great amounts, resulting in large negative enthalpies for forming solid-solution phases and/or intermetallic compounds. Accordingly, these empirical rules need to be modified with the new experimental data. In contrast, CALPHAD (acronym of the calculation of phase diagrams) method is demonstrated to be an effective approach to predict the phase formation in HEAs as a function of composition and temperature. Future perspectives on the design of light-weight HEAs are discussed in light of CALPHAD modeling and physical metallurgy principles.

Corrosion Resistant Plates for Pipes Operated in Sour Environments

Based on physical metallurgy principles, specialists of Severstal and I. P. Bardin Institute developed chemical composition and manufacture technology for microalloyed steel plates for sour service line pipes with minimum guaranteed tensile strength 510 MPa, produced by TMCP (thermo-mechanical controlled process). The key elements of newly developed technology were: low-carbon, low-manganese chemical composition with balanced microalloying; production of clean steel in melting shop; microstructure control during all stages of manufacture process.

Physical Metallurgy of High-Entropy Alloys

Two definitions of high-entropy alloys (HEAs), based on composition and entropy, are reviewed. Four core effects, i.e., high entropy, sluggish diffusion, severe lattice distortion, and cocktail effects, are mentioned to show the uniqueness of HEAs. The current state of physical metallurgy is discussed. As the compositions of HEAs are entirely different from that of conventional alloys, physical metallurgy principles might need to be modified for HEAs. The thermodynamics, kinetics, structure, and properties of HEAs are briefly discussed relating with the four core effects of HEAs. Among these, a severe lattice distortion effect is particularly emphasized because it exerts direct and indirect influences on many aspects of microstructure and properties. Because a constituent phase in HEAs can be regarded as a whole-solute matrix, every lattice site in the matrix has atomic-scale lattice distortion. In such a distorted lattice, point defects, line defects, and planar defects are different from those in conventional matrices in terms of atomic configuration, defect energy, and dynamic behavior. As a result, mechanical and physical properties are significantly influenced by such a distortion. Suitable mechanisms and theories correlating composition, microstructure, and properties for HEAs are required to be built in the future. Only these understandings make it possible to complete the physical metallurgy of the alloy world.

Metallographic Assessment of Al-12Si High-Pressure Die Casting Escalator Steps

A microstructural characterization study was performed on high-pressure die cast specimens extracted from escalator steps manufactured from an Al-12 wt.% Si alloy designed for structural applications. Black and white, color light optical imaging and scanning electron microscopy techniques were used to conduct the microstructural analysis. Most regions in the samples studied contained globular-rosette primary α-Al grains surrounded by an Al-Si eutectic aggregate, while primary dendritic α-Al grains were present in the surface layer. This dendritic microstructure was observed in the regions where the melt did not impinge directly on the die surface during cavity filling. Consequently, microstructures in the surface layer were nonuniform. Utilizing physical metallurgy principles, these results were analyzed in terms of the applied pressure and filling velocity during high-pressure die casting. The effects of these parameters on solidification at different locations of the casting are discussed.

Composition design of nanocrystalline bainitic steels by diffusionless solid reaction

NANOBAIN is the term used to refer to a new generation of advanced steels capable of producing by isothermal transformation at low homologous temperatures, T/Tm∼0.25 where Tm is the absolute melting temperature, a nanocrystalline microstructure, composed exclusively of two phases, thin plates of bainitic ferrite separated by C enriched austenite. Such alloys are exclusively designed on the basis of bainitic transformation theory and some physical metallurgy principles. In this work, by designing a new set of alloys capable of producing such microstructure, a further step toward the industrialization of NANOBAIN is taken. Some important industrial requirements, including circumventing the inclusion of expensive alloying elements and the need for faster transformations, are also considered. For all the alloys, the experimental results validate the design procedure and they illustrate that the NANOBAIN concept is a step closer to industrialization, probing that it is possible to obtain nanocristalline bainite in simpler alloy systems and in shorter times than those reported previously.

Cellular automaton simulation of hot deformation of TRIP steel

The recrystallization process of hot deformed austenite of TRIP steel was predicted using Cellular Automaton (CA) combined with the principles of physical metallurgy. A model was developed for prediction of the dynamic and static recrystallization microstructure evolution and properties of hot deformed austenite for TRIP steel. The theoretical modeling of recrystallization was based on dislocation density. The microstructure evolution of austenite recrystallization of TRIP steel (such as the grain shape and size, volume fraction, kinetics curve of recrystallization) was predicted both visually and quantitatively. The distribution and variation of the dislocation density and flow stress were also obtained, and the effects of silicon content on recrystallization for TRIP steel were analyzed. The CA calculation results were in good agreement with the measured ones.

Optimization of Stability of Retained Austenite in TRIP-Aided Steel Using Data-Driven Models and Multi-Objective Genetic Algorithms

The important parameters that determine the properties of transformation-induced-plasticity (TRIP) -aided steel are the amount of retained austenite phase present in its initial microstructure and its stability. A large value of carbon equivalent leads to a high amount of retained austenite in the initial microstructure of these steels at room temperature. Looking at it from another angle, a high value of carbon equivalent is undesirable, as it adversely affects the weldability. In this study, we have attempted to resolve this conflict by bringing in the notion of Pareto optimality. Through an evolutionary neural network that evolved through multi-objective genetic algorithms, data-driven models were constructed for both carbon equivalent and fraction transformed. The effect of individual variables on the extent of austenite transformation as inferred by the model was found to be consistent with the principles of physical metallurgy of TRIP-aided steel. Next, using a predator–prey genetic algorithm, a bi-objective optimization task was conducted for simultaneous minimization of carbon equivalent and the extent of transformation. The resulting Pareto frontier was carefully analyzed, and the transformation behavior of different TRIP-assisted steels was also predicted for different straining conditions. Further need for optimizing the heat-treatment schedule is highlighted through selective experimentation.

A Review of the Physical Metallurgy related to the Hot Press Forming of Advanced High Strength Steel

The automotive industry requirements for vehicle weight reduction, weight containment, improved part functionality and passenger safety have resulted in the increased use of steel grades with a fully martensitic microstructure. These steel grades are essential to improve the anti-intrusion resistance of automotive body parts and the related passenger safety during car collisions. Standard advanced high strength steel (AHSS) grades are notoriously difficult to be press formed; they are characterized by elastic springback, poor stretch flangeability and low hole expansion ratios. Hot press forming has therefore received much attention recently as an alternative technology to produce AHSS automotive parts. In this contribution, the physical metallurgy principles of the hot press forming process are reviewed. The effect of composition on CCT curves of standard CMnB hot press forming steels is discussed taking the deformation during press forming into account. Furthermore,the effect of the static strain ageing processes occurring during the paint baking cycle on the in-service mechanical properties of press hardened steel will be presented. The influence of temperate and strain rate on the flow stress during press forming and the final room temperature mechanical properties will be discuss ed. Moreover, the issues related to coatings on B-alloyed CMn hot press forming steel will be critically reviewed. In particular the combined effects of thermal cycle and deformation on the degradation of the Al-10%Si coating will be discussed in detail. Finally, the properties of both Al-based and Zn-based coating systems are compared, and the possibility of the formation of a diffusion barrier during press forming is discussed.

Overview of Pearlitic Rail Steel: Accelerated Cooling, Quenching, Microstructure, and Mechanical Properties

Abstract Railway networks form an integral part of the infrastructure development of a developing country with ever-increasing passenger and freight volume. Increase in train speed, pay load, reliability, and safety are the major thrust areas for railways requiring more stringent mechanical properties such as wear, deformation resistance, and fatigue life from the railway steel. Steel chemistry control and thermomechanical processing significantly affect final properties and performance of the railway steel. For example, for a given steel composition, a number of stable or metastable microstructures can be obtained by controlling heat treatment operations. Conventional rail steels primarily contain nearly eutectoid pearlitic microstructure, which is dependent on the criticality of the application. An overview of the physical metallurgy principles involved during the manufacturing of rail steel will be provided here. The primary focus of this review is thermal processing including quenching and accelerated cooling of the rail steel. In addition, other important aspects relating to design and production of rail steel are discussed, including: impact of steel chemistry on the phase diagram, effect of thermomechanical processing on microstructure, and influence of microstructure or residual stress on mechanical properties.

Global recrystallisation model of low carbon sheet steels with different cementite contents

This paper discusses the influence of Particle Stimulated Nucleation (PSN) on the overall process of nucleation and subsequent grain growth in low carbon (LC) steels and proposes a global recrystallisation kinetics model based on fundamental physical metallurgy principles. The model takes into account the cementite content, which depends on the nominal carbon content, and the second phase particle size. The deformation field around cementite particles and its influence on processes is analysed. Finally, experimental evidence is given to show the accuracy of the theoretical predictions.

Artificial Neural Network: Some Applications in Physical Metallurgy of Steels

Artificial neural networks are parameterized nonlinear models used for empirical regression and classification modelling. Their flexibility enables them to discover very complex relationships among large number of variables with complex interdependencies. Hence it is a very appropriate technique for developing predictive models in the short term. This article discusses the development of neural network models based on a Bayesian framework and some applications of the same. These examples include: (i) prediction of yield and tensile strengths of hot-rolled low-carbon ferrite-pearlite steel plates as a function of composition and rolling parameters, (ii) estimation of bainite plate thickness, (iii) estimation of retained austenite as a function of process parameters in Transformation Induced Plasticity aided (TRIP-aided) steels, and (iv) analysis of strain-induced transformation behavior of retained austenite during uniaxial tensile testing of TRIP-aided steels. While examples (i), (ii), and (iv) provide an overview of the work reported earlier, example (iii) is reported here in open literature for the first time. In all these four cases, it has been shown that the results are consistent with the established physical metallurgy principles.

Development of new 11%Cr heat resistant ferritic steels with enhanced creep resistance for steam power plants with operating steam temperatures up to 650 °C

The goal of developing new heat resistant 11%Cr ferritic–martensitic steels with sufficient creep and oxidation resistance up to 650 °C was pursued within a joint project following an alloying concept based on physical metallurgy principles. The highest creep strength combined with good oxidation resistance was achieved for a Ta-alloyed test melt (11 wt.% Cr, W, Co, Mo, V, 0.09 wt.% Ta, relatively high contents of C and B). The microstructural evolution during creep was investigated by transmission electron microscopy for the Ta-alloyed melt in comparison to a sister melt where Ta was exchanged for 0.04%Nb. It is proposed that fine particles of types MX and M 23C 6 (M: metallic element, X: interstitial elements) are the cause of the outstanding creep resistance of the Ta-alloyed melt.

Mapping the input–output relationship in HSLA steels through expert neural network

Modification of the architecture of the artificial neural network is done to accommodate the information available from the knowledge base in the field of materials science for thermomechanically processed HSLA steel. The complicated architectures of these networks are made to satisfy the well-understood physical metallurgy principles, which administer the property response to the combined actions of the compositional and process parameters. The networks developed have been found to give very good convergence during training. The number of epochs required to reach the targeted error was found less for these networks than the conventional networks.

MODELLING TMCP OF HSLA STEELS BY PETRI NEURAL NETWORK TECHNIQUE

The optimization of the architecture of the Petri neural network (PNN) model for thermomechanically controlled processed (TMCP) high strength low alloy (HSLA) steel is done through the comparison of test errors with training errors at different model complexities. The development of the PNN models on the basis of phenomenological relationships between the input and the output variables of TMCP steels has been done. Physical metallurgy principles which govern the property response to the combined actions of chemical composition and process parameters and describe the operative strengthening mechanisms are embedded in the Petri neural network in such a way that the metallurgical reasoning are duly regarded in TMCP steels in predictive activities. It has been found that the Petri neural network is a good tool for modelling the effect of alloy composition and process parameters on the mechanical properties of HSLA steels with certain limitations.L’optimisation de l’architecture du modèle de réseau neuronal de Pétri (PNN) de l’acier haute résistance faiblement allié (HSLA), traité par contrôle thermomécanique (TMCP), est faite par comparaison des erreurs d’évaluation par rapport aux erreurs d’entraînement à différents niveaux de complexité du modèle. On a effectué le développement des modèles PNN en se basant sur les relations phénoménologiques entre les variables d’entrée et de sortie des aciers TMCP. Les principes de métallurgie physique qui gouvernent la réponse de la propriété aux actions combinées de la composition chimique et des paramètres de traitement et qui décrivent les mécanismes fonctionnels de renforcement sont inclus dans le réseau neuronal de Pétri de manière telle que le raisonnement métallurgique est proprement considéré dans les aciers TMCP dans les activités prédictives. On a trouvé que le réseau neuronal de Pétri était un bon outil pour la modélisation de l’effet de la composition de l’alliage et des paramètres de traitement sur les propriétés mécaniques des aciers HSLA, avec certaines limitations.

Modeling the Effect of Copper on Hardness of Microalloyed Dual Phase Steel through Neural Network and Neuro-fuzzy Systems

The effects of copper along with some microalloying elements and the processing parameters are modeled with artificial neural network and adaptive neuro-fuzzy inference system. Both the tools are found to be useful for modeling the effect of copper and other alloying additions along with the processing parameters on the hardness of microalloyed DP steels. In case of the neural network, the proposed committee of models is found to be effective in handling the problem of mapping the input-output relation in these steels. The increase in the number of rules is found to improve the predictability of the neuro-fuzzy inference system. The predictions made by both the models substantiate the knowledge of physical metallurgy principles.

Effect of minor alloying additions on glass formation in bulk metallic glasses

This paper provides a comprehensive review of recent work on the effect of minor alloying additions on glass formation in bulk metallic glasses. Recently, minor alloying addition or microalloying technology has shown to have dramatic effects on the glass formation and thermal stability of many bulk metallic glasses. This technology basically involves adding small amounts of alloying additions (usually, less 2 at%) to existing bulk metallic glasses for the purpose of further improving their glass forming ability. Experimental evidences indicate that alloying additions of small atoms with atomic radius <0.12 nm (such as B and Si) or large atoms with radius >0.16 nm (such as Y and Sc) are most effective in enhancing glass forming ability. The beneficial effects of these elements are discussed in terms of physical metallurgy principles.