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This study aims to focus on numerical simulation investigations of phase transformation during cooling of 55SiMnMo steel, which is commonly applied to improve mechanical properties.

A mathematical model based on the finite element method (FEM) and the phase transformation kinetics model has been proposed to predict microstructure changes during continuous cooling of 55SiMnMo steel. This model can be employed to analyze the variation of austenite, special upper bainite and lump-like composite structure with cooling time at different cooling rates.

According to the continuous cooling experiments, when the cooling rate is lower than 0.1°C/s, the special upper bainite is the only transformation product which decreases with increasing cooling rate; when the cooling rate is above 0.5°C/s, the transformation products include special upper bainite and lump-like composite structure. Meanwhile, the results of continuous cooling experiment verified the correctness of this finite element model.

This model has a great value for proper controlling of the cooling process which can improve the quality of hollow drill steel and increase the service life of the final product.

Shape memory alloys are a fascinating class of materials because they combine both structural and functional properties. These properties strongly depend on temperature. One consequence of this dependency yields the characteristic shape‐memory effect: shape memory alloys can recover processed reference configurations after significant plastic deformations simply upon a change of temperature. For real materials, such processes incorporate characteristic hysteresis. This paper aims at an understanding of these materials from an atomistic point of view.

2D molecular‐dynamics (MD) simulations describing a chain consisting of 32 linked Lennard‐Jones crystals are presented. The crystals consist of nested lattices of two atom species. Distinct lattice structures can be identified, interpreted as austenite and (variants of) martensite. Temperature and/or load‐induced phase transitions between these configurations are observed in MD simulations. Previously, the thermal equation of state of one isolated crystal was investigated and its phase stability was discussed in detail. In the multi‐crystal chain considered in the present paper, individual crystals contribute collectively to the thermo‐mechanical behavior of the assembly.

The paper presents the results of numerical experiments with this polycrystalline chain under strain‐, load‐ and/or temperature‐control. The results show that with the assumption of simple Lennard‐Jones potentials of interaction between atoms in individual crystals and linking these crystals allows to reproduce the features associated with the fascinating behavior of shape memory alloys, including pseudo‐plasticity, pseudo‐elasticity and the shape memory effect.

Owing to the special setup chosen, interfaces are missing between adjacent crystals in the chain assembly. The paper shows that in this situation load‐induced austenite/martensite transitions do not exhibit hysteresis in tension/compression cycles. This observation indirectly supports mesoscopic‐level work in the literature which explicitly introduces interface energy to model such hysteresis.

This paper aims to present a numeric modeling of the electromagnetic and thermal phenomena for the non‐ferromagnetic alloys casting equipment that control the solidification structure's formation.

The paper uses a numerical modeling program for the controlled solidification process modeling to allow visualization of the thermal field variation.

The probes cast using this method will have a uniform structure, without micro‐fissures. If the solid's part growth of the molten is controlled, we can stop the process that creates solidification centers, thus the material structure will be homogenous. A solution for this matter is given by the control of the solidification structure using forced heating of the liquid part with eddy currents and forced cooling of the solid part. The evolution of the area with eddy currents and the cooled one controls the phase transformation.

The paper presents a procedure to solve the coupled electromagnetic and thermal field problem in the casting systems techniques: solidification with controlled phase variation layer.

The purpose of this paper is to present the numerical modeling of the coupled electromagnetic and thermal fields for the controlled solidification process of non‐ferrous alloys.

The paper uses a numerical modeling program for the controlled solidification process modeling to allow visualization of the model and collection of the data. The data are then processed using spreadsheets in order to obtain graphic representations for the variations of the measured values.

The probes cast using this method will have a uniform structure, without micro‐fissures. If the solid part growth of the molten is controlled, the process can be stopped that creates solidification centers, thus the material structure will be homogenous. A solution for this matter is given by the control of the solidification structure using forced heating of the liquid part with eddy currents and forced cooling of the solid part. The evolution of the area with eddy currents and the cooled one controls the phase transformation.

It is presented here a computation method for the thermal field diffusion and the results of the numerical modelling of the thermal and electromagnetic fields during casting process with controlled solidification layer for the non‐ferrous alloys.

This paper aims to develop efficient and simple models for thermal distribution, melt pool dimensions and controlled phase change in the laser additive manufacturing (AM) of bulk and powder particles ceramic materials.

This paper proposes new analytical models for the AM of bulk and powder bed ceramic materials. A volumetric moving heat source, along with the complete melting of bulk and powder particle materials, is taken into account. Different values of laser absorption coefficient in solid and liquid states have been used to investigate the phase transformation. Furthermore, the pores and voids dimensions are also included in the modeling. Theoretical predictions have been compared with the experimental analyses and finite element simulations in laser to silicon nitride and laser to alumina interaction. The analysis focuses on the impact of laser power and scanning speed on the melt pool width and depth evolution into the bulk substrate and powder bed.

This study shows that the powder particles exhibit a higher thermal distribution value than the bulk substrate because of voids in the powder layer. The laser beam experiences multiple reflections in the presence of porosity/voids, thus increasing the surface absorption coefficient, which becomes relevant with the increment in the pore/void dimension. A direct relationship has been found between the laser power and melt pool dimensions, while the scanning speed displayed an inverse relationship for the melt pool width and length. Larger melt dimensions were inferred in the case of laser–powder particle interaction compared with laser–bulk substrate interaction. A close correlation was found between the analytical simulations, experimental investigations and numerical simulation results within the range of 4%–8%.

This paper fulfills an identified need to develop efficient and simplified models for ceramics laser AM by taking into account different laser absorption coefficients in solid and liquid form, voids and pores dimensions and controlled phase transformation to avoid vapors and plasma formation. The limitation of the finite element simulation model is that the solution is strongly dependent on the mesh quality and accuracy directly linked to the computation efficiency and time. A finer mesh requires a longer computing time than a coarse mesh. Finite element simulations require, however, specialized skills.

The premise of this research is that the coupling of reinforcement learning algorithms and computational dynamics can be used to design efficient control strategies and to improve the cooling of hot components by quenching, a process that is classically carried out based on professional experience and trial-error methods. Feasibility and relevance are assessed on various 2-D numerical experiments involving boiling problems simulated by a phase change model. The purpose of this study is then to integrate reinforcement learning with boiling modeling involving phase change to optimize the cooling process during quenching.

The proposed approach couples two state-of-the-art in-house models: a single-step proximal policy optimization (PPO) deep reinforcement learning (DRL) algorithm (for data-driven selection of control parameters) and an in-house stabilized finite elements environment combining variational multi-scale (VMS) modeling of the governing equations, immerse volume method and multi-component anisotropic mesh adaptation (to compute the numerical reward used by the DRL agent to learn), that simulates boiling after a phase change model formulated after pseudo-compressible Navier–Stokes and heat equations.

Relevance of the proposed methodology is illustrated by controlling natural convection in a closed cavity with aspect ratio 4:1, for which DRL alleviates the flow-induced enhancement of heat transfer by approximately 20%. Regarding quenching applications, the DRL algorithm finds optimal insertion angles that adequately homogenize the temperature distribution in both simple and complex 2-D workpiece geometries, and improve over simpler trial-and-error strategies classically used in the quenching industry.

To the best of the authors’ knowledge, this constitutes the first attempt to achieve DRL-based control of complex heat and mass transfer processes involving boiling. The obtained results have important implications for the quenching cooling flows widely used to achieve the desired microstructure and material properties of steel, and for which differential cooling in various zones of the quenched component will yield irregular residual stresses that can affect the serviceability of critical machinery in sensitive industries.

This paper aims to present a broad review of near-a titanium alloys for high-temperature applications.

Following a brief introduction of titanium (Ti) alloys, this paper considers the near-α group of Ti alloys, which are the most popular high-temperature Ti alloys developed for a high-temperature application, particularly in compressor disc and blades in aero-engines. The paper is relied on literature within the past decade to discuss phase stability and microstructural effect of alloying elements, plastic deformation and reinforcements used in the development of these alloys.

The near-a Ti alloys show high potential for high-temperature applications, and many researchers have explored the incorporation of TiC, TiB SiC, Y₂O₃, La₂O₃ and Al₂O₃ reinforcements for improved mechanical properties. Rolling, extrusion, forging and some severe plastic deformation (SPD) techniques, as well as heat treatment methods, have also been explored extensively. There is, however, a paucity of information on SiC, Y₂O₃ and carbon nanotube reinforcements and their combinations for improved mechanical properties. Information on some SPD techniques such as cyclic extrusion compression, multiaxial compression/forging and repeated corrugation and straightening for this class of alloys is also limited.

This paper provides a topical, technical insight into developments in near-a Ti alloys using literature from within the past decade. It also outlines the future developments of this class of Ti alloys.

Coil shape quality is the external representation of strip product quality, and it is also a direct reflection of strip production process level. This paper aims to predict the coil shape results in advance based on the real-time data through the designed algorithm.

Aiming at the strip production scale and coil shape application requirements, this paper proposes a strip coil shape defects prediction algorithm based on Siamese semi-supervised denoising auto-encoder (DAE)-convolutional neural networks. The prediction algorithm first reconstructs the information eigenvectors using DAE, then combines the convolutional neural networks and skip connection to further process the eigenvectors and finally compares the eigenvectors with the full connect neural network and predicts the strip coil shape condition.

The performance of the model is further verified by using the coil shape data of a steel mill, and the results show that the overall prediction accuracy, recall rate and F-measure of the model are significantly better than other commonly used classification models, with each index exceeding 88%. In addition, the prediction results of the model for different steel grades strip coil shape are also very stable, and the model has strong generalization ability.

This research provides technical support for the adjustment and optimization of strip coil shape process based on the data-driven level, which helps to improve the production quality and intelligence level of hot strip continuous rolling.

REAL World Graphics has formally launched its new range of high preformance real time 3D image generators aimed at the visual simulation and animation markets. Known as Super Reality, these modular and scaleble systems use parallel arrays of Intel i860 RISC microprocessors running at 40MHz to achieve remarkable price/ performance characteristics. Super Reality offers such features as photo‐texturing, antialiasing and Gouraud shading at a cost which puts graphics supercomputing within reach of those who previously could not afford it. All systems and graphics functions are programmed in ‘C’ making Super Reality a totally flexible product that could be easily applied to other applications such as general array processing.

Some functional properties of engineering materials, i.e. physical, mechanical and thermal ones, depend directly on the microstructure, which is a result of processes occurring in the material during the forming and thermomechanical processing. The proper microstructure can be obtained in many cases by the phase transformation. This phenomenon is one of the most important processes during hot forming and heat treatment. The purpose of this paper is to develop a new comprehensive hybrid model for modeling diffusion phase transformations. A problem has been divided into several tasks and is carried out on several stages. The purpose of this stage is a development of the structure of a hybrid model, development of an algorithm used in the diffusion module and one-dimensional heat flow and diffusion modeling. Generally, the processes of phase transformations are studied well enough but there are not many tools for their complex simulations. The problems of phase transformation simulation are related to the proper consideration of diffusion, movement of phase boundaries and kinetics of transformation. The proposed new model at the final stage of development will take into account the varying grain growth rate, different shape of growing grains and will allow for proper modeling of heat flow and carbon diffusion during the transformation in many processes, where heating, annealing and cooling can be considered (e.g. homogenizing and normalizing).

One of the most suitable methods for modeling of microstructure evolution during the phase transformation is cellular automata (CA), while lattice Boltzmann method (LBM) suits for modeling of diffusion and heat flow. Then, the proposed new hybrid model is based on CA and LBM methods and uses high performing parallel computations.

The first simulation results obtained for one-dimensional modeling confirm the correctness of interaction between LBM and CA in common numerical solution and the possibility of using these methods for modeling of phase transformations. The advantages of the LBM method can be used for the simulation of heat flow and diffusion during the transformation taking into account the results obtained from the simulations. LBM creates completely new possibilities for modeling of phase transformations in combination with CA.

The studies are focused on diffusion phase transformations in solid state in condition of low cooling rate (e.g. transformation of austenite into ferrite and pearlite) and during the heating and annealing (e.g. transformation of the ferrite-pearlite structure into austenite, the alignment of carbon concentration in austenite and growth of austenite grains) in carbon steels within a wide range of carbon content. The paper presents the comprehensive modeling system, which can operate with the technological processes with phase transformation during heating, annealing or cooling.

A brief review of the modeling of phase transformations and a description of the structure of a new CA and LBM hybrid model and its modules are presented in the paper. In the first stage of model implementation, the one-dimensional LBM model of diffusion and heat flow was developed. The examples of simulation results for several variants of modeling with different boundary conditions are shown.