Metallurgical Model Research Articles

Development and experimental validation of a thermo-metallurgical-mechanical model of the laser metal deposition (LMD) process

The current paper presents the development and experimental validation of a thermo-metallurgical-mechanical model for a multi-pass Laser Metal Deposition (LMD) process, with the objective of accurately predicting the resultant constituent phases and residual stresses. The thermal model is calibrated using temperature readings and is shown to accurately capture the transient temperature field and extent of the fusion zone associated with the multi-pass LMD process. The metallurgical model incorporates the kinetics of ongoing solid-state phase transformations (SSPTs) and tempering reactions. The predictions are validated via hardness measurements, demonstrating a very good agreement with the predictions. The developed mechanical model predicts the residual stress field, which is validated using X-ray diffraction measurements, and the comparison also shows a very good agreement between the predictions and measurements. The validated numerical model is then used to explore alternative LMD strategies showing that the choice of deposition strategy can significantly impact resultant constituent phases and residual stresses.

The road to carbon neutrality in the metallurgical industry: Hydrogen metallurgy processes represented by hydrogen-rich coke oven gas, short-process metallurgy of scrap and low-carbon policy

Abstract With the acceleration of global industrialization, the concentration of carbon dioxide is increasing in the atmosphere, and its negative impacts have seriously affected all walks of human life, so achieving carbon neutrality has become an urgent task for achieving sustainable development. As an important energy-intensive industry, the metallurgical industry occupies an important position in the global carbon-neutral agenda. In China, the metallurgical industry is actively researching and developing a new green metallurgical model of “replacing carbon with hydrogen”, exploring the feasibility of utilizing renewable energy sources to produce hydrogen from electrolysis to reduce iron ore, and at the same time utilizing hydrogen-rich coke oven gas to get rid of the over-reliance on coke; at the same time, the government’s policies provide support and incentives to elevate sustainable development and technological innovation in the metallurgical industry. support and incentives to elevate sustainable development and technological innovation in the metallurgical industry. Against this background, this paper describes the key initiatives taken by the metallurgical industry in the process of achieving carbon neutrality, including the metallurgy using hydrogen processes using hydrogen-rich coke oven gas as a source of reducing gas, short-process metallurgy of scrap, and technology that reduce emissions and save energy. Through case studies and policy analyses of new green metallurgy, this study demonstrates the potential and achievements of the metallurgical industry in achieving global carbon neutrality. It concludes with a call for the metallurgical industry to continue to strengthen innovation and work with governments and academia to pave the way toward carbon neutrality. Through new metallurgical technologies, improved energy and resource efficiency, and sustainable development, the metallurgical industry will make a significant contribution to the goal of global carbon neutrality.

In situ HEXRD experimental study and prediction of microstructures and internal stresses during heat treatment of carburized and carbonitrided low-alloyed steels

Thermochemical surface treatments of steels, such as carburizing and carbonitriding are used to improve mechanical properties, especially resistance to fatigue. Carbonitriding can achieve excellent properties, but the origins remain to be understood, as it has been less studied than carburizing. Fundamental understanding has to be established regarding the formation of microstructures and internal stresses as well as their couplings, during the cooling which follows the enrichment treatment.For this purpose, an original experimental method is introduced to follow in situ by High-Energy X-Ray Diffraction the phase transformation kinetics as well as the evolutions of the internal stresses during cooling, inside laboratory scale, 3 mm-thick samples with C and/or N composition gradients. The usual trends are confirmed regarding carburizing: the carbon-enriched case is the last to transform and the surface ends up with compression residual stresses. Conversely, for carbonitriding, unusual evolutions of microstructure and internal stresses are observed. The presence of nitrogen reduces the hardenability in the enriched case. This modifies the chronology of the phase transformations and this leads to tensile residual stresses at the surface.A coupled thermal, mechanical and metallurgical model predicting the phase transformation kinetics and the evolutions of internal stresses is set up. It achieves quantitative predictions and it shows that, in the studied cases, the phase transformation plasticity strains are the main origin of the residual stresses.

An ANN Hardness Prediction Tool Based on a Finite Element Implementation of a Thermal–Metallurgical Model for Mild Steel Produced by WAAM

WAAM has emerged as a promising technique for manufacturing medium- and large-scale metal parts due to its high material deposition efficiency and automation level. However, its high heat accumulation and complex thermal evolution strongly affect the resulting microstructures and mechanical properties. The heterogeneous and unpredictable nature of these properties hinder the widespread application of WAAM in the steel construction industry. In this study, an artificial neural network (ANN) hardness model is developed, based on a thermal–metallurgical model for mild steel. The objective is to establish non-linear relationships between the input process parameters and the desired output, i.e., hardness. The thermal–metallurgical model utilizes a well-distributed heat source model, a death-and-birth algorithm, and a metallurgical model to simulate the temperature field and to calculate the microstructure phase fraction. The temperature prediction errors at four thermocouple positions are mostly below 20%. Because of the limited experimental data, twenty-five simulation experiments are performed using the L25 orthogonal array based on the Taguchi method. The analysis of variance (ANOVA) reveals that the travel speed has the greatest impact on hardness. With the dataset from the thermal–metallurgical model, an ANN model to predict hardness is developed. A comparison to experimental data shows excellent performance and accuracy, with the Mean Absolute Percentage Error (MAPE) of ANN predictions within 10% of the targeted hardness.

Multi-physical simulation of an inductive heat treatment process on 50CrMo4 steel rods and validation by HEXRD cross-sectional measurements

A multi-physical finite element model for the induction hardening of a 50CrMo4 steel rod was implemented in Abaqus, including an electromagnetic, thermal, mechanical, and metallurgical model describing the individual phenomena taking place during the process. For induction heating, a linearization scheme for the nonlinear permeability of steel was implemented. The model was validated by recreating a previously published experiment with the model and using the calculated residual stress data as a comparison to the measured stresses. To ensure a valid comparison, the sample preparation process and measurement methodology were considered for the simulation and a good agreement between the measured and calculated stress distributions was obtained. Thus the distribution and time evolution of several quantities such as temperature, phase fractions and plastic strain can be examined to gain insight into the formation of residual stresses.

ACCELERATING ORE SINTERING MATHEMATICAL MODEL USING GPU

The study aims to enhance the efficiency and computational speed of the ore sintering model through the utilization of graphics processing units (GPUs). The purpose of this research is to address the growing demand for faster and more scalable simulations in the field of ore sintering, a crucial process in the production of iron and steel. Methodology involves the integration of parallel computing capabilities offered by GPUs into the existing ore sintering model. By leveraging the parallel processing power of GPUs, the computational workload is distributed across multiple cores, significantly reducing the simulation time. Results demonstrate a substantial acceleration in the ore sintering simulation process. Comparative analyses between CPU and GPU implementations reveal a remarkable reduction in computation time, thereby enabling real-time or near-real-time simulations. The achieved speedup not only enhances the efficiency of ore sintering modeling but also opens avenues for exploring larger and more complex scenarios. This is the successful integration of GPU parallel computing into the ore sintering model, showcasing the adaptability of advanced computational technologies to traditional industrial processes. The study contributes to the field by bridging the gap between computational power and metallurgical simulations, demonstrating the potential for GPU acceleration in other areas of metallurgical processes. Practical significance of this research is underscored by its potential to revolutionize the ore sintering industry. Faster simulations facilitate quicker decision-making in process optimization, leading to improved energy efficiency and reduced environmental impact. This research sets the stage for the broader adoption of GPU acceleration in metallurgical modeling, signaling a paradigm shift towards more efficient and sustainable industrial practices

Predictive insights for copper recovery: A synergistic approach integrating variability data and machine learning in the geometallurgical study of the Tizert deposit, Morocco

This work involved the application of machine learning (ML) to predict copper recovery through variability data in the context of the geometallurgical study of the Tizert deposit in Morocco. For this purpose, geological and metallurgical variability investigations were conducted on drilling samples from Tizert ore, leading to the consolidation of a robust database for ML algorithms. The dataset comprised 345 observations across seven variables, namely GPS coordinates, geological units, lithological facies, the main copper deportment, and metallurgical responses from flotation tests, as well as the copper grade and the oxidation rate. The geological and mineralogical variability revealed two geological units: The Basal Series and Tamjout Dolomite unit, hosting copper mineralization dominated by chalcocite and malachite. Besides, the metallurgical evaluation demonstrated recovery variability, primarily influenced by oxidized copper. Copper oxides exhibit less efficient flotation than sulfides, with recovery decreasing as the oxidation rate increases. The prediction of copper recovery was performed using the variability data through the XGBoost algorithm. The resulting model showed good performance on the test dataset, with a coefficient of determination R2 of 0.83 and a RMSE of 2.47, improving the accuracy of copper recovery predictions. In addition, the model elucidated the most significant variables impacting Tizert ore behavior, notably the casing, copper-bearing mineral, and oxidation rate. These variables were considered key factors for advancing Tizert ore valuation and geometallurgical model development. Overall, metallurgical modeling based on the ML approach provided an innovative way to integrate variables into mine planning and processing. The use of such a predictive model gave valuable insights for improving efficiency, understanding the metallurgical behavior, optimizing production, and reducing costs along the value chain of the Tizert deposit. Further data processing efforts are underway, to improve the assessment of copper recovery quality for the Tizert ore and the development of a complete geometallurgical model on a plant scale.

Numerical and experimental assessment of liquid metal embrittlement in externally loaded spot welds

Zinc-based surface coatings are widely applied with high-strength steels in automotive industry. Some of these base materials show an increased brittle cracking risk during loading. It is necessary to examine electrogalvanized and uncoated samples of a high strength steel susceptible to liquid metal embrittlement during spot welding with applied external load. Therefore, a newly developed tensile test method with a simultaneously applied spot weld is conducted. A fully coupled 3D electrical, thermal, metallurgical and mechanical finite element model depicting the resistant spot welding process combined with the tensile test conducted is mandatory to correct geometric influences of the sample geometry and provides insights into the sample’s time dependent local loading. With increasing external loads, the morphology of the brittle cracks formed is affected more than the crack depth. The validated finite element model applies newly developed damage indicators to predict and explain the liquid metal embrittlement cracking onset and development as well as even ductile failure.

A Comprehensive Material Model for the Super-Duplex Stainless Steel SAF2507 in a Welding Environment

The aim of this work is to describe a reliable methodology for determining parameters of a material model suitable for implementation in a welding simulation using the finite element method (FEM). The adopted methodology employs a multi-scale approach integrating a microstructure evolution model, a representative volume element (RVE) calibrated through experimental methods, including a thermal–mechanical simulator, and electron backscatter diffraction (EBSD) experiments. The result is a complete material model, which covers thermal, mechanical and metallurgical material models for SAF2507 (EN 1.4410), that shows promising results and was successfully implemented in finite element (FE) code. A direct comparison of experimental and calculated results shows a deviation of up to 12% for the phase fraction of austenite and 25% for the mean grain diameter of ferrite.

A comprehensive review on recent laser beam welding process: geometrical, metallurgical, and mechanical characteristic modeling

A comprehensive review on recent laser beam welding process: geometrical, metallurgical, and mechanical characteristic modeling

Correction to: A comprehensive geometrical, metallurgical, and mechanical characteristic dynamic model of laser beam welding process

Correction to: A comprehensive geometrical, metallurgical, and mechanical characteristic dynamic model of laser beam welding process

A comprehensive geometrical, metallurgical, and mechanical characteristic dynamic model of laser beam welding process

A comprehensive geometrical, metallurgical, and mechanical characteristic dynamic model of laser beam welding process

Thermo-mechanical and metallurgical preliminary analysis of SiC MOSFET gate-damage mode under short-circuit based on a complete transient multiphysics 2D FEM

For the first time, a complete 2D transient multiphysics electro-thermo-mechanical and metallurgical model of a 1.2 kV-80mΩ gate-planar SiC MOSFET power chip has been developed in a single FEM software. This model is used to quantify the short-circuit critical time and energy density with respect to the local SiO2 interlayer strength at high temperatures and the Al source-metal solidus-liquidus phase transition temperature. Repetitive experimental short-circuits were conducted to confirm the gate-aging existence in coherence with the critical values extracted from the proposed model.

Quantification of softening kinetics in cold-rolled pure aluminum and copper using High-Temperature Scanning Indentation

The High-Temperature Scanning Indentation (HTSI) method enables the continuous monitoring of various mechanical properties, including Young's modulus, hardness, and creep properties, during specific thermal cycles. In this study, the HTSI method is applied to cold-rolled samples of commercially pure aluminum and oxygen-free high conductivity (OFHC) copper. The observed variations in hardness during heating are attributed to the underlying restoring mechanisms, namely static recovery and static recrystallization. Inverse analyses are performed using established metallurgical models of restoration. The results highlight the influence of the initial deformation state and heating rate on the kinetics parameters. The findings are further supported by Electron-Back Scattering Diffraction measurements. The study concludes by demonstrating the HTSI method's capability to quantify restoration parameters as a function of temperature through a limited number of well-designed HTSI experiments.

Numerical Modeling for the Prediction of Microstructure and Mechanical Properties of Quenched Automotive Steel Pieces.

In this work, we present an efficient numerical tool for the prediction of the final microstructure, mechanical properties, and distortions of automotive steel spindles subjected to quenching processes by immersion in liquid tanks. The complete model, which consists of a two-way coupled thermal-metallurgical model and a subsequent (one-way coupled) mechanical model, was numerically implemented using finite element methods. The thermal model includes a novel generalized solid-to-liquid heat transfer model that depends explicitly on the piece's characteristic size, the physical properties of the quenching fluid, and quenching process parameters. The resulting numerical tool is experimentally validated by comparison with the final microstructure and hardness distributions obtained on automotive spindles subjected to two different industrial quenching processes: (i) a batch-type quenching process with a soaking air-furnace stage prior to the quenching, and (ii) a direct quenching process where the pieces are submerged directly in the liquid just after forging. The complete model retains accurately, at a reduced computational cost, the main features of the different heat transfer mechanisms, with deviations in the temperature evolution and final microstructure lower than 7.5% and 12%, respectively. In the framework of the increasing relevance of digital twins in industry, this model is a useful tool not only to predict the final properties of quenched industrial pieces but also to redesign and optimize the quenching process.

Exploring computational techniques for simulating residual stresses for thin wall multi-joint hexagon configurations for a laser directed energy deposition process

Laser cladding is a directed energy deposition process and can lead to high residual stresses, which can compromise the quality of the specimen. As a result, it is crucial to accurately predict and investigate the residual stress distribution in cladded parts and understand the formation mechanisms. In this study, a thermo-mechanical metallurgical simulation model of the laser cladding process was developed for three different deposition sequences for a thin wall hexagon with inner junctions to investigate the formation of residual stress and distortion. The study was performed for single and multilayer scenarios. Two types of computational techniques, the detailed transient approach and the imposed thermal cycle approach, were performed and comparisons conducted. Consistent results were observed when comparing the resultant stress patterns for the single layer; subsequently, the imposed thermal cycle method was applied for the five-layer models. A preheat scenario is explored. This reduced the computational cost significantly, but the stress patterns were not similar. This indicates that building up worn regions at the top of a thin-walled component, such as a roll die, needs to be investigated further as unique issues have been highlighted. The differences between the implemented computational techniques are described as well as the advantages and disadvantages of each. Knowledge obtained from these case studies provides a foundation for efficient and rapid optimization of laser cladding processes, with the aim of minimizing residual stress in both simple and complex laser cladding structures.

The Impact of Froth Launders Design in an Industrial Flotation Bank Using Novel Metallurgical and Hydrodynamic Models

In flotation cells, especially in large flotation units, froth management is a crucial variable that should be considered during the design phase or optimized to improve the performance of existing flotation circuits. This paper presents a simulation evaluation of the effect of launder design on the metallurgical performance of an industrial flotation circuit consisting of five TankCell® e630 (630 m3) cells in a Cu rougher duty. This analysis was carried out using a new industrial simulator that includes novel metallurgical and hydrodynamic models, developed from a wide database collected from many industrial concentrators. This tool is currently incorporated into HSC Chemistry® software and allows evaluating the effect of launder design on mineral froth recovery, water recovery, entrainment, and other variables. The industrial flotation circuit was evaluated under different launder design scenarios, considering an actual flotation circuit that includes TankCell® e630 cells for calibration and as a reference (baseline). Firstly, two different designs were evaluated in the full circuit: a standard launder design and a new launder technology. It was found that the new launder technology enabled improvement of the mineral recovery along the circuit, mainly for coarse particles, due to the improvement in froth mineral recovery. Next, a partial upgrade of the launder design along the circuit was analysed. Thus, the new launder technology was evaluated in the first and the last two cells of the bank. The results showed that upgrading the launders in different cells along the circuit delivered an increase in the final recovery with respect to the baseline, with a partial impact on the concentrate grade. However, these changes are less than those when evaluating the full upgrade scenario. A partial launder upgrade either in the first or last two cells of the bank showed similar final recoveries, but the latter enabled a slightly higher concentrate grade (about 1% higher) to be achieved. Finally, the evaluation of launder design using the industrial simulator made it possible to estimate its effect on variables that are not commonly obtained from plant surveys, including superficial gas rates at the froth surface level, froth recovery per particle size, collection recovery per particle size and liberation, gangue entrainment, and bubble loading grade.

Prediction of the Consumption of Raw Materials and Fuels for the Blast Furnace

This article was conducted within the framework of project reg. no. CZ.11.4.120/0.0/0.0/16_013/0002594, programme, Interreg V-A Czech Republic-Poland, Microprojects Fund 2014–2020 in the Euroregion Silesia. It is focused in the area of modelling technological processes and the presentation of the main principles of these models. The modelling of technological processes is important in terms of its applicability to process prediction in industry. A complex of analytical and predictive metallurgical models was developed within VSB-TUO. The original mathematical model of coke degradation in a blast furnace makes it possible to calculate the minimum consumption of coke from the dynamic balance for different values of the ratio of direct and indirect reduction. As part of the graphic output, it determines the practically and theoretically achievable minimum coke consumption points. The use of the model enables the determination of a real reserve in reducing the amount of coke.

Comparison of the Prediction of BOF End‐Point Phosphorus Content Among Machine Learning Models and Metallurgical Mechanism Model

The five machine learning models (MLM) of ridge regression, gradient boosting regression (GBR), support vector regression, random forest regression (RFR), convolutional neural network, and a metallurgical mechanism model (MMM) are compared in predicting the end‐point P content in the basic oxygen furnace steelmaking process. The prediction accuracy of MMM is much lower than those of five MLM. The GBR and RFR models have the best performance, with the correlation coefficient values of 0.599 and 0.608, respectively. The smallest mean absolute relative error value of 0.155 and the root mean square error value of 0.00319 are obtained with GBR and RFR, respectively. The values of correlation coefficient after data distribution optimization for all MLM are increased two times higher than before. The second blowing time, lime weight, and oxygen consumption amount are evaluated to have the greatest impacts on the end‐point P content. The end‐point P content decreases with decreasing the second blowing time and with increasing the lime weight and the oxygen consumption amount. The GBR and RFR models are optimized by removing the variables with little impacts on the end‐point P content. The highest prediction accuracy is obtained when 14 variables are remained.

Recovery and enrichment of acid from metallurgical wastewater model by electrodialysis integrated diffusion dialysis system using poly(ethylene) based IEMs

Here in, we report an energy efficient and sustainable integrated approach for the recovery of different acids (viz. HCl, HNO3 and H2SO4) from metallurgical acidic effluent models. Taking advantage of high-permselectivity and counter-ion transport number in membrane driven processes, the pilot scale diffusion dialysis (DD) cell was integrated with electrodialytic (ED) cell in series and comparative performance is evaluated. The poly(ethylene) based inter-penetrating type cation and anion exchange membranes (In-CEM and In-AEM) was prepared and have counter-ion transport number (tm) in range 0.80–0.95 and ionic conductivities between 8 and 12 mS cm−1. The process efficacy was evaluated for the recovery of HCl, HNO3 and H2SO4 in terms of proton dialysis coefficient (vH+), separation factor (Sf) and metal-ion leakage (LFen+). The reclamation studies were corroborated based on the counter-ion diffusivity, thermodynamic parameters, dissociation coefficient and ion’s intrinsic transmembrane features for different acids. DD as an individual technology showed vH+ and Sf between 0.35 and 0.50 × 10-3 mh−1 and 6–7. In contrast, using integrated system, the vH+ and Sf were improved by ∼30 and ∼15 folds respectively. The highest achievable vH+ was 14 × 10-3 mh−1, 11 × 10-3 mh−1 and 10.2 × 10-3 mh−1 whilst, Sf was 88.6, 91.0 and 106.0 for HCl, HNO3 and H2SO4, respectively. Also, the autopsy analysis on the stability of IEMs and integrated assembly was evaluated and no significant change was observed which shows long-term durability of membranes. From these results, we anticipate that integrated systems with designed membrane stand out to be a competitive device model for acid enrichment application from industrial acidic wastewater.