Search Results (543)

As the use of data models and data science techniques in industrial processes grows exponentially, the question arises: to what extent can these techniques impact the future of manufacturing processes? This article examines the potential future impacts of these models based on an assessment of existing trends and practices. The drive towards digital-oriented manufacturing and cyber-based process optimization and control has brought many opportunities and challenges. On one hand, issues of data acquisition, handling, and quality for proper database building have become important subjects. On the other hand, the reliable utilization of this available data for optimization and control has inspired much research. This research work discusses the fundamental question of how far these models can help design and/or improve existing processes, highlighting their limitations and challenges. Furthermore, it reviews state-of-the-art practices and their successes and failures in material process applications, including casting, extrusion, and additive manufacturing (AM), and presents some quantitative indications. Full article

Steel is an important product in many engineering sectors; however, steelmaking remains one of the largest ${\mathrm{CO}}_2$ emitters. Therefore, new governmental policies drive the steelmaking industry toward a cleaner and more sustainable operation such as the gas-based direct reduction–electric arc furnace process. To become carbon neutral, utilizing more scrap is one of the feasible solutions to achieve this goal. Addressing knowledge gaps regarding scrap heterogeneity (size, shape, and composition) is essential to evaluate the effects of increased scrap ratios in basic oxygen furnace (BOF) operations. This review systematically examines heat and mass transfer correlations relevant to scrap melting in BOF steelmaking, with a focus on low Prandtl number fluids (thick thermal boundary layer) and dense particulate systems. Notably, a majority of these correlations are designed for fluids with high Prandtl numbers. Even for the ones tailored for low Prandtl, they lack the introduction of the porosity effect which alters the melting behavior in such high temperature systems. The review is divided into two parts. First, it surveys heat transfer correlations for single elements (rods, spheres, and prisms) under natural and forced convection, emphasizing their role in predicting melting rates and estimating maximum shell size. Second, it introduces three numerical modeling approaches, highlighting that the computational fluid dynamics–discrete element method (CFD–DEM) offers flexibility in modeling diverse scrap geometries and contact interactions while being computationally less demanding than particle-resolved direct numerical simulation (PR-DNS). Nevertheless, the review identifies a critical gap: no current CFD–DEM framework simultaneously captures shell formation (particle growth) and non-isotropic scrap melting (particle shrinkage), underscoring the need for improved multiphase models to enhance BOF operation. Full article

Stacking fault tetrahedra (SFTs) are typically considered improbable in high stacking fault energy metals like aluminum. Using molecular statics and dynamics simulations, we reveal the formation, growth, and transformation of SFTs in aluminum via vacancy aggregation. Three types—perfect, truncated, and defective SFTs—are characterized by their structure, formation energy, and binding energy across a range of vacancy cluster sizes. Formation energies of perfect and truncated SFTs follow a scaling relation; beyond a critical size, truncated SFTs become thermodynamically favored, indicating a size-dependent transformation pathway. Binding energy and structure evolution exhibit quasi-periodic behavior, where vacancies initially adsorb at the vertices or the midpoints of the edges of a perfect SFT, then aggregate along one facet, triggering fault nucleation and a binding energy jump as the system reconstructs into a new perfect SFT. Molecular dynamics simulations further confirm the SFT nucleation and growth via vacancy aggregation, consistent with thermodynamic predictions. SFTs exhibit notable thermal mobility, enabling coalescence and evolution into vacancy-type dislocation loops. BCC-like $V_5$ clusters are identified as potential nucleation precursors. These findings explain the nanoscale, low-temperature nature of SFTs in aluminum and offer new insights into defect evolution and control in FCC metals. Full article

In a previous article, an estimation procedure for calculating the liquidus and solidus lines of binary equilibrium phase diagrams was presented. In this article, keeping the thermodynamic basics, the estimation method for the approximate calculation of the liquidus and solidus surfaces of ternary phase diagrams was further developed. It is shown that the procedure has a hierarchical structure, and the ternary functions contain the binary functions. The applicability of the method is checked by calculating the liquidus and solidus surfaces of the Ag-Au-Pd isomorphous ternary equilibrium phase diagram. The application of each level of the developed four-level procedure depends on the data available and the aim. It is shown that in the case of a concentration range close to the base alloy pure element, the liquidus and solidus surfaces of the ternary equilibrium phase diagram can be calculated from the liquidus and solidus functions of the binary equilibrium phase diagrams with a few K errors, which is 0.2 at% at 10 K/at% slope. The equilibrium phase diagrams were available in graphical form, so the data obtained via digitalisation of the diagrams for the calculations was used. The functions describe the slope of the surfaces, and the approximate method developed for the calculation of the partition ratios is also shown. Full article

The identification of suitable alloy compositions for the formation of bulk metallic glasses (BMGs) is a key challenge in materials science. In this study, we developed machine learning (ML) models to predict the critical casting diameter ($D_{max}$) of Fe-based BMGs, enabling rapid assessment of glass-forming ability (GFA) using composition-based and calculated thermophysical features. Three datasets were constructed: one based on alloy molar fractions, one using thermophysical quantities calculated via the CALPHAD method, and another utilizing Magpie-derived features. The performance of various ML models was evaluated, including support vector machines (SVM), XGBoost, and ensemble methods. Models trained on thermophysical features outperformed those using only molar fractions, with XGBoost and SVM models achieving test $R^2$ scores of up to 0.63 and 0.60, respectively. Magpie features yielded similar results but required a larger feature set. To enhance predictive accuracy, we explored data augmentation using the PADRE method and a modified version (PADRE-2). While PADRE-2 demonstrated slight improvements and reduced data redundancy, the overall performance gains were limited. The best-performing model was an ensemble combining SVM and XGBoost models trained on thermophysical and Magpie features, achieving an $R^2$ score of 0.69 and MAE of 0.69, comparable to published results obtained from larger datasets. However, predictions for high $D_{max}$ values remain challenging, highlighting the need for further refinement. This study underscores the potential of leveraging thermophysical features and advanced ML techniques for GFA prediction and the design of new Fe-based BMGs. Full article

The constitutive model significantly influences the accuracy of predicting the complex rheological behavior of hot isostatically pressed powders. The temperature plays a crucial role in determining material properties during hot isostatic pressing (HIP), making it essential to account for its effect on the yield model parameters to more accurately describe the densification evolution of powders. In this study, HIP experiments were conducted using two different process schemes, and the shrinkage deformation of the envelope under each scheme was analyzed. High-temperature uniaxial compression experiments were performed on HIP samples with varying densities to analyze and characterize the stress–strain response of the powder during HIP. A mesoscopic particle-scale high-temperature uniaxial compression model was developed based on the discrete element method (DEM), and the strain and stress values corresponding to different densities in the high-temperature uniaxial compression simulations were validated through experimental comparison. The strain evolution during the uniaxial compression process was analyzed, and the relationship between the parameters of the Shima–Oyane model and the temperature was established, leading to the development of a temperature-compensated Shima–Oyane model. Based on the obtained parameters at various densities and temperatures, a yield stress map for the nickel-based alloy was constructed. The accuracy of this model was verified by comparing experimental results with finite element method (FEM) simulations. The findings of this study contribute to a more precise prediction of densification behavior in thermally driven isostatic pressing. Full article

Wire electrical discharge machining (WEDM), as a significant branch of non-traditional machining technologies, is widely applied in fields such as mold manufacturing and aerospace due to its high-precision machining capabilities for hard and complex materials. This paper systematically reviews the research progress in WEDM process optimization from two main perspectives: traditional optimization methods and artificial intelligence (AI) techniques. Firstly, it discusses in detail the applications and limitations of traditional optimization methods—such as statistical approaches (Taguchi method and response surface methodology), Adaptive Neuro-Fuzzy Inference Systems, and regression analysis—in parameter control, surface quality improvement, and material removal-rate optimization for cutting metal materials in WEDM. Subsequently, this paper reviews AI-based approaches, traditional machine-learning methods (e.g., neural networks, support vector machines, and random forests), and deep-learning models (e.g., convolutional neural networks and deep neural networks) in aspects such as state recognition, process prediction, multi-objective optimization, and intelligent control. The review systematically compares the advantages and disadvantages of traditional methods and AI models in terms of nonlinear modeling capabilities, adaptability, and generalization. It highlights that the integration of AI by optimization algorithms (such as Genetic Algorithms, particle swarm optimization, and manta ray foraging optimization) offers an effective path toward the intelligent evolution of WEDM processes. Finally, this investigation looks ahead to the key application scenarios and development trends of AI techniques in the WEDM field for cutting metal materials. Full article

Aluminum, boron, and titanium microalloyed into high-strength low-alloy boron steel exhibit a complex interplay, competing for nitrogen, with titanium demonstrating the highest affinity, followed by boron and aluminum. This competition affects the formation and distribution of nitrides, impacting the microstructure and mechanical properties of the steel. Titanium protects boron from forming BN and facilitates the nucleation of acicular ferrite, enhancing toughness. The segregation of boron to grain boundaries, rather than its precipitation as boron nitride, promotes the formation of martensite and thus the through-hardenability. Aluminum nitride is critical in controlling grain size through a pronounced pinning effect. In this study, we employ energy- and wavelength-dispersive X-ray spectroscopy and computer-aided particle analysis to analyze the phase content of 12 high-purity vacuum induction-melted samples. The primary objective of this study is to correctly describe the microstructural evolution in the Fe-Al-B-Ti-C-N system using the Calphad approach, with special emphasis on correctly predicting the dissolution temperatures of nitrides. A multicomponent database is constructed through the incorporation of available binary and ternary descriptions, employing the Calphad approach. The experimental findings regarding the solvus temperature of the involved nitrides are employed to validate the accuracy of the thermodynamic database. The findings offer a comprehensive understanding of the relative phase stabilities and the associated interplay among the involved elements Al, B, and Ti in the Fe-rich corner of the system. The type and size distribution of the stable nitrides in microalloyed steel have been demonstrated to exert a substantial influence on the properties of the material, thereby rendering accurate predictions of phase stabilities of considerable relevance. Full article

Non-uniform friction and lubrication are the key factors affecting the surface quality of the casting billet. Based on the three-layer structure of the casting powder in the mold, the frictional stress in the mold was calculated and analyzed by using the relationship between the frictional stress and the thickness and viscosity of the liquid slag film, and the lubrication state between the cast billet and the mold was evaluated. Based on the actual production data of 40Mn2 steel and combined with the numerical simulation results of the solidification and shrinkage process of the molten steel in the mold by ANSYS 2022 R1 software, the frictional stress on the cast billet in the mold was calculated. It was found that within the range of 44~300 mm from the meniscus, the friction between the cast billet and the mold was mainly liquid friction, and the friction stress value increased from 0 to 145 KPa. Within 300–720 mm from the meniscus, the billet shell is in direct contact with the mold. The friction between the cast billet and the mold is mainly solid-state friction, and the friction stress value increases from 10.6 KPa to 26.6 KPa. It indicates that the excessive frictional stress inside the mold causes poor lubrication of the cast billet. By reducing the taper of the mold and optimizing the physical and chemical properties of the protective powder, within the range of 44~550 mm from the meniscus, the friction between the cast billet and the mold is mainly liquid friction, and the friction stress value varies within the range of 0–200 Pa. It reduces the frictional stress inside the mold, improves the lubrication between the billet shell and the mold, and completely solves the problem of mesh cracks on the surface of 40Mn2 steel cast billets. Full article

In this paper, we propose the use of burnishing internal cylindrical surfaces with a hard tool in a mandrel shape. The burnishing force is exerted mainly by the press slide, which has pushing properties, moving the burnisher through the hollow tube. The burnishing of hollow surfaces is used as the finishing step for elements such as tubes. The purpose of using the burnishing method may be, for example, to increase the smoothness and accuracy of the object, for the improvement of its functional and operational properties, for economic reasons, or to increase its resistance to corrosion and fatigue. The depth of plastic deformation and the accuracy of processing are the main differences in the machining effects for individual burnishing methods. The selection of the burnishing conditions depends on the method of exerting pressure from the burnishing elements on the machined surface, which can be elastic or rigid. Computer simulations of the burnishing process were performed in FORGE^® NxT 2.1 software. A numerical analysis was performed using a three-dimensional triangular mesh. The theoretical and experimental research was determined to have very good compatibility, as determined by the numerically calculated results and by the mean deviation of residual stress method. This research analyzed the stress and strain state after the burnishing process, and a depth of deformation of approximately 20 μm to 30 μm in the material was determined. Full article

High-chromium cast irons (HCCIs) have emerged as preferred materials for critical wear-resistant components operating under extreme conditions, owing to their excellent wear resistance, low cost, and good castability. They are widely used in metallurgy, energy, and mechanical engineering industries. The evolution of solidification microstructure directly governs the final properties of HCCIs, making the in-depth investigation of their solidification behavior of great significance. This paper provides a comprehensive review of recent experimental and simulation-based advances in understanding the solidification microstructure evolution of HCCIs. The effects of alloy composition, cooling rate, and inoculation treatments on microstructure development and phase distribution during solidification are critically analyzed. Furthermore, the application of simulation techniques—including thermodynamic modeling, phase-field method, cellular automata, and finite element analysis—is discussed in detail, highlighting their roles in revealing the mechanisms of microstructural evolution. Finally, the current challenges and potential future research directions in the study of the solidification behavior of high-chromium cast irons are outlined. Full article

Variations in the warm formability of AA7075 sheets are primarily attributed to differences in precipitate morphology resulting from distinct thermal histories. To better understand this relationship, this study systematically investigates the influence of precipitate characteristics—quantified by the product of precipitate volume fraction and average radius—on forming limits across various thermal routes in warm forming processes. A modified Cockcroft–Latham ductile fracture model incorporating this microstructural parameter was developed, calibrated against experimental data from warm isothermal Nakajima tests, and implemented within a finite element framework. The proposed model enables the accurate prediction of forming limit curves with minimal experimental effort, thereby significantly reducing the reliance on extensive mechanical testing. Building upon the validated FE model, a practical methodology for rapid R-value estimation under warm forming conditions was established, involving the design of specimen geometries optimised for isothermal Nakajima testing. This approach achieved R-value predictions within 5% deviation from conventional uniaxial tensile test results. Furthermore, experimental results indicated that AA7075 sheets exhibited nearly isotropic deformation behaviour under retrogression warm forming conditions. Overall, the methodology proposed in this study bridges the gap between formability prediction and process simulation, offering a robust and scalable framework for the industrial optimisation of warm forming processes for high-strength aluminium alloys. Full article

Final electromagnetic stirring (F-EMS) effectively improves macrosegregation and central porosity in round bloom continuous casting, while the flow and solidification of molten steel under F-EMS have a direct impact on metallurgical properties. Fluid flow and solidification behavior in a 600 mm round bloom continuous casting process with F-EMS were simulated. The influence of the liquid fraction model on strand temperature distribution was investigated. The flow of molten steel was analyzed under both continuous and alternate stirring modes. The results indicated that in continuous stirring mode, the stirring velocity fluctuates between peaks and troughs over a specific period. The closer the F-EMS is to the meniscus, the larger the mushy zone area and the higher the stirring velocity. Due to the 10+ s rise time for current intensity, a 25 s forward and reverse stirring duration is recommended for Φ600 mm round bloom continuous casting with F-EMS. Full article

This study examined machinability aspects in terms of the main cutting force and surface roughness in dry CNC turning of graphene-reinforced composite aluminum with 0.5 wt%. The cutting speed, feed rate and depth of cut influence were investigated in regard to the responses of main cutting force Fz and surface roughness Ra when turning high-purity aluminum (Al 96.83%) and graphene-reinforced aluminum with 0.5% graphene nanoplatelets for comparative analysis. A customized central composite design of the experiments with nine runs was established, and the results were assessed through analysis of variance and response surface regression. Full quadratic prediction models were generated based on the experimental results and they were examined for their validity and efficiency in predicting the response of the main cutting force and surface roughness of the machined graphene-reinforced composite aluminum. The NSGA-II algorithm was finally applied for simultaneously minimizing the main cutting force and surface roughness by providing a well-spread Pareto front of non-dominated solutions. The results indicated that the feed rate was the dominant parameter affecting both objectives, namely the main cutting force and surface roughness, while the NSGA-II algorithm was capable of delivering advantageous solutions for enhancing machinability with less than 10% error predictions when comparing simulated and actual machining results. Full article

A recently developed approach to the partition function with very high efficiency was applied to study the equation of state (EOS) of metal nickel (Ni) up to 3000 K and concurrently 500 GPa. The theoretical results agree very well with previous hydrostatic experiments at room temperature, and at high temperatures, the deviation of our calculated pressures from the latest hydrostatic experiments up to 109 GPa is less than 4.16%, 4.95%, and 5.53% at 1000, 2000, and 3000 K, respectively. Furthermore, an analytical EOS model with only two parameters was developed for common metals at high temperatures, and the analytical EOS of metal Ni was obtained to produce the map of pressure over the temperature–volume plane, which should be helpful to understand the thermodynamic properties of Ni-based alloys. Full article