Is Thermal Analysis Able to Provide Carbon and Silicon Contents of Cast Irons?

The determination of carbon activities in the Fe–C–Si system is important in thermodynamic calculations and in particular for predicting equilibrium compositions in slag–metal reactions. In this research, the interaction parameters required to describe the activity of carbon were calculated from previously published carbon solubility data. These parameters were referred to as ternary interaction parameters &phis; and were fitted to equations as a function of the carbon and silicon contents and temperature. Subsequently, the various interaction parameter formalisms were utilised to calculate the activity of carbon. In dilute solutions, all formalisms were in excellent agreement with a previously published regular solution model. At low silicon and high carbon contents, the unified formalism was in excellent agreement, while the substitutional formalism was in good agreement with the regular solution model. However, in high silicon alloys with low carbon contents, the substitutional and unified formalisms showed only limited agreement with the regular solution model.

Twenty‐one durum wheat genotypes originating from different geographic areas were grown during 3 successive years. The trials were characterised by different precipitation regimes. Carbon isotope discrimination (Δ), carbon content (CC) and ash content (ma) were assessed in the flag leaf during anthesis, then in the kernel at full maturity. Differences between the 3 years, due to water availability, were noted for Δ, ma, CC and yield. Genotypic differences were also noted within each year for all the traits studied. Some genotypes from the Middle East exhibited higher flag leaf and kernel Δ than those originating from the West of the Mediterranean basin. The kernel Δ was strongly correlated with grain yield (GY). The leaf Δ correlated with GY only under strong water limitation and with biomass production (BP) in favourable water conditions. For the flag leaf, Δ was correlated with ma and with CC. Silicon content was then assessed in the flag leaf and in the kernel on a subset of 10 genotypes differing in their Δ values. Strong positive correlations were noted between silicon content and Δ and ma for the flag leaf. However, no clear relationship was found between silicon content and GY. The results obtained in this study confirm the validity of kernel Δ as a predictive criterion for GY under water stress and suggest the possible use of kernel ma as an alternative criterion to select genotypes with higher water stress tolerance.

Effects of ferrite content and concentrations of carbon and silicon on weld solidification cracking susceptibility of stainless steels

In recent years, the booming automotive, home appliance, and construction industries have driven a surge in market demand for cold-rolled steel strips. In response to the “carbon neutrality” initiative, the steel industry has accelerated its green transformation. The high added-value characteristics of cold-rolled strip steel highlight the importance of quality control. However, its production process is complex, with intertwined parameters. This paper aims to establish a model that can accurately reflect the production mechanism. For Problem 1, this paper innovatively combines principal component analysis (PCA) with the Pearson correlation coefficient to precisely screen out the key parameters that have a decisive impact on hardness. For Problem 2, a composite model based on random forest and BP neural network is constructed. This model can capture the subtle changes in production data in real time and accurately predict product quality, providing strong support for production decision-making. For Problem 3, the advanced optimization tool of simulated annealing algorithm is introduced. Through the intelligent optimization of complex process parameters, improvements in production efficiency and product quality are achieved. For Problem 1, our objective focused on identifying the key parameters significantly affecting the hardness of cold-rolled steel strips. In the preliminary data processing stage, we addressed missing values and outliers through linear interpolation and moving median filtering, followed by data standardization using the Z-score method. This rigorous preprocessing established a reliable data foundation for subsequent modeling. We then employed two complementary statistical approaches for feature selection and model development: PCA and Pearson correlation analysis. PCA identified critical variables based on their contribution rates to variance, while Pearson correlation coefficients quantitatively assessed the strength of linear relationships. These mutually validating methodologies collectively established robust correlations between hardness and twelve key process parameters. The final identified significant parameters include: thickness, width, carbon content, silicon content, strip steel speed, heating furnace temperature, soaking furnace temperature, slow cooling furnace temperature, over-aging furnace temperature, rapid cooling furnace temperature, quenching temperature, and temper mill tension. For Problem 2, which requires establishing a data-driven online quality inspection model for steel strips and analyzing its performance, we first introduced the random forest algorithm to significantly enhance model effectiveness and computational efficiency. Through this approach, we extracted the most representative feature subset, revealing that carbon content and rapid cooling furnace temperature are the most critical factors in predicting steel strip product quality, followed by physical dimensions, with silicon content and other heat treatment parameters also exhibiting certain influence. Building upon this optimized feature subset, we employed a backpropagation neural network to construct the online prediction model. Through hyperparameter tuning, we optimized the neural network architecture configuration, determining that 12 hidden layers are optimal for the acceleration/deceleration phases while 9 hidden layers achieve peak performance during stable operation phases. Finally, we conducted efficiency optimization and performance evaluation of the model, with results demonstrating excellent prediction performance at 97.6733% accuracy, prediction errors within 5%, and overall satisfactory prediction outcomes. For Problem 3, which requires establishing a comprehensive and efficient solution for optimizing process parameters of steel strips, simulated annealing algorithm was adopted in this study to globally search for optimal solutions, given the difficulties in developing mechanistic models due to the complex interdependencies among control parameters. The methodology encompasses parameter initialization, key parameter identification, objective function formulation, constraint handling, result validation, and model integration. Building upon the results of Problem 1, we first identified critical parameters and construct the objective function, followed by setting practical production constraints. The simulated annealing algorithm performs global optimization while effectively addressing constraint limitations through a penalty function mechanism. The optimal solution yields: strip speed of 201 m/s, heating furnace temperature at 710°C, soaking furnace temperature at 645°C, slow cooling furnace temperature at 608°C, over-aging furnace temperature at 355°C, rapid cooling furnace temperature at 67°C, quenching temperature at 45°C, and temper mill tension at 2420 kN, achieving optimal mechanical properties under these conditions.

A model to predict the solidification of spheroidal graphite (SG) cast iron during cooling process was developed. The model treated the problem of the ternary Fe-C-Si system and aimed at simulating the evolution of the solidifying microstructure. When the eutectic temperature is reached, eutectic grains are formed. The rate of nucleation depends on the melt characteristics and supercooling. The nucleation rate was assumed to be presented by power law function of undercooling. The growth of the austenite grain was controlled by carbon diffusion through austenite. Carbon and silicon concentrations at different interfaces are calculated from the Fe-C-Si equilibrium ternary phase diagram and Sheil equation was applied to calculate the silicon content in the bulk liquid and the concentration of carbon in the liquid was determined to keep the eutectic reaction. The model was applied to a four-step cylindrical sand casting which has a different diameter. The simulated results were compared with the experimental ones using a post processing program which was also developed to simulate the microstructural evolution graphically. The results showed relatively good agreement and the usefulness of the proposed model was confirmed.

Various types of high strength steel have been developed to improve the impact safety and reduce the weight of cars. For the steels used in anti-crash equipments, mechanical properties, especially, at high strain rates such as 103/s is of importance. In this study, 0.110%C–1.44%Si–1.29Mn–0.65%Cr–0.29%Mo containing hot rolled dual phase steel with 780 MPa grade in tensile strength was employed as a base steel and the effects of carbon and silicon on static (strain rate: 10−3/s)/ dynamic (strain rate: 103/s) mechanical properties of the dual phase steel were investigated. Carbon and silicon contents were changed in a range of 0.076–0.190% and 1.44–2.39%, respectively. Grain size of the steels was varied by hot rolling reduction: 53% (named coarse grain process), 73% (middle grain process) and 88% (fine grain process). Dynamic absorbed energy up to 10% tensile strain had a linear relationship with tensile strength, regardless of microstructures, i.e., neglecting carbon and silicon contents, and hot rolling conditions. All absorbed energy to fracture had a close relationship with tensile strength–ductile balance parameter (tensile strength×total elongation), reflecting microstructural change through chemical and rolling conditions. All the processed 0.190% C steels, and the fine grain processed 1.93% Si and 2.39% Si steels showed the highest all absorbed energy of all the steels tested. The 0.190% C steel was characterized by almost 100% martensite with some content of retained austenite, and the 1.93% Si and 2.39% Si steels were fine grained ferrite+martensite. It was found that carbon improves all absorbed energy through increase in volume fraction of martensite and silicon raises it through solid solution hardening of ferrite matrix.

Using the plasma transferred arc (PTA) process to conduct surface remelting, the surface region of flake graphite cast iron can be hardened. To study the effect of silicon content on the microstructure of the hardened surface layer, flake graphite cast iron specimen of about 3.1 ∼ 3.2 wt pet carbon and various silicon contents from 1.47 to 3.41 wt pet were selected for the PTA surface remelting experiment. The remelting current and voltage were set at 100 A and 32 V. The results show a rapidly solidified microstructure with large amounts of eutectic cementite. Reducing the silicon content increases the volume fraction of eutectic cementite and thus increases the hardness. The increase of cementite volume fraction with decreasing silicon content is linear. Reducing the silicon content is also effective in suppressing the formation of porosities.

Generally, a three-step heat treatment consisting of austenitizing, quenching and austempering is necessary to produce ADI. Using an in situ heat treatment the nodular cast iron casting was ejected at a temperature above 870°C and directly quenched to 300–400°C for austempering. Because the austenitization is no longer necessary in this method a significant reduction of energy and production time can be achieved. To guarantee the casting to be ejected with a well defined temperature distribution a permanent mould casting process was used.For predicting the microstructural evolution during the solidification and the subsequent ausferrite reaction during austempering a simulation model was developed for ternary Fe-C-Si alloys. For the simulation it was assumed that eutectic grains are formed below the eutectic temperature and that their growth rate is controlled by carbon diffusion through the austenite shell. Carbon and silicon concentrations at the phase interfaces are calculated from the Fe-C-Si phase diagram obtained from the thermodynamic phase equilibrium calculation tool, ChemAppTM. The pursuing solid state transformation is described by the growth of the ferrite shell into the austenite, which is controlled by carbon diffusion in a quasi-steady state. The distribution of temperature, nodule counts, nodule sizes and phase fractions in the casting were calculated from mass conservation. The transformation from austenite to ausferrite was calculated by the modified Avrami equation with the additivity rule. The simulation results were verified by experimental results. Mechanical properties show interesting potentials for application.

Solidification behavior and porosity in electron-beam powder bed fusion of Co–Cr–Mo alloys: Effect of carbon concentrations

The developed model of diffusion‐limited and diffusionless solidification of a eutectic alloy describes the relation “undercooling (ΔT)‐velocity (V)‐interlamellar spacing (λ)” for two cases. Namely, when the solidification front velocity V is smaller than the solute diffusion speed in bulk liquid VD, V < VD, the model predicts a regime of eutectic solidification similarly to known classical models. If the solidification front velocity V is higher than the diffusion speed, V > VD, the solidification is mainly controlled by kinetic and thermal undercoolings. New expressions for the solute distribution coefficient and slope of the liquidus lines are supplied. The influence of the model parameters on the growth kinetics during eutectic solidification is discussed. Model predictions are compared with experimental data for the solidification of an Fe–B alloy with eutectic composition. Computational results show that the model agrees well with experimental data especially for low and high undercoolings, extending the undercooling range that can be covered by sharp interface modeling.

Auger electron spectroscopy on both fracture surfaces of an (001)/(011) asymmetrical grain boundary

1. The mechanical characteristics of Kh20N14S2 steel and the resistance to prolonged exposure at 700, 800, 850, 900, and 950\dgC are similar to those of Kh23N18 steel. The decrease in the concentration of carbon and the increase in the silicon concentration in Kh20N14S2 steel decreases the strength. 2. During prolonged heating at 700\2-950\dgC Kh20N14S2 steel undergoes structural transformations with formation of the \gs-phase, which decreases the impact strength and the ductility and somewhat increases the hardness. The impact strength of the aged steel is considerably higher at the aging temperature than at 20°C. 3. Kh20N14S2 steel with 2.2\2-3% Si has good resistance to carburization. The resistance to carburization increases with increasing concentrations of silicon in the steel. The resistance of Kh20N14S2 steel to scale formation is considerably higher than that of Kh23N18 steel. 4. Kh20N14S2 steel containing up to 0.12% C and 2.2\2-3% Si can be used for pipes in pyrolysis furnaces in place of Kh23N18 steel.

In the paper we obtained the expression of cementite free energy and determined the solubility of manganese and silicon in Fe3C cementite depending on the temperature. Investigation was carried out for alloys with carbon content of 0.55-0.60 % (wt.), silicon content of 0.95-1.0 % (wt.), manganese content of 0.8-0.9% (wt.), the rest was iron. The smelting of Fe-Mn-Si-C system alloys was carried out in the alundum crucible furnace in argon atmosphere. The cooling rate of alloys after casting was 10 K/s. Microstructure analysis along with X-ray diffraction analysis was used to determine the structural state of the alloys. In addition, the physical characteristics of the alloys studied in this paper were determined, such as alloy chemical dependence of ultimate strength, extension and contraction ratio, impact toughness and hardness. The results obtained in this paper showed that the iron-based alloy with the content of carbon of 0.57 % (wt.), silicon of 0.97 % (wt.) and manganese of 0.85 % (wt.)) had the superior microstructure and physical properties. The microstructure of alloys studied in the paper is represented by pearlite, which makes up to 95 % of the volume. In the alloys we revealed the highly dispersed inclusions of Fe2.7Mn0.3C, Fe0.25Mn1.4C0.6 and Fe9SiC0.4 carbides, whose volume ratio was up to 1.5 %, the rest was ferrite. As it is known, the structural constituent of pearlite is cementite. The cementite has a significant effect on the physical properties of alloys. Application of quasi-chemical method enables to calculate the free energy of silicon and manganese doped with cementite and to determine the temperature dependence of silicon and manganese content in cementite. It is ascertained that there is a slight increase of carbon content in cementite (up to 28.79 % (atoms). Manganese can replace up to 12 % of iron atoms, and silicon can replace up to 4.5 % of iron atoms, depending on temperature. The calculated data obtained in this paper are in good agreement with those found experimentally by other authors.

The article presents crystallization process of silicon molybdenum ductile cast iron (SiMo). The alloy with 5% silicon content and with variable amounts of Mo in a range of 0-1% was chosen for the research. The carbon content in the analysed alloys did not exceed 3,1%. The studies of crystallization process were based on thermal - derivative analysis (TDA). Chemical composition of all examined samples was analysed with the use of LECO spectrometer. Additionally, the carbon and the sulphur content was determined basing on carbon and sulphur LECO analyser. For metallographic examination, the scanning electron microscopy (SEM) with EDS analyser was used. Disclosed phases have been also tested with the use of X-ray diffraction. The results allowed the description of crystallization processes of silicon molybdenum ductile cast iron using thermal - derivative analysis (TDA). Conducted studies did not allow for the clear identification of all complex phases containing molybdenum, occurring at the grain boundaries. Therefore, the further stages of the research could include the use of a transmission electron microscope to specify the description of complex compounds present in the alloy.

ABSTRACTElectrical resistivity of commercially produced plain carbon manganese steel has been experimentally measured at room temperature (28–30°C) using four-probe method. Resulting data were used to generate both regression based and artificial neural network-based models for prediction of electrical resistivity from the chemical composition of steel. It was found that both models were capable of predicting the resistivity within ±5% error band. Analysis of data also indicated carbon to be the most influential element to increase resistivity followed by manganese and silicon. A comprehensive literature review indicates no such advanced resistivity prediction model is available in the public literature for commercially produced steel with wide variation in carbon content (0.03 0.85 wt-%), manganese content (0.35–1.50 wt-%) and silicon content (0.015–0.90 wt-%).