Steel Temperature Research Articles

Revealing dislocation activity modes during yielding and uniform deformation of low-temperature tempered steel by acoustic emission

Revealing dislocation activity modes during yielding and uniform deformation of low-temperature tempered steel by acoustic emission

Influence of Mo carbides and two-stage tempering methodology on the susceptibility of medium carbon martensitic steel to hydrogen embrittlement

The objective of the present study was to enhance the hydrogen embrittlement (HE) resistance of the quenched and tempered martensitic steel via the interplay of heat treatment variance and precipitation of nanosized carbides. For this purpose, one-stage tempering and two-stage tempering methodologies were implemented, and steel was alloyed with Mo to instigate the precipitation of Mo carbides. The results revealed that two-stage tempered steel exhibited superior resistance to HE, as a result of reduced dislocation density and higher quantity of Mo2C. To discern the role and trapping behaviour of Mo2C carbides, Thermal Desorption Spectroscopy (TDS) combined with electrochemical hydrogen charging was utilized. Precipitated nanosized Mo2C exhibited the ability to trap hydrogen. On the contrary, an increase of dislocations and higher diffusible hydrogen content in one-stage tempered steel promoted deterioration of mechanical properties which was investigated by Slow Strain Rate Test (SSRT) and fracture surface morphology analysis. In addition, the effective diffusion coefficient for one-stage tempered steel was lower, as dislocations served as additional trap sites.

Effect of hydrogen on the pitting mechanism of tellurium-modified 38MnVS6 non-quenched and tempered steel in alkaline NaCl solution

This study investigates the influence of hydrogen on the pitting behavior of tellurium-modified 38MnVS6 Non-quenched and Tempered steel (NQTS). Hydrogen primarily accumulates at the interface between inclusions and the matrix, reducing the local pH of pits in corrosive solution. For specimens charged for 12 h, hydrogen blisters gradually rise in size and number with the increase of tellurium (Te) content, resulting in higher local stress and facilitating the pitting formation.

The surface tension of boiling steel surfaces

Material properties of metals and metal alloys at high temperatures are often unknown, but necessary to understand physical mechanisms for prediction and improvement of high temperature processes, such as laser beam technologies. Surface tension is an elementary property that was measured in this study above the boiling temperature of steel using a laser-induced vapor channel in a steel substrate and the extraction of the vapor channel diameter from in-situ X-ray observations. The measurement principle is based on the pressure balance inside the keyhole, where the recoil pressure from keyhole wall vaporization works against the surface tension pressure from the surrounding melt pool. An increase in surface tension at increasing temperatures above the boiling point was measured against theoretical expectations. In order to create the keyhole shapes measured, the surface tension must increase to counterbalance the increasing recoil pressure.

Failure analysis and heat treatment process optimization of NOS525 rotary flat binaural hot forging die

A NOS525 steel rotary flat binaural hot forging die frequently failed too early due to collapse and wear in service. In this paper, the rotating planar binaural failure die was taken as the research object. Firstly, the sample design and sample processing of the failure die with different depths and directions were carried out. The material composition, hardness and metallographic structure of the samples were analyzed, and the tensile and impact properties were tested by direct-reading spectrometer, Rockwell hardness tester, optical microscope, scanning electron microscope, MTS testing machine and impact testing machine, etc. Meanwhile, with the help of Deform-HT software and JMatPro software, the coupling simulation of hardness, temperature and structure of the heat treatment of NOS525 steel was realized, and the hardness change of the material after heat treatment was predicted and verified by heat treatment test. The research results show that the composition of the die material is abnormal, especially the excessive P and C elements, at the same time, there is a phenomenon of quenching overheating and insufficient tempering, resulting in insufficient strength, toughness and hardness of the die material, in addition, the die surface is subjected to the cold and hot alternating load, and finally, the die surface collapses and wear too soon, resulting in its failure. Through the combination of heat treatment simulation optimization and test, the optimal heat treatment temperature of NOS525 steel was finally determined, that is, the quenching temperature of 1090℃, the first tempering temperature of 560℃, the second tempering temperature of 600℃, the third tempering temperature of 600℃, so that the mechanical properties of the material have been significantly improved.

Prediction of martensite start temperature of steel combined with expert experience and machine learning

ABSTRACT The martensite start temperature (MS) plays a pivotal role in formulating heat treatment regimes for steel. This paper, through the compilation of experimental data from literature and the incorporation of expert knowledge to construct features, employs machine learning algorithms to predict the MS of steel. The study highlights that the ETR algorithm attains optimal prediction accuracy, and the inclusion of atomic features enhances the model’s performance. Feature selection is accomplished by evaluating linear and nonlinear relationships between data using the Pearson correlation coefficient (PCC), variance inflation factor (VIF), and maximum information coefficient (MIC). Subsequently, the performance of machine learning models on unknown data is compared to validate the model’s generalization ability. The introduction of SHAP values for model interpretability analysis unveils the influencing mechanisms between features and the target variable. Finally, utilizing a specific steel type as an illustration, the paper underscores the practical value of the model.

A new method for predicting reference temperature of ferritic steel using finite element analysis and master curve method

Fracture toughness is an important parameter for measuring material properties in industry. In order to obtain data on the fracture toughness of a material it is often necessary to carry out a large number of fracture toughness experiments. The cost of the experiments is high and the experimental process is cumbersome. Moreover, in practice, there are often not a large number of materials that meet the requirements for the experiment. The relationship between different experimental ambient temperatures and fracture toughness can be determined by determining the reference temperature T0 in the master curve method. In this paper the load-displacement curves of the specimens are obtained using finite element analysis. Determine the J integrals corresponding to the initiating load Pint and the maximum load Pc on the load-displacement curve using the Load Slope Compliance Changing Rate method. Determine the reference temperature T0 in conjunction with the single temperature method. On the one hand, in order to validate the method, finite element analyses were performed on specimens of different thicknesses, temperatures, materials and types. This method was also applied to obtain fracture toughness data and to determine the reference temperature T0. On the other hand, combining the master curve method and combining the experimental data of 16MnDR, 16MnR and 20MnMoNi55 ferritic steels were verified. The experimental results show that the results predicted by this method are in good agreement with the actual results. However, it has a certain conservative type. It provides a good solution for practical engineering. The accuracy of fracture toughness and reference temperature of ferritic steels of different thicknesses can be verified by few experiments.

Unleashing the potential of nano-bainite bearing steels: Controllable selection of microstructure evolution enables concurrent improvement of toughness and hardness by pre-cold deformation

In the face of increasingly demanding service conditions for high-end bearings, the need for bearing steels that demonstrate superior mechanical properties, specifically a robust balance of strength and toughness, is growing. The adoption of near-net-shaped cold rolling methods has emerged as a leading international technology to enhance bearing performance. In this study, the effect mechanisms of pre-cold deformation on the phase transformation kinetics, microstructure evolution and mechanical properties of nano-bainite bearing steels are systematically studied. Results reveal that pre-cold deformation treatment effectively reduces the Ac1 temperature of the experimental steel, expedites nano-bainite transformation and promotes strong variant selection during bainite phase transformation. With the increase in pre-cold deformation from 0 % to 50 %, the size of the prior austenite grains decreases from 6.7 μm to 3.3 μm, and the size of carbides in specimens becomes more uniform, whilst the refinement of austenite grains can considerably reduce the thickness of bainitic ferrite plates. The decrease in grain size and the increase in V1–V2 variant pairs lead to an improvement in the toughness of the nano-bainite bearing steel from 64.9 J/cm2 to 114.3 J/cm2. A marginal increase in the hardness of the bearing steel is observed with the increase in pre-cold deformation. Furthermore, this study discusses the evolution mechanism of microstructure in the subsequent phase transformation by different microstructures in the early stage. Results show that pre-cold deformation leads to the transition of the martensite twin substructure in the specimens from {112} <111> twins to twinned variants. These insights offer critical understanding into the microstructural underpinning the enhanced performance of nano-bainite bearing steels subjected to pre-cold deformation treatment.

Oil and Gas Structures: Forecasting the Fire Resistance of Steel Structures with Fire Protection under Hydrocarbon Fire Conditions

The hydrocarbon temperature–time curve is widely used instead of the standard curve to describe the temperature in the environment of structural surfaces exposed to fire in oil and gas chemical facilities and tunnels. This paper presents calculations of the ratio of time to reach critical temperatures at different nominal fire curves for steel structures such as bulkheads and columns with different types of fireproofing. The thermophysical properties of the fireproofing materials were obtained by solving the inverse heat conduction problem using computer simulation. It was found that the time interval for reaching critical temperatures in structures with different types of fireproofing in a hydrocarbon fire decreased, on average, by a factor of 1.2–1.7 compared to the results of standard fire tests. For example, for decks and bulkheads with mineral wool fireproofing, the K-factor of the ratio of the time for reaching the critical temperature of steel under the standard curve to the hydrocarbon curve was 1.30–1.62; for plaster, it was 1.56; for cement boards, it was 1.34; for non-combustible coatings, it was 1.38–2.0; and, for epoxy paints, it was 1.71. The recommended values of the K-factor for fire resistance up to 180 min (incl.) were 1.7 and, after 180 min, 1.2. The obtained dependencies would allow fireproofing manufacturers to predict the insulation thickness for expensive hydrocarbon fire experiments if the results of fire tests under standard (cellulosic) conditions are known.

Influence of Nickel on Microstructure and Mechanical Properties in Medium-Carbon Spring Steel.

The effects of adding nickel on the phase transition temperature, microstructure, and mechanical properties of medium-carbon spring steel have been investigated. The results show that adding nickel reduces the martensite start (Ms) temperature, improves hardenability, and refines the sub-microstructure of the martensite, thereby improving yield stress. The yield strength of martensitic steel increases by approximately 100 MPa due to a synergistic combination of grain refinement strengthening and dislocation strengthening, with an increase in the nickel content from 0 wt.% to 1 wt.%. The cryogenic impact toughness of martensitic steel also improved with a higher nickel content due to packet and block refinement and an increase in the proportion of high-angle grain boundaries (HAGBs).

Analysis of Shift in Nil-Ductility Transition Reference Temperature for RPV Steels Due to Irradiation Embrittlement Using Probability Distributions and Gamma Process

Reactor pressure vessel (RPV) steels are highly susceptible to irradiation embrittlement due to prolonged exposure to high temperature, high pressure, and intense neutron irradiation. This leads to the shift in nil-ductility transition reference temperature—∆RTNDT. The change in ∆RTNDT follows a certain distribution pattern and is impacted by factors including chemical composition, neutron fluence, and irradiation temperature. Existing empirical procedures can estimate ∆RTNDT based on fitting extensive irradiation embrittlement data, but their reliability has not been thoroughly investigated. Probability statistical distributions and the Gamma stochastic process were performed to model material property degradation in RPV steels from a pressurized water reactor due to irradiation embrittlement, with the probability models considered being normal, Weibull, and lognormal distributions. Comparisons with existing empirical procedures showed that the Weibull distribution model and the Gamma stochastic model demonstrate good reliability in predicting ∆RTNDT for RPV steels. This provides a valuable reference for studying irradiation embrittlement in RPV materials.

Effects of Mg–Ca treatment and Ca treatment on impact toughness and morphology of sulfides in 45MnVS non-quenched and tempered steel

Effects of Mg–Ca treatment and Ca treatment on impact toughness and morphology of sulfides in 45MnVS non-quenched and tempered steel

Effect of V and Mn addition on the HAZ softening and tensile properties of friction stir welded martensitic steel

This study explores the suppression of heat-affected zone (HAZ) softening in friction stir welding (FSW) joints formed above the A3 temperature of martensitic steels by leveraging short-time secondary hardening induced by vanadium (V) addition. The HAZ microstructure was examined using scanning electron microscopy (SEM), transmission electron microscopy (TEM), and atom probe tomography (APT). Mechanical properties were evaluated through Vickers hardness (HV) measurements and tensile tests. Analysis of hardness distribution indicates effective HAZ softening suppression with V addition, attributed to the precipitation of fine and high-density V carbides within the HAZ. The inhibition of HAZ softening was correlated not only with V content but also with the A1 temperature of the steels, regulated by the manganese (Mn) content. However, at 1 mass% V content, intergranular brittle fracture occurred within the HAZ due to continuously distributed fine carbides and Mn segregation along the prior austenite boundary.

Irradiation Effects on Tensile Properties of Reduced Activation Ferritic/Martensitic Steel: A Micromechanical-Damage-Model-Based Numerical Investigation

The tensile properties of reduced activation ferritic/martensitic (RAFM) steels are significantly influenced by neutron irradiation. Here, a mechanism-based model taking account of the typical ductile damage process of void nucleation, growth, and coalescence was used to study the temperature and irradiation effects. The elastic–plastic response of RAFM steels irradiated up to 20 dpa was investigated by applying the GTN model coupled with different work hardening models. Through a numerical study of tensile curves, the GTN parameters were identified reasonably and satisfying simulation results were obtained. A combination of Swift law and Voce law was used to define the flow behavior of irradiated RAFM steels. The deformation localization could be adjusted effectively via setting the nucleation parameter εn close to the strain where necking occurs. Because εn changed with uniform elongation, εn decreased with the testing temperature and rose with an irradiation temperature above 300 °C. The nucleation parameter fn increased with the testing temperature for RAFM steels before irradiation. For irradiated RAFM steels, fn barely changed when the irradiation temperature was below 300 °C and then it rose at a higher irradiation temperature. Meanwhile, the ultimate strength of the simulated and experimental curves showed good agreement, indicating that this method can be applied to engineering design.

Surface Tension Estimation of Steel above Boiling Temperature

Surface tension is an important characteristic of materials. In particular at high temperatures, surface tension values are often unknown. However, for metals, these values are highly relevant in order to enable efficient industrial processing or simulation of material behavior. Plasma, electron or laser beam processes can induce such high energy inputs, which increase the metal temperatures to, and even above, boiling temperatures, e.g., during deep penetration welding or remote cutting. Unfortunately, both theoretical and experimental methods experience challenges in deriving surface tension values at high temperatures. Material models of metals have limitations in explaining complex ion interactions, and experimentally measuring temperature and surface tension at high temperatures is a challenge for methods and equipment. Therefore, surface wave analysis was conducted in this work to derive surface tension values around the boiling temperature of steel and identify trends. In addition, a simple ion interaction calculation was used to simulate the impacting parameters that define the surface tension. Since both the experimental values and simulation results indicate an increasing trend in surface tension above the boiling temperature, it is concluded that the dominating attractive forces above this temperature should increase with increasing temperature and lead to increasing surface tension forces in the surface layers of liquid metal.

A data-driven approach to model the martensitic transformation temperature in strain-induced metastable austenitic steels

Strain-induced metastable austenitic stainless steels form an important class of materials in metallurgical industries for their wide range of applications. These steels undergo austenite-martensite phase transformation at temperatures above martensite start temperature, Ms, at which martensite is formed on mechanical deformation, also known as the Md temperature. Md temperature depends on several factors related to the steel and is important from alloy design perspective. In the literature, there are quite a few equations based on composition of the steels for the prediction of Md temperature. However, it is well known that the transformation from austenite to martensite is dependent on the austenite grain size as well as deformation conditions i.e. strain, strain rate and temperature of deformation. In the present work, the role of those parameters has also been considered. The model is implemented using fourteen input parameters viz., composition, grain size, amount of strain, temperature of deformation, and strain rate. The architecture of the neural network model is optimized rigorously to predict the Md temperature on a par with actual value. It has been shown that grain size and strain rate have very negligible influence whereas strain and temperature of deformation have quite strong role. Md temperature is increased with increasing strain whereas the temperature of deformation shows opposite dependence on it. An empirical equation thus, has been established to calculate the Md temperature of a steel as a function of its composition, grain size, temperature of deformation and strain. The final optimized model is then deployed to predict the Md temperature of different steels and predictions are found to be in close agreement to the experimentally measured Md temperatures. The developed model is general and can be extended to include other parameters as well as various other steel alloys.

Enhancing the Tensile Properties and Ductile-Brittle Transition Behavior of the EN S355 Grade Rolled Steel via Cost-Saving Processing Routes.

Structural rolled steels are the primary products of modern ferrous metallurgy. Consequently, enhancing the mechanical properties of rolled steel using energy-saving processing routes without furnace heating for additional heat treatment is advisable. This study compared the effect on the mechanical properties of structural steel for different processing routes, like conventional hot rolling, normalizing rolling, thermo-mechanically controlled processing (TMCP), and TMCP with accelerating cooling (AC) to 550 °C or 460 °C. The material studied was a 20 mm-thick sheet of S355N grade (EN 10025) made of low-carbon (V+Nb+Al)-micro-alloyed steel. The research methodology included standard mechanical testing and microstructure characterization using optical microscopy, scanning and transmission electronic microscopies, energy dispersive X-ray spectrometry, and X-ray diffraction. It was found that using different processing routes could increase the mechanical properties of the steel sheets from S355N to S550QL1 grade without additional heat treatment costs. TMCP followed by AC to 550 °C ensured the best combination of strength and cold-temperature resistance due to formation of a quasi-polygonal/acicular ferrite structure with minor fractions of dispersed pearlite and martensite/austenite islands. The contribution of different structural factors to the yield tensile strength and ductile-brittle transition temperature of steel was analyzed using theoretical calculations. The calculated results complied well with the experimental data. The effectiveness of the cost-saving processing routes which may bring definite economic benefits is concluded.

Intelligent prediction for the temperature of molten steel after RH treatment

The cost of steelmaking can be saved by $0.1 per ton steel if the temperature of molten steel after RH (Ruhstahl Heraeus) treatment drops by 1 degree, so it is necessary to control the temperature of molten steel after RH treatment accurately in order to satisfy the superheat of molten steel in tundish. Such a temperature control depends on the hit rate greater 90% if the error range is [Formula: see text]. It is more than 10 years since intelligent models were introduced to predict the temperature of molten steel in RH, but such an issue is still far from being solved. Thus, it is necessary to find some methods to solve this problem from the viewpoints of data preprocess, feature selection and data modelling. Numerical results show that there are nine key factors to affect the temperature of molten steel after RH treatment: the temperature, the oxygen mass fraction and the carbon mass fraction of molten steel before RH treatment, the vacuum time, and the oxygen consumption, the oxygen mass fraction after decarburisation, the aluminium consumption, the thickness of the slag layer, the headroom. And these nine key factors can be reduced to six principal components by the principal component analysis method. Especially, data preprocess can deduce the prediction error and improve the hit rate effectively. If the error range is [Formula: see text], the hit rate is up 45% for back-propagation neural network and back-propagation neural network optimised by particle swarm optimisation when the data set jumps from 711 samples to 471 samples, and the hit rate reach 92.2% in the case of back-propagation neural network optimised by particle swarm optimisation after data preprocess and feature selection.

Combined effect of natural fibre and steel fibre on the thermal-mechanical properties of UHPC subjected to high temperature

This research investigates the combined effect of steel and natural fibre on the mechanical properties and spalling resistance of ultra-high performance concrete (UHPC) before and after exposure to elevated temperatures. The effects of two parameters, namely temperature (ambient temperature, 200 °C, 400 °C and 600 °C) and fibre type (solely macro end hooked steel fibre (MHS) and hybrid of micro straight steel fibre (MSS), macro end hooked steel (MHS) fibre and micro jute (MJ) fibre) on the mechanical properties, spalling resistance and mass loss of UHPC were studied.Micrographs from scanning electron microscope (SEM) test were utilized to appraise the steel fibre and jute fibre before and after exposure to elevated temperatures. The test results exhibited that the residual tensile strength and flexural strength were significantly declined after exposure to high temperatures for UHPC specimens reinforced with macro steel fibre only. Moreover, explosive thermal spalling occurred for this group of specimens under 600 °C. Whereas the combined use of jute fibre and micro-macro steel fibre was an effective approach to prevent the explosive thermal spalling under 600 °C and improve the residual tensile strength, flexural strength and toughness of UHPC. Furthermore, preliminary empirical expressions have been developed in this study to predict the residual mechanical properties of UHPC as a function of natural fibre content, steel fibre content and temperature.

Process control and energy saving in the ladle stage of a metal casting process through physics-based and ANN-based modelling approaches

The process and temperature control of metal casting applications is of utmost importance both to guarantee the good quality of the final product and also to pursue an energy saving policy. For this purpose, in this paper two different modelling approaches have been proposed to predict the liquid steel temperature inside a ladle for metal casting, shortly before the casting process. The first is a physics-based grey-box model relying on equations for the characterization of the heat transfer mechanisms inside the ladle structure, whereas the second approach relies on artificial neural networks (ANNs). Both methods have been calibrated with experimental data of a case study plant, and subsequently assessed and compared in terms of prediction accuracy. Results show that the physics-based approach is able to predict the casting temperature with a higher mean absolute error (MAE) of 14 °C, whereas the ANNs predictions result to be better, with MAEs around 6 °C. On the other hand, it has been demonstrated that the ANNs approach may lack of reliability, especially if input data strongly differ from the calibration dataset, whereas the physics-based approach results to be more consistent and trustworthy. Finally, an energy analysis is conducted to demonstrate the feasibility of the model in evaluating the potential energy saving compared with situations in which decisions are taken by operators without the aid of a model predictive control.