Austenite Formation Temperature Research Articles

Kinetics of Austenite Formation in a Medium-Carbon, Low-Alloy Steel with an Initial Martensite Microstructure: Influence of Prior Austenite Grain Size

The impact of prior austenite grain size (PAGS) on the kinetics of austenite formation with an initial martensite microstructure was investigated in a medium-carbon, low-alloy steel. Two distinct PAGS of 117 and 330 μm, representing the range of grain sizes encountered in industries, were considered. In this analysis, high-resolution dilatometry was used to study the formation of austenite during continuous heating experiments. The analysis of the dilatometry results revealed that grain refinement accelerated the rate of austenite formation without impacting its austenite formation temperature. Intermittent quenching tests were conducted to elucidate the nucleation and growth mechanisms of austenite formation using a combination of optical, scanning electron microscopy (SEM), and electron backscatter diffraction (EBSD). The differences in austenite formation kinetics as a function of prior austenite grain size were quantified and modeled in the framework of diffusion-controlled nucleation and growth theories using the genetic algorithm optimization.

Chemical Composition Optimization and Isothermal Transformation of δ‐Transformation‐Induced Plasticity Steel for the Third‐Generation Advanced High‐Strength Steel Grade

A new low‐manganese transformation‐induced plasticity steel is designed with optimized nickel content to achieve superior strength and ductility while minimizing the use of expensive nickel. The steel is optimized using JMatPro software, then cast, and hot rolled. To assess the effect of intercritical annealing on austenite (martensite at room temperature) volume fraction and carbon content, hot‐rolled steel samples quenched from different annealing temperatures (680–1100 °C) are used. Additionally, hot‐rolled steel coupons are intercritically annealed at about 50% austenite formation temperature (740 °C) and then subjected to isothermal treatments at 300–425 °C for varying times (10–90 min). After optimizing these treatments to maximize retained austenite (RA), tensile specimens are heat‐treated first at 740 °C and then isothermally at 325 °C. Thermodynamic calculations suggest that aluminum combined with silicon may lead to the δ ferrite formation, and even minimal nickel content can stabilize a considerable amount of austenite. In the experimental studies, it is shown that lower‐temperature bainitic holding enhances austenite stability by enriching the carbon content. Optimized two‐stage heat treatments yield up to 25.8% RA, with a tensile strength of 867.2 MPa and elongation of 40.6%, achieving a strength‐elongation product of 35.2 GPa×%, surpassing the third‐generation advanced high‐strength steel grades minimum requirement of 30 GPa×%.

Phase transition kinetics and microstructures of A508-3 steel under continuous cooling process

The phase transition kinetics of A508-3 steel during continuous cooling were studied by high temperature transition instrument which was based on thermodilatometry in this paper. The microstructures during continuous heating and cooling were also observed. The transition points were determined to be austenite formation temperatures, A c1 = 757 °C, A c3 = 823 °C, and martensitic transformation start and final temperatures, Ms = 382 °C, Mf = 222 °C at the cooling rate of 5 °C/s. The curves of Austenite transition fraction and time during continuous heating show a typical S shape. And the Austenite will show a maximum transition rate value at the middle temperature. The volume fraction of new phases translated from Austenite as functions of time and temperature during the cooling process can be obtained using the lever law. The pearlite transitions can occur in high temperature region at cooling rate smaller than 0.1 °C/s. And Bainite can be gotten at a lower temperature region. The S type curves can be used to describe the relationships between the transition volume fraction and time. Increasing the cooling rate enhances the volume fraction of Martensite at the cooling rate ≥5 °C/s. And only Martensite transition can be observed at the cooling rate > 10 °C/s. And Ms and Mf increase with increasing cooling rate. The Martensite transition kinetics equation with changing temperature were obtained. Finally, the continuous cooling transformation curves of A508-3 steel was constructed, which can be used to predict the microstructure and design the heat treatment technologies.

Microstructural mechanisms controlling the mechanical behaviour of ultrafine grained martensite/austenite microstructures in a metastable stainless steel

This study unravels the microstructural mechanisms controlling the mechanical behaviour and austenite mechanical stability in ultrafine grained austenite/martensite (α′/γ) microstructures, created varying the austenitisation heating rate (0.1–10 °C/s) and temperature within the start-finish austenite formation temperatures (AS − AF) in a cold-rolled semi-austenitic stainless steel. A wide spectrum of strength-ductility combinations; i.e. strengths of 900–2100 MPa and elongations up to 25%, were characterised by sub-size tensile testing. The nanoprecipitation of Ni3(Ti,Al) in martensite during heating to low austenitisation temperatures rises the strength, while the martensite recovery, enhanced at low heating rates, improves the work-hardening. The high strength of martensite partially suppresses the formation of mechanically-induced martensite during loading, which is enabled with the increase of austenite volume fraction and contributes positively to the work-hardening. The heating rate barely affects the mechanical properties of microstructures austenitised close to AF. The austenite ultrafine grain size controls the yield strength, while the decrease in austenite mechanical stability and the α′/γ composite effect increase remarkably the work-hardening with respect to dual (α′/γ) microstructures with larger martensite volume fractions and fully austenitised microstructures. These results will enable the design of microstructures with controlled mechanical behaviour for a wider spread use of similar steel grades.

The effect of increasing Cu and Ni on a significant enhancement of mechanical properties of high strength low alloy, low carbon steels of HSLA-100 type

High strength low alloy steels (HSLAs) containing copper, nickel and low amount of Carbon are a new generation of high strength low alloy steels that are widely used in shipbuilding. Strengthening elements such as Cu, Ni, and to some extend C, play a significant role in this family of steels. By increasing the amount of Cu, Ni, and by selection of proper final temperature of rolling, austenitization and aging, it is possible to enhance the yield strength of this steel up to 160 ksi (1102 MPa) while at the same time toughness remains at an acceptable level. In this investigation, the effect of increasing Cu and Ni on mechanical properties of these steels was elaborated. Four steels with varying amounts of Cu and Ni were cast, and hot rolled by a thermomechanical controlled processing. The steels were then austenitized at 920 °C for 45 min followed by quenching in water. Finally, the steels were aged at 650 °C for one hour. Optical microscope, and Filed Emission Scanning Electron Microscope (FE-SEM) were used to study microstructural evolution. Mechanical properties variation was evaluated by tensile and microhardness tests. Results showed that by increasing the amount of these two elements, hardness and tensile strength were increased in thermomechanically controlled processed, austenitized and aged specimens. Yield strength values of the rolled samples were somehow scattered which was due to a slight difference in final rolling temperature and the subsequent rapid cooling. Yield strength under austenitization and quenching conditions increased by increasing the amount of Cu and Ni which could be due to the presence of Nb (C, N) precipitates. Increasing the amount of Cu precipitates after quench could also be another reason for yield strength increment. Increasing the amount of Cu and Ni in samples containing 4.16 and 7.65 wt% for each of these alloys led to the reduction of Ac1 (austenite formation temperature). Selecting aging temperature (650 °C) higher than the austenite formation temperature could probably lead to the growth of the carbides, softening of martensite layers and lower amount of dislocations density hence for the reduction of yield strength of the aged steels. As well, with the addition of the mentioned amounts of Cu and Ni, yield strength of 705 MPa (102.2 ksi), tensile strength of 1072 MPa (155.4 ksi), elongation of 14% and area reduction of 63% were obtained in the aged steels of this investigation.

Investigation and analysis of the possibility of diffusionless phase transformations in the surface layer of a part under the action of grinding temperatures

The possibility of diffusionless phase transformations in the surface layer of the ground part under the influence of instantaneous grinding temperature is investigated and analyzed. This is important because the phase Feα-Feγ transformations that may occur when grinding parts lead to the appearance of so-called grinding burns, which 2-3 times reduce the reliability and durability of the part in the working mechanism. The mechanism of phase transformations, the critical temperature of these transformations and the associated processing modes that provide this temperature are determined. This allows a reasonable approach to the definition of grinding modes and, if necessary, the application of the cooling method. In addition, the problem of optimizing grinding regimes can be solved if the processing performance is taken as a target function, and grinding temperature as a limitation. With rapid surface heating of the hardened steel part by the grinding temperature above the Ac1 line, there is a reverse martensitic transformation Feα→Feγ. The martensite range during cooling Mn-Mk to a large extent covers negative temperatures. Therefore, austenite is partially fixed in the surface layer, forming a so-called quenching burn. Dependences for determining the formation temperature for steel of any chemical composition, give the possibility to maintain the value of grinding temperature below this level during grinding. The mechanism of diffusionless reverse martensitic transformation in the high-speed surface heating by cutting grains (instantaneous temperature) is considered. The heating rate and the effect of the pressure produced by the abrasive grain on the metal during the chip removal are experimentally determined. Thus, the possibility of diffusionless phase transformation is substantiated and the dependences for the calculation of austenite formation temperatures are given, which in turn provides the opportunity to calculate safe processing regimes

Functionally-graded shape memory alloy by diffusion annealing of palladium-coated NiTi plates

Diffusion annealing of palladium-coated Ti-Ni plates was performed at temperatures ranging from 900 °C to 1,000 °C, to accomplish a compositional gradient in Ti-rich, Ti-Ni shape memory alloys. The aim of this study was to increase the transformation temperatures and transformation temperature intervals. Palladium diffusion profiles were measured by energy dispersive spectroscopy, and the corresponding approximate diffusion coefficients of the annealed specimens were calculated. The Gaussian solution of Fick’s second law for the one-dimensional lattice diffusion of a tracer was used. The transformation behavior studies were performed by differential scanning calorimetry. It was depicted that annealed specimens show longer transformation intervals compared to the bare alloy. In addition, annealed specimens showed improved shape memory properties that were attributed to the lower amount of Ti2Ni precipitates in the diffusion layer. The shape memory behaviour of the samples was detected using micro-indentation at room temperature, followed by heating them above the austenite formation temperature to calculate the shape recovery ratio.

Martensite transformations in the surface layer at grinding of parts of hardened steels

The mechanism of direct and inverse martensitic transformation in the surface layer of a ground part of hardened steel under the influence of the grinding temperature was investigated. It is shown that the reverse martensitic transformation is carried out during grinding according to the martensite-perlite-austenite scheme. The possibility of high-speed tempering of martensite to perlite is shown, under the action of a contact grinding temperature, which, with a further increase in temperature, turns into austenite, forming an extremely harmful defect in the working surface –quenching burn. In the present work, it is shown that the quenching burn in the form of austenite is happened by the M–P–A diffusion mechanism. This made it possible to obtain graphical and analytical dependencies, using which it is possible to calculate the Ac1 temperature points of the formation of austenite for virtually any steel grade depending on the content of carbon and alloying elements. The latter circumstance makes it possible to create such grinding conditions (the type of abrasive, the characteristics of the wheel, the use of coolant, treatment regime), at which the austenite formation temperature is not attained and the quenching does not happen. Thus, as a result of the studies, it is possible to determine safe grinding temperatures for steels of different chemical compositions and treatment regimens that do not cause an increase in the grinding temperature above a certain level.

Prediction of Austenite Formation Temperatures Using Artificial Neural Networks

For the modeling and design of heat treatments, in consideration of the development/ transformation of the microstructure, different material data depending on the chemical composition, the respective microstructure/phases and the temperature are necessary. Material data are, e.g. the thermal conductivity, heat capacity, thermal expansion and transformation data etc. The quality of thermal simulations strongly depends on the accuracy of the material data. For many materials, the required data - in particular for different microstructures and temperatures - are rare in the literature. In addition, a different chemical composition within the permitted limits of the considered steel alloy cannot be predicted. A solution for this problem is provided by the calculation of material data using Artificial Neural Networks (ANN). In the present study, the start and finish temperatures of the transformation from the bcc lattice to the fcc lattice structure of hypoeutectoid steels are calculated using an Artificial Neural Network. An appropriate database containing different transformation temperatures (austenite formation temperatures) to train the ANN is selected from the literature. In order to find a suitable feedforward network, the network topologies as well as the activation functions of the hidden layers are varied and subsequently evaluated in terms of the prediction accuracy. The transformation temperatures calculated by the ANN exhibit a very good compliance compared to the experimental data. The results show that the prediction performance is even higher compared to classical empirical equations such as Andrews or Brandis. Therefore, it can be assumed that the presented ANN is a convenient tool to distinguish between bcc and fcc phases in hypoeutectoid steels.

Correlation between warm deformation characteristics and mechanical properties of a new TRIP-assisted Fe–MN–Ni steel

The warm deformation behavior of a Fe–12Mn–3Ni steel was investigated and the governing deformation mechanisms were determined. The compressive deformation was applied at various temperatures from room temperature to reverse austenite formation temperature. The subsequent mechanical properties were evaluated through a shear punch testing method. The phase characterization was carried out using scanning electron microscopy, X-ray diffraction analysis and ferritescope measurements. The results showed that the dynamic α′-martensite transformation occurs during deformation at relatively low temperatures (below 100°C). In the moderate temperature range (i.e. 200–400°C), α′-martensite formation takes place holding a lower kinetic at upper temperature limit due to the higher stability of austenite. At temperatures around 500°C, the fraction of α′-martensite decreases due to α′→γ reverse transformation phenomenon. These findings are confirmed by stacking fault energy and γ→α′ chemical driving force thermodynamic calculations. According to shear punch results on processed specimens, a good combination of strength and ductility is achieved for the deformed material at 500°C. This is related to the occurrence of TRIP effects due to the higher fraction of retained austenite in the microstructure.

Bayesian Regularization Neural Networks for Prediction of Austenite Formation Temperatures (Ac1 and Ac3)

A neural network with a feed forward topology and Bayesian regularization training algorithm is used to predict the austenite formation temperatures (Ac1 and Ac3) by considering the percentage of alloying elements in chemical composition of steel. The data base used here involves a large variety of different steel types such as structural steels, stainless steels, rail steels, spring steels, high temperature creep resisting steels and tool steels. Scatter diagrams and mean relative error (MRE) statistical criteria are used to compare the performance of developed neural network with the results of Andrew's empirical equations and a feed forward neural network with “gradient descent with momentum” training algorithm. The results showed that Bayesian regularization neural network has the best performance. Also, due to the satisfactory results of the developed neural network, it was used to investigate the effect of the chemical composition on Ac1 and Ac3 temperatures. Results are in accordance with materials science theories.

Application of thermal analysis to monitor the quality of hypoeutectic cast irons during solidification in sand and metal moulds

By exploring the effects of carbon equivalent (CE = 3.4–4.2%) and inoculation (Ca, Ba, Al–FeSi alloy), the thermal analysis parameters, representative temperatures and undercooling events, during solidification of cast irons are compared with chill tendency (carbides) in cast irons. Cooling curve analyses were conducted directly in the thermal centre of samples solidified in resin sand and metal moulds to explore the utility of thermal analysis in actual casting shapes. As a reference, a Quik-cup™ system was employed with the same cooling modulus (CM = 0.75 cm). The austenite formation temperature is lower at higher CE, with higher temperatures of both the start of the eutectic reaction and end of solidification. This was more pronounced for metal mould solidification and for un-inoculated irons, respectively. The transition from white iron through mottled iron up to grey iron macrostructure is illustrated by variations of eutectic undercooling compared to the metastable equilibrium temperature, as affected by increasing carbon equivalent and applied inoculation. Inoculation affected both austenite and eutectic formation, by preventing excess eutectic undercooling during solidification, even at low carbon equivalent. The differences in solidification patterns of sand and metal mould castings are discussed, for un-inoculated and inoculated irons, at different carbon equivalent values.

DYNAMIC PROPERTIES FOR MODELING AND SIMULATION OF MACHINING: EFFECT OF PEARLITE TO AUSTENITE PHASE TRANSITION ON FLOW STRESS IN AISI 1075 STEEL

The Pulse-Heated Kolsky Bar Laboratory at the National Institute of Standards and Technology (NIST) has been developed for the measurement of dynamic properties of metals. With this system, a small sample can be pre-heated from room temperature to several hundred degrees C in less than a second, prior to rapid loading in compression at strain rates up to the order of 104 per second. A major focus of this research program has been on investigating the influence of the heating rate and time at temperature on the flow stress of carbon steels, for application to the modeling and simulation of high-speed machining operations. The unique pulse heating capability of the NIST Kolsky bar system enables flow stress measurements to be obtained under conditions that differ significantly from those in which the test specimens have been pre-heated to a high temperature more slowly, because there is less time for thermally activated microstructural processes such as dislocation annealing, grain growth, and solid state phase transformations to take place. New experimental results are presented on AISI 1075 pearlitic steel samples that were pulse-heated up to and beyond the austenite formation temperature of the material (723 °C). The data show that the flow stress decreased by about 50 % due to a phase transformation in the microstructure of the material from the stronger pearlitic phase to the weaker austenitic phase. As a result, the constitutive response behavior of the material cannot be modeled by a fixed-parameter constitutive model, like the Johnson-Cook flow stress model that is widely used in computer simulations of high-speed machining processes.

Austenite Formation Temperature Prediction in Steels Using an Artificial Neural Network

Determination of the temperature at which Austenite is formed is one of the important parameters in the heat treatment process. Chemical composition is an effective factor on these temperatures, particularly in steels that are used in various industries. In this research we have made an attempt to determine these temperatures based on the chemical composition of the steel. The technique used for this purpose is feedforward Artificial Neural Network (ANN) with the Back Propagation (BP) learning algorithm. A comparison is made between Ac1, Ac3 temperatures predicted with this model and those from the empirical equation as well as the experimental values obtained from costly and time-consuming tests in scientific and industrial centers for various steels. This comparison indicates that at Ac1, a better agreement exists between the ANN-predicted results and experimental values than the results from the empirical equation and experimental values. At Ac3, the results from the empirical equation are closer to those of the experimental than those predicted from the ANN. This was due to the dispersion of the data set used.

Materialometrical approach of predicting the austenite formation temperatures

Artificial neural network model—one of materialometrical approaches was developed basing on experimental data collected from domestic and foreign literatures to predict the austenite formation temperatures (Ac3 and Ac1) of steels. Scatters diagrams and statistical criteria showed that the prediction performance of artificial neural network is superior to that of Andrews formulae. Moreover, the quantitative effects of alloying elements on Ac3 and Ac1 temperatures were analysed using neural network models, the results showed that there exists nonlinear relationship between contents of alloying elements and the Ac3 and Ac1 temperatures which is mainly related to the interaction among the alloying elements in steels.

Dynamic recrystallization by rapid heating followed by compression for a 17Ni–0.2C martensite steel

A 17Ni–0.2C martensite steel was rapidly heated below the austenite formation temperature and deformed in compression. Continuous dynamic recrystallization was observed. The behavior is similar to that in a ferrite–pearlite steel but the onset strain is much smaller. Sub-micron ferrite grains were obtained through the dynamic recrystallization.

Gaussian process modelling of austenite formation in steel

The present paper introduces the Gaussian process model for the empirical modelling of the formation of austenite during the continuous heating of steels. A previous paper has examined the application of neural networks to this problem, but the Gaussian process model is a more general probabilistic model which avoids some of the arbitrariness of neural networks, and is somewhat more amenable to interpretation. It is demonstrated that the model leads to an improvement in the significance of the trends of the Ac1 and Ac3 temperatures as a function of the chemical composition and heating rate. In some cases, these predicted trends are more plausible than those obtained with the neural network analysis. Additionally, it is shown that many of the trace alloying elements present in steels are irrelevant in determining the austenite formation temperatures.

Effect of carbon on hot ductility of as-cast low alloy steels.

The effect of C on hot ductility of low alloy steels has been studied in view of surface cracking of continuously cast (CC) slabs. As the ductility was not affected by C content in hot tensile test of the reheated specimens, the well-known C dependency of surface cracking susceptibility in CC slabs can be ascribed to the microstructural change during the solidification process. Austenite grain size of as-cast materials was found to depend largely on C content, i.e., the maximum grain size in 0.10-0.15% C region. This can be explained by the higher austenite formation temperature in these C region. Austenite grain growth rapidly occurred after the complete transformation or solidification into γ phase, as the strong pinning effect of the second phase such as δ-ferrite or liquid phase on γ grain boundary migration was relieved. Carbon dependency of γ grain size became more marked with increasing cooling rate up to that of ordinary continuous casting.Such coarsening of γ grains enhanced intergranular fracture, resulting in ductility loss inversely proportional to the γ grain size. Uneven surface solidification in the mold due to the peritectic reaction will produce much coarse γ structure because of the local delay of cooling. Surface cracking susceptibility will also be largely accelerated by this mechanism. Carbon range where surface cracking susceptibility was the largest varied with the chemical compositions. This shift can be explained in terms of the effect of alloying elements such as Mn on the peritectic composition.

低合金鋼凝固材の高温延性におよぼす炭素の影響

The effect of C on hot ductility of low alloy steels has been studied in view of surface cracking of continuously cast (CC) slabs. As the ductility was not affected by C content in hot tensile test of the reheated specimens, the well-known C dependency of surface cracking susceptibility in CC slabs can be ascribed to the microstructural change during the solidification process. Austenite grain size of as-cast materials was found to depend largely on C content, i.e., the maximum grain size in 0.10-0.15% C region. This can be explained by the higher austenite formation temperature in these C region. Austenite grain growth rapidly occurred after the complete transformation or solidification into γ phase, as the strong pinning effect of the second phase such as δ-ferrite or liquid phase on γ grain boundary migration was relieved. Carbon dependency of γ grain size became more marked with increasing cooling rate up to that of ordinary continuous casting.Such coarsening of γ grains enhanced intergranular fracture, resulting in ductility loss inversely proportional to the γ grain size. Uneven surface solidification in the mold due to the peritectic reaction will produce much coarse γ structure because of the local delay of cooling. Surface cracking susceptibility will also be largely accelerated by this mechanism. Carbon range where surface cracking susceptibility was the largest varied with the chemical compositions. This shift can be explained in terms of the effect of alloying elements such as Mn on the peritectic composition.