Kinetics of Multi-step Austenite to Lath Martensite Transformation in Maraging Steels 300 and 350 During Continuous Cooling

Abstract

Maraging steels are a class of ultra-high-strength materials hardened by the precipitation of intermetallic compounds in a lath martensite matrix. This study comparatively analyzes the martensitic transformation kinetics of maraging steels 300 and 350 during continuous cooling using dilatometry (DIL). Through DIL data analysis, martensite start temperature, M_S, and transformed volume fraction of martensite as function of temperature were determined. The findings demonstrate that both steel grades undergo a multi-stage lath martensitic transformation during continuous cooling. Notably, the higher cobalt content in the maraging steel 350 not only influences the M_S temperature but also induces faster initial transformation kinetics. Consequently, the transformation in the maraging steel 350 proceeds in fewer stages than in the maraging steel 300. This phenomenon is attributed to the cobalt-induced reduction in stacking fault energy (SFE) of austenite, which significantly enhances the autocatalytic nature of the martensitic transformation.

Keywords:
maraging steels; kinetics of martensitic transformation; lath martensite; autocatalysis

1. Introduction

Maraging steels are a class of high-strength martensitic steels recognized for their superior balance of strength and fracture toughness compared to traditional high-carbon steels¹-³. In conventionally processed maraging steels, this property combination is achieved through solution annealing followed by cooling to form a soft, and ductile lath martensite matrix, which is subsequently age-hardened via the fine precipitation of intermetallic compounds¹-⁶. More recently, additive manufacturing technologies have enabled the fabrication of complex maraging steel components; however, this approach introduces greater microstructural heterogeneity compared to conventional processing routes⁵,⁷-⁹. Due to this combination of high strength and toughness, maraging steels are employed in high-performance aerospace, automotive, defense, and nuclear applications⁵,¹⁰.

The continuous cooling transformation from austenite to lath martensite is a diffusionless, shear-dominated phase transformation involving the collective movement of atoms within the crystal lattice without altering chemical composition⁶,¹¹-¹³. For the transformation to initiate at the martensite start temperature (M_s), the thermodynamic driving force, defined by the difference in Gibbs free energy between austenite and martensite, must overcome the interfacial energy and the elastic/plastic strain energy associated with the shape deformation⁶,¹¹,¹².

A significant effect of the martensitic transformation on the microstructure is the induction of a considerable amount of plastic strain, which generates crystal defects such as dislocations, vacancies and atoms in interstitial positions. This occurs because the stress field surrounding the transformed region cannot be fully accommodated by elastic strain alone¹¹,¹². This phenomenon plays a crucial role in maraging steels, as the α’ lath martensite exhibits a high density of dislocation tangles, which in turn induces the precipitation of intermetallic compounds during aging⁴-⁷,¹⁴,¹⁵.

The behavior of the martensitic transformation in maraging steels is usually explained using the Fe-Ni binary system²-⁵. The kinetics of the martensitic transformation are faster than the reactions that lead to equilibrium phases, such as austenite and ferrite. Furthermore, the start temperature of the martensite-to-austenite transformation, A_S, occurs at higher temperatures than those observed for the martensitic transformation start, M_S, due to the thermal hysteresis of these transformations²-⁵,¹⁴,¹⁶. Martensite reversion occurs starting at 520 °C for samples aged for long periods¹⁵ or from 640 °C during continuous heating¹⁴, while the M_S occurs between 170 and 200 °C upon continuous cooling⁴,¹⁴,¹⁶. With this continuous cooling, the driving force for the martensitic transformation increases as the temperature decreases, which results in a lower thermal stability of the austenite³,¹²,¹⁷.

The type of martensite formed in steels, lath martensite or twinned martensite, depends on the M_S temperature and the stacking fault energy (SFE) of the alloy³. Thus, high M_S temperatures and low stacking fault energies induce the formation of lath martensite, whereas low M_S temperatures and high stacking fault energies favor the formation of twinned martensite²,³. Therefore, in maraging steels, the addition of alloying elements that lower the M_S temperature and promote precipitation, such as nickel and molybdenum, is balanced by increasing the cobalt content, which raises the M_S temperature and lowers the stacking fault energy of the austenite²,³,⁵.

From a kinetic perspective, martensitic transformations during continuous cooling can proceed in an athermal (dependent only on temperature) or isothermal (dependent on both temperature and time) modes⁶,¹⁷. However, in continuous cooling of steels, the transformation can be strongly modulated by autocatalytic effects¹⁷-²³. Autocatalysis occurs when the elastic and plastic strain induces the nucletion of new martensite units (such as primary laths or blocks) locally alter the stress state of adjacent parent austenite, creating localized stress concentrations that trigger secondary nucleation events⁶,¹²,¹⁷-²³. When autocatalytic response is intense, it can induce distinct kinetic bursts or multi-stage transformation behavior, where the local kinetics accelerate and decelerate periodically during continuous cooling⁶,²⁴,²⁵.

Variations in the rate of martensitic transformation exhibiting multi-stage behavior can be effectively detected using dilatometry and differential thermal analysis⁶,²⁴,²⁵. Villa et al.²⁴ observed several distinct stages of martensitic transformation during the continuous cooling of a 16Cr-5Ni-3Cu steel. They suggested that this behavior stems from strain accommodation and interfacial energy introduced by the transformation itself, which locally stabilize the austenite and temporarily suppress martensite growth, thereby retarding its kinetics. Consequently, further undercooling is required to increase the thermodynamic driving force and trigger a subsequent acceleration of the process. Similarly, Mao et al.²⁵ identified a multi-stage transformation in RAFM steels and rationalized it using a localized stress field theory. According to this concept, martensite nucleation initiates around stress concentrations at crystal defects, such as dislocation tangles. The formation of these initial martensite units exerts an autocatalytic effect by generating secondary stress fields that promote the nucleation of neighboring clusters. However, if the stress field magnitude in the surrounding austenite becomes insufficient to sustain continuous growth, an additional temperature drop is necessary for stress to re-accumulate at defects and restart the transformation, producing a new kinetic peak. Finally, Loewy et al.⁶ observed a similar multi-stage transformation in an experimental Fe-Ni-Co-Mo maraging steel, proposing that the sequence of kinetic peaks results from the consecutive, coordinated formation of adjacent blocks within a lath martensite packet, where the initial block alters the local strain state of the adjacent austenite to induce subsequent block nucleation.

Although several previous studies have analyzed the kinetics of precipitation and martensite reversion in various grades of maraging steels⁷,¹⁴-¹⁶,²⁶-³⁷, there are still gaps in the literature regarding the kinetic analysis of the martensitic transformation in commercial Ni-Co-Mo-Ti maraging steels. Despite the known effect of cobalt on the M_s temperature of maraging steels⁴,⁵, systematic studies comparing its specific influence on the martensitic transformation kinetics during continuous cooling remain scarce⁶, particularly regarding its effect on the multi-stage transformation behavior in these alloys. In this context, the present study aims to comparatively analyze the martensitic transformation kinetics in maraging steels 300 and 350 during continuous cooling using dilatometry (DIL).

2. Materials and Methods

2.1. Materials and microstructural characterization

The samples were prepared from 10 mm thick disks, which were sectioned from a 98.5-mm diameter maraging steel 300 bar and a 140-mm diameter maraging steel 350 bar. The chemical composition of the bars is presented in Table 1.

Table 1
Chemical composition (wt.%) of the maraging steels 300 and 350 (as-received bars).

Grade	Ni	Co	Mo	Ti	Al	S*	C*	O*	N*
300	18.69	8.99	5.01	0.80	0.086	30	10	9	1.5
350	17.79	11.85	4.83	1.46	0.088	25	25	9	7

• ^*
  in ppm

The microstructural characterization of the steels in the as-received condition was performed using optical microscopy (OM), and X-ray diffraction (XRD). The XRD measurements were carried out on samples mechanically polished with 3 μm and 1 μm diamond paste. For these measurements, a continuous scan with a step size of 0.02º in the 2θ from 40º to 100º was employed using an X’Pert Pro diffractometer. A sample spinner was also used to reduce preferred orientation and improve statistics. Furthermore, optical microscopy investigations were conducted using an Olympus BX60M microscope on samples that were mechanically polished and etched with Vilella’s reagent.

2.2. Dilatometric analysis

Dilatometry is a widely used technique for the kinetic analysis of phase transformations in steels³⁷-⁴². In this study, the dilatometric analysis of the martensitic transformation in the maraging steel 300 and 350 samples was performed using an Adamel-Lhomargy LK02 quenching dilatometer. The specimens were machined to 2 mm in diameter and 12 mm in length, extracted along the radial direction of the disks. Heating was carried out under low vacuum (0.1 mbar pressure) at a heating rate of 1 °C/s, from 20 °C to 900 °C. Subsequently, the samples were held at this temperature for 3 min, followed by cooling at 1 °C/s under a helium gas flow. The dimensional changes of the specimens were monitored using a linear variable differential transformer (LVDT), while temperature changes were recorded using a Type K (chromel-alumel) thermocouple.

It is important to emphasize that the dwell time of 3 min at 900 °C and the cooling rate of 1 °C /s were selected due to the susceptibility of maraging steels to $\text{TiC}$ grain-boundary precipitation in primary austenite. This phenomenon is exacerbated under low cooling rates or prolonged exposure within the 760 to 1010 °C range⁴³,⁴⁴. Furthermore, previous studies report a tendency toward intermetallic phase precipitation within the austenitic field (ausaging) during holding between 500 and 700 °C, which alters matrix composition and consequently affects the M_s temperature⁴,⁴⁵,⁴⁶. Although not a critical factor for the Adamel-Lhomargy LK02 specimen dimensions, excessively high cooling rates can induce significant thermal gradients across the specimen, compromising dilatometric measurement accuracy and leading to discrepancies between measured and actual transformation temperatures⁴⁷. Therefore, the 1 °C/s cooling rate represents an optimal compromise by suppressing alloying element segregation and precipitation in primary austenite while enabling precise dimensional tracking during the martensitic transformation, closely matching the typical air-cooling behavior from 820 °C in industrial processing².

Figure 1 shows that the start (M_S) and finish (M_F) temperatures of the martensitic transformation were estimated by the deviation from linearity of the dilatometric curve. The estimated error for the M_S and M_F temperatures was 2 °C. Furthermore, the volume fraction of formed martensite, f_M, was calculated by applying the lever rule directly to this curve, as described by Equation 1⁶,⁴⁸:

$f_M=\frac{\frac{\text{ΔL}\left(\text{T}\right)}{{\text{L}}_0}-\frac{{\text{ΔL}}_A\left(\text{T}\right)}{{\text{L}}_0}}{\frac{{\text{ΔL}}_M\left(\text{T}\right)}{{\text{L}}_0}-\frac{{\text{ΔL}}_A\left(\text{T}\right)}{{\text{L}}_0}}$ (1)

where ΔL(T)/Lo represents the relative change in length of the specimen as a function of temperature, ΔL_A(T)/Lo is the value on the extrapolated line corresponding to the linear thermal expansion of austenite, and ΔL_M(T)/Lo is the value on the extrapolated line corresponding to the linear thermal expansion of martensite.

Figure 1
Schematic representation of the determination of M_s and M_f temperatures, as well as the transformed fraction during martensitic transformation, derived from the dilatometric curve of maraging steel 350 at a cooling rate of 1 °C/s using an Adamel-Lhomargy LK02 dilatometer.

In addition to obtaining the transformed volume fraction from the dilatometric data, the linear thermal expansion coefficient of the sample, α_L, was obtained through the derivative of the dilatometric curve¹⁴ using Equation 2:

${\alpha }_L=\frac{1}{L_0}\frac{d\left(\Delta L\right)}{dT}$ (2)

where ΔL is the change in length of the specimen, L₀ is the initial length, and T is the temperature. The linear thermal expansion coefficient allows for evaluating the sensitivity of the specimen's dimensional changes as a function of temperature during a phase transformation.

3. Results and Discussions

3.1. Characterization of specimen in ‘as-received’ condition

Figure 2 shows the microstructure of the maraging steels 300 and 350 in the as-received condition, revealed by etching with Vilella's reagent. The images evidence that the samples exhibit a lath martensite microstructure, which is a typical characteristic of maraging steels after solution annealing⁵,⁴⁹-⁵¹, revealing sub-structural features of the lath martensite, as packet and block boundaries for both alloys.

Figure 2
Optical micrographs showing the as-received lath martensite microstructure etched with Vilella's reagent for: (a) maraging 300 steel, and (b) maraging 350 steel⁵¹.

Figure 3 presents the X-ray diffractograms of the samples in the as-received condition. In both cases, a body-centered cubic (BCC) crystal structure typical of lath martensite is observed. Considering the detection limit of this technique, it can be stated that the microstructure is fully martensitic⁵,²⁸,⁵⁰.

Figure 3
X-ray diffractogram for maraging steels 300 and 350 for samples in ‘as-received’ condition⁵¹.

3.2. Dilatometric analysis of martensitic transformation

Figure 4 shows the relative change in length of the specimens during the heating and cooling cycles for the maraging steels 300 and 350. It is noted that the precipitation of intermetallic compounds and martensite reversion induce a dimensional contraction during the heating stage. Conversely, the martensitic transformation induces a relative length change of approximately 0.8% for maraging steel 300, and 0.6% for maraging steel 350 upon cooling, exhibiting remarkable dimensional stability during the phase transformation. This occurs due to the change from the face-centered cubic (FCC) crystal structure of austenite to the body-centered cubic (BCC) structure of martensite, which has a lower atomic packing factor³-⁵,⁷,¹⁴. Furthermore, it is observed that the difference in chemical composition between the analyzed maraging steels influences the behavior of precipitation and reversion during heating⁴,⁷,¹⁴, as well as the martensitic transformation during cooling³. It is worth noting that Yeo⁵² highlighted the important role of cobalt in increasing the thermal hysteresis effect, associated with the difference between the martensite reversion start temperature (A_S) and the martensitic transformation start temperature (M_S), in ternary Fe-22.5%Ni alloys containing cobalt. Therefore, this alloying element is also expected to play a fundamental role in the martensitic transformation kinetics of the maraging steels 300 and 350.

Figure 4
Relative length change vs. temperature for maraging steels 300 and 350 during thermal cycling at 1 °C/s.

Changes in chemical composition directly influence the stability of austenite by modifying the thermodynamic driving force of the martensitic transformation. Consequently, the Gibbs free energy of the system is affected, resulting in changes to the M_S temperature¹⁷. Given the correlation between the M_S temperature and the resulting martensite morphology (twinned or lath), where low M_S temperatures favor the formation of twinned martensite²-⁴, several empirical equations using linear and nonlinear multivariate regressions have been developed to quantify the effect of individual alloying elements on the M_S temperature of steels⁵³-⁵⁹, as summarized in Table 2. However, it should be noted that these classical equations do not account for the influence of titanium and aluminum on the M_S temperature⁵³-⁵⁶. On the other hand, Guo and Sha⁵⁷ proposed an equation to predict the M_S temperature based on artificial neural network (ANN) modeling and a database containing M_S values for maraging steels and low-carbon PH (precipitation-hardening) steels. More recently, Barbier⁵⁸ developed a more general equation for the M_S temperature, based on the generalization of the model proposed by van Bohemen⁵⁶. This approach considered broader composition ranges and a larger number of alloying elements, utilizing a database comprising 1,000 distinct chemical compositions and their corresponding M_s temperatures. Eres-Castellanos and coauthors⁵⁹ also proposed a linear regression model derived from a filtered dataset of up to 886 alloys, covering a wide range of alloying elements, such as Al, V, Co, W, and Cu. All these equations are summarized in Table 2.

Table 2
Comparison of several empirical equations obtained by linear and nonlinear regressions for predicting Ms temperature⁵³-⁵⁹.

Reference	Ms empirical equation	Range (wt%)
Andrews53	539 – 423C – 30.4Mn – 12.1Cr – 17.7Ni – 7.5Mo	0.11-0.6C; 0.04-4.87Mn; 0-4.61Cr; 0-5.04Ni; 0-5.4Mo
Kung and Rayment54	539 – 423C – 30.4Mn – 12.1Cr – 17.7Ni – 7.5Mo + 10Co – 7.5Si	Max. Values:
		0.5C; 12.2Cr; 2.1Mn; 12.5Ni; 4.2Mo; 3.2Si; 9Co
Carapella55	496(1 – 0.62C)(1 - 0.092Mn)(1 – 0.033Si)(1 – 0.045Ni)(1 – 0.07Cr)(1 – 0.029Mo)(1 – 0.013W)(1 + 0.012Co)	Not specified
van Bohemen56	565 – 600[1 – exp(-0.96C)] – 31Mn – 13Si – 10Cr – 18Ni – 12Mo	0.14-1.80C; 0.04-1.95Mn; 0.00-1.95Si; 0.00-3.11Cr; 0.00-5.25Ni; 0.00-0.53Mo
Guo and Sha57	549 – 500C + 15.9Al – 3.8Co – 18.3Cr – 6Cu – 22Mn + 0.2Mo – 1.6Nb – 17.5Ni – 10Si – 29Ti – 54V	0.001-0.085C; 0-3.2Al; 0-20.0Co; 0-26.0Cr; 0-4.0Cu; 0-8.0Mn; 0-17.5Mo; 0-3.1Nb; 0-25.5Ni; 0-2.9Si; 0-2.5Ti; 0-10.0V; 0-9.6W
Barbier58	545 – 601.2[1 – exp(-0.868C)] – 34.4Mn – 13.7Si – 9.2Cr –17.3Ni – 15.4Mo + 10.8V + 4.7Co – 1.4Al – 16.3Cu – 361Nb – 2.44Ti –3448B	0.002-1.86C; 0-10.24Mn; 0-1.9Si; 0-17.98Cr; 0-29.55Ni; 0-5.4Mo; 0-1.19V; 0-16.08Co; 0-3.007Al; 0-2.17Cu; 0-0.11Nb; 0-1.614Ti; 0-0.004B
Eres-Castellanos et al.59	512 – 331.3C – 211.5N – 31.7Mn – 18.1Si – 8.8Cr – 16.6Ni – 6.8Mo + 5.5V + 3.2Co – 13.3Al – 3.0W – 9.2Cu	Max. values:
		1.80C; 10.24Mn; 3.8Si; 17.98Cr; 31.54Ni; 8.00Mo; 2.10V; 16.08Co; 3.00Al; 5.46W; 3.04Cu; 2.65N

Table 3 presents the comparison between the experimental M_S temperatures for the maraging steels 300 and 350 and the values predicted by the models of Andrews⁵³, Kung and Rayment⁵⁴, Carapella⁵⁵, van Bohemen⁵⁶, Guo and Sha⁵⁷, Barbier⁵⁸, and Eres-Castellanos and coauthors⁵⁹. Figure 5 also compares the relative deviation of each predictive model against experimental measurements. It is observed that the experimental M_S values are consistent; however, the M_S temperature of the maraging steel 350 is higher due to its higher cobalt content compared to the maraging 300²,³,⁶⁰. Furthermore, the values predicted by the classical equations tend to present significant deviations from the measured values, especially those by Kung and Rayment⁵⁴ and Carapella⁵⁵, which can be attributed to the specific composition ranges used in their original studies. Although the predictions from the Guo and Sha⁵⁷ equation are closer to the experimental data than those of the classical equations, the model fails by predicting a higher M_S temperature for the maraging steel 300 than for the 350. This contradicts the established knowledge that cobalt increases the M_S temperature in maraging steels³-⁵,⁶⁰. Finally, it was found that the Barbier⁵⁸ and Eres-Castellanos et al.⁵⁹ models provided the most accurate predictions for the experimental M_S values of the analyzed alloys, exhibiting maximum relative deviations of only 10%. This high accuracy stems from the larger training databases used to construct these models, as well as compositional ranges that closely align with highly alloyed maraging steels compared to classical empirical equations (for instance, Andrews⁵³ model was based on a database with 184 M_s temperature values). Notably, the model by Eres-Castellanos et al.⁵⁹ achieved high precision despite omitting titanium content from its expression.

Table 3
Comparison between the experimental M_S temperatures obtained by dilatometry for the maraging steels 300 and 350 and the values predicted by the models of Andrews⁵³, Kung and Rayment⁵⁴, Carapella⁵⁵, van Bohemen⁵⁶, Guo and Sha⁵⁷, Barbier⁵⁸, and Eres-Castellanos et al.⁵⁹.

Maraging	Experimental values (°C)		Predicted values for Ms (°C)
	MS	MF	Andrews	Kung and Rayment	Carapella	Van Bohemen	Guo and Sha	Barbier	Eres-Castellanos et al.
300	203	67	173	260	77	168	166	184	195
350	207	56	189	305	97	185	151	214	220

Figure 5
Comparison of the relative deviation (%) between experimental and calculated M_s temperatures for maraging steels 300 and 350, evaluated across different empirical equations reported in the literature⁵³-⁵⁹.

It is worth noting that the sensitivity of M_s to chemical composition is also evident in higher-strength grades such as maraging 400 (13Ni-15Co-10Mo). For this alloy, Hornbogen and Rittner⁴⁵ reported an M_s temperature between 237 °C and 246 °C, which is higher than those of maraging 300 (203 °C) and maraging 350 (Table 3). This behavior stems from the reduced Ni content combined with elevated Co to offset the austenite-stabilizing effect of Mo, demonstrating how subtle balance between Co and Ni controls the M_s threshold in cobalt-bearing maraging steels⁵,³⁶. Another important aspect is that conventionally processed maraging 300 steel exhibits a higher M_s temperature than its additive manufacturing counterpart, which is attributed to the chemical segregation of alloying elements that localizes austenite stabilization and promotes retained austenite. Noh et al.⁶¹ reported that the partitioning of alloying elements in maraging 300 steel produced by L-DED leads to microstructural variations in M_s temperatures. Specifically, regions that foster retained austenite exhibit M_s temperatures ranging from 55 °C to 115 °C, whereas nickel-depleted regions present higher M_s temperatures between 160 °C and 200 °C.

Figure 6a presents the variation of the linear thermal expansion coefficient as a function of temperature, while Figure 6b shows the transformed volume fraction versus temperature for the maraging steels 300 and 350. It is noted that the linear thermal expansion coefficient reaches more negative values in the initial stages of the martensitic transformation (between 195 °C and 205 °C) for the maraging steel 350 compared to the maraging 300. This indicates initially faster transformation kinetics in the maraging 350 steel, which is corroborated by Figure 6b. Subsequently, the rapid expansion of the maraging steel 300 specimen between 150 °C and 190 °C, evidenced by the pronounced variation in its thermal expansion coefficient in Figure 6a, causes its transformed fraction to surpass that of the maraging 350 steel starting at 181 °C. Furthermore, Figure 6b shows that the difference between the M_s temperature and the temperature at which the transformed fraction reaches 50% is 25 °C for maraging 300 steel and 33 °C for maraging 350 steel as a result of the autocatalytic nature of the martensitic transformation¹²,¹⁷-²³,⁶¹,⁶².

Figure 6
Monitoring of the martensitic transformation from dilatometric data for maraging steel 300 and 350 samples: (a) linear thermal expansion coefficient versus temperature; (b) transformed fraction as a function of temperature.

Figure 7 shows that the transformation rate (df/dt) as a function of temperature (Figure 7a) and transformed fraction (Figure 7b) exhibits several peaks during the martensitic transformation in the maraging steels 300 and 350. This multi-stage kinetic behavior, characterized by successive accelerations and decelerations, reflects the autocatalytic nature of the transformation driven by stress concentrations at crystalline defects²⁵. As initial martensite clusters nucleate, they modify the local stress field in the surrounding austenite. When the thermodynamic driving force becomes temporarily insufficient to sustain continuous growth, the transformation rate slows down, requiring further undercooling for stress re-accumulation and subsequent block nucleation, as proposed by Loewy et al.⁶ and Villa et al.²⁴.

Figure 7
Kinetics of the martensitic transformation from dilatometric data for maraging steel 300 and 350 samples: (a) rate of change of the transformed fraction versus temperature; (b) rate of change of the transformed fraction as a function of the transformed fraction.

Figure 7 also evidences that the transformation rate (df/dt) as a function of temperature (Figure 7a) and transformed fraction (Figure 7b) exhibits higher values in the initial stages for the maraging 350 steel compared to the maraging 300, which corroborates the behavior observed in Figure 6a. The addition of cobalt to maraging steels lowers the stacking fault energy (SFE)⁶⁰. This effect favors the dissociation of dislocations into partial dislocations, resulting in an increased density of dislocations that act as nucleation sites for the martensitic transformation¹²,¹⁸. It is worth noting that Lu et al.⁶³ demonstrated, through first-principles quantum mechanical calculations, that cobalt exerts a similar effect in lowering the SFE in austenitic stainless steels.

It is also observed in Figure 7 that the transformation rate of the maraging 300 steel exhibits more peaks than that of the maraging 350, indicating that cobalt plays a relevant role in the martensitic transformation kinetics. Since cobalt lowers the stacking fault energy (SFE) in maraging steels⁶⁰ and consequently facilitates the formation of partial dislocations that induce martensite nucleation¹², the autocatalytic effect becomes more pronounced in the maraging steel 350. Consequently, the martensitic transformation in maraging 300 steel exhibits lower autocatalytic activation and requires continuous additional cooling to provide the thermodynamic driving force necessary for the phase transformation to proceed.

Figure 8 summarizes the role of cobalt and its impact on stacking fault energy (SFE) during multi-stage martensitic transformation kinetics in maraging steels 300 and 350. As shown in Figures 8a and 8b, maraging steel 350 exhibits a higher partial dislocation density in austenite than maraging 300, which is attributed to its higher cobalt content reducing the SFE⁶⁰. During cooling, this increased cobalt content promotes the refinement of martensitic blocks and packets, yielding a finer microstructure in maraging steel 350⁶⁴,⁶⁵, as depicted in Figures 8c–d and 2a–b. Furthermore, the reduced SFE in maraging 350 provides a higher density of martensite nucleation sites. The initial martensite formation induces a localized stress field accompanied by defect generation²⁴,²⁵. Consequently, maraging 350 shows a stronger autocatalytic effect due to this stress field (Figures 8e and 8f), where the high dislocation density continuously triggers the nucleation of additional martensite blocks, ultimately resulting in a more refined microstructure than in maraging 300 (Figures 8g and 8h). A potential implication of this finding for additive manufacturing is that any loss of mechanical strength could be compensated by cobalt additions, which would promote microstructural refinement⁶⁴,⁶⁵ and enhance precipitation hardening through a higher precipitate density³,⁵.

Figure 8
Schematic illustration of the sequence of steps during the martensitic transformation in maraging 300 (a, c, e, g) and maraging 350 (b, d, f, h) steels. The diagram highlights the role of higher cobalt content in lowering the stacking fault energy (SFE), which increases the density of initial partial dislocations (b) and enhances autocatalytic nucleation across multiple packet boundaries in maraging steel 350 (d, f), ultimately yielding a more refined, fully martensitic matrix (h).

5. Conclusions

The dilatometric experiments and analyses performed on the maraging steels 300 and 350 yielded the following conclusions:

1. Dilatometry is a reliable technique for monitoring the multi-step transformation from austenite to lath martensite in maraging steels 300 and 350.

2. The martensitic transformation in the maraging steels 300 and 350 exhibits variations in its velocity, occurring in multiple stages.

3. Cobalt not only influences the M_S temperature of maraging steels, but also induces faster transformation kinetics in the initial stages, as observed in the maraging steel 350.

4. In the maraging 350 steel, the transformation occurs in fewer stages compared to the maraging 300 steel. This is attributed to its higher cobalt content, which lowers the stacking fault energy (SFE) and, consequently, enhances the autocatalytic nature of the martensitic transformation.

6. Acknowledgments

The authors are grateful to EPUSP and SENAI FIEMG CETEC-MG for the provided experimental facilities, CNPq and FAPEMIG for the financial support and to Nilton J. L. Oliveira for the dilatometric testing. The authors employed Gemini 1.5 Flash (Google) for linguistic refinement and stylistic enhancements. Following this automated process, a rigorous technical review was conducted by the authors, who assume full responsibility for the integrity and accuracy of the final content.

7. Data Availability

The data that support the findings of this study are available from the corresponding author, L. G. Carvalho, upon reasonable request. The raw datasets are not publicly deposited due to their continued use in ongoing departmental research on phase transformation kinetics.

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*

e-mail: [e-mail]

Associate Editor:

Aloisio Klein.

Editor-in-Chief:

Luiz Antonio Pessan.

Publication Dates

• Publication in this collection
  09 Nov 2026
• Date of issue
  2026

History

• Received
  15 May 2026
• Reviewed
  14 Sept 2026
• Accepted
  04 Oct 2026