Construction method and application of material model for predicting structure evolution in forging process of 100CrMo7-3 bearing steel

Abstract

The invention relates to the field of bearing steel forging, in particular to a construction method and application of a material model for predicting structural evolution of 100CrMo7-3 bearing steel in the forging process, and the model comprises an austenite grain growth model, a thermal deformation constitutive equation, an austenite dynamic recrystallization integral number model and an austenite dynamic recrystallization grain size model. By establishing the model, the whole chain prediction evolved from a microstructure to a macroscopic forging load is realized for the first time. By adopting the model, a process window can be locked only through single simulation, the grain size of the core of the forge piece is successfully compressed to be below an industry critical value, and compared with a traditional method, the forging load prediction precision is improved in a leap-over mode.

Description

Technical Field

[0001] This invention relates to the field of bearing steel forging, and more particularly to a method for constructing and applying a material model for predicting the microstructure evolution during the forging process of 100CrMo7-3 bearing steel. Background Technology

[0002] With the trend of large-scale wind power equipment developing towards higher power, the reliability of bearings, as core transmission components, faces severe challenges. Especially for megawatt-class wind turbines, their main shaft bearings need to withstand extreme alternating loads and complex operating conditions. Traditional bearing steels such as GCr15 are no longer sufficient to meet performance requirements due to insufficient hardenability. In recent years, high-alloy 100CrMo7-3 bearing steel has gradually become the preferred material for large bearings due to its excellent strength-toughness matching and hardenability depth. However, the widespread application of this material is encountering multiple technical barriers: First, to balance material cost and performance, the manufacturing of large bearings generally adopts the "forging + heat treatment" process, in which the forging process plays a decisive role in grain refinement. Studies have shown that for every 10μm reduction in austenite grain size, the bearing contact fatigue life can be increased by 15%-20%. However, as the bearing size increases, the forging ratio decreases significantly, and the grain refinement efficiency decreases by 30%-40% under conventional processes, resulting in excessive grain size in the core region of the forging, which becomes the main cause of fatigue failure.

[0003] A more serious problem lies in the lack of process optimization methods. Due to the high chromium and molybdenum content in 100CrMo7-3 steel, which significantly alters phase transformation kinetics, its high-temperature austenitic microstructure evolution differs fundamentally from that of common bearing steels. For example, at the same forging temperature, the dynamic recrystallization activation energy ratio and critical recrystallization strain of 100CrMo7-3 steel are significantly higher than those of GCr15 steel. This means that directly applying the process window of existing steel grades will lead to abnormal grain growth. Unfortunately, current industry research on the high-temperature behavior of high-alloy bearing steels is almost nonexistent: on the one hand, the core microstructure of large forgings is difficult to observe directly experimentally; on the other hand, thermal simulation testing machines are limited by sample size and cannot reproduce the low strain rate and multiaxial compressive stress state unique to large forgings, resulting in a significant deviation between laboratory data and actual production.

[0004] Existing technologies attempt to overcome this predicament through numerical simulation. For example, patent CN202211556429.X proposes a forging process optimization method based on grain size simulation, but its proposed model has a deviation of 35%-40% when predicting the rheological stress of 100CrMo7-3 steel. Especially in the low temperature and high strain rate range, the prediction error is further amplified because the hindering effect of molybdenum on dislocation movement is not considered. Another typical solution, CN201510926383.X, constructs a recrystallization model for 20CrMnTiH steel, but the recrystallization mechanism of this low-carbon steel is completely different from that of high-carbon bearing steel. Directly applying this model will significantly overestimate the recrystallization fraction. In addition, existing model systems do not cover the "austenite stabilization" phenomenon unique to high-alloy bearing steel: when the temperature exceeds 1100℃, the proportion of undissolved carbides in 100CrMo7-3 steel is 2-3 times higher than that of conventional steels. These nanoscale precipitates strongly inhibit grain boundary migration, making the traditional Arrhenius-type grain growth model completely ineffective. This strong coupling effect of composition, microstructure, and process led to the failure of simulation strategies for migrating from materials such as GCr15 and 20CrMnTiH.

[0005] The industry is currently forced to adopt an experience-based trial-and-error approach: repeatedly adjusting parameters such as forging temperature and deformation per pass, incurring significant trial production costs to explore the process window. Therefore, how to establish a physical model system adapted to the properties of high-alloy bearing steel and solve the problem of measurability of the microstructure evolution of large-scale workpieces remains a major technological hurdle for the industry. Summary of the Invention

[0006] This invention aims to overcome the shortcomings of existing technologies where models for steel grades such as GCr15, 300M, and 20CrMnTiH cannot be applied to 100CrMo7-3 bearing steel. This results in the need to repeatedly adjust parameters such as forging temperature and deformation per pass during the forging process of 100CrMo7-3 bearing steel, incurring significant trial production costs to explore the process window. Therefore, this invention provides a method for constructing and applying a material model to predict the microstructure evolution of 100CrMo7-3 bearing steel during the forging process, thereby overcoming the aforementioned deficiencies.

[0007] To achieve the above-mentioned objectives, the present invention is implemented through the following technical solution: In a first aspect, the present invention provides a method for constructing a material model for predicting the microstructure evolution during the forging process of 100CrMo7-3 bearing steel, comprising the following steps: (S.1) 100CrMo7-3 bearing steel was held at different temperatures for different times and then water-quenched to room temperature. The austenite grain size was statistically analyzed, and the austenite grain size data based on different holding temperatures and times were fitted to obtain an austenite grain growth model. (S.2) Hot compression was performed on 100CrMo7-3 bearing steel under different compression temperatures, different strain rates and the same amount of deformation. Based on the true stress-true strain curves obtained from the deformation, the constitutive equation of hot deformation of 100CrMo7-3 bearing steel was fitted. (S.3) Based on the obtained true stress-true strain curve of the hot deformation process of 100CrMo7-3 bearing steel, the curve is processed to obtain the volume fraction model of austenite dynamic recrystallization. (S.4) The original austenite grain boundary corrosion was performed on the quenched specimen after hot compression deformation, and the austenite grain size was statistically determined. The austenite grain size model was obtained by fitting the austenite grain size data based on different deformation temperatures and strain rates.

[0008] As described in the background section, in the wave of large-scale wind turbine bearings, 100CrMo7-3 high-alloy bearing steel has become a core material for bearing megawatt-level loads due to its excellent hardenability. However, this has also put it in a dilemma: on the one hand, the increased size of forgings leads to a sharp decline in the grain refinement capability of traditional forging processes, and the coarse grain problem in the core causes a sharp drop in bearing fatigue life; on the other hand, the excessive chromium and molybdenum elements in this steel cause the mature grain growth model in conventional steels to fail instantly. Therefore, simply and crudely transplanting the physical assumptions of other steels to 100CrMo7-3 steel will produce surprising deviations.

[0009] To address the aforementioned impasse, this invention recognizes that the key to breaking the deadlock lies in establishing a "microstructure decoding system" perfectly suited to the characteristics of high-alloy steel. To this end, this solution first reconstructs the experimental observation paradigm. Firstly, addressing the challenge of capturing high-temperature austenite, it creatively uses a specific temperature sequence of heat treatment and water quenching to "freeze" the fleeting austenite grains at room temperature, exposing microstructural details previously hidden beneath the hardened layer to the microscope for the first time. More groundbreakingly, it designs a hot compression experiment covering the actual deformation rate of large forgings, accurately reproducing the strain rate gradient in actual production on micro-samples.

[0010] Based on the innovation of the above experimental paradigm, this method has for the first time drawn a complete genetic map of the high-temperature behavior of 100CrMo7-3 steel: In step (S.1), through the heat preservation experiment across extreme temperature ranges, the anomalous inhibitory effect of carbide precipitation on grain growth was revealed. When the temperature exceeds 1100℃, the monotonically rising grain growth curve in the traditional model shows a rare plateau. This subversive law is accurately encoded in the grain growth model. Step (S.2) captures the unique contribution of high molybdenum content to rheological stress and finds a sharp increase in stress in the low temperature and high strain rate range. The constitutive equation derived from this completely corrects the systematic underestimation of forging load in the existing model. Particularly crucial are steps (S.3)-(S.4), which, by analyzing the subtle fluctuations of the true stress-strain curve, quantify for the first time the delay effect of carbide pinning on dynamic recrystallization and successfully establish a coupled prediction model of recrystallization volume fraction and grain size, solving the industry puzzle of "all or nothing" recrystallization in high alloy steel. The synergistic effect of these four models is like a digital twin customized for 100CrMo7-3 steel, enabling for the first time a full-chain prediction from microstructure evolution to macro forging load.

[0011] The wind turbine bearing forging guided by this model can lock the process window with just a single simulation, successfully compressing the core grain size of the forging to below the industry critical value, and achieving a leapfrog improvement in forging load prediction accuracy compared to traditional methods. More significantly, it ends the era of relying on trial and error for the development of high-alloy bearing steel. Process solutions that once required months of verification can now be optimized in hours in the digital space, resulting in an order-of-magnitude reduction in the development cycle of large bearings.

[0012] Preferably, in step (S.1), the heat preservation temperature is 920-1150℃ and the heat preservation time is 0-600 min.

[0013] Preferably, in step (S.2), the compression temperature is 900-1150℃ and the strain rate is 0.01-10 s⁻¹. -1 The deformation is 0.6-1.0.

[0014] Preferably, the expression for the austenite grain growth model fitted in step (S.1) is: ; In the formula: D t D is the austenite grain size at time t; o t represents the initial austenite grain size; R represents the time; and T represents the gas constant.

[0015] Preferably, the expression for the thermal deformation constitutive equation fitted in step (S.2) is: ; In the formula: ε̇ is the strain rate; σ is the true stress; R is the gas constant; T is the temperature.

[0016] Preferably, the expression for the austenite dynamic recrystallization volume fraction model fitted in step (S.3) is as follows: ; In the formula: X drx ε is the volume fraction of austenite dynamic recrystallization; ε is the true strain; ε c The critical strain at which dynamic recrystallization of austenite occurs; ε 0.5 The true strain corresponding to the occurrence of 50% dynamic recrystallization of austenite; ; ε 0.5 The true strain corresponds to the occurrence of 50% dynamic recrystallization of austenite; ε̇ is the strain rate; R is the gas constant; T is the temperature.

[0017] Preferably, the expression for the austenite dynamic recrystallization grain size model fitted in step (S.4) is as follows: ; In the formula: d drx ε̇ is the grain size of austenite during dynamic recrystallization; R is the gas constant; and T is the temperature.

[0018] Secondly, the present invention also provides a method for applying the model constructed by the method described above. Import the model into finite element simulation software to predict at least one of the following during the forging process of 100CrMo7-3 bearing steel: Austenite grain size distribution; Dynamic recrystallization volume fraction; Forging load variation curve.

[0019] As a preferred option, the forging process parameters are optimized based on the prediction results to make the average austenite grain size of the forging ≤200 μm and the thickness of the rigid zone ≤30 mm.

[0020] Thirdly, this application also provides a manufacturing process for 100CrMo7-3 bearing steel forgings, including the following steps: - Construct the model using any of the methods mentioned above; - Determine forging parameters through finite element simulation; - By forging and quenching 100CrMo7-3 bearing steel according to the determined forging parameters, 100CrMo7-3 bearing steel forgings are obtained.

[0021] Therefore, this application has the following beneficial effects: This invention establishes an austenite grain growth model, a hot deformation constitutive equation, a dynamic recrystallization volume fraction model, and a dynamic recrystallization grain size model for predicting the microstructure evolution during the forging process of 100CrMo7-3 bearing steel. This achieves, for the first time, a complete chain prediction from microstructure evolution to macroscopic forging load. Using this model, the process window can be locked in a single simulation, successfully compressing the core grain size of the forging to below the industry critical value, and achieving a significant improvement in forging load prediction accuracy compared to traditional methods. Attached Figure Description

[0022] Figure 1 Comparison of austenitic grain growth model and actual grain size for 100CrMo7-3 bearing steel.

[0023] Figure 2 The experimental and predicted values ​​of true stress-true strain are compared at different deformation temperatures; where: (a) 900 ℃; (b) 1000 ℃; (c) 1100 ℃; (d) 1500 ℃; (e) a summary comparison of the predicted and experimental results.

[0024] Figure 3 Verification of forging load, temperature, and defects; where (a) is the hammer load diagram; (b) is the workpiece surface temperature; and (c) is the depth of the "end face defect".

[0025] Figure 4 This refers to the original austenite grain boundary characteristics of the workpiece from the subsurface position after forging.

[0026] Figure 5 The DEFORM simulation results after upsetting are as follows: (a) Dynamic recrystallization volume fraction - distance from the top surface; (b) Average grain size - distance from the top surface. Detailed Implementation

[0027] The present invention will be further described below with reference to specific embodiments. Those skilled in the art will be able to implement the present invention based on these descriptions. Furthermore, the embodiments of the present invention described below are generally only some, not all, of the embodiments of the present invention. Therefore, all other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort should fall within the scope of protection of the present invention.

[0028] Example 1 I. Construction of a microstructure evolution model for the forging process of 100CrMo7-3 bearing steel To study the austenite evolution and austenite grain refinement behavior during the forging process of 100CrMo7-3 bearing steel, it is necessary to establish an austenite growth model, a hot deformation constitutive equation, an austenite dynamic recrystallization volume fraction model, and an austenite dynamic recrystallization grain size model.

[0029] 1. Establishment of the austenite grain growth model A furnace heating experiment was conducted on 100CrMo7-3 bearing steel. After holding at different temperatures for varying times, the steel was water-quenched to room temperature, and the original austenite grain boundaries were etched out. The austenite grain size was then statistically analyzed. The holding temperatures were 920℃, 1000℃, 1100℃, and 1150℃, and the holding times were 0, 5, 10, 30, 120, 360, and 600 min, respectively. Based on the austenite grain size data under different holding temperatures and times, the following austenite grain growth model for 100CrMo7-3 bearing steel was fitted: ; In the formula: D t D is the austenite grain size at time t; o t represents the initial austenite grain size; R represents the time; and T represents the gas constant.

[0030] The austenite grain growth model of 100CrMo7-3 bearing steel was compared with the actual grain size. The comparison results are as follows: Figure 1 As shown, from Figure 1 As can be seen from the data, the comparison is quite accurate.

[0031] 2. Establishment of the thermal deformation constitutive model Hot compression was performed on 100CrMo7-3 bearing steel using a thermodynamic simulation testing machine at compression temperatures of 900℃, 1000℃, 1100℃, and 1150℃, and strain rates of 0.01, 0.1, 1, and 10 s⁻¹, respectively. -1 The deformation was 0.8. Based on the true stress-true strain curves obtained from the deformation, the constitutive equation for the hot deformation of 100CrMo7-3 bearing steel was fitted: ; In the formula: ε̇ is the strain rate; σ is the true stress; R is the gas constant; T is the temperature.

[0032] The model calculation results are compared with the actual experimental results, and the comparison results are as follows: Figure 2 As shown, from Figure 2 The results show that the model calculation results match the actual experimental values ​​well overall, and R0 is high. 2 The accuracy of the thermal deformation constitutive model is 0.972, indicating that the model has a high degree of accuracy.

[0033] 3. Establishment of a dynamic recrystallization volume fraction model for austenite Based on the obtained true stress-true strain curves of the hot deformation process of 100CrMo7-3 bearing steel, the curves are processed to obtain the austenite dynamic recrystallization volume fraction model: ; In the formula: X drx ε is the volume fraction of austenite dynamic recrystallization; ε is the true strain; ε c The critical strain at which dynamic recrystallization of austenite occurs; ε 0.5 The true strain corresponding to the occurrence of 50% dynamic recrystallization of austenite; ; ε 0.5 The true strain corresponds to the occurrence of 50% dynamic recrystallization of austenite; ε̇ is the strain rate; R is the gas constant; T is the temperature.

[0034] 4. Establishment of a dynamic recrystallization grain size model for austenite The original austenite grain boundaries were etched on the quenched specimens after hot compression deformation, and the austenite grain size model for dynamic recrystallization was obtained by fitting data based on austenite grain size under different deformation temperatures and strain rates. ; In the formula: d drx ε̇ is the grain size of austenite during dynamic recrystallization; R is the gas constant; and T is the temperature.

[0035] II. Verification and Application of the Microstructure Evolution Model in the Forging Process of 100CrMo7-3 Bearing Steel The established 100CrMo7-3 bearing steel material model was imported into DEFORM software to establish a material model for studying the microstructure evolution law during the hot deformation process of 100CrMo7-3 bearing steel during forging. Based on the actual process of on-site forging, a simulation study was carried out. The load curve, austenite recrystallization behavior and austenite grain size calculated by the simulation are consistent with the actual results, verifying the accuracy of the model.

[0036] The raw material for on-site upsetting was a Ф252×410 mm bar. It was heated to 1150℃, held for 2 hours, and then removed from the furnace for upsetting to 180 mm. After upsetting, the billet was placed in a water bath and quenched to room temperature. Due to the large billet size and the delayed quenching, the observed austenite grains after upsetting showed coarsening. Software calculations were needed to account for this coarsening effect.

[0037] 1. Verification of forging Forging load, temperature and defect verification, such as Figure 3 As shown. Among them, Figure 3The hammer load simulated by the software shown in Figure a has the same trend and similar values ​​as the actual hammer load on site. This indicates that the model conforms to the actual process and also reflects the high accuracy of the rheological stress model constructed above.

[0038] Secondly, the surface temperature of the workpiece before forging was measured to be 1030 ℃ and after forging was measured to be 995 ℃. Figure 3 b is the simulated workpiece surface temperature change curve, where the workpiece surface temperature is 1027℃ before forging and 1010℃ after forging. Comparing the workpiece surface temperature before and after forging measured on-site with the numerical simulation, it can be seen that the constructed finite element model conforms to the actual production process.

[0039] Thirdly, due to the characteristics of the forging process, the temperature drops rapidly in the upper and lower end faces of the workpiece in contact with the hammer, making metal deformation in this area more difficult. Furthermore, as hot working continues, the metal on the side of the workpiece, after contacting the hammer, flips upwards, folding against the original end face circumference, thus creating an "end face defect." Figure 3 c indicates that the simulation results show that the average depth of the four "end face defects" is 1.2 mm, which is close to the average depth of 1.3 mm of the forged workpiece produced on site, further demonstrating the reliability of the finite element model.

[0040] 2. Verification of recrystallization volume fraction in various parts of the entire workpiece In actual forging, a rigid zone forms at the contact point between the billet and the hammer due to temperature drop and friction. This zone does not deform or recrystallize during upsetting, and the austenite grain size remains coarse. Therefore, the depth of the rigid zone can be determined by the austenite grain size variation pattern, as shown in the following results. Figure 4 As shown in the figure, the austenite grains near the end of the workpiece are relatively coarse. In the region above 31 mm, the average original austenite grain size is 865.0-980.0 µm. When the depth exceeds 31 mm, the average original austenite grain size decreases significantly. Specifically, the original austenite grain size in the 21-23 mm region decreases to 684.0 µm, and in the 33-35 mm region, it further decreases to 525.0 µm. Therefore, the rigid zone depth of the forged workpiece produced on-site is 31 mm.

[0041] To determine the depth of the rigid region in the simulation results, Figure 5The simulation results show the variation of austenite recrystallization volume fraction and austenite grain size with workpiece depth. As shown in the figure, in the region within 27 mm of the upper surface, the dynamic recrystallization volume fraction after forging is consistently below 0.02, and the average austenite grain size is not significantly different from the initially set average grain size (900.0 µm), indicating that this region is a rigid zone where strain is insufficient for dynamic recrystallization. However, in the region beyond 27 mm, partial dynamic recrystallization occurs, resulting in a significant refinement of the average grain size.

[0042] In summary, comparing the results of on-site production with those of the finite element simulation, it can be seen that the hammer load exhibits the same trend, the workpiece surface temperature is similar before and after forging, the difference in the depth of the "end face defect" is only 7.7%, and the difference in the depth of the "rigid zone" is only 9.7%. These phenomena all indicate that the finite element model constructed in this paper is consistent with the actual production process.

[0043] 3. Verification of austenite grain size in various parts of the entire workpiece According to the finite element simulation results, except for the rigid region and a few nearby areas, 100% recrystallization occurred in other locations during the upsetting process. Considering the growth of austenite grain size during the quenching process, the austenite grain size at the 1 / 4 diameter position of the workpiece height is 225 μm, and the austenite grain size at the shear position is 110 μm. The actual test results show that the austenite grain sizes at the 1 / 4 diameter position of the workpiece height and the shear position are 255 μm and 106 μm, respectively, with deviations of 11.8% and 3.8%, respectively. The simulation results are consistent with the actual results.

[0044] In summary, based on the 100CrMo7-3 material model, in-situ forging experiments were conducted. The simulation results and experimental results were compared in all aspects, including load, austenite recrystallization distribution, and austenite recrystallization grain size. The simulation results showed good agreement with the actual results, indicating that the austenite growth model, hot deformation constitutive equation, austenite dynamic recrystallization volume fraction, and austenite recrystallization grain size model of 100CrMo7-3 bearing steel established in this invention are accurate and can be used for research on load, austenite microstructure evolution, and austenite grain refinement during in-situ forging.

[0045] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A method for constructing a material model for predicting the microstructure evolution during the forging process of 100CrMo7-3 bearing steel, characterized in that, Includes the following steps: (S.1) 100CrMo7-3 bearing steel was held at different temperatures for different times and then water-quenched to room temperature. The austenite grain size was statistically analyzed, and the austenite grain size data based on different holding temperatures and times were fitted to obtain an austenite grain growth model. (S.2) Hot compression was performed on 100CrMo7-3 bearing steel under different compression temperatures, different strain rates and the same amount of deformation. Based on the true stress-true strain curves obtained from the deformation, the constitutive equation of hot deformation of 100CrMo7-3 bearing steel was fitted. (S.3) Based on the obtained true stress-true strain curve of the hot deformation process of 100CrMo7-3 bearing steel, the curve is processed to obtain the volume fraction model of austenite dynamic recrystallization. (S.4) The original austenite grain boundary corrosion was performed on the quenched specimen after hot compression deformation, and the austenite grain size was statistically determined. The austenite grain size model was obtained by fitting the austenite grain size data based on different deformation temperatures and strain rates.

2. The method according to claim 1, characterized in that, In step (S.1), the heat preservation temperature is 920-1150℃ and the heat preservation time is 0-600 min.

3. The method according to claim 1, characterized in that, In step (S.2), the compression temperature is 900-1150℃, and the strain rate is 0.01-10 s⁻¹. -1 The deformation is 0.6-1.

0.

4. The method according to claim 1, characterized in that, The expression for the austenite grain growth model fitted in step (S.1) is as follows: ； In the formula: D t D is the austenite grain size at time t; o t represents the initial austenite grain size; R represents the time; and T represents the gas constant.

5. The method according to claim 1, characterized in that, The expression for the thermal deformation constitutive equation fitted in step (S.2) is as follows: ； In the formula: ε̇ is the strain rate; σ is the true stress; R is the gas constant; T is the temperature.

6. The method according to claim 1, characterized in that, The expression for the austenite dynamic recrystallization volume fraction model fitted in step (S.3) is as follows: ； In the formula: X drx ε is the volume fraction of austenite dynamic recrystallization; ε is the true strain; ε c The critical strain at which dynamic recrystallization of austenite occurs; ε 0.5 The true strain corresponding to the occurrence of 50% dynamic recrystallization of austenite; ； ε 0.5 The true strain corresponds to the occurrence of 50% dynamic recrystallization of austenite; ε̇ is the strain rate; R is the gas constant; T is the temperature.

7. The method according to claim 1, characterized in that, The expression for the austenite dynamic recrystallization grain size model fitted in step (S.4) is as follows: ； In the formula: d drx ε̇ is the grain size of austenite during dynamic recrystallization; R is the gas constant; and T is the temperature.

8. A method for applying a model constructed according to any one of claims 1-7, characterized in that, Import the model into finite element simulation software to predict at least one of the following during the forging process of 100CrMo7-3 bearing steel: Austenite grain size distribution; Dynamic recrystallization volume fraction; Forging load variation curve.

9. The application method according to claim 8, characterized in that, Based on the prediction results, the forging process parameters were optimized to ensure that the average austenite grain size of the forging is ≤200 μm and the thickness of the rigid zone is ≤30 mm.

10. A manufacturing process for 100CrMo7-3 bearing steel forgings, characterized in that, Includes the following steps, - Construct the model using any one of claims 1-7; - Determine forging parameters through finite element simulation; - By forging and quenching 100CrMo7-3 bearing steel according to the determined forging parameters, 100CrMo7-3 bearing steel forgings are obtained.

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