Influence of forging parameters on austenitic grain size for DIN 20MnCr5 steel

Influence of forging parameters on austenitic grain size for DIN 20MnCr5 steel | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Influence of forging parameters on austenitic grain size for DIN 20MnCr5 steel Rafael Menezes Nunes, Melina Vasconcellos Dilélio, Guilherme Vieira Braga Lemos This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9545076/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Some automotive components such as axles, gears and bearings are often subjected to carburizing to increase hardness, fatigue and wear resistance, while maintaining an adequate toughness towards the core. Nonetheless, a high temperature achieved can result in distortion and abnormal grain growth; thus, being harmful to mechanical properties. Undesirable grain growth can be controlled through suitable upstream processes to carburizing such as hot forging. This work aims at investigating the influence of hot forging conditions on the austenitic grain size of a DIN 20MnCr5 steel. To that, experiments were conducted considering the forging variables such as a prior heating time (3h, 4h, 5h and 6h), different material conditions (billets from continuous casting and hot rolled bars), speed (110 strokes/min and 200 strokes/min), and chemical composition (variation in the aluminum and nitrogen contents). Grain size was measured in carburized, quenched in oil and tempered samples via Leica Materials software based on the planimetric method as per ISO 643 standard. From the outcomes, it was noted that the most refined and homogeneous microstructure was obtained for hot rolled bars forged at 200 strokes/min. DIN 20MnCr5 Forging Austenitic Grain Size Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. INTRODUCTION Thermochemical carburizing treatment is widely used in the manufacturing of automotive components, such as gears, shafts, and bearings aiming at an improvement of fatigue and wear resistance. On the other hand, carburizing might achieve an abnormal austenitic grain growth due to prolonged time exposure at high temperatures, which could lead to undesirable mechanical properties. Thus, grain growth can affect the performance of these components by a detrimental effect on fatigue, and increased distortions (Kubota and Ochi, 2007 ). To avoid or minimize the above-mentioned issues, changes in the manufacturing processes cannot be neglected, including an adjustment in the chemical composition, an optimization in the pre-carburizing processes (as forging operations), and heat treatments for preventing the formation of coarse and heterogeneous grains. In general, in a hot forging operation, a preform is transformed from a simple geometry to a more complex one, where plastic deformation and recovery processes occur simultaneously, followed by the recrystallization. For steels, the forging process is undertaken in the austenitic region, and variables such as deformation, temperature, and time usually affect the microstructure achieved (Poliak & Jonas, 1996 ). The processing method also influences the kinetics of recrystallization and the growth of austenitic grains (Huang and Logé., 2016). Regarding the chemical composition, an alternative to prevent grain growth is to use secondary phase particles or precipitates, as they would anchor the grain size by the addition of titanium, vanadium, niobium, and aluminum (Bhadeshia, 2015). However, the performance of these added elements also depends on factors such as their content, strain rate and forging temperature. Additions of Al and N have been related to the formation of nitrides, with their addition being more efficient when they comply with the stoichiometric ratio (Al/N = 1.94); otherwise, anomalous growth may occur (Manohar et al., 1998 ). This investigation proposes an evaluation of distinct hot forging parameters applied to DIN 20MnCr5 steel to observe the resulting austenitic grain size after thermochemical carburizing treatment. Thus, the effects of different starting materials (billets from continuous casting and hot rolled bars), various heating times, forging speeds, and chemical compositions on grain refinement were analyzed. 2. MATERIALS AND METHODS A DIN 20MnCr5 steel grade, whose chemical composition range is shown in Table (1), was used in this work. The chemical composition of this steel was determined using with an ARL – 4460 Optical Emission Spectrometer. Table 1 Chemical composition of DIN 20MnCr5 steel (wt.%). Min %C %Si %Mn %P %S %Cr %Ni %Cu %Al %N 0.17 - 1.1 - - 1.0 - - 0.02 0.009 Max 0.22 0.4 1.4 0.025 0.035 1.3 0.3 0.3 0.05 0.014 At first, the samples were prepared from billets obtained by continuous casting, with a cross-sectional dimension of 240 mm x 240 mm, and hot-rolled bars, with a diameter of 101.60 mm. Then, the 68 samples were subjected to different experimental cycles as shown in Table (2). The samples were heated to 1200ºC in an industrial gas furnace and then sent for forging operations, carried out using two pneumatic hammers with speeds of 110 strokes/min and 200 strokes/min. The final forged thickness ranged from 30 to 40 mm, followed by cooling in still air. Afterwards, carburizing, oil quenching, and tempering cycles were conducted to examine the austenitic grain structure. The heat treatments were performed in furnaces using the solid carburizing method, with graphite as the carburizing element. The samples were held at 960°C ± 10°C for 4–6 hours, followed by oil quenching. The tempering cycle was carried out at 170°C ± 10°C for 1.5–3 hours. For metallographic analysis, the samples were initially prepared using abrasive sandpapers ranging from 400 to 1200 mesh on a Struers® automatic system, followed by polishing and then using Picral etching to reveal the grain boundaries. The resulting microstructures were verified by using an Olympus optical microscope with an image acquisition system, and then images were captured at 100x magnification. The grain size distributions were compiled using Leica Materials Workstation software®, scanning 15 fields per sample according to the planimetric method of the ISO 643 standard. Table 2 Test conditions for the present study. Billets obtained by continuous casting Starting Material Heating Time (h) Forging strokes per Minute (Strokes/min) Billet 3 110 Billet 4 110 Billet 5 110 Billet 6 110 Hot Rolled Bar 1,5 110 Hot Rolled Bar 1,5 200 Based on the preliminary results, the most important parameters were defined, and a DoE matrix was set. The main parameters considered were speed (strokes/min), starting material condition, %Aluminum, %Nitrogen, and the response variable was the average % of grains > Index 5. Boxplot graphs compiling the results of the average austenitic grain size and % of grains above index 5 were then generated using Minitab® software version 21.0. 3. RESULTS AND DISCUSSION 3.1 Grain size – starting condition: billets The initial microstructural condition plays a key role in the recrystallization behavior of forged steels. In this context, billets obtained by continuous casting tend to present higher heterogeneity due to segregation and coarse prior grains, which directly affect the uniformity of recrystallization during hot deformation. As a result, greater dispersion in grain size is observed, particularly for shorter and intermediate heating times. By using billets from continuous casting as a starting material condition for forging, four samples were tested for the evaluation of the austenitic grain size (with heating times of 3h, 4h, 5h, and 6h). Figures (1) shows the boxplot of results for the average austenitic grain size, while Figure (2) presents the average percentage of grains with sizes above index 5, according to ISO 643 standard. The highest result amplitudes were observed for billets with heating times of 3h and 5h. According to Humphreys & Hatherly ( 2017 ), Hot rolled bars typically have a more refined and homogeneous microstructure compared to continuous casting billets, which often contain larger initial grains, segregation, and porosity. During hot forging, these initial microstructural differences affect recrystallization and grain growth behavior. Figure 3 shows the distribution of the average percentages of austenitic grain sizes for billets forged after different pre-forging heating times (3 h, 4 h, 5 h, and 6 h). The results indicate that heating time strongly influences the homogeneity of the microstructure. Billets heated for 3 h and 5 h presented the widest dispersions, reflecting heterogeneous microstructures with a higher occurrence of coarse grains. In contrast, the 6 h condition displayed the lowest dispersion, pointing to a more stable recrystallization process and reduced abnormal grain growth. The 4 h condition was statistically associated with the lowest median and the highest percentage of fine grains (index 0 = 15.15%), suggesting a tendency toward refinement. Finally, micrographs of all the conditions are shown in Figure (4). The corresponding micrographs in Fig. 4 further illustrate these trends. After 3 h of heating (Fig. 4 A), the microstructure is heterogeneous, with coarse grains and at least one abnormally large grain, confirming the broad dispersion shown in the statistical results. For 4 h (Fig. 4 B), although the quantitative data revealed a higher proportion of fine grains, the micrograph still shows the presence of an oversized grain, indicating localized abnormal growth despite the general refinement tendency. At 5 h (Fig. 4 C), the microstructure is again characterized by coarse grains and significant heterogeneity, with a very large grain clearly visible, which is consistent with the greater scatter found in the grain-size distributions. Finally, after 6 h of heating (Fig. 4 D), the microstructure appears to be more uniform, with reduced variation in grain size and more equiaxed morphology. This supports the observation that longer heating promotes a more stable recrystallization and minimizes abnormal grain growth. Taken together, the results from Figs. 3 and 4 demonstrate that heating times of 3 h and 5 h tend to promote coarse, heterogeneous microstructures with localized abnormal grain growth, while the 6 h condition yields the most uniform distribution. Although the 4 h condition provided the smallest median and the highest fraction of fine grains, its microstructure still contained isolated abnormally large grains, highlighting that statistical refinement does not necessarily preclude local heterogeneities. According to Humphreys (2004), recrystallization mechanisms are strongly dependent on temperature and stored energy, in which the samples were heated for 22, 66, and 200 minutes at temperatures of 900 ºC, 1000 ºC, 1100 ºC, and 1200 ºC, and subsequently forged at 1120 ºC. It was found that the phenomenon of complete recrystallization—static, dynamic, and metadynamic — was only observed under the highest time and temperature conditions (200 minutes at 1200 ºC). Equiaxed grains were present throughout the entire sample cross-section, with an average diameter of 26 µm. These findings agree well with the ones from the current work, where the lowest result dispersion corresponded to the longest holding time at temperature (6 hours). In this investigation, the DOE method was employed to analyze the test results. The parameters and criteria for this methodology are presented in Table (3). The definition of the quantitative values for the % of the aluminum and nitrogen contents aimed to approximate the stoichiometric Al/N ratio for nitride formation (In Table 3 : 0.023%/0.012% = 1.92), as well as to ensure reasonable values considering the range of the chemical composition allowed for this DIN 20MnCr5 steel. Table (4) presents the average results for the percentage of grains with a size above index 5. By analyzing the compiled results, it is evident that the % of aluminum and nitrogen outweighed the influence of pre-forging heating time. The best results, based on the % of grains above index 5 (more refined structures), were associated with higher additions of these elements (Al and N). The high-heating time, high-Aluminum and high-Nitrogen yielded 88.62% of grains above index 5, while the low-heating time, High-Aluminum and high-Nitrogen achieved 88.26%; thus, presenting only 0.40% of difference between them. The most critical conditions were for the low-Aluminum and low-Nitrogen pairs, with the worst performance seen for the high-heating time condition (49.3%). These current results are in line with the studies of Kubota and Ochi ( 2007 ), indicating that a more refined grain structure is achieved when aluminum and nitrogen are added in higher contents, provided the stoichiometric ratio for nitride formation is maintained. In a similar way as in the study of Parrish ( 1999 ), for not-so-high exposure times, the % aluminum has a more significant effect on grain growth behavior than that of the heating time. Table 3 Parameters and criteria selected for the DOE methodology for billets with different heating times. Parameter Criterion 1 Criterion 2 Heating time Low (≤ 4h) High (> 4h) Aluminum Low (≤ 0.023%) High (> 0.023%) Nitrogen Low (≤ 0.012%) High (> 0.012%) Table 4 Results of the average percentage of grains with size above index 5 for different combinations proposed by the DOE method for billets. Heating time Aluminum Nitrogen Grain Size > Index 5 High High High 88.62 Low High High 88.26 Low High Low 84.52 High High Low 84.41 High Low High 82.58 Low Low High 81.22 Low Low Low 70.77 High Low Low 49.3 3.2 Grain size – starting condition: hot-rolled bars By using hot-rolled bars, two conditions were tested for the evaluation of austenitic grain size: forging at 110 strokes/min and 200 strokes/min. Figures (5) shows the boxplot results of the average austenitic grain size, while Figure (6) presents the average % of grains with sizes above index 5, according to ISO 643. Outliers were excluded from the analysis. The greatest dispersions were found for bars forged at 110 strokes/min. Although medians for the average austenitic grain size are similar for both conditions, the differences become more significant when analyzing the percentage of grains above index 5. The observed grain refinement at higher forging speeds (200 strokes/min) can be primarily attributed to the enhancement of dynamic recrystallization (DRX) mechanisms. Higher strain rates increase the stored deformation energy, which acts as a driving force for nucleation of new grains, promoting a finer and more homogeneous microstructure. In contrast, at lower forging speeds (110 strokes/min), the deformation conditions favor recovery and partial recrystallization rather than full DRX. This leads to heterogeneous grain structures and the persistence of coarse grains, as observed in both billets and hot-rolled bars. The role of aluminum and nitrogen is strongly associated with the formation of AlN precipitates, which exert a Zener pinning effect on grain boundaries. When the Al/N ratio approaches the stoichiometric condition (~ 1.9), precipitate distribution becomes more effective in restricting grain boundary mobility, thus suppressing abnormal grain growth. However, this effect becomes less significant at high strain rates, where recrystallization kinetics dominate over precipitate pinning. Additionally, the longer heating times (6 h) promote homogenization and complete recrystallization prior to deformation, reducing microstructural heterogeneities inherited from casting. Nevertheless, excessive holding times may also favor grain coarsening due to precipitate dissolution, indicating a trade-off between homogenization and grain growth control. Recent studies also indicate the strong influence of strain rate on DRX kinetics (Jiang et al., 2025 ) These results suggest that grain refinement in DIN 20MnCr5 steel is governed by the combined effects of strain rate, precipitation state, and initial microstructure, rather than by a single dominant parameter. From the microstructural point of view, it should be noted that the lower speed led to more heterogeneous materials. The grain size distributions are shown in Figure (7), where a high % of grains with abnormal growth was found at 110 strokes/min, including, for example, 7.88% of grains with index 1. Abnormal grain growth was not observed at 200 strokes/min. The micrographs of the materials forged at 110 strokes/min and 200 strokes/min are shown in Figure (8), where abnormal grain growth is observed in the first condition. The conditions evaluated by the DOE method for the hot-rolled bars are presented in Table (5). The same criteria for the quantitative limits of aluminum and nitrogen used in section 3.1 was applied here. The greatest influence on the formation of finer grains was the forging speed. In this context, at 200 strokes/min, the effect of chemical composition can be considered insignificant, with grain values above index 5 ranging from 98.84% to 99.99%. However, at 110 strokes/min, the effect of aluminum and nitrogen contents cannot be neglected, with refined grains ranging from 76.4% to 92.28%. The best combinations were obtained when Al/N ratio approached the ideal stoichiometric value for aluminum nitride formation. (Sellars and Whiteman, 1979 ) and Fang (2015) also observed that for low deformation speeds, grain growth is interrupted only when grains meet their neighboring grains. The process resembles static recrystallization, and the grain refinement may not occur, and only the recovery phenomenon takes place. Humphreys (2004) found that deformation speed influences the kinetics of metadynamic recrystallization. High speeds resulted in high recrystallization rates, thus leading to a fine-grained structure. It is also justified that, with an increase in applied speed, there is also an improvement in the stored deformation energy, which acts as a driving force for dynamic recrystallization. Recent approaches have also employed artificial intelligence techniques to model DRX behavior under varying strain rate conditions, demonstrating good agreement with experimental observations (Mha et al., 2023 ). At higher strain rates (200 strokes/min), the increased dislocation density leads to higher stored deformation energy, which promotes the nucleation of new grains and results in a refined and homogeneous microstructure. Conversely, at lower strain rates (110 strokes/min), the deformation conditions favor recovery and partial recrystallization, leading to heterogeneous grain structures and the persistence of coarse grains. This behavior is consistent with classical recrystallization theory and recent studies, which indicate that insufficient strain rates limit DRX kinetics and promote grain coarsening. Table 5 Results of the average percentage of grains with size above index 5 for different combinations proposed by the DOE method for hot-rolled bars. Speed (strokes/min) Aluminum Nitrogen Mean of % Grain Size > Index 5 200 Low High 99.99 200 High Low 99.43 200 Low Low 98.91 200 High High 98.84 110 Low Low 92.28 110 High High 91.41 110 High High 78.61 110 High Low 76.4 3.3 Grain Size – raw material: general results Figure (9) and Figure (10) present the boxplot graphs that compile the austenitic grain outcomes for all tested conditions (billets and hot-rolled bars). The worst experimental condition that led to a more heterogeneous (higher dispersions) and coarser structures corresponded to the billets forged at 110 strokes/min, followed by the hot-rolled bars with the same parameter. As shown in Figure (11), abnormal grain growth was present in both conditions, with a substantial occurrence of grains with sizes between 0–4. According to Lin and Chen ( 2011 ), hot-rolled materials are the most suitable for forging operations due to their more homogeneous microstructure, as during the rolling process, dynamic, static, and metadynamic recrystallization occurs throughout the passes during the forming rolls (Jonas et al. 2009 ). However, it was verified that the microstructure of the hot-rolled bars prior to forging already had coarse grains in some regions of the samples (see Fig. 12 ). The occurrence of abnormal grain growth indicates that, despite recrystallization taking place during rolling, it did not proceed uniformly. Therefore, forging at 110 strokes/min was not efficient in eliminating these grains, unlike the speed of 200 strokes/min. The test variables and the DOE experimental matrix are presented in Table (6). The parameters studied were strokes speed in the forging stage, starting material condition, % Al, % N. The response variable was Average % Grains > Index 5. Table 6 Results of the average percentage of grains with size above index 5 for different combinations proposed by the DOE method for all experimental conditions. Speed (Strokes/min) Starting Material Al N Mean % Grains > index 5 200 Hot Rolled Bar Low High 99.99 200 Hot Rolled Bar High Low 99.43 200 Hot Rolled Bar Low Low 98.91 200 Hot Rolled Bar High High 98.84 110 Hot Rolled Bar Low Low 92.28 110 Hot Rolled Bar High High 91.41 110 Billet High High 88.74 110 Billet High Low 84.45 110 Billet Low High 82.13 110 Hot Rolled Bar Low High 78.61 110 Hot Rolled Bar High Low 76.4 110 Billet Low Low 57.41 Although forging might eliminate the casting structure for billets, chemical composition segregations can be present (Zhang and Thomas, 2006 ). These segregation regions may influence in the formation of precipitates that act as grain anchors, recrystallization rates, grain growth, and the formation of non-uniform textures, and they can remain after hot working as distinct chemical composition bands (Sellars and Whiteman, 1979 ). Billets have a more heterogeneous prior microstructure as compared to that of the hot-rolled bars, and thus resulting in changes in the behavior of the recrystallization phenomenon. The recrystallization potential varies from grain to grain, so not all grains undergo the process, often only undergoing recovery, which justifies the higher dispersion of results for these starting materials (Xu et al., 2022 ). Overall, the results demonstrate that the evolution of austenitic grain size is controlled by the interaction between initial microstructure, deformation conditions, and precipitation state. While strain rate governs the kinetics of recrystallization, the initial heterogeneity of the material and the effectiveness of precipitate pinning determine the stability and uniformity of the resulting grain structure. 4. CONCLUSIONS An investigation on the influence of hot forging conditions on the austenitic grain size of DIN 20MnCr5 steel was undertaken. The current outcomes can be summarized as follows: Effect of billets and heating time: For billets obtained from continuous casting, the lowest result dispersion was observed at the 6 h pre-forging heating time, indicating a more stable recrystallization process. However, the most significant factor for obtaining refined grains was the chemical composition, especially higher aluminum and nitrogen contents. Under High-Al/High-N conditions, the percentage of grains above index 5 ranged from 88.26% to 88.62%. Effect of forging speed on hot-rolled bars: For hot-rolled bars, the forging speed had the strongest influence. At 200 strokes/min, the microstructures showed minimal abnormal grain growth and were significantly more homogeneous compared to 110 strokes/min. Role of chemical composition: At 200 strokes/min, the effect of aluminum and nitrogen content was negligible, with > 98.8% of grains above index 5 for all conditions. At 110 strokes/min, however, the Al/N ratio played an important role, with refined grains ranging from 76.4% to 92.3%, the best results being obtained when the stoichiometric ratio for AlN formation was approached. Comparison between billets and hot-rolled bars: When both materials were forged at 110 strokes/min, billets presented greater dispersion in grain size than hot-rolled bars. Nevertheless, both starting materials exhibited abnormal grain growth (grain sizes between 0–4), reflecting the influence of their prior microstructures. Most homogeneous condition: Among all tested conditions, the hot-rolled bars forged at 200 strokes/min consistently presented the most homogeneous grain-size distribution, with stable microstructures largely free from abnormal growth, regardless of Al and N contents. Declarations ACKNOWLEDGMENT The authors acknowledge the financial support provided by the CAPES, Brazil, under Finance Code 88881.844968/2023-01 Program PROEX Academic Excellence Program, which made this research possible. Conflict of interest The authors declare that there are no conflicts of interest related to this work. The research was conducted independently, without any commercial, financial, or personal influences that could have affected the interpretation of the results or the conclusions presented. Contributions Conceptualization, Dilélio, M. V. and Nunes, R. M..; methodology, Nunes, R. M. and Dilélio, M. V.; formal analysis, Dilélio, M. V., Nunes, R. M. and Lemos, G. V.B..; investigation, Dilélio, M. V..; data evaluation, Dilélio, M. V. and Nunes, R. M..; writing, Lemos, G. V.B. and Nunes, R. M. 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Journal of Materials Research and Technology 38, 4791–4805. https://doi.org/10.1016/j.jmrt.2025.08.224 Supplementary Files 3Noveltystatement.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 20 May, 2026 Reviewers invited by journal 07 May, 2026 Editor assigned by journal 06 May, 2026 First submitted to journal 05 May, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9545076","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":636234996,"identity":"a50c6670-b158-4344-a58d-1ef0f5903cb7","order_by":0,"name":"Rafael Menezes Nunes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYFACxgcMDAVQ9gcGORDFRkALswEDgwFU9wwGYxK1MPMQo8W8/TDjhw8GDHkGt5sPf7ZtM8iX9z/A9rgCjxaZM8nMkjMMGIoN7hxLk85tM7DceCOB3fAMHi0SDPkHpHkMGBI33MgxY85t+2NgOIOBTbIBnxb+x8y//4C15H/+bNlmYGDYf4CAFolkNmkGiC0M0oxALfIMCYS0PGaz7DGQSJx5I81MsuecgQGQ3W6I32HJzDd+VNgk9t1IfvzhRxnQlv7Dxx7i0wLTiWAaHGAkQgMKkCdVwygYBaNgFAx7AAC2EkdZWwhQ4gAAAABJRU5ErkJggg==","orcid":"","institution":"UFRGS","correspondingAuthor":true,"prefix":"","firstName":"Rafael","middleName":"Menezes","lastName":"Nunes","suffix":""},{"id":636234997,"identity":"659cb88d-dc25-448c-bbb4-7a3ff7a00a7c","order_by":1,"name":"Melina Vasconcellos Dilélio","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Melina","middleName":"Vasconcellos","lastName":"Dilélio","suffix":""},{"id":636234998,"identity":"00e8202f-8f67-4d40-9ae4-f31d36855c5b","order_by":2,"name":"Guilherme Vieira Braga Lemos","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Guilherme","middleName":"Vieira Braga","lastName":"Lemos","suffix":""}],"badges":[],"createdAt":"2026-04-27 17:59:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9545076/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9545076/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109437423,"identity":"262d2fec-8417-473c-9676-3dd2423ccb73","added_by":"auto","created_at":"2026-05-18 06:31:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46430,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot results of the average austenitic grain size for different heating times of the billets.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9545076/v1/8901f4014b15806c3e1d34b4.jpg"},{"id":109437426,"identity":"96cfc0c0-5097-4f94-8a62-cb1655b20500","added_by":"auto","created_at":"2026-05-18 06:31:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43116,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot results of the average percentage of grains with size above index 5 for different heating times of the billets.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9545076/v1/74fa558a6c8e617c99fe0f9b.jpg"},{"id":109799495,"identity":"a63900da-2c55-44f1-b574-dd72f58cc2e7","added_by":"auto","created_at":"2026-05-22 15:29:51","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":136824,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the average percentages of grain sizes for forged billets. (A) 3 hours, (B) 4 hours, (C) 5 hours and (D) 6 hours.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9545076/v1/5a78bd3db94d3ba65545574e.jpg"},{"id":109759353,"identity":"22f558d4-0ecf-43bb-b59c-5a571218ee3e","added_by":"auto","created_at":"2026-05-22 07:26:44","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":224123,"visible":true,"origin":"","legend":"\u003cp\u003eMicrographs of austenitic grain size at 100X (Picral etching). Billets heated: a) 3 hours b) 4 hours c) 5 hours d) 6 hours.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9545076/v1/02869c790a8b53c96e5a7d21.jpg"},{"id":109760191,"identity":"84a485dc-29d5-4d6d-8ef9-d12b1cf5947f","added_by":"auto","created_at":"2026-05-22 07:28:17","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":39567,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot results of the average austenitic grain size for different forging speeds of hot-rolled bars.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9545076/v1/ec2b07cb32af7ac3fc8ff0c2.jpg"},{"id":109437428,"identity":"581a4bc4-6fe1-4d0e-915d-6ce9cc8eacfd","added_by":"auto","created_at":"2026-05-18 06:31:56","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":41112,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot results of the average percentage of grain size for different forging speeds of hot-rolled bars.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9545076/v1/822f165c5f0b8bccafe5f798.jpg"},{"id":109760896,"identity":"138a9942-cdab-42da-9caf-e7c6e6114c59","added_by":"auto","created_at":"2026-05-22 07:29:17","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":97333,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the average percentages of grain sizes obtained for hot-rolled bars.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9545076/v1/7a84ab1f9b11b56d29d20495.jpg"},{"id":109437429,"identity":"e66f7803-7b32-4790-ba7f-f7a26c0198f7","added_by":"auto","created_at":"2026-05-18 06:31:56","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":168473,"visible":true,"origin":"","legend":"\u003cp\u003eMicrographs of austenitic grain size, 100X (Picral etching). a) Hot-rolled bar forged at a speed of 110 strokes/min b) Hot-rolled bar forged at a speed of 200 strokes/min.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9545076/v1/9c760caa20d5b36eeca0f836.jpg"},{"id":109437432,"identity":"c1dae94c-0979-486b-8218-ae342ed9596d","added_by":"auto","created_at":"2026-05-18 06:31:56","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":43711,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot results of the average austenitic grain size for all tested conditions. HRF – 110 – hot rolled bar forged 110 strokes/min; HRF 200 hot rolled bar forged 200 strokes/min.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9545076/v1/23ce4d6dc6a826121e86409d.jpg"},{"id":109437431,"identity":"b89ef7b4-05ec-4865-9f51-24f5a0b9fa96","added_by":"auto","created_at":"2026-05-18 06:31:56","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":40255,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot results of the average percentage of grains with size above index 5 for all experimental conditions. HRF – 110 – hot rolled bar forged 110 strokes/min; HRF – 200 - Hot rolled bar forged 200 strokes/min.\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9545076/v1/eb25bc438c76702a0958f03b.jpg"},{"id":109760216,"identity":"f28aeef8-f9c0-45e8-90e3-8e331db43b80","added_by":"auto","created_at":"2026-05-22 07:28:19","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":100316,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the average percentages of grain sizes obtained for all test conditions.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9545076/v1/277c92064a6d3c8e93023361.jpg"},{"id":109760697,"identity":"aa3acaaf-285f-44ba-adc5-509c22f35774","added_by":"auto","created_at":"2026-05-22 07:29:01","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":231313,"visible":true,"origin":"","legend":"\u003cp\u003eMicrographs of the austenitic grain size of hot-rolled bars prior to forging. An abnormal grain growth is clearly seen. 100X, Picral etching.\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9545076/v1/e214235224cc9fb8b297b504.jpg"},{"id":109906400,"identity":"a37c8703-5875-45ef-a431-bead8758212b","added_by":"auto","created_at":"2026-05-25 06:40:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1467882,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9545076/v1/02a0123d-7616-49dd-bafb-472db67f01ba.pdf"},{"id":109437427,"identity":"6ad7c701-c9c8-45a8-8a19-002296cd653b","added_by":"auto","created_at":"2026-05-18 06:31:56","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":92447,"visible":true,"origin":"","legend":"","description":"","filename":"3Noveltystatement.docx","url":"https://assets-eu.researchsquare.com/files/rs-9545076/v1/1c0f4ab7c0ad84e073fa933d.docx"}],"financialInterests":"","formattedTitle":"Influence of forging parameters on austenitic grain size for DIN 20MnCr5 steel","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThermochemical carburizing treatment is widely used in the manufacturing of automotive components, such as gears, shafts, and bearings aiming at an improvement of fatigue and wear resistance. On the other hand, carburizing might achieve an abnormal austenitic grain growth due to prolonged time exposure at high temperatures, which could lead to undesirable mechanical properties. Thus, grain growth can affect the performance of these components by a detrimental effect on fatigue, and increased distortions (Kubota and Ochi, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). To avoid or minimize the above-mentioned issues, changes in the manufacturing processes cannot be neglected, including an adjustment in the chemical composition, an optimization in the pre-carburizing processes (as forging operations), and heat treatments for preventing the formation of coarse and heterogeneous grains.\u003c/p\u003e \u003cp\u003eIn general, in a hot forging operation, a preform is transformed from a simple geometry to a more complex one, where plastic deformation and recovery processes occur simultaneously, followed by the recrystallization. For steels, the forging process is undertaken in the austenitic region, and variables such as deformation, temperature, and time usually affect the microstructure achieved (Poliak \u0026amp; Jonas, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The processing method also influences the kinetics of recrystallization and the growth of austenitic grains (Huang and Log\u0026eacute;., 2016). Regarding the chemical composition, an alternative to prevent grain growth is to use secondary phase particles or precipitates, as they would anchor the grain size by the addition of titanium, vanadium, niobium, and aluminum (Bhadeshia, 2015). However, the performance of these added elements also depends on factors such as their content, strain rate and forging temperature. Additions of Al and N have been related to the formation of nitrides, with their addition being more efficient when they comply with the stoichiometric ratio (Al/N\u0026thinsp;=\u0026thinsp;1.94); otherwise, anomalous growth may occur (Manohar et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis investigation proposes an evaluation of distinct hot forging parameters applied to DIN 20MnCr5 steel to observe the resulting austenitic grain size after thermochemical carburizing treatment. Thus, the effects of different starting materials (billets from continuous casting and hot rolled bars), various heating times, forging speeds, and chemical compositions on grain refinement were analyzed.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cp\u003eA DIN 20MnCr5 steel grade, whose chemical composition range is shown in Table\u0026nbsp;(1), was used in this work. The chemical composition of this steel was determined using with an ARL \u0026ndash; 4460 Optical Emission Spectrometer.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChemical composition of DIN 20MnCr5 steel (wt.%).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMin\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%Si\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%Mn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%S\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%Cr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e%Ni\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%Cu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e%Al\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e%N\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMax\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAt first, the samples were prepared from billets obtained by continuous casting, with a cross-sectional dimension of 240 mm x 240 mm, and hot-rolled bars, with a diameter of 101.60 mm. Then, the 68 samples were subjected to different experimental cycles as shown in Table\u0026nbsp;(2).\u003c/p\u003e \u003cp\u003eThe samples were heated to 1200\u0026ordm;C in an industrial gas furnace and then sent for forging operations, carried out using two pneumatic hammers with speeds of 110 strokes/min and 200 strokes/min. The final forged thickness ranged from 30 to 40 mm, followed by cooling in still air. Afterwards, carburizing, oil quenching, and tempering cycles were conducted to examine the austenitic grain structure. The heat treatments were performed in furnaces using the solid carburizing method, with graphite as the carburizing element. The samples were held at 960\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u0026deg;C for 4\u0026ndash;6 hours, followed by oil quenching. The tempering cycle was carried out at 170\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u0026deg;C for 1.5\u0026ndash;3 hours.\u003c/p\u003e \u003cp\u003eFor metallographic analysis, the samples were initially prepared using abrasive sandpapers ranging from 400 to 1200 mesh on a Struers\u0026reg; automatic system, followed by polishing and then using Picral etching to reveal the grain boundaries. The resulting microstructures were verified by using an Olympus optical microscope with an image acquisition system, and then images were captured at 100x magnification. The grain size distributions were compiled using Leica Materials Workstation software\u0026reg;, scanning 15 fields per sample according to the planimetric method of the ISO 643 standard.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTest conditions for the present study. Billets obtained by continuous casting\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStarting Material\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeating Time (h)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eForging strokes per Minute (Strokes/min)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBillet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBillet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBillet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBillet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot Rolled Bar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot Rolled Bar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the preliminary results, the most important parameters were defined, and a DoE matrix was set. The main parameters considered were speed (strokes/min), starting material condition, %Aluminum, %Nitrogen, and the response variable was the average % of grains\u0026thinsp;\u0026gt;\u0026thinsp;Index 5. Boxplot graphs compiling the results of the average austenitic grain size and % of grains above index 5 were then generated using Minitab\u0026reg; software version 21.0.\u003c/p\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Grain size \u0026ndash; starting condition: billets\u003c/h2\u003e \u003cp\u003eThe initial microstructural condition plays a key role in the recrystallization behavior of forged steels. In this context, billets obtained by continuous casting tend to present higher heterogeneity due to segregation and coarse prior grains, which directly affect the uniformity of recrystallization during hot deformation. As a result, greater dispersion in grain size is observed, particularly for shorter and intermediate heating times.\u003c/p\u003e \u003cp\u003eBy using billets from continuous casting as a starting material condition for forging, four samples were tested for the evaluation of the austenitic grain size (with heating times of 3h, 4h, 5h, and 6h). Figures\u0026nbsp;(1) shows the boxplot of results for the average austenitic grain size, while Figure (2) presents the average percentage of grains with sizes above index 5, according to ISO 643 standard. The highest result amplitudes were observed for billets with heating times of 3h and 5h. According to Humphreys \u0026amp; Hatherly (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Hot rolled bars typically have a more refined and homogeneous microstructure compared to continuous casting billets, which often contain larger initial grains, segregation, and porosity. During hot forging, these initial microstructural differences affect recrystallization and grain growth behavior.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure 3 shows the distribution of the average percentages of austenitic grain sizes for billets forged after different pre-forging heating times (3 h, 4 h, 5 h, and 6 h). The results indicate that heating time strongly influences the homogeneity of the microstructure. Billets heated for 3 h and 5 h presented the widest dispersions, reflecting heterogeneous microstructures with a higher occurrence of coarse grains. In contrast, the 6 h condition displayed the lowest dispersion, pointing to a more stable recrystallization process and reduced abnormal grain growth. The 4 h condition was statistically associated with the lowest median and the highest percentage of fine grains (index 0\u0026thinsp;=\u0026thinsp;15.15%), suggesting a tendency toward refinement. Finally, micrographs of all the conditions are shown in Figure (4).\u003c/p\u003e \u003cp\u003eThe corresponding micrographs in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e further illustrate these trends. After 3 h of heating (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), the microstructure is heterogeneous, with coarse grains and at least one abnormally large grain, confirming the broad dispersion shown in the statistical results. For 4 h (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), although the quantitative data revealed a higher proportion of fine grains, the micrograph still shows the presence of an oversized grain, indicating localized abnormal growth despite the general refinement tendency. At 5 h (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), the microstructure is again characterized by coarse grains and significant heterogeneity, with a very large grain clearly visible, which is consistent with the greater scatter found in the grain-size distributions. Finally, after 6 h of heating (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), the microstructure appears to be more uniform, with reduced variation in grain size and more equiaxed morphology. This supports the observation that longer heating promotes a more stable recrystallization and minimizes abnormal grain growth.\u003c/p\u003e \u003cp\u003eTaken together, the results from Figs.\u0026nbsp;3 and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrate that heating times of 3 h and 5 h tend to promote coarse, heterogeneous microstructures with localized abnormal grain growth, while the 6 h condition yields the most uniform distribution. Although the 4 h condition provided the smallest median and the highest fraction of fine grains, its microstructure still contained isolated abnormally large grains, highlighting that statistical refinement does not necessarily preclude local heterogeneities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to Humphreys (2004), recrystallization mechanisms are strongly dependent on temperature and stored energy, in which the samples were heated for 22, 66, and 200 minutes at temperatures of 900 \u0026ordm;C, 1000 \u0026ordm;C, 1100 \u0026ordm;C, and 1200 \u0026ordm;C, and subsequently forged at 1120 \u0026ordm;C. It was found that the phenomenon of complete recrystallization\u0026mdash;static, dynamic, and metadynamic \u0026mdash; was only observed under the highest time and temperature conditions (200 minutes at 1200 \u0026ordm;C). Equiaxed grains were present throughout the entire sample cross-section, with an average diameter of 26 \u0026micro;m. These findings agree well with the ones from the current work, where the lowest result dispersion corresponded to the longest holding time at temperature (6 hours).\u003c/p\u003e \u003cp\u003eIn this investigation, the DOE method was employed to analyze the test results. The parameters and criteria for this methodology are presented in Table\u0026nbsp;(3). The definition of the quantitative values for the % of the aluminum and nitrogen contents aimed to approximate the stoichiometric Al/N ratio for nitride formation (In Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: 0.023%/0.012% = 1.92), as well as to ensure reasonable values considering the range of the chemical composition allowed for this DIN 20MnCr5 steel. Table\u0026nbsp;(4) presents the average results for the percentage of grains with a size above index 5.\u003c/p\u003e \u003cp\u003eBy analyzing the compiled results, it is evident that the % of aluminum and nitrogen outweighed the influence of pre-forging heating time. The best results, based on the % of grains above index 5 (more refined structures), were associated with higher additions of these elements (Al and N). The high-heating time, high-Aluminum and high-Nitrogen yielded 88.62% of grains above index 5, while the low-heating time, High-Aluminum and high-Nitrogen achieved 88.26%; thus, presenting only 0.40% of difference between them. The most critical conditions were for the low-Aluminum and low-Nitrogen pairs, with the worst performance seen for the high-heating time condition (49.3%). These current results are in line with the studies of Kubota and Ochi (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), indicating that a more refined grain structure is achieved when aluminum and nitrogen are added in higher contents, provided the stoichiometric ratio for nitride formation is maintained. In a similar way as in the study of Parrish (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), for not-so-high exposure times, the % aluminum has a more significant effect on grain growth behavior than that of the heating time.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters and criteria selected for the DOE methodology for billets with different heating times.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCriterion 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCriterion 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeating time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow (\u0026le;\u0026thinsp;4h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;4h)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAluminum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow (\u0026le;\u0026thinsp;0.023%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;0.023%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow (\u0026le;\u0026thinsp;0.012%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;0.012%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the average percentage of grains with size above index 5 for different combinations proposed by the DOE method for billets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeating time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAluminum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNitrogen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrain Size\u0026thinsp;\u0026gt;\u0026thinsp;Index 5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Grain size \u0026ndash; starting condition: hot-rolled bars\u003c/h2\u003e \u003cp\u003eBy using hot-rolled bars, two conditions were tested for the evaluation of austenitic grain size: forging at 110 strokes/min and 200 strokes/min. Figures\u0026nbsp;(5) shows the boxplot results of the average austenitic grain size, while Figure (6) presents the average % of grains with sizes above index 5, according to ISO 643. Outliers were excluded from the analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe greatest dispersions were found for bars forged at 110 strokes/min. Although medians for the average austenitic grain size are similar for both conditions, the differences become more significant when analyzing the percentage of grains above index 5.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe observed grain refinement at higher forging speeds (200 strokes/min) can be primarily attributed to the enhancement of dynamic recrystallization (DRX) mechanisms. Higher strain rates increase the stored deformation energy, which acts as a driving force for nucleation of new grains, promoting a finer and more homogeneous microstructure.\u003c/p\u003e \u003cp\u003eIn contrast, at lower forging speeds (110 strokes/min), the deformation conditions favor recovery and partial recrystallization rather than full DRX. This leads to heterogeneous grain structures and the persistence of coarse grains, as observed in both billets and hot-rolled bars.\u003c/p\u003e \u003cp\u003eThe role of aluminum and nitrogen is strongly associated with the formation of AlN precipitates, which exert a Zener pinning effect on grain boundaries. When the Al/N ratio approaches the stoichiometric condition (~\u0026thinsp;1.9), precipitate distribution becomes more effective in restricting grain boundary mobility, thus suppressing abnormal grain growth. However, this effect becomes less significant at high strain rates, where recrystallization kinetics dominate over precipitate pinning.\u003c/p\u003e \u003cp\u003eAdditionally, the longer heating times (6 h) promote homogenization and complete recrystallization prior to deformation, reducing microstructural heterogeneities inherited from casting. Nevertheless, excessive holding times may also favor grain coarsening due to precipitate dissolution, indicating a trade-off between homogenization and grain growth control. Recent studies also indicate the strong influence of strain rate on DRX kinetics (Jiang et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThese results suggest that grain refinement in DIN 20MnCr5 steel is governed by the combined effects of strain rate, precipitation state, and initial microstructure, rather than by a single dominant parameter.\u003c/p\u003e \u003cp\u003eFrom the microstructural point of view, it should be noted that the lower speed led to more heterogeneous materials. The grain size distributions are shown in Figure (7), where a high % of grains with abnormal growth was found at 110 strokes/min, including, for example, 7.88% of grains with index 1. Abnormal grain growth was not observed at 200 strokes/min.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe micrographs of the materials forged at 110 strokes/min and 200 strokes/min are shown in Figure (8), where abnormal grain growth is observed in the first condition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe conditions evaluated by the DOE method for the hot-rolled bars are presented in Table\u0026nbsp;(5). The same criteria for the quantitative limits of aluminum and nitrogen used in section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e was applied here.\u003c/p\u003e \u003cp\u003eThe greatest influence on the formation of finer grains was the forging speed. In this context, at 200 strokes/min, the effect of chemical composition can be considered insignificant, with grain values above index 5 ranging from 98.84% to 99.99%. However, at 110 strokes/min, the effect of aluminum and nitrogen contents cannot be neglected, with refined grains ranging from 76.4% to 92.28%. The best combinations were obtained when Al/N ratio approached the ideal stoichiometric value for aluminum nitride formation. (Sellars and Whiteman, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) and Fang (2015) also observed that for low deformation speeds, grain growth is interrupted only when grains meet their neighboring grains. The process resembles static recrystallization, and the grain refinement may not occur, and only the recovery phenomenon takes place. Humphreys (2004) found that deformation speed influences the kinetics of metadynamic recrystallization. High speeds resulted in high recrystallization rates, thus leading to a fine-grained structure. It is also justified that, with an increase in applied speed, there is also an improvement in the stored deformation energy, which acts as a driving force for dynamic recrystallization.\u003c/p\u003e \u003cp\u003eRecent approaches have also employed artificial intelligence techniques to model DRX behavior under varying strain rate conditions, demonstrating good agreement with experimental observations (Mha et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At higher strain rates (200 strokes/min), the increased dislocation density leads to higher stored deformation energy, which promotes the nucleation of new grains and results in a refined and homogeneous microstructure. Conversely, at lower strain rates (110 strokes/min), the deformation conditions favor recovery and partial recrystallization, leading to heterogeneous grain structures and the persistence of coarse grains. This behavior is consistent with classical recrystallization theory and recent studies, which indicate that insufficient strain rates limit DRX kinetics and promote grain coarsening.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the average percentage of grains with size above index 5 for different combinations proposed by the DOE method for hot-rolled bars.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpeed (strokes/min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAluminum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNitrogen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean of % Grain Size\u0026thinsp;\u0026gt;\u0026thinsp;Index 5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Grain Size \u0026ndash; raw material: general results\u003c/h2\u003e \u003cp\u003eFigure (9) and Figure (10) present the boxplot graphs that compile the austenitic grain outcomes for all tested conditions (billets and hot-rolled bars).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe worst experimental condition that led to a more heterogeneous (higher dispersions) and coarser structures corresponded to the billets forged at 110 strokes/min, followed by the hot-rolled bars with the same parameter. As shown in Figure (11), abnormal grain growth was present in both conditions, with a substantial occurrence of grains with sizes between 0\u0026ndash;4. According to Lin and Chen (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), hot-rolled materials are the most suitable for forging operations due to their more homogeneous microstructure, as during the rolling process, dynamic, static, and metadynamic recrystallization occurs throughout the passes during the forming rolls (Jonas et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, it was verified that the microstructure of the hot-rolled bars prior to forging already had coarse grains in some regions of the samples (see Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e). The occurrence of abnormal grain growth indicates that, despite recrystallization taking place during rolling, it did not proceed uniformly. Therefore, forging at 110 strokes/min was not efficient in eliminating these grains, unlike the speed of 200 strokes/min.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe test variables and the DOE experimental matrix are presented in Table\u0026nbsp;(6). The parameters studied were strokes speed in the forging stage, starting material condition, % Al, % N. The response variable was Average % Grains\u0026thinsp;\u0026gt;\u0026thinsp;Index 5.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the average percentage of grains with size above index 5 for different combinations proposed by the DOE method for all experimental conditions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpeed (Strokes/min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStarting Material\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean % Grains\u0026thinsp;\u0026gt;\u0026thinsp;index 5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot Rolled Bar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e99.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot Rolled Bar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e99.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot Rolled Bar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot Rolled Bar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot Rolled Bar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot Rolled Bar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBillet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBillet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBillet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot Rolled Bar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot Rolled Bar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBillet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAlthough forging might eliminate the casting structure for billets, chemical composition segregations can be present (Zhang and Thomas, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). These segregation regions may influence in the formation of precipitates that act as grain anchors, recrystallization rates, grain growth, and the formation of non-uniform textures, and they can remain after hot working as distinct chemical composition bands (Sellars and Whiteman, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). Billets have a more heterogeneous prior microstructure as compared to that of the hot-rolled bars, and thus resulting in changes in the behavior of the recrystallization phenomenon. The recrystallization potential varies from grain to grain, so not all grains undergo the process, often only undergoing recovery, which justifies the higher dispersion of results for these starting materials (Xu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, the results demonstrate that the evolution of austenitic grain size is controlled by the interaction between initial microstructure, deformation conditions, and precipitation state. While strain rate governs the kinetics of recrystallization, the initial heterogeneity of the material and the effectiveness of precipitate pinning determine the stability and uniformity of the resulting grain structure.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. CONCLUSIONS","content":"\u003cp\u003eAn investigation on the influence of hot forging conditions on the austenitic grain size of DIN 20MnCr5 steel was undertaken. The current outcomes can be summarized as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eEffect of billets and heating time: For billets obtained from continuous casting, the lowest result dispersion was observed at the 6 h pre-forging heating time, indicating a more stable recrystallization process. However, the most significant factor for obtaining refined grains was the chemical composition, especially higher aluminum and nitrogen contents. Under High-Al/High-N conditions, the percentage of grains above index 5 ranged from 88.26% to 88.62%.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEffect of forging speed on hot-rolled bars: For hot-rolled bars, the forging speed had the strongest influence. At 200 strokes/min, the microstructures showed minimal abnormal grain growth and were significantly more homogeneous compared to 110 strokes/min.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRole of chemical composition: At 200 strokes/min, the effect of aluminum and nitrogen content was negligible, with \u0026gt;\u0026thinsp;98.8% of grains above index 5 for all conditions. At 110 strokes/min, however, the Al/N ratio played an important role, with refined grains ranging from 76.4% to 92.3%, the best results being obtained when the stoichiometric ratio for AlN formation was approached.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eComparison between billets and hot-rolled bars: When both materials were forged at 110 strokes/min, billets presented greater dispersion in grain size than hot-rolled bars. Nevertheless, both starting materials exhibited abnormal grain growth (grain sizes between 0\u0026ndash;4), reflecting the influence of their prior microstructures.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMost homogeneous condition: Among all tested conditions, the hot-rolled bars forged at 200 strokes/min consistently presented the most homogeneous grain-size distribution, with stable microstructures largely free from abnormal growth, regardless of Al and N contents.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eACKNOWLEDGMENT\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the financial support provided by the CAPES, Brazil, under Finance Code 88881.844968/2023-01 Program PROEX Academic Excellence Program, which made this research possible.\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest related to this work. The research was conducted independently, without any commercial, financial, or personal influences that could have affected the interpretation of the results or the conclusions presented.\u003c/p\u003e\n\u003cp\u003eContributions\u003c/p\u003e\n\u003cp\u003eConceptualization, Dil\u0026eacute;lio, M. V. and Nunes, R. M..; methodology, Nunes, R. M. and Dil\u0026eacute;lio, M. V.; formal analysis, Dil\u0026eacute;lio, M. V., Nunes, R. M. and Lemos, G. V.B..; investigation, Dil\u0026eacute;lio, M. V..; data evaluation, Dil\u0026eacute;lio, M. V. and Nunes, R. M..; writing, Lemos, G. V.B. and Nunes, R. M.\u003c/p\u003e\n\u003cp\u003eAvailability of Data and Materials\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study, including processing parameters, austenitic grain size measurements, and microstructural characterization data, are available from the corresponding author on reasonable request. Relevant data supporting the findings of this study are included within the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBhadeshia, H.K.D.H., 2012. Steels for bearings. Progress in Materials Science 57 (2), 268\u0026ndash;435. https://doi.org/10.1016/j.pmatsci.2011.06.002 \u003c/li\u003e\n\u003cli\u003eFang, Y., Chen, X., Madigan, B., Cao, H., Konovalov, S., 2016. Effects of strain rate on the hot deformation behavior and dynamic recrystallization in China low activation martensitic steel. Fusion Engineering and Design 103, 21\u0026ndash;30. https://doi.org/10.1016/j.fusengdes.2015.11.036 \u003c/li\u003e\n\u003cli\u003eHuang, K., Log\u0026eacute;, R.E., 2016. A review of dynamic recrystallization phenomena in metallic materials. Materials \u0026amp; Design 111, 548\u0026ndash;574. https://doi.org/10.1016/j.matdes.2016.09.012 \u003c/li\u003e\n\u003cli\u003eHumphreys, F.J., 1997. 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Philosophical Magazine 103 (17), 1603\u0026ndash;1625. https://doi.org/10.1080/14786435.2023.2224090 \u003c/li\u003e\n\u003cli\u003eZhang, L., Thomas, B.G., 2006. State of the art in the control of inclusions during steel ingot casting. Metallurgical and Materials Transactions B 37, 733\u0026ndash;761. https://doi.org/10.1007/s11663-006-0057-0 \u003c/li\u003e\n\u003cli\u003eZhao, Y., Cheng, N., Zhu, H., Wang, X., Xu, X., 2025. Dynamic recrystallization mechanisms and thermomechanical processing optimization in Q1300 ultrahigh-strength steel. Journal of Materials Research and Technology 38, 4791\u0026ndash;4805. https://doi.org/10.1016/j.jmrt.2025.08.224 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"DIN 20MnCr5, Forging, Austenitic Grain Size","lastPublishedDoi":"10.21203/rs.3.rs-9545076/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9545076/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSome automotive components such as axles, gears and bearings are often subjected to carburizing to increase hardness, fatigue and wear resistance, while maintaining an adequate toughness towards the core. Nonetheless, a high temperature achieved can result in distortion and abnormal grain growth; thus, being harmful to mechanical properties. Undesirable grain growth can be controlled through suitable upstream processes to carburizing such as hot forging. This work aims at investigating the influence of hot forging conditions on the austenitic grain size of a DIN 20MnCr5 steel. To that, experiments were conducted considering the forging variables such as a prior heating time (3h, 4h, 5h and 6h), different material conditions (billets from continuous casting and hot rolled bars), speed (110 strokes/min and 200 strokes/min), and chemical composition (variation in the aluminum and nitrogen contents). Grain size was measured in carburized, quenched in oil and tempered samples via Leica Materials software based on the planimetric method as per ISO 643 standard. From the outcomes, it was noted that the most refined and homogeneous microstructure was obtained for hot rolled bars forged at 200 strokes/min.\u003c/p\u003e","manuscriptTitle":"Influence of forging parameters on austenitic grain size for DIN 20MnCr5 steel","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 06:31:47","doi":"10.21203/rs.3.rs-9545076/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-05-20T19:20:23+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-07T12:29:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-07T00:47:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2026-05-05T08:56:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"83a9a365-2945-40af-8a88-e7625c2f01d7","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"","date":"2026-05-20T19:20:23+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-07T12:29:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-07T00:47:18+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T06:31:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 06:31:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9545076","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9545076","identity":"rs-9545076","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}