Impact of Build Plate Packing and Preheat on the Grain Size Evolution in Powder Bed Fusion – Electron Beam of Metals

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Torrent, Philipp Krooss, Thomas Niendorf This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6566196/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Jan, 2026 Read the published version in Progress in Additive Manufacturing → Version 1 posted You are reading this latest preprint version Abstract Additive manufacturing (AM) relies particularly on the precise control of thermal paths to achieve desired microstructural and mechanical properties of the processed material. Complex geometrical shapes often associated with AM result in varying cross sections and consequently fluctuating energy inputs, which can cause localized differences in thermal history that influence material response. The present work explores the impact of variations in thermal history during the AM process on the final properties of built components. In this research, the influence of scan area and preheat on the material’s properties is investigated. The number of specimens on the build plate was altered without changing the process parameters to examine how scan area affects overall process temperature and microstructure. Additionally, using the same build layout and melt parameters, the preheat step was varied to explore further impact on final microstructure. The results show that changes in the number of built specimens significantly impact the energy input, build chamber temperature, grain size, and material strength. Although adjusting the preheat also led to changes, the effects were less pronounced. These findings highlight the importance of understanding and controlling thermal conditions in AM processes to ensure consistent material properties and prevent unexpected component failures. pure iron intrinsic heat treatment grain refinement powder bed fusion electron beam melting Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction For a specific alloy, the thermal path can be a key to adjust the microstructure and mechanical properties. It is well known that by varying processing temperatures, holding times, or cooling velocities, the properties of a piece of metal can be significantly modified according to the needs. Nowadays, it is known that these adjustments are based on the adjustment of the grain size, segregations portion and size, or phase composition. Additionally, the residual stress state can be manipulated by the chosen heat treatment. For a wide range of alloys, the theoretical backgrounds are well investigated and depicted in phase diagrams or time-temperature-transformation (TTT) diagrams. Hence, the strict dependency of a microstructural design on the course of a developed thermal path is known. Technical components, especially those for which production technologies of additive manufacturing (AM) are chosen, often feature complex geometrical shapes. As a function of the geometry, varying cross sections in subsequent layers are in direct correlation to varying areas being melted and, thus, to a changing energy input throughout the build process. This in turn may cause undetected localized differences in the thermal history experienced by the material, and hence influence the mechanical response of the built component under load. In powder bed AM (PB-AM), the energy input is applied layer by layer, leading to a characteristic temperature profile throughout the build process. Initially, a defined volume increment undergoes melting and solidification, followed by progressively decreasing periodic reheating as the build plate lowers. This results in a sawtooth-like cyclic temperature profile over time, which can be interpreted as an intrinsic heat treatment. In allotropic alloys, this can lead to numerous phase transformations during processing, playing a crucial role in shaping the microstructure. Here, iron will be regarded, which shows a phase transformation path of liquid → δ → γ → α. Depending on the global temperature level in the build chamber, the aforementioned volume increment can trespass a higher or lower number of δ ↔ γ and γ ↔ α phase transformations (at 1392°C and 911°C at 1 bar, respectively), each giving the option of one grain to break down into several smaller grains. A critical thermal window must therefore exist in which the number of phase transformations is maximized, resulting in the highest potential for grain refinement. Above this range, the material may remain too long in a stable high-temperature phase, while below it, the temperature may be insufficient to induce repeated transformations — in both cases reducing the grain refinement potential. In this way, grain size and nature of grain boundaries is significantly affected in PB-AM. Furthermore, high temperatures can allow for annealing phenomena as recrystallization and recovery, also contributing to the final microstructural appearance. The described effects have already been mentioned in previous studies (cf. [ 1 – 6 ]) and is the process related background for the investigations in this work. Accurately determining the actual temperature at the active processing layer remains a significant challenge. While the thermo couple is commonly placed underneath the build plate, the recorded thermal signal is influenced by multiple layers of material – ranging from fully dense metal to semi-sintered and loose powder – each with distinct and evolving thermal conductivities. Furthermore, the dynamic nature of the electron beam introduces highly localized and transient heat inputs and a complex thermal path not directly reflected in the smoothed response of the thermocouple. As a result, no direct or reliable correlation exists yet between the sub-plate thermo couple reading and the actual temperature at the melt pool or solidification front. Instead, temperature estimations rely on calibrated models or indirect inference, as demonstrated by Plotkowski et al. [ 7 ] and Raplee et al. [ 8 ]. In these studies, semi-analytical models and infra-red thermography were applied to estimate local thermal gradients in electron beam powder bed fusion of metals (PBF-EB/M) and correlate them with microstructural features. This highlights the complexity but also the necessity of robust thermal analysis and/or modeling when studying phase transformations and microstructure evolution in PBF-EB/M. The aim of this study is not to determine the exact temperature at the processing layer, but rather to investigate how variations in heat input, arising from build layout and preheat strategy, can affect the resulting microstructures in pure iron. By identifying correlations between process-induced thermal conditions and grain refinement, this work seeks to contribute to a deeper understanding of thermal histories in PBF-EB/M and to explore whether such effects can be controlled or counteracted through process design. For this sake, a PBF-EB/M system is used to build specimens equal in geometry and without altering the melt parameters. The number of specimens built on a build plate is varied in order to investigate how the scan area influences the overall process temperature, microstructure, and mechanical response of the material. By increasing or decreasing the number of specimens, the energy distribution and thermal history during the build process is altered. Furthermore, to explore additional ways to control the microstructure, the preheat step using the same build plate layout and melt parameters is varied. This allows to evaluate if adjusting the preheat temperature provides an additional degree of freedom to fine-tune the microstructural characteristics of the built specimens. These variations aim to provide a deeper understanding of how localized thermal conditions in PB-AM processes affect the final properties of manufactured components. By understanding and controlling thermal variables, a safe transitioning from initial parameter studies, which uses typically smaller geometries, to mass production of more complex geometries can be ensured. Furthermore, the reliability and performance of additively manufactured components can be improved while unexpected anomalies in material properties can be avoided. Materials and experimental methods Commercially pure iron (cp-Fe) powder in a particle size distribution of nominally 63–150 µm provided by Eckard TLS (Germany) was processed on an Arcam A2X (GE ARCAM AB, Mölndal, Sweden), which was operated at a constant acceleration voltage of 60 kV. The presinter , i.e. the heating of the build plate before the first raking, was set to nominally 400°C and hold for 10 minutes. The lowest current possible on this machine (5 mA) still leads to higher build plate temperatures so that the processes then started at around 500°C. subsequently, with a constant layer thickness 0f 0.05 mm, a varying number of 40 mm high cuboid blocks of l melt × l melt = 10 × 10 mm² was built on a 100 × 100 mm² build plate. After the process start, the built room temperatures recorded by the thermo couple type K attached underneath the build plate differed depending on the varied preheat as well as the melted area. The preheat area l heat × l heat was 80 × 80 mm², and the current I heat was set to 12 mA, the beam speed v heat to 12,000 mm⋅s − 1 and the hatching h heat was 1 mm. The numbers of repetition (NoR) were set to 15 as standard, but varied from 10 to 25 in the respective NoR Study . After preheating, the melting was conducted with I melt , v melt , and h melt of 12.25 mA, 4,000 mm s − 1 , and 0.08 mm, respectively. 4 × 4 blocks were built as a standard and varied from 2 × 2 to 6 × 6 in the Packing Study . Figure 1 shows a representative build plate layout. Again, in the present study, two different strategies were considered, i.e. the Packing Study (variation in the packing density on the build plate) and the NoR Study (variation of the numbers of repetition). The specimens built and investigated in the packing study will be referred to as Fe 15/m , with m giving information on the number of built blocks on one plate using always 15 NoR. The specimens in the NoR Study will be referred to as Fe n/4×4 , with n being the index for the NoR in jobs in which 4 × 4 = 16 blocks have been built. Finally, after manufacturing, the blocks were cut from the build plate and dog-bone shaped tensile specimen with a cross section of 4.5 mm² (cf. Figure 1 ) extracted via electrical discharge machining. Although in [ 9 ] it is found that microstructure, density and hardness are not affected by the part’s position on the build plate for PBF-EB/M processed IN 718, a center block was chosen from the 4 × 4 layouts, and from the more densely packed layouts of the Packing Study blocks from the second row (cf. Figure 2 ) were chosen to minimize heat affected sample to sample variations. These specimens were then used for both, the microstructural investigation as well as the mechanical testing, and the tested region was always in the middle of the formerly 40 mm high blocks (cf. Figure 1 ). After grinding the specimen to P2500 and polishing one surface to 0.05 µm, the microstructure was contemplated by means of a scanning electron microscope (SEM, TESCAN CAMSCAN MV 2300, Brno, Czech Republic) equipped with an electron backscatter diffraction (EBSD) detector. The collected data were analyzed using TSL orientation imaging microscopy software, version 7 (EDAX, Mahwah, NJ, USA). From EBSD data, grain sizes (GS) are determined giving the diameter of circles with the same area. Tensile tests were conducted on a MTS Criterion Model 43 (MTS Systems Corp., Eden Prairie, MN, USA) at a displacement rate of 2 mm⋅min − 1 , recording the elongation till 30 % ith an extensometer with a gauge length of 5 mm. After 30 %,the elongation was calculated from the crosshead displacement. Two specimens per condition were tested. Results and discussion Packing Study Figure 1 depicts the thermal paths recorded by the thermo couple underneath the build plate. For the characterization, apart from the 2 × 2, blocks built in the second row were chosen cf. circles in Fig. 1 ). In this way, “cooler” specimens from the edge as well as “hotter” specimens from the center of the build plate were avoided. As a consequence of the varying packing density and thus the thermal path, obvious microstructural changes appear, as becomes visible in the inverse pole figure (IPF) maps. When examining the microstructure of the built samples, it is evident that changes in process temperatures influence the microstructure: Whereas the texture and general grain morphology remains similar, specimens from the densest build plates exhibit the largest GS. As the build plate packing decreases and the process takes place at lower temperatures, the GS decreases, reaching the smallest size in the 3 × 3 process. With even less dense packing (Fe 15/2×2 ), the GS again slightly increases. Hence, GS development does not strictly corelate to the process temperature level. This indicates that different driving forces may impact microstructural development: GS seems to increase at both high and low temperatures, while a certain build temperature level results in the smallest GS. High temperatures and long process times can promote grain growth to reduce grain boundary energy [ 10 ], which may explain the larger GS in processes with high density of built blocks. However, it is not the process with the lowest build density that leads to the smallest GS, but rather the intermediate processes with maximum detected temperatures between 650°C and 700°C. According to the concept of intrinsic heat treatment induced grain refinement introduced earlier, this temperature range likely facilitates the highest number of allotropic phase transformations during processing, thereby offering the greatest potential for grain refinement. At lower process temperatures, although the driving force for grain coarsening is reduced, the influence of grain refinement through multiple phase transformations also diminishes, weakening the grain refinement mechanism and resulting in increasing GS. Hence, a measured build chamber temperature of 650°C to 700°C results in the lowest grain sizes in the Packing Study . Yet, the thermo couple is located underneath the build plate as described above, and separated from the active processing region by a complex and evolving material stack (solidified bulk, sintered powder, and loose powder). Estimation of the thermal circumstances via the thermo couple is hence inappropriate [ 11 , 12 ]. Even though for iron a α ↔ γ transformation is known to occur at 911°C (at 1 bar), a clear correlation between temperature and GS shall be dispensed here for this reason. Figure 3 shows representative stress-strain curves and the mean yield and ultimate tensile strengths (YS and UTS, respectively) as well as GS of the compared specimens. Regarding these results, a correlation of GS and strengths becomes obvious, revealing highest strengths for the Fe 15/3×3 specimens. Contrary, Fe 15/6×6 shows the lowest material resistance to quasistatic loading. This correlation is thus in agreement with Hall-Petch relation [ 13 ]. The changes depicted herein are not related to the geometry of the individual specimens or applied process parameters, but solely to the packing density of the build plate and it’s influence on the overall chamber temperature and, ultimately, on the phase transformation occurring during the process. As mentioned in the introduction above, the chamber temperature can lie within a range that promotes maximal grain refinement – or fall outside (above or below) this range, thereby reducing the refinement effect. The chamber temperature is usually set via the presinter step and maintained at a desired level through the preheat during the whole built process. This raises the question of whether the effects shown in this section can be intentionally manipulated or counteracted via tailored preheat strategies which shall be examined in the following section. NoR Study For this purpose, standard build jobs with 4 × 4 cuboid blocks were performed in which the NoR were varied, while all other parameters were kept constant. In Fig. 4 the temperature paths, resulting microstructure and mechanical properties are shown. As with build plate packing, changing the NoR also affects the layer time, i.e. the interval between two successive raking steps. Also, the total introduced heat per layer changes accordingly, finally affecting the built chamber temperature recorded by the thermocouple underneath the build plate. With 8 NoR , the temperature is insufficient for proper sintering of the loose powder in a new layer, leading to powder blasts and process interruption. Therefore, at least 10 NoR are necessary to create an adequate degree of sintering and thus allow for a stable build process. Obviously, the built chamber temperatures increase with increasing NoR (cf. Figure 4 ). In comparison to the results from the Packing Study , the differences in the recorded temperatures in the NoR Study are less pronounced, indicating a lower variation in total energy input, which will be discussed in the remainder of this work. Still, a significant change in GS is visible, revealing the highest GS (around 50 µm) for the process with highest NoR . Again, the values for the GS correlate with the mechanical response to quasistatic loading. Lower NoR result in a smaller GS and thus, according to the Hall-Petch relationship, in a higher strength. However, 25 NoR lead to highest GS, whereas GS remain in between 30 and 40 µm for lower NoR , and no explicit extremum can be seen in this study. In the NoR Study , the coolest build process shows a maximum build plate temperature at around 680°C, whereas in the Packing Study even lower build plate temperatures (around 620°C), were achieved. According to Fig. 2 , in the Packing Study the GS increased again at build plate temperatures below 650°C. This trend may be attributed to a reduced influence of grain refinement via multiple phase transformations below this specific temperature regime. It is assumed that at lower chamber temperatures, fewer phase transformations occur during processing, which in turn may reduce grain refinement. Similarly, at higher temperatures, the number of transformations may also decrease, and conventional grain growth could become more dominant. However, this interpretation is based on the current experimental observations and should be further substantiated in future studies or with dedicated phase transformation analyses. In the NoR Study, a correlation of GS and built plate temperature is not obvious (Fig. 4 ). The process temperature resulting from the lowest possible NoR is found to be higher (680°C) than that resulting from the lowest build plate packing in the Packing Study (620°C). While an increase of GS at even lower temperatures – following the trend observed in the Packing Study – is possible, it could not be clearly identified within the process window considered here. In other words: effective grain refinement induced by intrinsic heat treatment could be active in case of the lowest used NoR , but is not proven if GS does not increase on both ends of the study. Thermal background In [ 14 ] it is discussed how PBF-EB/M manufactured strut geometries are impacted by changing energy input, heat accumulation, beam return time etc. and hence melt pool geometries change in accordance to the melted cross section in a layer, influencing in turn the final microstructure in specific component’s regions. In the results presented herein, the microstructural appearance only changes in regard of the GS and not in the grain shape or aspect ratio. The fact that the Packing Study could run stably at lower temperatures again shows that the temperature recorded by the thermocouple is not an appropriate indicator for the thermal household in the region of processing [ 11 ]. Furthermore, time also plays a significant role, as sintering the material is a matter of both temperature and time. In the Packing Study , processing 2 × 2 blocks at 15 NoR resulted in shorter layer times and hence the shortest total process times. Nevertheless, the preheat time remained constant, allowing for sufficient sintering in each layer. As a result, this setup resulted in the lowest stable process temperatures. In comparison, building 4 × 4 blocks at 10 NoR in the NoR Study led to higher process temperatures than the coolest case in the Packing Study. Further reducing the NoR to 8 caused instabilities, suggesting that the NoR cannot be arbitrarily lowered to reduce process temperature. This is likely due to the reduced available sintering time, as well as insufficient time for electron beam energy to dissipate during the preheat step when applying lower NoR. To assess the energy input during the PBF-EB/M process, the volume energy Q vol can be calculated using the well-known Eq. 1 [ 1 , 4 , 15 , 16 ], in which the product of the acceleration voltage U und the used beam current I is divided by spatial factors as the hatch h , the beam speed v and the layer thickness d : \(\:{Q}_{vol}=\frac{U\cdot\:I}{h\cdot\:v·d}\) [Equation 1] Q vol with the unit J·mm − 1 can be applied for both, the preheat and the melt step. To understand why lower process temperatures are possible in the Packing Study , Eq. 2 is abstracted from Eq. 1 and introduced to explain the overall inserted Q total in each single layer (regardless of the layer time). Q total is composed by the energy input while preheating, Q preheat , and while melting, Q melt . Since only the inserted energy – regardless of the absorption by the constant layer thickness – shall be regarded, d can be deleted. Instead, the preheated and melted surface l × l for heating and melting have to be regarded, and obviously the NoR in the preheat and the number of melted blocks n in the melting (cf. values in experimental section and schematic shown in Fig. 1 ). \(\:{Q}_{total}={Q}_{preheat}+{Q}_{melt}={\left[U\cdot\:I\cdot\:\frac{l\times\:l}{h\cdot\:v}\cdot\:NoR\right]}_{preheat}+{\left[U\cdot\:I\cdot\:\frac{l\times\:l}{h\cdot\:v}\cdot\:n\right]}_{melt}\:\) [Equation 2] Q total now has the unit J . In the Packing Study , Q preheat remains constant and Q melt increases with increasing number of built blocks, while in the NoR Study , Q melt remains constant and Q heat increases with increasing NoR. By inserting the values in the above Eq. 2, the nominal Q total can be calculated for every process run for the two studies compared herein, and the values are depicted in below Table 1 . Table 1: Q preheat , Q melt and Q total for the processes run, calculated with Equation 2 (kJ, rounded to 0.01 kJ). For direct comparison, also T max (°C) of every process as well as the GS (µm) of the built structures are shown. Q total was varied in a much larger scale in the Packing Study and hence the temperature varied in a greater manner. The correlation of the GS, max process temperature and Q total is graphically shown in Fig. 5 . Obviously, the high values for Q total and resulting maximum process temperatures differ for the Packing as well as the NoR Study . Changing the number of built blocks in the Packing Study allowed for a much wider Q total and hence temperature window compared to the variation in the NoR . In the right chart, it becomes obvious that it is at lower process temperatures that the GS increases again in the Packing Study . In the present study, no direct information about the true processing temperature near the melt pool is available. The number of phase transformations is not only a matter of reaching critical transformation temperatures but also depends on the frequency and amplitude of the underlying thermal cycles. While the actual thermal path still remains unknown, all blocks were designed with identical geometries and processed using the same melting parameters to minimize variation and isolate the effects of build layout and heat accumulation. It can furthermore be assumed that heat accumulation in between closely packed blocks is higher, while heat dissipation is facilitated in less densely packed configurations. This assumption though can only be proven with adequate thermal simulations, which remain scarce in the literature. In a simulative study by Zhang et al. [ 17 ], the peak build plate temperature varied by up to 25.7% when comparing processes with one versus four build parts, suggesting that higher build densities lead to greater heat accumulation. This, in turn, influences the thermal history of the parts and potentially affects microstructural evolution through altered phase transformation dynamics. However, this study was conducted using a laser-based PB-AM system to process Ti-6Al-4V. A precise assessment of the actual processing temperatures is beyond the scope of the present study. Instead, the aim here is to contribute to a broader understanding of thermal effects, phase transformations, and microstructure evolution in PBF-EB/M processes, particularly with respect to build job configuration and preheat strategy. Conclusions In the presented work, cp-Fe was processed on a PBF-EB/M machine, and two studies are compared in which the heat development in an Arcam A2X build chamber was manipulated in different ways. The resulting microstructural variations and their consequences on quasistatic mechanical load were then demonstrated. In the Packing Study , the loading of a build plate, i.e., the overall melted area was varied. This variation is a matter of the CAD construction and/or the build layout. In contrast, the NoR Study involved intentional changes to process parameters. While the latter case is a conscious act of interfering in the build chamber temperature, microstructural changes as a result of build plate packing can be unexpected or even unnoticed until the component’s unexpected failure. Within the range of performed changes, the number of built specimens had a strong impact on the inserted energy Q total , the developed build chamber temperature, the GS and finally the strength of the material. While altering the NoR also led to process and material changes, these effects were less pronounced. As visualized in Fig. 5 , higher process temperatures are a driving force for grain growth, while at a certain thermal level an opposite driving force is active, where multiple phase transformations can lead to a grain refinement. In the presented study, this thermal level is in between 650°C and 700°C, but it is important to note that this information is based on a thermo couple being spatially separated from the active fusion zone by complex, evolving material layers. Hence, direct correlation remains difficult without advanced modeling. This complexity limits the ability to determine the absolute number of phase transformations during processing based on chamber temperature alone. Nevertheless, the goal of the present work is not to provide exact temperature values at the phase transformation front, but rather to highlight the thermally driven mechanisms that may occur depending on build job configuration and preheat conditions. The mentioned driving forces, i.e. grain growth at elevated temperatures versus refinement in a particular thermal window, ultimately shape microstructure and mechanical performance and should be considered when scaling from laboratory to industrial builds. The study underscores the critical importance of understanding heat development in PBF-EB/M processes and its impact on microstructure and component strength. It particularly highlights the potential risks of unanticipated microstructural changes due to build plate packing variations, which might not be evident until component failure occurs. This research aims to raise awareness about the consequences of varying specimen or part numbers during PBF-EB/M processing, emphasizing that parameters optimized for small-scale specimens may not be directly applicable to larger, real-world components. Declarations Author Contribution C.J.J.T.: Investigation, Conceptualization, Writing - review&editing, Writing - original draft, VisualizationP.K.: Writing—review & editing, SupervisionT.N.: Writing—review & editing, Supervision, Funding acquisitionAll authors have read and agreed to the published version of the manuscript Acknowledgements This work was supported by Deutsche Forschungsgemeinschaft (DFG) under grant number 413259151. References Günther J, Brenne F, Droste M, et al. Design of novel materials for additive manufacturing - Isotropic microstructure and high defect tolerance. Sci Rep. 2018;8:1298. Körner C. Additive manufacturing of metallic components by selective electron beam melting — a review. International Materials Reviews. 2016;61:361–377. Lejček P, Čapek J, Roudnická M, et al. Selective laser melting of iron: Multiscale characterization of mechanical properties. Materials Science and Engineering: A. 2021;800:140316. Burkhardt C, Wendler M, Lehnert R, et al. 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Torrent","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIie3RMQrCMBSA4fd4YJeqqyDoFSKdnHoVs9jJG4gECumizs0t6uaoFHTxAAEdOjnrIgoqtjq4SCy4OORfkiEfjyQANtsf1wJAgaLYOgAN01H3tTDvTagk4cXpcsR3JvvDcX4Nkm0oMB6O/DoB6oNpirv2lNqwQbJbCkxWKVchUDc2kUafqCpzonl0ySqLHkuh0nS/kZtkAdNcYHYf+X5Brt8IStZ7kpkkTCgnpuu7mxXhRHodVRA1TXmcYtgdG4gTSYKLbLVrOshwfMpfLAqX+mwa86HX99hsNpvthx5E8kh9lLPHHQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Kassel","correspondingAuthor":true,"prefix":"","firstName":"Christof","middleName":"J.J.","lastName":"Torrent","suffix":""},{"id":452006458,"identity":"0fe1562a-1a28-4d69-b4fd-aee215a2cbd4","order_by":1,"name":"Philipp Krooss","email":"","orcid":"","institution":"University of Kassel","correspondingAuthor":false,"prefix":"","firstName":"Philipp","middleName":"","lastName":"Krooss","suffix":""},{"id":452006460,"identity":"e42f789d-d44a-4e49-aa0d-9b05bdad4ecf","order_by":2,"name":"Thomas Niendorf","email":"","orcid":"","institution":"University of Kassel","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Niendorf","suffix":""}],"badges":[],"createdAt":"2025-04-30 15:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6566196/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6566196/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40964-025-01444-3","type":"published","date":"2026-01-12T16:29:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82354661,"identity":"32b71188-8986-4aee-b587-90afba5c9cdc","added_by":"auto","created_at":"2025-05-09 11:10:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55035,"visible":true,"origin":"","legend":"\u003cp\u003eBuild plate layout for the standard built job.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6566196/v1/07acccf12dd7952ea5189b9a.png"},{"id":82354663,"identity":"fb8151d9-0907-4760-89b7-1c762d953505","added_by":"auto","created_at":"2025-05-09 11:10:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1203973,"visible":true,"origin":"","legend":"\u003cp\u003eThermal paths recorded underneath the build plates with build layouts as shown. EBSD IPF maps of the samples processed with different packing densities. All IPFs are indexed in normal direction.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6566196/v1/a42f57acfa8599f6fd8ff85b.png"},{"id":82354665,"identity":"d0a9557d-40b7-452a-b7ff-60cb01f8e0cc","added_by":"auto","created_at":"2025-05-09 11:10:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80937,"visible":true,"origin":"","legend":"\u003cp\u003emechanical values of the specimens built in different packings. The bar diagram on the right-hand side gives direct comparison of strengths as well as GS.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6566196/v1/43d1a88716e183dc2bde203e.png"},{"id":82356558,"identity":"87f06023-c607-4f0b-8f84-e23d00b21df5","added_by":"auto","created_at":"2025-05-09 11:18:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1271384,"visible":true,"origin":"","legend":"\u003cp\u003ethermal path; bar diagram UTS, YS, and GS; resulting microstructure.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6566196/v1/a174a491d4d04da0961bfb6c.png"},{"id":82356557,"identity":"6a27ff8c-6eb7-407e-a3bb-55c2b8869890","added_by":"auto","created_at":"2025-05-09 11:18:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":44810,"visible":true,"origin":"","legend":"\u003cp\u003eSelected process conditions: Q\u003csub\u003etotal\u003c/sub\u003e according to Equation 2, max process temperature plotted over the GS. In the two compared studies different Q\u003csub\u003etotal\u003c/sub\u003e resulted in each layer, leading to different maximum process temperatures. The two main driving forces for GS evolution are grain growth at elevated temperatures as well as multiple phase transformations, each being more prominent in different thermal levels. (Note that the phase transformation does not take place where the line is located; this level is the thermo couple signal.) The GS finally correlates with the material’s resistance upon quasistatic loading (cf. Figures 3 and 4).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6566196/v1/2d26df685f128cdc61babf42.png"},{"id":100617579,"identity":"9f436922-082e-47fc-9826-fcf5d6348dd5","added_by":"auto","created_at":"2026-01-19 17:54:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3124850,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6566196/v1/5afefa0d-38b0-4a80-9146-b0ef0f6baa02.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Build Plate Packing and Preheat on the Grain Size Evolution in Powder Bed Fusion – Electron Beam of Metals","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFor a specific alloy, the thermal path can be a key to adjust the microstructure and mechanical properties. It is well known that by varying processing temperatures, holding times, or cooling velocities, the properties of a piece of metal can be significantly modified according to the needs. Nowadays, it is known that these adjustments are based on the adjustment of the grain size, segregations portion and size, or phase composition. Additionally, the residual stress state can be manipulated by the chosen heat treatment. For a wide range of alloys, the theoretical backgrounds are well investigated and depicted in phase diagrams or time-temperature-transformation (TTT) diagrams. Hence, the strict dependency of a microstructural design on the course of a developed thermal path is known.\u003c/p\u003e \u003cp\u003eTechnical components, especially those for which production technologies of additive manufacturing (AM) are chosen, often feature complex geometrical shapes. As a function of the geometry, varying cross sections in subsequent layers are in direct correlation to varying areas being melted and, thus, to a changing energy input throughout the build process. This in turn may cause undetected localized differences in the thermal history experienced by the material, and hence influence the mechanical response of the built component under load.\u003c/p\u003e \u003cp\u003eIn powder bed AM (PB-AM), the energy input is applied layer by layer, leading to a characteristic temperature profile throughout the build process. Initially, a defined volume increment undergoes melting and solidification, followed by progressively decreasing periodic reheating as the build plate lowers. This results in a sawtooth-like cyclic temperature profile over time, which can be interpreted as an intrinsic heat treatment. In allotropic alloys, this can lead to numerous phase transformations during processing, playing a crucial role in shaping the microstructure. Here, iron will be regarded, which shows a phase transformation path of liquid \u0026rarr; δ \u0026rarr; γ \u0026rarr; α. Depending on the global temperature level in the build chamber, the aforementioned volume increment can trespass a higher or lower number of δ \u0026harr; γ and γ \u0026harr; α phase transformations (at 1392\u0026deg;C and 911\u0026deg;C at 1 bar, respectively), each giving the option of one grain to break down into several smaller grains. A critical thermal window must therefore exist in which the number of phase transformations is maximized, resulting in the highest potential for grain refinement. Above this range, the material may remain too long in a stable high-temperature phase, while below it, the temperature may be insufficient to induce repeated transformations \u0026mdash; in both cases reducing the grain refinement potential. In this way, grain size and nature of grain boundaries is significantly affected in PB-AM. Furthermore, high temperatures can allow for annealing phenomena as recrystallization and recovery, also contributing to the final microstructural appearance. The described effects have already been mentioned in previous studies (cf. [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]) and is the process related background for the investigations in this work.\u003c/p\u003e \u003cp\u003eAccurately determining the actual temperature at the active processing layer remains a significant challenge. While the thermo couple is commonly placed underneath the build plate, the recorded thermal signal is influenced by multiple layers of material \u0026ndash; ranging from fully dense metal to semi-sintered and loose powder \u0026ndash; each with distinct and evolving thermal conductivities. Furthermore, the dynamic nature of the electron beam introduces highly localized and transient heat inputs and a complex thermal path not directly reflected in the smoothed response of the thermocouple. As a result, no direct or reliable correlation exists yet between the sub-plate thermo couple reading and the actual temperature at the melt pool or solidification front. Instead, temperature estimations rely on calibrated models or indirect inference, as demonstrated by Plotkowski et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and Raplee et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In these studies, semi-analytical models and infra-red thermography were applied to estimate local thermal gradients in electron beam powder bed fusion of metals (PBF-EB/M) and correlate them with microstructural features. This highlights the complexity but also the necessity of robust thermal analysis and/or modeling when studying phase transformations and microstructure evolution in PBF-EB/M.\u003c/p\u003e \u003cp\u003eThe aim of this study is not to determine the exact temperature at the processing layer, but rather to investigate how variations in heat input, arising from build layout and preheat strategy, can affect the resulting microstructures in pure iron. By identifying correlations between process-induced thermal conditions and grain refinement, this work seeks to contribute to a deeper understanding of thermal histories in PBF-EB/M and to explore whether such effects can be controlled or counteracted through process design. For this sake, a PBF-EB/M system is used to build specimens equal in geometry and without altering the melt parameters. The number of specimens built on a build plate is varied in order to investigate how the scan area influences the overall process temperature, microstructure, and mechanical response of the material. By increasing or decreasing the number of specimens, the energy distribution and thermal history during the build process is altered. Furthermore, to explore additional ways to control the microstructure, the \u003cem\u003epreheat\u003c/em\u003e step using the same build plate layout and melt parameters is varied. This allows to evaluate if adjusting the \u003cem\u003epreheat\u003c/em\u003e temperature provides an additional degree of freedom to fine-tune the microstructural characteristics of the built specimens. These variations aim to provide a deeper understanding of how localized thermal conditions in PB-AM processes affect the final properties of manufactured components. By understanding and controlling thermal variables, a safe transitioning from initial parameter studies, which uses typically smaller geometries, to mass production of more complex geometries can be ensured. Furthermore, the reliability and performance of additively manufactured components can be improved while unexpected anomalies in material properties can be avoided.\u003c/p\u003e"},{"header":"Materials and experimental methods","content":"\u003cp\u003eCommercially pure iron (cp-Fe) powder in a particle size distribution of nominally 63\u0026ndash;150 \u0026micro;m provided by Eckard TLS (Germany) was processed on an Arcam A2X (GE ARCAM AB, M\u0026ouml;lndal, Sweden), which was operated at a constant acceleration voltage of 60 kV. The \u003cem\u003epresinter\u003c/em\u003e, i.e. the heating of the build plate before the first raking, was set to nominally 400\u0026deg;C and hold for 10 minutes. The lowest current possible on this machine (5 mA) still leads to higher build plate temperatures so that the processes then started at around 500\u0026deg;C. subsequently, with a constant layer thickness 0f 0.05 mm, a varying number of 40 mm high cuboid blocks of \u003cem\u003el\u003c/em\u003e\u003csub\u003e\u003cem\u003emelt\u003c/em\u003e\u003c/sub\u003e \u0026times; \u003cem\u003el\u003c/em\u003e\u003csub\u003e\u003cem\u003emelt\u003c/em\u003e\u003c/sub\u003e = 10 \u0026times; 10 mm\u0026sup2; was built on a 100 \u0026times; 100 mm\u0026sup2; build plate. After the process start, the built room temperatures recorded by the thermo couple type K attached underneath the build plate differed depending on the varied \u003cem\u003epreheat\u003c/em\u003e as well as the melted area. The \u003cem\u003epreheat\u003c/em\u003e area \u003cem\u003el\u003c/em\u003e\u003csub\u003e\u003cem\u003eheat\u003c/em\u003e\u003c/sub\u003e \u0026times; \u003cem\u003el\u003c/em\u003e\u003csub\u003e\u003cem\u003eheat\u003c/em\u003e\u003c/sub\u003e was 80 \u0026times; 80 mm\u0026sup2;, and the current \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003eheat\u003c/em\u003e\u003c/sub\u003e was set to 12 mA, the beam speed \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003eheat\u003c/em\u003e\u003c/sub\u003e to 12,000 mm\u0026sdot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and the hatching \u003cem\u003eh\u003c/em\u003e\u003csub\u003e\u003cem\u003eheat\u003c/em\u003e\u003c/sub\u003e was 1 mm. The numbers of repetition (NoR) were set to 15 as standard, but varied from 10 to 25 in the respective \u003cem\u003eNoR Study\u003c/em\u003e. After preheating, the melting was conducted with \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003emelt\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003emelt\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eh\u003c/em\u003e\u003csub\u003e\u003cem\u003emelt\u003c/em\u003e\u003c/sub\u003e of 12.25 mA, 4,000 mm s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 0.08 mm, respectively. 4 \u0026times; 4 blocks were built as a standard and varied from 2 \u0026times; 2 to 6 \u0026times; 6 in the \u003cem\u003ePacking Study\u003c/em\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows a representative build plate layout.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAgain, in the present study, two different strategies were considered, i.e. the\u003c/p\u003e \u003cp\u003e \u003cem\u003ePacking Study\u003c/em\u003e (variation in the packing density on the build plate) and the \u003cem\u003eNoR Study\u003c/em\u003e (variation of the numbers of repetition). The specimens built and investigated in the \u003cem\u003epacking study\u003c/em\u003e will be referred to as Fe\u003csub\u003e15/m\u003c/sub\u003e, with \u003cem\u003em\u003c/em\u003e giving information on the number of built blocks on one plate using always 15 NoR. The specimens in the \u003cem\u003eNoR Study\u003c/em\u003e will be referred to as Fe\u003csub\u003en/4\u0026times;4\u003c/sub\u003e, with n being the index for the \u003cem\u003eNoR\u003c/em\u003e in jobs in which 4 \u0026times; 4\u0026thinsp;=\u0026thinsp;16 blocks have been built. Finally, after manufacturing, the blocks were cut from the build plate and dog-bone shaped tensile specimen with a cross section of 4.5 mm\u0026sup2; (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) extracted via electrical discharge machining. Although in [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] it is found that microstructure, density and hardness are not affected by the part\u0026rsquo;s position on the build plate for PBF-EB/M processed IN 718, a center block was chosen from the 4 \u0026times; 4 layouts, and from the more densely packed layouts of the \u003cem\u003ePacking Study\u003c/em\u003e blocks from the second row (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were chosen to minimize heat affected sample to sample variations. These specimens were then used for both, the microstructural investigation as well as the mechanical testing, and the tested region was always in the middle of the formerly 40 mm high blocks (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After grinding the specimen to P2500 and polishing one surface to 0.05 \u0026micro;m, the microstructure was contemplated by means of a scanning electron microscope (SEM, TESCAN CAMSCAN MV 2300, Brno, Czech Republic) equipped with an electron backscatter diffraction (EBSD) detector. The collected data were analyzed using TSL orientation imaging microscopy software, version 7 (EDAX, Mahwah, NJ, USA). From EBSD data, grain sizes (GS) are determined giving the diameter of circles with the same area. Tensile tests were conducted on a MTS Criterion Model 43 (MTS Systems Corp., Eden Prairie, MN, USA) at a displacement rate of 2 mm\u0026sdot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, recording the elongation till 30 % ith an extensometer with a gauge length of 5 mm. After 30 %,the elongation was calculated from the crosshead displacement. Two specimens per condition were tested.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003ePacking Study\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the thermal paths recorded by the thermo couple underneath the build plate. For the characterization, apart from the 2 \u0026times; 2, blocks built in the second row were chosen cf. circles in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In this way, \u0026ldquo;cooler\u0026rdquo; specimens from the edge as well as \u0026ldquo;hotter\u0026rdquo; specimens from the center of the build plate were avoided. As a consequence of the varying packing density and thus the thermal path, obvious microstructural changes appear, as becomes visible in the inverse pole figure (IPF) maps.\u003c/p\u003e\n \u003cp\u003eWhen examining the microstructure of the built samples, it is evident that changes in process temperatures influence the microstructure: Whereas the texture and general grain morphology remains similar, specimens from the densest build plates exhibit the largest GS. As the build plate packing decreases and the process takes place at lower temperatures, the GS decreases, reaching the smallest size in the 3 \u0026times; 3 process. With even less dense packing (Fe\u003csub\u003e15/2\u0026times;2\u003c/sub\u003e), the GS again slightly increases. Hence, GS development does not strictly corelate to the process temperature level. This indicates that different driving forces may impact microstructural development: GS seems to increase at both high and low temperatures, while a certain build temperature level results in the smallest GS. High temperatures and long process times can promote grain growth to reduce grain boundary energy [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e], which may explain the larger GS in processes with high density of built blocks.\u003c/p\u003e\n \u003cp\u003eHowever, it is not the process with the lowest build density that leads to the smallest GS, but rather the intermediate processes with maximum detected temperatures between 650\u0026deg;C and 700\u0026deg;C. According to the concept of intrinsic heat treatment induced grain refinement introduced earlier, this temperature range likely facilitates the highest number of allotropic phase transformations during processing, thereby offering the greatest potential for grain refinement. At lower process temperatures, although the driving force for grain coarsening is reduced, the influence of grain refinement through multiple phase transformations also diminishes, weakening the grain refinement mechanism and resulting in increasing GS. Hence, a measured build chamber temperature of 650\u0026deg;C to 700\u0026deg;C results in the lowest grain sizes in the \u003cem\u003ePacking Study\u003c/em\u003e. Yet, the thermo couple is located underneath the build plate as described above, and separated from the active processing region by a complex and evolving material stack (solidified bulk, sintered powder, and loose powder). Estimation of the thermal circumstances via the thermo couple is hence inappropriate [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. Even though for iron a \u0026alpha; \u0026harr; \u0026gamma; transformation is known to occur at 911\u0026deg;C (at 1 bar), a clear correlation between temperature and GS shall be dispensed here for this reason.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows representative stress-strain curves and the mean yield and ultimate tensile strengths (YS and UTS, respectively) as well as GS of the compared specimens. Regarding these results, a correlation of GS and strengths becomes obvious, revealing highest strengths for the Fe\u003csub\u003e15/3\u0026times;3\u003c/sub\u003e specimens. Contrary, Fe\u003csub\u003e15/6\u0026times;6\u003c/sub\u003e shows the lowest material resistance to quasistatic loading. This correlation is thus in agreement with Hall-Petch relation [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe changes depicted herein are not related to the geometry of the individual specimens or applied process parameters, but solely to the packing density of the build plate and it\u0026rsquo;s influence on the overall chamber temperature and, ultimately, on the phase transformation occurring during the process. As mentioned in the introduction above, the chamber temperature can lie within a range that promotes maximal grain refinement \u0026ndash; or fall outside (above or below) this range, thereby reducing the refinement effect. The chamber temperature is usually set via the presinter step and maintained at a desired level through the preheat during the whole built process. This raises the question of whether the effects shown in this section can be intentionally manipulated or counteracted via tailored \u003cem\u003epreheat\u003c/em\u003e strategies which shall be examined in the following section.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eNoR Study\u003c/h3\u003e\n\u003cp\u003eFor this purpose, standard build jobs with 4 \u0026times; 4 cuboid blocks were performed in which the \u003cem\u003eNoR\u003c/em\u003e were varied, while all other parameters were kept constant. In Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e the temperature paths, resulting microstructure and mechanical properties are shown.\u003c/p\u003e\n\u003cp\u003eAs with build plate packing, changing the \u003cem\u003eNoR\u003c/em\u003e also affects the layer time, i.e. the interval between two successive raking steps. Also, the total introduced heat per layer changes accordingly, finally affecting the built chamber temperature recorded by the thermocouple underneath the build plate. With 8 \u003cem\u003eNoR\u003c/em\u003e, the temperature is insufficient for proper sintering of the loose powder in a new layer, leading to powder blasts and process interruption. Therefore, at least 10 \u003cem\u003eNoR\u003c/em\u003e are necessary to create an adequate degree of sintering and thus allow for a stable build process. Obviously, the built chamber temperatures increase with increasing \u003cem\u003eNoR\u003c/em\u003e (cf. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). In comparison to the results from the \u003cem\u003ePacking Study\u003c/em\u003e, the differences in the recorded temperatures in the \u003cem\u003eNoR Study\u003c/em\u003e are less pronounced, indicating a lower variation in total energy input, which will be discussed in the remainder of this work. Still, a significant change in GS is visible, revealing the highest GS (around 50 \u0026micro;m) for the process with highest \u003cem\u003eNoR\u003c/em\u003e. Again, the values for the GS correlate with the mechanical response to quasistatic loading. Lower \u003cem\u003eNoR\u003c/em\u003e result in a smaller GS and thus, according to the Hall-Petch relationship, in a higher strength. However, 25 \u003cem\u003eNoR\u003c/em\u003e lead to highest GS, whereas GS remain in between 30 and 40 \u0026micro;m for lower \u003cem\u003eNoR\u003c/em\u003e, and no explicit extremum can be seen in this study.\u003c/p\u003e\n\u003cp\u003eIn the \u003cem\u003eNoR Study\u003c/em\u003e, the coolest build process shows a maximum build plate temperature at around 680\u0026deg;C, whereas in the \u003cem\u003ePacking Study\u003c/em\u003e even lower build plate temperatures (around 620\u0026deg;C), were achieved. According to Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, in the Packing Study the GS increased again at build plate temperatures below 650\u0026deg;C. This trend may be attributed to a reduced influence of grain refinement via multiple phase transformations below this specific temperature regime. It is assumed that at lower chamber temperatures, fewer phase transformations occur during processing, which in turn may reduce grain refinement. Similarly, at higher temperatures, the number of transformations may also decrease, and conventional grain growth could become more dominant. However, this interpretation is based on the current experimental observations and should be further substantiated in future studies or with dedicated phase transformation analyses.\u003c/p\u003e\n\u003cp\u003eIn the NoR Study, a correlation of GS and built plate temperature is not obvious (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The process temperature resulting from the lowest possible \u003cem\u003eNoR\u003c/em\u003e is found to be higher (680\u0026deg;C) than that resulting from the lowest build plate packing in the \u003cem\u003ePacking Study\u003c/em\u003e (620\u0026deg;C). While an increase of GS at even lower temperatures \u0026ndash; following the trend observed in the \u003cem\u003ePacking Study\u003c/em\u003e \u0026ndash; is possible, it could not be clearly identified within the process window considered here. In other words: effective grain refinement induced by intrinsic heat treatment could be active in case of the lowest used \u003cem\u003eNoR\u003c/em\u003e, but is not proven if GS does not increase on both ends of the study.\u003c/p\u003e\n\u003ch3\u003eThermal background\u003c/h3\u003e\n\u003cp\u003eIn [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e] it is discussed how PBF-EB/M manufactured strut geometries are impacted by changing energy input, heat accumulation, beam return time etc. and hence melt pool geometries change in accordance to the melted cross section in a layer, influencing in turn the final microstructure in specific component\u0026rsquo;s regions. In the results presented herein, the microstructural appearance only changes in regard of the GS and not in the grain shape or aspect ratio.\u003c/p\u003e\n\u003cp\u003eThe fact that the \u003cem\u003ePacking Study\u003c/em\u003e could run stably at lower temperatures again shows that the temperature recorded by the thermocouple is not an appropriate indicator for the thermal household in the region of processing [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, time also plays a significant role, as sintering the material is a matter of both temperature and time. In the \u003cem\u003ePacking Study\u003c/em\u003e, processing 2 \u0026times; 2 blocks at 15 \u003cem\u003eNoR\u003c/em\u003e resulted in shorter layer times and hence the shortest total process times. Nevertheless, the \u003cem\u003epreheat\u003c/em\u003e time remained constant, allowing for sufficient sintering in each layer. As a result, this setup resulted in the lowest stable process temperatures. In comparison, building 4 \u0026times; 4 blocks at 10 \u003cem\u003eNoR\u003c/em\u003e in the \u003cem\u003eNoR Study\u003c/em\u003e led to higher process temperatures than the coolest case in the \u003cem\u003ePacking Study.\u003c/em\u003e Further reducing the \u003cem\u003eNoR\u003c/em\u003e to 8 caused instabilities, suggesting that the \u003cem\u003eNoR\u003c/em\u003e cannot be arbitrarily lowered to reduce process temperature. This is likely due to the reduced available sintering time, as well as insufficient time for electron beam energy to dissipate during the \u003cem\u003epreheat\u003c/em\u003e step when applying lower NoR.\u003c/p\u003e\n\u003cp\u003eTo assess the energy input during the PBF-EB/M process, the volume energy \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003evol\u003c/em\u003e\u003c/sub\u003e can be calculated using the well-known Eq. 1 [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e], in which the product of the acceleration voltage \u003cem\u003eU\u003c/em\u003e und the used beam current \u003cem\u003eI\u003c/em\u003e is divided by spatial factors as the hatch \u003cem\u003eh\u003c/em\u003e, the beam speed \u003cem\u003ev\u003c/em\u003e and the layer thickness \u003cem\u003ed\u003c/em\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{vol}=\\frac{U\\cdot\\:I}{h\\cdot\\:v\u0026middot;d}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e [Equation 1]\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQ\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003evol\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e with the unit \u003cem\u003eJ\u0026middot;mm\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e can be applied for both, the \u003cem\u003epreheat\u003c/em\u003e and the \u003cem\u003emelt\u003c/em\u003e step. To understand why lower process temperatures are possible in the \u003cem\u003ePacking Study\u003c/em\u003e, Eq. 2 is abstracted from Eq. 1 and introduced to explain the overall inserted \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003etotal\u003c/em\u003e\u003c/sub\u003e in each single layer (regardless of the layer time). \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003etotal\u003c/em\u003e\u003c/sub\u003e is composed by the energy input while preheating, \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003epreheat\u003c/em\u003e\u003c/sub\u003e, and while melting, \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003emelt\u003c/em\u003e\u003c/sub\u003e. Since only the inserted energy \u0026ndash; regardless of the absorption by the constant layer thickness \u0026ndash; shall be regarded, \u003cem\u003ed\u003c/em\u003e can be deleted. Instead, the preheated and melted surface \u003cem\u003el \u0026times; l\u003c/em\u003e for heating and melting have to be regarded, and obviously the \u003cem\u003eNoR\u003c/em\u003e in the \u003cem\u003epreheat\u003c/em\u003e and the number of melted blocks n in the \u003cem\u003emelting\u003c/em\u003e (cf. values in experimental section and schematic shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{total}={Q}_{preheat}+{Q}_{melt}={\\left[U\\cdot\\:I\\cdot\\:\\frac{l\\times\\:l}{h\\cdot\\:v}\\cdot\\:NoR\\right]}_{preheat}+{\\left[U\\cdot\\:I\\cdot\\:\\frac{l\\times\\:l}{h\\cdot\\:v}\\cdot\\:n\\right]}_{melt}\\:\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e [Equation 2]\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQ\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003etotal\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e now has the unit \u003cem\u003eJ\u003c/em\u003e. In the \u003cem\u003ePacking Study\u003c/em\u003e, \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003epreheat\u003c/em\u003e\u003c/sub\u003e remains constant and \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003emelt\u003c/em\u003e\u003c/sub\u003e increases with increasing number of built blocks, while in the \u003cem\u003eNoR Study\u003c/em\u003e, \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003emelt\u003c/em\u003e\u003c/sub\u003e remains constant and \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eheat\u003c/em\u003e\u003c/sub\u003e increases with increasing \u003cem\u003eNoR.\u003c/em\u003e By inserting the values in the above Eq. 2, the nominal \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003etotal\u003c/em\u003e\u003c/sub\u003e can be calculated for every process run for the two studies compared herein, and the values are depicted in below Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1: Q\u003csub\u003epreheat\u003c/sub\u003e , Q\u003csub\u003emelt\u0026nbsp;\u003c/sub\u003eand Q\u003csub\u003etotal\u003c/sub\u003e for the processes run, calculated with Equation 2 (kJ, rounded to 0.01 kJ). For direct comparison, also T\u003csub\u003emax\u003c/sub\u003e (\u0026deg;C) of every process as well as the GS (\u0026micro;m) of the built structures are shown.\u003c/em\u003e\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" style=\"width: 1146px;\" width=\"1146\" height=\"581\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQ\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003etotal\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e was varied in a much larger scale in the \u003cem\u003ePacking Study\u003c/em\u003e and hence the temperature varied in a greater manner. The correlation of the GS, max process temperature and \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003etotal\u003c/em\u003e\u003c/sub\u003e is graphically shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Obviously, the high values for \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003etotal\u003c/em\u003e\u003c/sub\u003e and resulting maximum process temperatures differ for the \u003cem\u003ePacking\u003c/em\u003e as well as the \u003cem\u003eNoR Study\u003c/em\u003e. Changing the number of built blocks in the \u003cem\u003ePacking Study\u003c/em\u003e allowed for a much wider \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003etotal\u003c/em\u003e\u003c/sub\u003e and hence temperature window compared to the variation in the \u003cem\u003eNoR\u003c/em\u003e. In the right chart, it becomes obvious that it is at lower process temperatures that the GS increases again in the \u003cem\u003ePacking Study\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eIn the present study, no direct information about the true processing temperature near the melt pool is available. The number of phase transformations is not only a matter of reaching critical transformation temperatures but also depends on the frequency and amplitude of the underlying thermal cycles. While the actual thermal path still remains unknown, all blocks were designed with identical geometries and processed using the same melting parameters to minimize variation and isolate the effects of build layout and heat accumulation.\u003c/p\u003e\n\u003cp\u003eIt can furthermore be assumed that heat accumulation in between closely packed blocks is higher, while heat dissipation is facilitated in less densely packed configurations. This assumption though can only be proven with adequate thermal simulations, which remain scarce in the literature. In a simulative study by Zhang et al. [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e], the peak build plate temperature varied by up to 25.7% when comparing processes with one versus four build parts, suggesting that higher build densities lead to greater heat accumulation. This, in turn, influences the thermal history of the parts and potentially affects microstructural evolution through altered phase transformation dynamics. However, this study was conducted using a laser-based PB-AM system to process Ti-6Al-4V. A precise assessment of the actual processing temperatures is beyond the scope of the present study. Instead, the aim here is to contribute to a broader understanding of thermal effects, phase transformations, and microstructure evolution in PBF-EB/M processes, particularly with respect to build job configuration and preheat strategy.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn the presented work, cp-Fe was processed on a PBF-EB/M machine, and two studies are compared in which the heat development in an Arcam A2X build chamber was manipulated in different ways. The resulting microstructural variations and their consequences on quasistatic mechanical load were then demonstrated. In the \u003cem\u003ePacking Study\u003c/em\u003e, the loading of a build plate, i.e., the overall melted area was varied. This variation is a matter of the CAD construction and/or the build layout. In contrast, the \u003cem\u003eNoR Study\u003c/em\u003e involved intentional changes to process parameters. While the latter case is a conscious act of interfering in the build chamber temperature, microstructural changes as a result of build plate packing can be unexpected or even unnoticed until the component\u0026rsquo;s unexpected failure.\u003c/p\u003e \u003cp\u003eWithin the range of performed changes, the number of built specimens had a strong impact on the inserted energy \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003etotal\u003c/em\u003e\u003c/sub\u003e, the developed build chamber temperature, the GS and finally the strength of the material. While altering the \u003cem\u003eNoR\u003c/em\u003e also led to process and material changes, these effects were less pronounced. As visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, higher process temperatures are a driving force for grain growth, while at a certain thermal level an opposite driving force is active, where multiple phase transformations can lead to a grain refinement. In the presented study, this thermal level is in between 650\u0026deg;C and 700\u0026deg;C, but it is important to note that this information is based on a thermo couple being spatially separated from the active fusion zone by complex, evolving material layers. Hence, direct correlation remains difficult without advanced modeling. This complexity limits the ability to determine the absolute number of phase transformations during processing based on chamber temperature alone. Nevertheless, the goal of the present work is not to provide exact temperature values at the phase transformation front, but rather to highlight the thermally driven mechanisms that may occur depending on build job configuration and preheat conditions. The mentioned driving forces, i.e. grain growth at elevated temperatures versus refinement in a particular thermal window, ultimately shape microstructure and mechanical performance and should be considered when scaling from laboratory to industrial builds.\u003c/p\u003e \u003cp\u003eThe study underscores the critical importance of understanding heat development in PBF-EB/M processes and its impact on microstructure and component strength. It particularly highlights the potential risks of unanticipated microstructural changes due to build plate packing variations, which might not be evident until component failure occurs. This research aims to raise awareness about the consequences of varying specimen or part numbers during PBF-EB/M processing, emphasizing that parameters optimized for small-scale specimens may not be directly applicable to larger, real-world components.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.J.J.T.: Investigation, Conceptualization, Writing - review\u0026amp;editing, Writing - original draft, VisualizationP.K.: Writing\u0026mdash;review \u0026amp; editing, SupervisionT.N.: Writing\u0026mdash;review \u0026amp; editing, Supervision, Funding acquisitionAll authors have read and agreed to the published version of the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was supported by Deutsche Forschungsgemeinschaft (DFG) under grant number 413259151.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eG\u0026uuml;nther J, Brenne F, Droste M, et al. 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Additive Manufacturing. 2021;47:102253.\u003c/li\u003e\n\u003cli\u003eTorrent C, Kroo\u0026szlig; P, Niendorf T. On the Impact of Build Envelope Sizes on E-PBF Processed Pure Iron. Metall Mater Trans B. 2021. DOI: 10.1007/s11663-021-02368-3.\u003c/li\u003e\n\u003cli\u003ePlotkowski A, Kirka MM, Babu SS. Verification and validation of a rapid heat transfer calculation methodology for transient melt pool solidification conditions in powder bed metal additive manufacturing. Additive Manufacturing. 2017;18:256\u0026ndash;268.\u003c/li\u003e\n\u003cli\u003eRaplee J, Plotkowski A, Kirka MM, et al. Thermographic Microstructure Monitoring in Electron Beam Additive Manufacturing. Sci Rep. 2017;7:43554.\u003c/li\u003e\n\u003cli\u003eKotzem D, Arold T, Niendorf T, et al. Influence of specimen position on the build platform on the mechanical properties of as-built direct aged electron beam melted Inconel 718 alloy. Materials Science and Engineering: A. 2020;772:138785.\u003c/li\u003e\n\u003cli\u003eHumphreys FJ, Hatherly M. Recrystallization and Related Annealing Phenomena. [place unknown]: Elsevier Science; 2012.\u003c/li\u003e\n\u003cli\u003eSj\u0026ouml;str\u0026ouml;m W, R\u0026auml;nnar L-E, Botero C, et al. Process Developments in Electron-Beam Powder Bed Fusion Enabled by Near-Infrared Radiation. JMMP. 2024;8:211.\u003c/li\u003e\n\u003cli\u003eRahimi F, Pourabdollah P, Farhang Mehr F, et al. A macroscale heat transfer analysis of the build chamber in a commercial electron beam powder bed fusion (EB-PBF) additive manufacturing system during component fabrication. Additive Manufacturing:103831.\u003c/li\u003e\n\u003cli\u003eHansen N. Hall\u0026ndash;Petch relation and boundary strengthening. Scripta Materialia. 2004;51:801\u0026ndash;806.\u003c/li\u003e\n\u003cli\u003eKotzem D, Arold T, Niendorf T, et al. Damage Tolerance Evaluation of E-PBF-Manufactured Inconel 718 Strut Geometries by Advanced Characterization Techniques. Materials (Basel) [Internet]. 2020;13:247. Available from: https://www.mdpi.com/1996-1944/13/1/247.\u003c/li\u003e\n\u003cli\u003eScharowsky T, Bauerei\u0026szlig; A, K\u0026ouml;rner C. Influence of the hatching strategy on consolidation during selective electron beam melting of Ti-6Al-4V. Int J Adv Manuf Technol. 2017;92:2809\u0026ndash;2818.\u003c/li\u003e\n\u003cli\u003eTammas-Williams S, Zhao H, L\u0026eacute;onard F, et al. XCT analysis of the influence of melt strategies on defect population in Ti\u0026ndash;6Al\u0026ndash;4V components manufactured by Selective Electron Beam Melting. Materials Characterization. 2015;102:47\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eZhang W, Tong M, Harrison NM. Multipart Build Effects on Temperature and Residual Stress by Laser Beam Powder Bed Fusion Additive Manufacturing. 3D Print Addit Manuf. 2023;10:749\u0026ndash;761.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"pure iron, intrinsic heat treatment, grain refinement, powder bed fusion, electron beam melting","lastPublishedDoi":"10.21203/rs.3.rs-6566196/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6566196/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAdditive manufacturing (AM) relies particularly on the precise control of thermal paths to achieve desired microstructural and mechanical properties of the processed material. Complex geometrical shapes often associated with AM result in varying cross sections and consequently fluctuating energy inputs, which can cause localized differences in thermal history that influence material response. The present work explores the impact of variations in thermal history during the AM process on the final properties of built components. In this research, the influence of scan area and \u003cem\u003epreheat\u003c/em\u003e on the material\u0026rsquo;s properties is investigated. The number of specimens on the build plate was altered without changing the process parameters to examine how scan area affects overall process temperature and microstructure. Additionally, using the same build layout and melt parameters, the \u003cem\u003epreheat\u003c/em\u003e step was varied to explore further impact on final microstructure. The results show that changes in the number of built specimens significantly impact the energy input, build chamber temperature, grain size, and material strength. Although adjusting the \u003cem\u003epreheat\u003c/em\u003e also led to changes, the effects were less pronounced. These findings highlight the importance of understanding and controlling thermal conditions in AM processes to ensure consistent material properties and prevent unexpected component failures.\u003c/p\u003e","manuscriptTitle":"Impact of Build Plate Packing and Preheat on the Grain Size Evolution in Powder Bed Fusion – Electron Beam of Metals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 11:10:39","doi":"10.21203/rs.3.rs-6566196/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e3d439fd-39e7-427c-af37-5021a4a4a62c","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T17:23:26+00:00","versionOfRecord":{"articleIdentity":"rs-6566196","link":"https://doi.org/10.1007/s40964-025-01444-3","journal":{"identity":"progress-in-additive-manufacturing","isVorOnly":false,"title":"Progress in Additive Manufacturing"},"publishedOn":"2026-01-12 16:29:35","publishedOnDateReadable":"January 12th, 2026"},"versionCreatedAt":"2025-05-09 11:10:39","video":"","vorDoi":"10.1007/s40964-025-01444-3","vorDoiUrl":"https://doi.org/10.1007/s40964-025-01444-3","workflowStages":[]},"version":"v1","identity":"rs-6566196","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6566196","identity":"rs-6566196","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}