Laser Powder Bed Fusion Process Research Articles

Towards a Sustainable Laser Powder Bed Fusion Process via the Characterisation of Additively Manufactured Nitinol Parts

Is additive manufacturing (AM) a sustainable process? Can the process be optimised to produce sustainable AM parts and production techniques? Additive manufacturing offers the production of parts made of different types of materials in addition to the complex geometry that is difficult or impossible to produce by using the traditional subtractive methods. This study is focused on the optimisation of laser powder bed fusion (L-PBF), one of the most common technologies used in additive manufacturing and 3D printing. This research was carried out by modulating the build layer thickness of the deposited metal powder and the input volumetric energy density. The aim of the proposed strategy is to save the build time by maximizing the applied layer thickness of nitinol powder while retrieving the different AM part properties. The saving in the process time has a direct effect on the total cost of the produced part as a result of several components like electric energy, inert gas consumption, and labour. Nickel-rich nitinol (52.39 Ni at.%) was selected for investigation in this study due to its extremely high superplastic and shape memory properties in addition to the wide application in various industries like aerospace, biomedical, and automotive. The results obtained show that significant energy and material consumption can be found by producing near full dens AM parts with limited or no alteration in chemical and mechanical properties.

A comparative analysis of compressive and wear failure of laser-powder bed fused Stainless Steel 316L produced with different hatch spacing in stripe and contour patterns

The selection of hatch spacing is crucial in additive manufacturing processes to produce a defect free Stainless Steel 316L (SS) components, especially in laser powder bed fusion (LPBF) process. Improper selection of hatch spacing could lead to various failure regimes (like vertical cracking, porosity, and un-melted particles) in Stainless Steel 316L (SS) component produced through laser powder bed fusion (LPBF). Further, the failure regimes have a direct impact on the hardness, compression strength, friction, and wear properties of LPBF SS components. The present study has taken substantial efforts to analyse the impact of various hatch spacing (0.08 mm, 0.10 mm. and 0.12 mm) in stripe and contour patterns, in terms of morphology, mechanical, and tribological properties of LPBF SS components. Also, the present study elucidates how variations in hatch spacing influences the features like particle fusion, crack formation, density, hardness, surface roughness, compression, and wear properties. The stripe pattern with low hatch spacing of 0.08 mm showed better particle fusion and high surface roughness; whereas the high hatch spacing has produced voids, un-melted powder particles, and cracks in the stripe and contour pattern. High densities and hardness values were attributed to the stripe patterns with more effective melting and fusing of powder particles. XRD analysis revealed the stable phase compositions across various hatch spacing with a predominant FCC crystal structure. Residual stress distribution was significantly influenced by the hatch spacing for both stripe and contour patterns. Importantly, the low hatch space leads has promoted the tensile residual stress formation and vice versa for the higher hatch spacing. Stripe patterns showed higher compression strengths with buckling and shear mode as a compression failure. The stripe pattern demonstrated a low specific wear rate (SWR) and coefficient of friction (COF) compared to the contour pattern, with respect to the various hatch spacing. Furthermore, the simulated body fluid (SBF) lubrication regime played a significant role in reducing the COF. Patches, groves, furrows, and craters demonstrated the abrasive and adhesive failure for both stripe and contour pattern, which is further detailed in the article.

An integrated computation framework for predicting mechanical performance of single-phase alloys manufactured using laser powder bed fusion: A case study of CoCrFeMnNi high-entropy alloy

Establishing a process-structure-property (PSP) relationship is crucial for understanding and optimizing the mechanical properties of components via laser powder bed fusion (L-PBF) additive manufacturing (AM). However, the parameters pertinent to the PSP relationship are encapsulated in a “black box” with a high dimension, resulting in relying on trial-and-error approaches to elucidate hidden associations becoming impracticable, while the acquisition of microscopic data proves financially burdensome. Thus this paper presents an integrated computation framework based on machine learning (ML) for predicting the tensile yield strengths and post-yield hardening rates of single-phase alloys fabricated through L-PBF, for efficient process optimization purposes. Research herein focuses on single-phase alloys to be built via L-PBF, taking CoCrFeMnNi high-entropy alloy as a study case, and provide an efficient integrated computation solution. The methodology proposed in this paper generates as-built microstructures associated with the varying L-PBF process parameters via cellular automata (CA). Subsequently, the mechanical responses of these microstructures are estimated by employing crystal plasticity finite element (CPFE) simulations. In addition, the approach raised in the paper leverages particle-level microstructure descriptors instead of average feature descriptors for the entire representative volume element (RVE) structure in an ML framework to predict the characteristics of the newly randomly growing grains. This research demonstrate the practicality and instructiveness of the methodology for modeling realistic grains. Then, trained ML model exhibits promising accuracy for the prediction of mechanical properties. This work showcases an integrated computation framework combining advanced materials science and ML techniques for enhancing understanding of the L-PBF process and offering valuable insights into process optimization for L-PBF-built diverse materials.

Tensile and High Cycle Fatigue Performance at Room and Elevated Temperatures of Laser Powder Bed Fusion Manufactured Hastelloy X.

The application potential of additive manufacturing nickel-based superalloys in aeroengines and gas turbines is extensive, and evaluating their mechanical properties is crucial for promoting the engineering application in load-bearing components. In this study, Hastelloy X alloy was prepared using the laser powder bed fusion process combined with solution heat treatment. The tensile and high cycle fatigue properties were experimentally investigated at room temperature as well as two typical elevated temperatures, 650 °C and 815 °C. It was found that, during elevated-temperature tensile deformation, the alloy exhibits significant serrated flow behavior, primarily observed during the initial stage of plastic deformation at 650 °C but occurring throughout the entire plastic deformation process at 815 °C. Notably, when deformation is small, sawtooth fluctuations are significantly higher at 815 °C compared to 650 °C. Irregular subsurface lack of fusion defects serve as primary sources for fatigue crack initiation in this alloy including both single-source and multi-source initiation mechanisms; moreover, oxidation on fracture surfaces is more prone to occur at elevated temperatures, particularly at 815 °C.

A thermodynamically consistent phase transformation model for multiphase alloys: application to Ti$$_6$$Al$$_4$$V in laser powder bed fusion processes

A thermodynamically consistent phase transformation model for multiphase alloys: application to Ti$$_6$$Al$$_4$$V in laser powder bed fusion processes

Transfer learning-based quality monitoring of laser powder bed fusion across materials

Laser powder bed fusion (LPBF) is a promising additive manufacturing (AM) technique that has gained rapid growth. Despite the unique advantages such as producing complex-shaped metal parts, LPBF has been still slowed by the poor process repeatability and part consistency. In-situ monitoring methods combining sensing sensors and machine leaning (ML) tools have attracted much attention for defect detection and quality identification. However, the current ML algorithms usually rely on the availability of massive labeled datasets, and the trained model for monitoring a certain powder material cannot be directly applied to monitoring another material with different properties. Moreover, for a new material, it is time-consuming and expensive to collect the sufficient labeled data for building the quality monitoring model from scratch. To this end, motivated by transfer learning (TL), we propose a TL-based quality monitoring approach across two different materials in the LPBF process. Three different sensing datasets of layer-wise images of the solidified layer, photodiode signals, and acoustic emission signals are separately transferred from the 316L stainless steel to the TC4 titanium alloy. A convolutional neural network (CNN)-based quality monitoring model is first built by the sufficient source datasets from 316L stainless steel material. Then, the pre-trained CNN model is fine-tuned with a small amount of the target datasets for quality monitoring on TC4 titanium alloy material. Experimental results demonstrate the effectiveness of the proposed method for quality monitoring from one material to another with limited target datasets, with a classification accuracy above 92 % just with 5 % of target datasets on all three transferred datasets.

Relating laser powder bed fusion process parameters to (micro)structure and to soft magnetic behaviour in a Fe-based bulk metallic glass

This work aims to establish fundamental processing-(micro)structure-property links in a commercial Fe-based Kuamet6B2 bulk metallic glass (BMG) processed by laser powder bed fusion (LPBF). With that purpose, amorphous powders were processed using a pulsed-wave system and a simple meander strategy. The laser power, the scan speed, and the hatch distance were varied over wide intervals within the conduction regime. The processability window leading to samples with good dimensional accuracy and mechanical stability was determined. Within this window, the manufactured samples were crystalline/amorphous composites and the crystalline regions were formed by equiaxed ultrafine and nanograins with random orientations. Processing parameters yielding the densest prints caused severe crystallization while, conversely, parameter sets allowing the material to retain a high amorphous fraction led to significant lack-of-fusion defects and residual cracking along directions perpendicular to crystalline/amorphous interfaces. Comparatively, for a fixed hatch distance, the scanning speed had a stronger effect than the laser power in the resulting amorphous fraction due to its stronger influence on the melt pool size and, in turn, on the corresponding HAZ volume. The saturation magnetization and the coercive field were inversely related to the amorphous fraction. This work allows to derive fundamental guidelines for the successful additive manufacturing of soft magnetic Fe-based bulk metallic glasses (BMGs) by laser powder bed fusion (LPBF).

Tailoring Microstructure and Mechanical Properties of Additively Manufactured Inconel 625 by Remelting Strategy in Laser Powder Bed Fusion

This study investigates the effect of laser power applied for a remelting scan in the laser powder bed fusion process on the formation of a bimodal microstructure and its impact on the mechanical properties of Ni-based Inconel 625 superalloy. Comparison of primary and remelting scans at similar surface energy densities revealed that the melt pools obtained in the remelting scan are smaller than in the primary scan. To achieve comparable remelted melt pool sizes, the 25 pct increase in energy is required. The shape and size of the remelted melt pools significantly affect the microstructure and material texture. The lower surface energy density in laser powder bed fusion favors the formation of a bimodal microstructure with large columnar grains and fine grain bands. Application of higher energy results in the formation of large columnar grains with Goss texture along build direction and separated by a large amount of low angle grain boundaries. Remelting scan also affects reduction of porosity and increasing of the area fraction of nanometric oxide inclusions. The study revealed that the samples subjected to a remelting laser scan and tensile tested along the direction of columnar grains exhibited higher ductility, which was associated with a slight decrease in the ultimate tensile strength compared to the samples that were not remelted. It was demonstrated that the remelting scan in the laser powder bed fusion process offers the possibility of improving the reliability of additively manufactured Inconel 625 superalloy by reducing porosity and tailoring its microstructure towards single-crystal-like, and thus improving the mechanical properties.Graphical

Influence of laser powder bed fusion parameters on microstructure and mechanical properties of Co–Cr dental alloy

This research investigates the impact of three process parameters of Laser Powder Bed Fusion (LPBF) - laser power, scanning speed, and base plate preheating temperature on the structure and mechanical properties of the EOS CoCr SP2 dental alloy. The LPBF process was used to fabricate dental Co–Cr alloy specimens for microstructural analysis and mechanical properties testing. Light and electron microscopy were used to determine microstructural parameters, including porosity, inclusions, and cracks. The material's chemical composition was analysed by EDS, while XRD and EBSD methods were used to determine the presence of microstructural phases and the crystallographic orientation of individual grains. The mechanical properties were evaluated through a static tensile test (Rp0.2, ε), a toughness test (KVa), and a three-point bending test to determine the flexural strength (Rms). In the microstructure, differences were observed that reflected statistically significant differences in mechanical properties (one-way analysis of variance (ANOVA) and Scheffé post hoc test (α = 0.05)) Using the base plate preheating temperature ϑp = 310 °C with a constant scanning speed v = 900 mm/s in combination with increasing laser power P from 160 W to 250 W the proportion of porosity decreased while the mechanical properties of toughness (KVa) and flexural strength (Rms) increase to maximum values.

Investigating post-processing impact on fatigue performance of LPBF Ti6Al4V with heat treatment, high pressure heat treatment, and dry electropolishing strategies

This study investigates the impact of post-processing techniques on the fatigue behaviour of specimens in Ti6Al4V made by laser powder bed fusion (LPBF) for relevant applications, such as motorsport. Despite the LPBF advantages in terms of complex design and weight reduction, challenges such as reduced fatigue performance persist. Therefore, the application of post-processing treatments can play a critical role. In addition to traditional sandblasting (SB), three post-processing conditions were investigated to enhance mechanical properties under quasi-static and fatigue loading conditions: heat treatment (HT) and the innovative High Pressure Heat Treatment (HPHT) along with dry electropolishing (EP) treatment. The results indicate that both the HT and HPHT treatments produced a macrostructure with equiaxial grains composed of fine α + β lamellae. Mechanical analyses demonstrated that the HPHT-SB-EP condition exhibits properties comparable to those of forged Ti6Al4V used in the automotive field, with a good balance between strength and ductility. The EP treatment effectively reduced surface roughness and inherent surface irregularities caused by the LPBF process. The best fatigue results were achieved with the HPHT-SB-EP condition, despite data scattering. An increase in fatigue resistance was observed by using SB at a higher pressure.

High precision detection of artificial defects in additively manufactured Ti6Al4V alloy via laser ultrasonic testing

Laser powder bed fusion (LPBF) is a cutting-edge metal additive manufacturing technology with promising applications in aerospace, biomedicine, and industrial automation. Its ability to fabricate intricate geometric shapes, achieve high surface precision, and deliver comprehensive results renders it highly advantageous. In LPBF process, several factors such as the interference of laser processing fluctuations, rapid cooling rate, and environmental gas, contribute to the initiation and expansion of defects including pores and cracks, limiting the application of LPBF components. Laser ultrasonic testing (LUT) is a promising non-destructive evaluation method that can accurately detected defects in LPBF manufactured components by non-contact generation and detection. In this paper, a multi-physical field LUT model for the pores and cracks in LPBF additively manufactured Ti6Al4V alloy is proposed. The interaction of ultrasound with defects produces defect reflection echo wave, diffracted wave, and transmission wave. The transmission and diffraction phenomena occur in the longitudinal sound waves at defects. Furthermore, the echo wave reflected at crack exhibited enhanced prominence and an irregular shape. Clear differences were observed in the effects of laser ultrasonic detection on different defect types, pore depths and sizes. Finally, the LUT technology achieved the detection of 90 μm pore defects in LPBF manufactured Ti6Al4V alloy.

Effect of process parameters on the roughness and tensile behavior of parts manufactured by the metals LPBF process

AbstractThe laser powder bed fusion process for metals (LPBF‐M) results in the development of stochastic surface features that significantly influence the interactions between parts and their surrounding environment, as well as their mechanical properties. The process parameters influence the surface quality, which is quantified by the surface roughness. Therefore, customizing the surface roughness during the build process can significantly contribute to obtaining ready‐to‐use parts, reducing the need for extensive surface posttreatments. This paper utilizes theoretical estimations of melt pool depth under iso‐linear energy density, iso‐power, and iso‐temperature manufacturing process parameter conditions. These estimations are then compared with experimental evaluations of surface roughness and tensile strength in upright‐built specimens to extract the trends in terms of the input energy versus roughness, and the input energy versus tensile behavior. The results show that iso‐energy values yield similar roughnesses due to the consistent expected melt pool depth. Moreover, an increase in melt pool depth generates higher surface roughness, while smaller melt pool dimensions result in improved roughness. Additionally, a comparison between the melt pool size and tensile test performance reveals a detrimental impact on the tensile strength for specimens estimated to have smaller melt pool depths.

Statistical parameterized physics-based machine learning digital shadow models for laser powder bed fusion process

This paper presents a statistical physics-based machine learning model for predicting defects, such as surface roughness and lack-of-fusion porosity, in the laser powder bed fusion of metals (PBF-LB/M) additive manufacturing process. The statistical physics-based model is calibrated and validated against controlled single-track experiments and used for statistical prediction for multi-layer and multi-track cases for PBF-LB/M defects. A mechanistic reduced-order-based stochastic calibration process is introduced to capture the stochastic nature of the melt pool. The calibrated physics-based digital shadow model is demonstrated for predicting the surface roughness of the National Institute of Standards and Technology (NIST) overhang part X4, with a difference of 9.3% compared to the experimental results. By leveraging data obtained from both the physics-based model and experiments, a machine learning model has been trained for fast predictions (inference time of 0.4 ms) with high accuracy (error bound of 6.7%). This model can predict melt pool geometries under various processing conditions, offering a control strategy for the PBF-LB/M process. Further, the trained machine learning model is showcased to demonstrate a control application of melt pool geometries (width and depth) for specific processing parameters. These developed models (physics-based and machine learning) serve as a digital shadow of the PBF-LB/M process, offering predictive capabilities to build a digital twin model for process control, optimization, and online monitoring.

Experimental investigation of the cooling performance of an additively manufactured prototype for nuclear fusion energy application

Metal Additive Manufacturing (MAM) is a non-traditional technology recently introduced to manufacture multifunctional mechanical components. In fact, recent developments in Laser Powder Bed Fusion (LPBF) process have enabled the production of materials characterized by high density and high thermal conductivity properties, such as copper alloys and pure copper, making the technology attractive for thermal science. In nuclear fusion energy applications, mechanical components often encounter extremely high heat fluxes. An innovative solution using unconventional integrated cooling channels is therefore required to safely manage the components. However, the high surface roughness in 3D-printed parts represents an intrinsic limitation of the LPBF technology: the cooling channels show high-pressure drops due to the high viscous dissipation generated by the rough surface. To address this challenge, a lab-scale prototype of an e-beam extraction grid for a fusion experiment with an original integrated cooling system was designed and manufactured. Additionally, a novel heating mask was designed and manufactured to reproduce the realistic heat load distribution on the grid during the experimental tests. The prototype was built using additive manufacturing with a CuCrZr copper alloy. The grid underwent heat treatment via solution annealing and age hardening, to increase thermal conductivity from about 100 W m−1 K−1 to almost 300 W m−1 K−1. The prototype was tested at three different constant heat fluxes by varying the water flow rate while measuring the pressure and the maximum temperatures of the grid. A CFD numerical model was also calibrated to estimate the thermo-hydraulic performance of the prototype under test conditions. The experimental and numerical results are presented in terms of overall thermal performance, maximum temperature, and pressure drop.

The effect of powder recycling on the mechanical performance of laser powder bed fused stainless steel 316L

Powder recycling refers to the reuse of unused powder feedstock in the laser powder bed fusion (PBF-LB/M) process. This approach is crucial for the economic viability and sustainability of PBF-LB/M, as powder accounts for a large proportion of the total production cost. However, through powder recycling, the physical and chemical properties of powder are liable to change. This variation in powder properties can subsequently lead to knock-on effects on the mechanical properties of a fully built component.This research has investigated the changes that occur to stainless steel 316L (SS316L) powder as a result of recycling. This includes changes to powder size distribution (PSD), flowability, chemistry and phase composition. Likewise, the impact that these changes have will also be assessed in PBF-LB/M SS316L components manufactured from powders after different levels of recycling and subjected to alternative post processing routes such as hot isostatic pressing (HIP). This comprehensive investigation involves a thorough examination of both macro- and microstructures, encompassing detailed analyses of chemical composition, microstructural features, and defects. The study aims to elucidate differences in mechanical behaviour through a series of experiments, including uniaxial tensile tests, Charpy impact assessments, and low cycle fatigue (LCF) experiments. Additionally, the investigation will be complemented by pitting potential tests, providing a holistic understanding of the material's performance and characteristics.Although moderate changes to powder were observed for both PSD and chemistry, this was found to be negligible and not enough to result in any adverse changes to part performance. In addition, the microstructure of SS316L remained stable across differing levels of powder recycling. Whereas the porosity content increased marginally as the fine particle content of powder was reduced, this was not found to be sufficient to affect the LCF performance of the material. After powder recycling, increases in ductility and Young’s modulus were attributed to a reduction in oxides present in the microstructure, which were sources of localised damage and deformation.

Designing materials by laser powder bed fusion with machine learning-driven bi-objective optimization

To exploit the full industrial potential of additive manufacturing (AM) beyond prototyping, the resource-consuming identification of the optimal processing conditions needs to be minimized. This task becomes more challenging when multiple properties of the part shall be simultaneously optimized. We utilize machine learning (ML) methods in a case study on laser powder bed fusion (LPBF) of a Zr-based glass-forming alloy. Our experiments show that processing parameters affect density and amorphicity opposingly, demonstrating the efficacy of our ML-based approach. We employ multi-objective optimization using Gaussian Process Regression to model and predict target properties and their uncertainties of parts fabricated by LPBF – a widely used metal AM technology. With density and amorphicity as target parameters, we optimize models using the Pareto front facilitated by the Non-Dominated Sorting Genetic Algorithm II. Despite deviations in the amorphicity data, we demonstrate this method to identify the high-performance region of the process parameters and its ability to be iteratively enhanced with additional experimental data. This bi-objective optimization approach provides a robust toolset for navigating LPBF processing. It can be easily extended to a larger set of target properties and transferred to further AM technologies.

Measurement of effective thermal conductivity of composite powders of 2D materials and metals for additive manufacturing

Laser Powder Bed Fusion (LPBF), a promising additive manufacturing technique for composites of 2D materials and metals, requires knowledge of thermophysical properties, such as the thermal conductivity of powder, for process optimization. In this study, we measured the effective thermal conductivity of the most representative Cu-graphene composite powder in the field of heat transfer applications. To measure thermal conductivity, we propose a differential scanning calorimetry (DSC) method to measure the thermal resistances of a powder bed under steady-state heat flow conditions. We observe that the thermal conductivity of a composite powder is 1,000 times lower than that of the bulk metal without 2D material addition; e.g., exhibiting ~ 0.30 W/mK in Cu-1wt.% graphene powder. Furthermore, we discuss powder size and morphology impact on thermal conductivity influencing the number of contact points and thermal contact resistance. Our findings contribute to understanding the thermal conductivity of composite powders in a powder bed and aid in optimizing LPBF processes.

The influence mechanisms of re-fused scanning on the surface roughness, microstructural evolution and mechanical properties of laser powder bed fusion processed AlMgScZr alloy

Laser powder bed fusion (LPBF) stands out as the foremost method for achieving intricate structure with superior design flexibility among additive manufacturing technologies. Within the LPBF process, surface roughness serves as a critical metric governing both fabrication precision and build quality. This study delves into the evolution of the top and side surface roughness as re-fused scanning times increase in LPBF-fabricated AlMgScZr alloy. Concurrently, the investigation evaluates alterations in microstructural characteristics and mechanical properties. Various characterization techniques, including confocal laser scanning microscope, scanning electron microscope, X-ray diffraction, electron backscatter diffraction, as well as tensile and microhardness tests, are employed. The findings concerning the top surface reveal that the initial application of re-fused scanning contributes to enhanced surface roughness. This improvement stems from refined grain structures and induced lattice distortions, facilitated by rapid heat dissipation and thermal recycling within the solidified layer. Consequently, microhardness increases. However, introducing the subsequent re-fused scanning exacerbates surface roughness and instigates Marongoni flow instability, magnesium depletion, and alterations in lattice structures. Moreover, with increasing the re-fused times, there is a noticeable enhancement in preferential crystallite growth orientation along the (200)α-Al crystal plane. Regarding the side surface, escalating re-fused times correlate with deteriorating roughness and alterations in molten pool shape and size, attributable to successive energy input. Despite these surface variations, minimal differences are observed in yield strength, tensile strength, and elongation. In essence, this research offers valuable insights into selecting optimal scanning methodologies during the LPBF process, thus guiding improvements in fabrication precision and component quality.

Data-driven Approach to Explore the Contribution of Process Parameters for Laser Powder Bed Fusion of a Ti-6Al-4V Alloy

In order to predict the process window of laser powder bed fusion (LPBF) for printing metallic components, the calculation of volumetric energy density (VED) has been widely calculated for controlling process parameters. However, because it is assumed that the process parameters contribute equally to heat input, the VED still has limitation for predicting the process window of LPBF-processed materials. In this study, an explainable machine learning (xML) approach was adopted to predict and understand the contribution of each process parameter to defect evolution in Ti alloys in the LPBF process. Various ML models were trained, and the Shapley additive explanation method was adopted to quantify the importance of each process parameter. This study can offer effective guidelines for fine-tuning process parameters to fabricate high-quality products using LPBF.

Dependences of the defect, microstructure and properties of 15-5 PH martensitic stainless steel on the laser powder bed fusion process parameters

15-5 PH martensitic stainless steel is widely utilized in the marine, chemical, and aerospace industries owing to its high strength and corrosion resistance properties. The highly tailored samples can be produced via laser powder bed fusion technology, however it is still unknown how to optimize process parameter windows and conduct mechanism research for 15-5 PH steel forming. The results show that fusion defects are mostly concentrated in the region with low laser power (P) of 200 W, and there is no obvious distribution pattern in the pore and porosity defects. Excellent dense samples can be formed mainly at medium P of 260 W and medium scanning speed (V) of 1200 mm/s. The proportion of body center cubic structure reaches 91.7 % for the hatch space (H) of 0.06 mm, and a heterogeneous layered structure with alternating fine and coarse grain layers is formed. The sample exhibits high strength and excellent plasticity, where the ultimate tensile strength, yield strength, elongation to fracture, impact toughness, and hardness are 1161 MPa, 825 MPa, 17.5 %, 122.5 J/cm2, and 34.6 HRC, respectively. The excellent comprehensive property is due to fine-grained strengthening, dislocation strengthening, solid solution strengthening and intrinsic factors/oxide dispersion strengthening.