Laser Powder Bed Fusion Process Parameters Research Articles

Influence of the process parameters on the microstructure and the machinability of additively manufactured Ti-5553 titanium alloy

Additive Manufacturing (AM) technologies, particularly laser powder bed fusion (LPBF), are revolutionising the production of complex geometries and lightweight structures. Furthermore, LPBF allows to tailor the microstructure and resulting properties of metallic materials. This study focuses on titanium alloys, crucial for high-performance applications like aircraft components and medical implants. Although AM enables near-net-shape fabrication, many titanium parts still require machining to meet surface and dimensional standards. Titanium’s challenging machinability is well-documented for cast and wrought alloys, but only less is known about additively manufactured variants. In this work, the machinability of an additively manufactured Ti-5Al-5V-5Mo-3Cr alloy (Ti-5553) is investigated, focusing on chip formation, cutting forces, and tool wear across different LPBF process parameters. Four LPBF parameter sets were validated, and results were compared to conventional wrought sample. The findings reveal significant variations in machinability linked to LPBF parameters. Specifically, the highest tool loads and wear were observed for samples produced with the highest energy density of EV = 37.0 J/mm3, likely due to α-phase precipitation. In contrast, samples with lower energy densities (<29.1 J/mm3) exhibited up to 100% longer tool life. Concluding, this study highlights how the machinability of Ti-based components can be significantly influenced by the LPBF processing parameters.

Investigation on the characteristics of porosity, melt pool in 316L stainless steel manufactured by laser powder bed fusion

Pores are among the primary defects in 316L stainless steel when fabricated through laser powder bed fusion (L-PBF). To better understand and precisely control the formation behavior of pores in the L-PBF-manufactured 316L stainless steel, effects of L-PBF process parameters on the melt pool and pore characteristics have been investigated. A method utilized for extreme values statistics analysis was also applied to investigate and predict the sizes of pores. Relationships among the count of measurements, the laser energy density, the reduced variate, and the maximum pore size were revealed. The findings showed an overall increase in melt pool depth as the laser energy densities was raised, yet the magnitude of this growth weakened as the laser energy density continued to increase. Conversely, it was discovered that the connection between the width of the melt pool and the density of laser energy exhibited a markedly less distinct correlation. As laser energy density escalated, there was a shift in the melt pool mode from conduction-based to a transitional phase, and upon further elevation of the laser energy density, it transitioned to keyhole mode. To obtain a reliable statistical analysis of pores, at least 40 measurements should be acquired for the estimation of the pore sizes by taking comprehensive consideration of time cost. Beside, in all three melt pool modes, at a fixed observation area, the maximum size of pores generally decreased with the escalation in laser energy densities.

Tailoring Laser Powder Bed Fusion Process Parameters for Standard and Off-Size Ti6Al4V Metal Powders: A Machine Learning Approach Enhanced by Photodiode-Based Melt Pool Monitoring

An integral part of laser powder bed fusion (LPBF) quality control is identifying optimal process parameters tailored to each application, often achieved through time-consuming and costly experiments. Melt pool dynamics further complicate LPBF quality control due to their influence on product quality. Using machine learning and melt pool monitoring data collected with photodiode sensors, the goal of this research was to efficiently customize LPBF process parameters. A novel aspect of this study is the application of standard and off-size powder feedstocks. Ti6Al4V (Ti64) powder was used in three size ranges of 15–53 µm, 15–106 µm, and 45–106 µm to print the samples. This facilitated the development of a process parameters tailoring system capable of handling variations in powder size ranges. Ultimately, per each part, the associated set of light intensity statistical signatures along with the powder size range and the parts’ density, surface roughness, and hardness were used as inputs for three regressors of Feed-Forward Neural Network (FFN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The laser power, laser velocity, hatch distance, and energy density of the parts were predicted by the regressors. According to the results obtained on unseen samples, RF demonstrated the best performance in the prediction of process parameters.

Effects of process parameters on the surface characteristics of laser powder bed fusion printed parts: machine learning predictions with random forest and support vector regression

Laser powder bed fusion (L-PBF) fuses metallic powder using a high-energy laser beam, forming parts layer by layer. This technique offers flexibility and design freedom in metal additive manufacturing (MAM). However, achieving the desired surface quality remains challenging and impacts functionality and reliability. L-PBF process parameters significantly influence surface roughness. Identifying the most critical factors among numerous parameters is essential for improving quality. This study examines the effects of key process parameters on the surface roughness of AlSi10Mg, a widely used aluminum alloy in high-tech industries, fabricated by L-PBF. Part orientation, laser power, scanning speed, and layer thickness were identified as crucial parameters via cause-and-effect analysis. To systematically examine their effects, the Taguchi method was employed within the framework of the design of experiment (DoE). Experimental results and statistical analysis revealed that laser power, scanning speed, and layer thickness significantly influence surface roughness parameters: arithmetic mean (Ra) and root mean square (Rq). Main effect plots and energy density analyses confirmed their impact on surface quality. Microscopic investigations identified surface flaws such as spattering, balling, and porosity contributing to poor quality. Given the complex interplay between parameters and surface quality, accurately predicting their effects is challenging. To address this, machine learning models, specifically random forest regression (RFR) and support vector regression (SVR), were used to predict the effects on surface roughness. The RFR model’s R2 values for predicting Ra and Rq are 97% and 85%, while the SVR model’s predictions are 85% and 66%, respectively. Evaluation metrics demonstrated that the RFR model outperformed SVR in predicting surface roughness.

The impact of post-processing heat treatment on the magnetic properties of additively manufactured Nd-Fe-B magnets

The effect of post-process heat treatment on magnetic properties by laser powder bed fusion (LPBF) additively manufactured Nd-Fe-B magnets was studied in dependence of heat treatment temperature and as built condition. The magnets show very different magnetic performance in the as built condition, which depends on the adjusted LPBF process parameters. Heat treatments subsequent to LPBF processing lead to a remarkable change in the magnetic properties, whereby the response characteristics of the magnets depend on the as built condition and magnets with low initial coercivity show a large potential of improvement in case of low temperature heat treatments. Crystallization of amorphous constitutes in the microstructure, whose volume fraction depends on the LPBF manufacturing parameter, is responsible for this improvement. Exceeding the optimal heat treatment temperatures leads to the precipitation of α-Fe and deteriorates coercivity but enhances remanence of the magnets. Magnets with low initial coercivity require higher heat treatment temperatures to obtain optimal magnetic performance and show an increased stability against overaging.

Experimental study and neural network model based prediction of layer thickness influence on LPBF IN625 single track geometry

The performance of interacting metal surfaces having sliding motion is enhanced by fabricating microfeatures on-top of the surfaces. Laser powder bed fusion (LPBF) is used to fabricate the microfeatures of IN625 on-top of a flat surface. In LPBF, geometrical characteristics of microfeatures are based on the energy supplied and the volume of material melted (layer thickness) at that instant of time. There is very few research reported in open literature on the effect of layer thickness on microfeatures geometry during LPBF. In this work, a series of single tracks is fabricated with different combinations of laser power and scanning speed for 75 μm, 100 μm, and 125 μm layer thickness and its effect on single track geometrical characteristics and microstructure was evaluated. It has been found that laser power and scanning speed have a significant influence on the track geometry irrespective of layer thickness. The minimum linear energy density of 2.5 J/mm is considered to fabricate the continuous track formation for investigated layer thickness. From statistical analysis, it is observed that the laser power and scanning speed shows higher contribution on track width and remelting depth, respectively. Whereas, layer thickness shows a higher influence on track height and contact angle of single track as compared to laser power and scanning speed. In 125 μm layer thickness, the volume of material melting is increased which encourages the gravity effect to deform the melt pool and produces larger track width compare to 100 μm layer thickness. Further, the cell spacing is gradually increased with layer thickness resulting increase in the solidification period. The shape controllability of the single track is significantly increased with layer thickness, but as keeps on increasing layer thickness due to the larger volume of molten metal, the melt pool losses ability to maintain convex shapes. An Artificial Neural Network (ANN) model is developed to understand the relationship between the LPBF process parameters and single track geometrical characteristics to predict the responses for a wide range of process parameters. The multiple response neural network model shows excellent agreement with experimental results with R2 value greater than 0.95.

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.

Impact of process parameters on the dynamic behavior of Inconel 718 fabricated via laser powder bed fusion

In this research, we investigate the dynamic behavior of Inconel 718 fabricated through laser powder bed fusion (L-PBF), addressing a notable knowledge gap regarding the correlation between process parameters and dynamic properties. The process parameters adopted are deducted from an extension of the Rosenthal solution, formulated to increase the process productivity while avoiding the typical production process defects. The dynamic Young modulus and the structural damping of the material are estimated as a function of the process parameters through ping tests reproducing the flexural vibrations of the specimens in as-built, solutioned, and aged conditions. The microstructure and porosity are investigated through metallographic analyses. The results show a substantial influence of the L-PBF process parameters on the dynamic Young modulus, which markedly increases as the energy density is reduced (23%) and progressively becomes more similar to the conventionally produced material. This influence stands in stark contrast to the relatively modest impact of heat treatments, which underlines a negligible effect of the process-induced residual stress. The structural damping remained approximately constant across all test conditions. The elastic response of the material is found to be primarily influenced by the different microstructures produced as the L-PBF process parameters varied, particularly in terms of the dimensions and shape of the solidification structures. The unexpected relationship between the dynamic Young modulus, energy density, and microstructure unveils the potential to fine-tune the material’s dynamic behavior by manipulating the process parameters, thereby carrying substantial implications for all the applications of additively manufactured components susceptible to significant vibratory phenomena.

Laser additive manufacturing of TiB2-modified Cu15Ni8Sn/GH3230 heterogeneous materials: Processability, interfacial microstructure and mechanical performance

Cu/Ni heterogeneous materials integrate excellent thermal conductivity and high-temperature mechanical properties, enabling them to be widely used in the aerospace domain. Differences in the thermal and physical properties of the Cu and Ni materials, however, make them difficult to be processed using the laser powder bed fusion (LPBF) additive manufacturing process. This study systematically examines the effects of various LPBF process parameters on microstructure, element diffusion, bonding strength and microhardness at the Cu/Ni interface, as well as investigating the mechanisms of defect formation within Cu/Ni heterogeneous materials. The results indicate that a reasonable control of laser energy input (<100 J/mm3) facilitates the Cu/Ni components through strong interfacial metallurgical bonding without pore defect formation. Compared to single-material Cu alloy, the ultimate tensile strength (UTS) of the horizontally bonded Cu/Ni specimen increased by 55.25 %, without significant reductions in elongation. The vertically bonded Cu/Ni tensile specimen fractured in the middle of the Cu region rather than the interfacial region, indicating superb interfacial bonding strength. Another advantage lies in the enhancement of thermophysical properties, with a 109.5 % increase in thermal conductivity achieved in the LPBF-fabricated Cu/Ni heterogeneous materials compared to the single-material Ni alloy. Quasi-static compression experiments indicated that the Cu/Ni lattice structure could absorb more energy when compressed parallel to the build direction (BD), compared to being perpendicular to the BD. This study provides guidance for the design and manufacture of high-performance Cu/Ni heterogeneous components via LPBF.

Superior mechanical properties of a Zr-based bulk metallic glass via laser powder bed fusion process control

Additive manufacturing has made the fabrication of large-dimensioned bulk metallic glasses (BMGs) achievable; however, questions remain regarding how to control the processing parameters to obtain dense and fully amorphous BMGs with desirable mechanical properties. Here, laser powder bed fusion (LPBF) was used to produce dense and fully amorphous Zr59.3Cu28.8Nb1.5Al10.4 BMG samples from two different starting powders within a large processing window of laser powers and scanning speeds. X-ray diffraction (XRD) revealed that fully amorphous XRD patterns were obtained for samples with high relative densities (>99 %) when the LPBF energy density ranged from ∼20 J/mm3 up to ∼33 J/mm3 for coarse powder with low oxygen and up to ∼30 J/mm3 for fine powder with higher oxygen. For the fully XRD amorphous samples, strength and hardness increased with increasing energy density while the relaxation enthalpy and ductility decreased. Transmission electron microscopy revealed that the softer samples contain larger medium range order clusters within the amorphous matrix. With higher LPBF energy density, high relative density was still achieved along with devitrification and embrittlement. While lower energy densities below ∼20 J/mm3 could retain a fully XRD amorphous structure, such samples had relative densities <99 %. When comparing the two powders, the coarse powder with four times lower oxygen content gave better glass forming ability, compression ductility up to 6 % plastic strain, and fracture toughness up to ∼38 MPa√m. These findings demonstrate that it is possible to tailor the structure and mechanical properties of BMGs by tuning the LPBF process parameters within a wide processing window.

COMMERCIALLY PURE (CP-TI) TITANIUM MEDICAL IMPLANT PRODUCTION USING LASER POWDER BED FUSION (L-PBF) TECHNOLOGY

Decreasing the bulk weight without losing the biomechanical properties of commercial pure titanium (Cp-Ti) medical implants is now possible by using Laser Powder Bed Fusion (L-PBF) technology. Gyroid lattice structures that have precious mechanical and biological advantages because of similarity to trabecular bone. The aim of the study was to design and develop L-PBF process parameter optimization for manufacturing gyroid lattice Cp-Ti structures. The cleaning process was then optimized to remove the non-melted powder from the gyroid surface without mechanical loss.Gyroid cubic designs were created with various relative densities by nTopology. L-PBF process parameter optimization was progressed using with Cp-Ti (EOS TiCP Grade2) powder in the EOS M290 machine to achieve parts that have almost full dense and dimensional accuracy. The metallography method was made for density. Dimensional accuracy at gyroid wall thicknesses was investigated between designed and manufactured via stereomicroscope, also mechanical tests were applied with real time experiment and numerical analysis (ANSYS). Mass loss and strut thickness loss were investigated for chemical etching cleaning process.Gyroid parts had 99,5% density. High dimensional accuracy was achieved during L-PBF process parameters optimization. Final L-PBF parameters gave the highest 19% elongation and 427 MPa yield strength values at tensile test. Mechanical properties of gyroid were controlled with changing relative density. A minute chemical etching provided to remove non-melted powders.Compression test results of gyroids at numerical and real-time analysis gave unrelated while deformation behaviors were compatible with each other. Gyroid Cp-Ti osteosynthesis mini plates will be produced with final L-PBF process parameters. MTT cytotoxicity test will be characterized for cell viability.Acknowledgements This project is granted by TUBITAK (120N943). Feza Korkusuz MD is a member of the Turkish Academy of Sciences (TÜBA).

Investigations into the interfacial characteristics of Inconel 718/stainless steel 316L fabricated by multi-material Laser Powder Bed Fusion.

The numerous advantages of Additive manufacturing (AM) are being utilized to print multi-material components for different applications. Although the AM method of laser powder bed fusion (LPBF) can print more complex and dimensionally accurate parts than the directed energy deposition (DED) method, printing multi-material components is challenging for LPBF. This study demonstrates an attempt at bimetallic 3D printing of stainless steel 316L and Inconel 718 by multi-material laser powder bed fusion and its characterization. A continuous wave (CW) fiber laser was used for the LPBF process and laser parameters for the bimetallic components were optimized. Microstructural studies were carried out with optical microscopy and scanning electron microscope (SEM) to investigate the interfacial characteristics for different numbers of interlayers. The thickness of the interfacial region was around 50-100 μm. Vickers microhardness (HV) and nanoindentation were performed at various locations around the fusion zone resulting in an average micro and nano hardness of 303 HV and 4.622 GPa respectively.

Effect of processing parameters on overhanging channel forming quality during laser powder bed fusion of AlSi10Mg

During the process of laser powder bed fusion (LPBF), the inclusion of support structure is often necessary for overhanging channel specimens, which significantly limits the applicability of complex designs. This study evaluates the influence of various process parameters on the forming quality of overhanging channel specimens without support structures, considering overhanging angles of 30° and 60°. A design of experiment (DoE) methodology was employed to investigate the correlation between forming quality and LPBF process parameters, including infill laser power, infill scanning angle, scanning angle, contour laser power, and contour scanning angle. The roundness and morphology of the overhanging channels were thoroughly examined. The results revealed that the influence of process parameters on roundness varied with different overhanging angles. In comparison to contour process parameters, infill process parameters exhibited a more significant effect on the forming quality of the overhanging channel. At a 30° overhanging angle, scanning angle emerged as the most influential parameter. As the overhanging angle increased to 60°, infill laser power had a notably stronger influence than other process parameters. The study also determined that the interactions between infill laser power and scanning speed, and between contour laser power and scanning speed, had negligible effects on the forming quality for the overhanging channel specimens. The minimum roundness values observed for the overhanging channels were 0.13 μm and 0.38 μm at 30° and 60° overhanging angles, respectively.

Correlation of Microstructural Events with Stress Corrosion Cracking Initiation Behaviour in Additively Manufactured Ni-Based Alloy 718: Microcapillary Electrochemical Technique Implementation

During the past decade, metal additive manufacturing has gained tremendous attention for the production of substitutive components for oil, gas, and nuclear industries as a consequence of the possibility of fabricating complex full-density components with fewer manufacturing processes compared to conventional methods. Laser powder bed fusion (L-PBF) is one of the most precise additive manufacturing techniques based on layer-by-layer production of the final component with a high-power laser beam. Most investigations to dat focus on comparing the mechanical properties of additively manufactured components with conventional counterparts. Process parameter optimization has been extensively studied for obtaining almost full-density components with comparable and in some cases even better mechanical performance compared to conventionally manufactured components. However, the corrosion and more specifically stress corrosion cracking (SCC) behaviour of L-PBF and in general, additively manufactured components has received less attention. Since the major concern in critical industries is to reduce the risk of SCC occurrence, the mechanistic study of the effect of various micro and submicron structural alterations on SCC risk assessment is of paramount importance.It is well recognized in the corrosion community that the most critical stage of SCC is related to the incubation/initiation stage. From the information gathered by various authors in the past decades, having a prolonged incubation/initiation stage of SCC could significantly increase the lifetime of components, which results from the balance between the formation and destruction reactions occurring in the passive layer-environment interface. Thus, it is essentially always the case that by retarding the initiation of cracks, we significantly increase the life cycle. Until now, traditional electrochemical techniques are not able to detect the SCC initiation stage as a consequence of the large surface area in contact with the solution and the shortcomings of the used electrochemical techniques. Thus, during the past decades, extensive research has been done on the crack propagation stage by utilizing various techniques and statistical models for the prediction of crack growth rate. This study reports for the first time the correlation of electrochemical polarization behaviour with the susceptibility of the material to SCC initiation. Such an approach fulfils the knowledge gap covering the initiation mechanism by proposing the microcapillary method as the method of choice for mechanistic analysis of initiation susceptibility.In the current investigation, various electrochemical techniques combined with high-resolution microstructural analysis were implemented to elucidate the importance of submicron characteristics of L-PBF fabricated components on SCC initiation risk assessment. L-PBF fabricated Ni-Fe-Cr based alloy 718 with a slight variation of laser power in the range of achieving full density was used for elucidating the effect of slight alterations in micro and nanostructure in corrosion and SCC initiation behaviour. It was found that even with a slight variation of L-PBF process parameters such as laser power, various alterations occurred in micron and submicron scale namely, a slight decrease in hardness, an increase in subgrain width, porosity, dislocation density, the density of nanovoids adjacent to subgrain boundaries, and presence of carbide particles. Such alterations consequently lead to significant electrochemical behaviour and SCC initiation susceptibility. Furthermore, the underlying mechanisms responsible for the observed variations in SCC initiation susceptibility had been explained as a consequence of alterations in subgrain width, dislocation density, intrinsic nano cavities density adjacent to subgrain boundaries, and the presence of nanoscale carbide particles adjacent to subgrain boundaries. From the obtained results, the microcapillary technique proved to be a very useful and powerful tool for assessing SCC initiation susceptibility for additive manufactured components with submicron scale structures.

Microstructural feature-driven machine learning for predicting mechanical tensile strength of laser powder bed fusion (L-PBF) additively manufactured Ti6Al4V alloy

The rapid solidification inherent in laser powder bed fusion (L-PBF) additive manufacturing (AM) introduces segregation phenomena and formation of non-equilibrium phases in duplex titanium alloy components, thereby impeding their suitability for high-reliability engineering applications. Consequently, heat treatment becomes indispensable for optimizing both the microstructure and mechanical properties to meet application requirements. This study aims to investigate the influence of varied annealing temperatures on the evolution of L-PBF-built Ti6Al4V alloy microstructure, subsequently elucidating their impact on tensile properties by analyzing of L-PBF process parameters, building orientations, and annealing temperatures. The findings reveal that annealing at 850 °C for 2 h facilitates the transformation of brittle martensite into a ductile lamellar (α + β) microstructure, thereby conferring excellent tensile properties upon the L-PBF-built Ti6Al4V alloy. Furthermore, to accurately predict the tensile strengths of the Ti6Al4V, we take into account the L-PBF process parameters and the as-built microstructures in a comprehensive manner, extracting the pertinent microstructural features. A machine learning (ML)-based model is built to facilitate accurate predictions. Accurate and reliable predictions are demonstrated by this model when applied to Ti6Al4V. This data-driven approach establishes a novel avenue for AM material property prediction and process parameter optimization.

Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks

Numerically generating synthetic surface topography that closely resembles the features and characteristics of experimental surface topography measurements reduces the need to perform these intricate and costly measurements. However, existing algorithms to numerically generated surface topography are not well-suited to create the specific characteristics and geometric features of as-built surfaces that result from laser powder bed fusion (LPBF), such as partially melted metal particles, porosity, laser scan lines, and balling. Thus, we present a method to generate synthetic as-built LPBF surface topography maps using a progressively growing generative adversarial network. We qualitatively and quantitatively demonstrate good agreement between synthetic and experimental as-built LPBF surface topography maps using areal and deterministic surface topography parameters, radially averaged power spectral density, and material ratio curves. The ability to accurately generate synthetic as-built LPBF surface topography maps reduces the experimental burden of performing a large number of surface topography measurements. Furthermore, it facilitates combining experimental measurements with synthetic surface topography maps to create large data-sets that facilitate, e.g. relating as-built surface topography to LPBF process parameters, or implementing digital surface twins to monitor complex end-use LPBF parts, amongst other applications.

Melt Pool Monitoring and X-ray Computed Tomography-Informed Characterisation of Laser Powder Bed Additively Manufactured Silver–Diamond Composites

In this study, silver (Ag) and silver–diamond (Ag-D) composites with varying diamond (D) content are fabricated using laser powder bed fusion (L-PBF) additive manufacturing (AM). The L-PBF process parameters and inert gas flow rate are optimised to control the build environment and the laser energy density at the powder bed to enable the manufacture of Ag-D composites with 0.1%, 0.2% and 0.3% D content. The Ag and D powder morphology are characterised using scanning electron microscopy (SEM). Ag, Ag-D0.1%, Ag-D0.2% and Ag-D0.3% tensile samples are manufactured to assess the resultant density and tensile strength. In-process EOSTATE melt pool monitoring technology is utilised as a comparative tool to assess the density variations. This technique uses in-process melt pool detection to identify variations in the melt pool characteristics and potential defects and/or density deviations. The resultant morphology and associated defect distribution for each of the samples are characterised and reported using X-ray computed tomography (xCT) and 3D visualisation techniques. Young’s modulus, the failure strain and the ultimate tensile strength of the L-PBF Ag and Ag-D are reported. The melt pool monitoring results revealed in-process variations in the build direction, which was confirmed through xCT 3D visualisations. Additionally, the xCT analysis displayed density variations for all the Ag-D composites manufactured. The tensile results revealed that increasing the diamond content reduced Young’s modulus and the ultimate tensile strength.

Controlled Crystallographic Texture Orientation in Structural Materials Using the Laser Powder Bed Fusion Process—A Review

Advantages of the laser powder bed fusion (LPBF) process over conventional processing methods include microstructural control capability and complex geometry parts fabrication. For most alloys, the microstructure–property relationship typically guides the LPBF process toward achieving application‐specific properties. Nonetheless, the LPBF process produces a high degree of anisotropy, primarily from the epitaxial preferred grain growth mechanisms during solidification and the consequent highly textured microstructures produced. The crystallographic texture‐controlled build concept through the LPBF process is a material design and processing approach gaining a lot of interest because of the promising unique and superior properties offered by the anisotropy accompanying the LPBF process. The numerous attempts at intentional texture‐controlled building or parts fabrication from structural materials using the LPBF process warrant an up‐to‐date review. Thus, this review presents solidification mechanisms that influence texture evolution during the LPBF process such as the melting mode, melt pool shape, influence of thermal gradient, and solidification rate. Furthermore, this review delves into the influence of LPBF process parameters on texture, accompanying microstructural evolution, and the intentional control of texture effects on properties such as mechanical, corrosion, and oxidation resistance.

Development of process-structure linkage for Inconel 718 processed by laser powder bed fusion: a numerical modeling approach

PurposeUnderstanding and tailoring the solidification characteristics and microstructure evolution in as-built parts fabricated by laser powder bed fusion (LPBF) is crucial as they influence the final properties. Experimental approaches to address this issue are time and capital-intensive. This study aims to develop an efficient numerical modeling approach to develop the process–structure (P-S) linkage for LPBF-processed Inconel 718.Design/methodology/approachIn this study, a numerical approach based on the finite element method and cellular automata was used to model the multilayer, multitrack LPBF build for predicting the solidification characteristics (thermal gradient G and solidification rate R) and the average grain size. Validations from published experimental studies were also carried out to ensure the reliability of the proposed numerical approach. Furthermore, microstructure simulations were used to develop P-S linkage by evaluating the effects of key LPBF process parameters on G × R, G/R and average grain size. A solidification or G-R map was also developed to comprehend the P-S linkage.FindingsIt was concluded from the developed G-R map that low laser power and high scan speed will result in a finer microstructure due to an increase in G × R, but due to a decrease in G/R, columnar characteristics are also reduced. Moreover, increasing the layer thickness and decreasing the hatch spacing lowers the G × R, raises the G/R and generates a coarse columnar microstructure.Originality/valueThe proposed numerical modeling approach was used to parametrically investigate the effect of LPBF parameters on the resulting microstructure. A G-R map was also developed that enables the tailoring of the as-built LPBF microstructure through solidification characteristics by tuning the process parameters.

Optimization of Laser Powder Bed Fusion Process for Forming Porous Ta Scaffold

To improve the performance of porous tantalum (Ta) manufactured by laser powder bed fusion (L-PBF) and meet its application requirements in medicine, the authors of this paper studied the influence of L-PBF process parameters on the strut surface morphology and mechanical performance. It was found that the powder layer thickness had a significant influence on the microstructure and mechanical properties based on statistical analysis. We proposed optimal process parameters of laser power of 150 W, scanning speed of 270 mm/s, thickness of 0.05 mm, and scanning spacing of 0.07 mm. After parameter optimization, we successfully obtained Ta samples with an elastic modulus of 1.352 ± 0.007 GPa and yield strength of 53.217 ± 0.114 MPa. The results show that the elastic modulus and yield strength of porous Ta samples with a porosity of 80% under the optimal process parameters are significantly superior to previous studies. The porous Ta scaffolds with higher mechanical properties fabricated with the optimized process parameters of L-PBF have significant value for applications in medicine.