Powder Bed Fusion Process Research Articles

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.

Semi-analytical formulation for single-track laser powder-bed fusion process to estimate melt-pool characteristics considering fluid-flow and marangoni effect

Semi-analytical formulation for single-track laser powder-bed fusion process to estimate melt-pool characteristics considering fluid-flow and marangoni effect

Evaluating quasi-static and fatigue performance of IN718 gyroid lattice structures fabricated via LPBF: Exploring relative densities

Additive manufacturing (AM) technology has unlocked the possibilities of fabricating complex structures of components such as lattice structures. Based on the literature review, the significance of the thickness effect on lattice structures has not been completely investigated yet. Therefore, this research aims to study the wall thickness effect on the mechanical performance of sheet-based lattice structures under quasi-static compression and fatigue loading conditions. For this aim, four geometries of lattice structures with designed relative densities of 12.5%, 25.0%, 37.5%, and 50.0% were fabricated via Laser Powder Bed Fusion (LPBF) process using Inconel alloy 718. The specimens were heat-treated to eliminate residual stresses and then tested under monotonic and cyclic compressive loading conditions to obtain the mechanical responses for each respective relative density. The experimental results from the quasi-static and fatigue compression tests on different geometries were compared and analyzed with the use of Scanning Electron Microscopy (SEM) to observe the fatigue failure locations and their dependency on the surface morphology of those failure locations. In addition, the actual relative densities were also calculated to evaluate the geometrical deviations and correct the experimental results. Overall, the experimental data revealed that there are strong associations between the lattices with various wall thicknesses and corresponding quasi-static and fatigue performance.

Additive manufacturing of a lightweight Al-Ca alloy by direct energy deposition and laser powder bed fusion

Abstract High strength and low density materials are needed to achieve lightweight design of components. Aluminum base metals alloyed with calcium are of potential interest because of the low density of calcium and its abundance. The additive manufacturing of dense and crack free samples out of an Al-10 wt.% Ca (Al-10Ca) alloy is presented. Both laser-based direct energy deposition (DED-LB) and laser powder bed fusion (LPBF) processes were applied to manufacture sample material. Preheating of the substrate plate is needed in LPBF to receive crack free samples. An analysis of the microstructure shows an Al-Al4Ca lamellar eutectic.

Enhanced flexural strength-to-weight ratio of Inconel718 lattice structure parts made of powder bed fusion process

The present work investigates the enhancement of the flexural strength-to-weight ratio on the lattice structure of additively manufactured Inconel-718 parts made using the powder bed fusion (PBF) process. ASTM-E290 standard three-point bending experiments and finite element modeling were conducted on solid and lattice structure parts. Four novel lattice structure unit cells with positive Maxwell’s number for stretch-dominant mechanical behavior were designed and used in the three-point bending specimens. The different lattice structure types investigated are strengthened-cubic (named Model_1), strengthened-cubic + kagome (named Model_2), strengthened-cubic + octet truss (named Model_3), and strengthened-cubic + kagome+octet truss (named as Model_4). Results obtained for the lattice structure specimens were compared with those obtained for solid specimens. The study revealed significant differences in strength-to-weight ratio among them, proving the tremendous potential of lattice structures in flexural loading applications by giving higher strength-to-weight ratios than solid specimens. Out of the four lattice structures and one solid structure investigated, Model_2 with only angular beams and Model_3 with only interior beams gave a better strength-to-weight ratio than solid specimens. Model_2 and Model_3 have beams along the unit cell’s body diagonal, leading to better performance. Out of all the lattice-structured specimens, Model_2 showed the highest strength-to-weight ratio. In the finite element model, the maximum equivalent strain was used as the criterion for failure. The failure sites found by finite element simulation coincided with those in the experiments.

A machine learning method to quantitatively predict alpha phase morphology in additively manufactured Ti-6Al-4V

sQuantitatively defining the relationship between laser powder bed fusion (LPBF) process parameters and the resultant microstructures for LPBF fabricated alloys is one of main research challenges. To date, achieving the desired microstructures and mechanical properties for LPBF alloys is generally done by time-consuming and costly trial-and-error experiments that are guided by human experience. Here, we develop an approach whereby an image-driven conditional generative adversarial network (cGAN) machine learning model is used to reconstruct and quantitatively predict the key microstructural features (e.g., the morphology of martensite and the size of primary and secondary martensite) for LPBF fabricated Ti-6Al-4V. The results demonstrate that the developed image-driven machine learning model can effectively and efficiently reconstruct micrographs of the microstructures within the training dataset and predict the microstructural features beyond the training dataset fabricated by different LPBF parameters (i.e., laser power and laser scan speed). This study opens an opportunity to establish and quantify the relationship between processing parameters and microstructure in LPBF Ti-6Al-4V using a GAN machine learning-based model, which can be readily extended to other metal alloy systems, thus offering great potential in applications related to process optimisation, material design, and microstructure control in the additive manufacturing field.

Evolution, Control, and Mitigation of Residual Stresses in Additively Manufactured Metallic Materials: A Review

Powder bed fusion (PBF) and directed energy deposition (DED) are the two major types of metal additive manufacturing (AM) processes, in which significant residual stresses could be generated in the as‐deposited materials and in turn affect part quality and performance. Therefore, how to control and mitigate residual stress has been a most pressing challenge for metal AM. Herein, the significant efforts made in literature to address this challenge for both PBF and DED processes are reviewed. The extensive survey covers the formation of residual stress in AM, influence of various AM process parameters, modeling techniques for predicting residual stresses, and post and in situ treatments for mitigating unfavorable residual stress distributions. More importantly, a critical analysis is provided to discuss the research gaps and opportunities, such that more exciting research will be stimulated to address the outstanding issues in this area and improve the quality and service life of AM‐produced components. As a result, once being better equipped with residual stress control knowledge and tools, the industry will be more confident in adopting metal AM techniques.

Additive manufacturing of a novel in-situ alloyed AISI316L-Cu stainless steel: Microstructure and antibacterial properties

Developing antibacterial metal implants is an innovative approach to reducing the risk of implant-related infections. In order to clarify the in-situ effect of copper contents on the microstructure and antibacterial properties of AISI316L stainless steel for advanced applications, Cu-bearing stainless steel was developed by the mean of the laser powder bed fusion process. This work demonstrates that using in-situ alloying with Cu addition can significantly promote antimicrobial activity against Escherichia coli and Staphylococcus aureus without significantly affecting microstructures. This methodology would apply to developing novel metallic materials towards new engineering applications of multi-function.

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.

Effects of process parameters on the microstructure of Inconel 718 during powder bed fusion based on cellular automata approach

ABSTRACT Using numerical simulation methods in additive manufacturing (AM) processes can effectively reduce the cost of process exploration. This study presents a multi-scale simulation study based on the powder bed model for grain growth in the laser powder bed fusion (L-PBF) process of the Inconel 718 material. The finite volume method (FVM) and cellular automaton (CA) model were used to reproduce the process. The simulation results were consistent with the experimental results in terms of melt pool morphology, grain structure, and average grain size. The processes of the competitive and epitaxial growth of grains were discussed. The effects of laser power, scanning speed, hatch distance, and scanning strategy on microstructure were further analyzed through pole figure, grain area distribution, CA nucleation and capture event. The results demonstrated the feasibility of this method in predicting the evolution of microstructures in the L-PBF process and provide a reference for future research.

Defect prediction in laser powder bed fusion with the combination of simulated melt pool images and thermal images

In laser powder bed fusion (LPBF) process, defects such as lack of fusion, porosity and keyhole originating from the layer-wise material deposition process, hinders its applications due to the absence of an effective prediction method. Thermal images of the melt pool are the most informative process signatures for in-process monitoring. Meanwhile, numerical simulation provides a better understanding of the melt pool fluid dynamics, the temperature field, and the quality of LPBFed metallic parts. In this paper, the thermal images are acquired from the high-speed thermal camera, and the simulation results from computational fluid dynamics (CFD) modelling. A hybrid neural network (HNN) model is proposed to fuse the data science features of thermal images and the physical knowledge features of simulated melt pool images for defect prediction in LPBF. The combination of features improves the defect identification accuracy to 97.25 %. Furthermore, a feature correlation algorithm employs the simulated melt pool features to improve the thermal image features, and replaces the physical feature extractor in the HNN. This physical supervision network (PSN) model saves the computational cost of CFD simulations. The high accuracy of 96 % of PSN model demonstrates that it is a promising method for defect identification in LPBF.

A Critical Review of Machine Learning Methods Used in Metal Powder Bed Fusion Process to Predict Part Properties

A Critical Review of Machine Learning Methods Used in Metal Powder Bed Fusion Process to Predict Part Properties

Microstructure gradient formation in electron-beam melting powder-bed fusion of a gamma-prime Ni-based superalloy

Microstructure gradient is formed during the additive manufacturing of Ni-based superalloy Haynes® 282® via electron-beam melting powder-bed fusion (EB-PBF) process. The precipitation of secondary phases occurs owing to EB-PBF in-situ heat treatment as consequence of high temperature powder bed pre-heating and multiple melting and re-heating thermal cycles, which can be summarised as a carbide precipitation heat treatment at 1000–1025 °C/8 h, aging between 875 and 975 °C/30 h, followed by a slow cooling step. Relatively large precipitates formed inter- and intragranularly with M3B2 boride and gamma-prime (γ') have sizes of approximately 5 μm and 110 nm, respectively, at the bottom of the build. The size of the precipitates decreases along the build direction. In the top region, the precipitate sizes of boride and γ' are 1 μm and 50 nm, respectively. MC carbide with a size of <1 μm precipitated throughout the build. A good correlation is observed in terms of the γ' size to hardness with average values of 350 HV at the bottom and 400 HV at the top. The ultimate tensile strength (UTS), yield strength (YS), and elongation (EL.) values at the bottom and top regions are 1080 ± 40 MPa, 805 ± 25 MPa, and 20 ± 5%; and 1105 ± 20 MPa, 825 ± 15 MPa, and 25 ± 5%, respectively. Overall, EB-PBF Haynes® 282® exhibits higher UTS and YS but lower EL than those of the as-received wrought alloy (950 ± MPa, 650 ± 15 MPa, and 40 ± 5%, respectively).

Enhanced structural integrity of Laser Powder Bed Fusion based AlSi10Mg parts by attaining defect free melt pool formations

This research aims to fabricate an AlSi10Mg parts using Laser Powder Bed Fusion technique with enhanced structural integrity. The prime novelty of this research work is eliminating the balling and sparring effects, keyhole and cavity formation by attaining effective melt pool formation. Modelling of the Laser Powder Bed Fusion process parameters such as Laser power, scanning speed, layer thickness and hatch spacing is carried out through Complex Proportional Assessment technique to optimize the parts' surface attributes and to overcome the defects based on the output responses such as surface roughness on scanning and building side, hardness and porosity. The laser power of 350 W, layer thickness of 30 µm, scan speed of 1133 mm/s, and hatch spacing of 0.1 mm produces significantly desirable results to achieve maximum hardness and minimum surface roughness and holding the porosity of < 1%. The obtained optimal setting from this research improves the structural integrity of the printed AlSi10Mg parts.

Intelligent Additive Manufacturing Architecture for Enhancing Uniformity of Surface Roughness and Mechanical Properties of Laser Powder Bed Fusion Components

In the Laser Powder Bed Fusion (L-PBF) process, 3D components with complex geometries are fabricated in a layer-by-layer fashion by using a controlled laser beam to selectively melt particular regions of the metal powder bed. However, due to the stochastic nature of the L-PBF process, the top surface roughness of each solidified layer tends to be different even when the optimal processing conditions for the different positions on the build plate are employed. As a result, the mechanical properties of the built components frequently vary from one component to the next. Accordingly, this study proposes an Intelligent Additive Manufacturing Architecture (IAMA) for controlling the surface roughness of each build layer through an appropriate adjustment of the laser re-melting parameters. The IAMA architecture comprises five modules, namely In-Situ Metrology (ISM), Ex-Situ Metrology (ESM), Automatic Virtual Metrology (AVM), Additive Manufacturing Simulation (AMS) and Intelligent Compensator (IC). The feasibility of the proposed architecture is demonstrated by comparing the top surface roughness of cubic and mechanical strengths of tensile test samples built using the proposed method with those built using a traditional L-PBF approach without surface roughness control. It is found that the samples fabricated using the IAMA approach have an average top surface roughness of 1.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu$</tex-math> </inline-formula> m and a standard deviation is 0.7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu$</tex-math> </inline-formula> m. By contrast, the samples produced using the traditional L-PBF approach have an average surface roughness of 13.45 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu$</tex-math> </inline-formula> m and a standard deviation of 2.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu$</tex-math> </inline-formula> m. In addition, the specimens produced with the assistance of IAMA architecture have an average tensile strength of 1013 MPa with a standard deviation of 69.5 MPa, while those printed without surface roughness control have an average tensile strength of 903 MPa with a standard deviation of 101.4 MPa <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —As L-PBF produce part in a layer-by-layer manner, therefore, the roughness on the top surface of previous layer have a strong influence on the printing quality of current layer. The variations of surface roughness will lead to the fluctuation of mechanical properties of the fabricated components. Additionally, to the best of author’s knowledge, current commercial L-PBF machines can not actively control the surface roughness of samples during the process. The proposed IAMA in this work can predict and control the roughness on the top surface of each layer during L-PBF process. Therefore, the constructed architecture has strong potential for integrating into the commercial L-PBF machine for controlling the surface roughness on the top of the parts during the manufacturing process. As a result, the quality of the fabricated components is expected to be in consistent. Accordingly, L-PBF machine equipped with IAMA will have a strong potential in applying for mass production of the components in aerospace and automobile industries.

An Empirical Model and Feedforward Control of Laser Powder Bed Fusion

Abstract Reliable process control for the laser powder bed fusion process, especially at the melt pool scale, remains an open challenge. One of the reasons for this is the lack of suitable control-oriented models and associated control design strategies. To address this issue, this paper (1) identifies an empirical control-oriented model of geometry-dependent melt pool behavior and (2) experimentally demonstrates melt pool regulation with a feedforward controller for laser power based on this model. First, the study establishes that the melt pool signature increases as the scan lines decrease in length. An empirical model of this behavior is developed and validated on different geometries at varying laser power levels. Second, the model is used to design a line-to-line feedforward controller that provides an optimal laser power sequence for a given geometry. Finally, this controller is validated experimentally and is demonstrated to suppress the in-layer geometry-related melt pool signal deviations for different test geometries.

An atomistic investigation on the sintering behaviour of AlSi10Mg nanoparticles during the laser powder bed fusion process

The laser powder bed fusion process represents an innovative manufacturing technique within the context of Industry 4.0. This method involves creating components in a layered manner by melting or compacting together individual powder particles. The process of compacting multiple-component metal powders significantly affects the mechanical and physical characteristics of the final product. In this particular investigation, an atomistic model is constructed to gain a deeper understanding of how AlSi10Mg metal powders with multiple components fuse together. This model is employed to examine how the compacting behavior changes for AlSi10Mg particles that are 4 nm in size at varying sintering temperatures: specifically, 800 K, 950 K, and 1100 K. The growth of interparticle connections (referred to as “necks”) and the ratio of shrinkage are quantified over time as the compacting process takes place. The results of the simulations reveal that increasing the sintering temperature leads to a wider neck, expanding from 51.81 Ã… to 53.63 Ã…. Correspondingly, the shrinkage ratio also increases as the sintering temperature rises. These findings offer insights into enhancing the manufacturing conditions and material formulations for multi-component nano powders.

A defect detection framework using three-dimensional convolutional neural network (3D-CNN) with in-situ monitoring data in laser powder bed fusion process

Laser powder bed fusion (L-PBF) is a promising additive manufacturing (AM) technology for manufacturing complex-shaped metallic parts with high density. Since L-PBF has been rapidly developed and applied in various industries, the quality assurance of the printed part using the process became a topic of primary importance. In this respect, in-situ monitoring techniques have received increased attention in recent years for aiding the quality control of L-PBF process and the certification of the products. This study proposes a novel defect detection framework with a three-dimensional convolutional neural network (3D-CNN) and in-situ monitoring of light intensity for detecting both the lack-of-fusion and keyhole-induced defects. The proposed 3D-CNN model works with a 3D moving window to perform the local inspection using the measured light intensities in three-dimensional space to classify the type of defect. Furthermore, the model predicts the local volume fraction to provide insights into the degree of defect. To perform the classification and regression with a single 3D-CNN, the joint classification and regression approach was adopted to train the model using the results obtained with micro-computed tomography as the ground truth. In order to build the training dataset, the samples with artificial defects were fabricated in different process regimes with energy densities ranging from 19.84J/mm3 to 110.12J/mm3. After the training process, the proposed model was evaluated with the test specimens which contain randomized defects generated due to the excessively low and high energy input. The results showed that the proposed 3D-CNN-based defect detection framework can detect pores greater than 80μm induced by both lack-of-fusion and keyhole mode melting. The sensitivity of the proposed framework was evaluated, showing the true positive rate of 75.69% and 70.47% for lack-of-fusion and keyhole defects with a void volume larger than 0.0003mm3. The prediction of local volume fraction with R2 score of 0.91 was also achieved with the proposed 3D-CNN approach.

Mechanical properties of 17-4 PH steel manufactured by powder bed fusion

The mechanical properties, such as the modulus of elasticity, yield strength, and ultimate tensile strength, were validated to precipitate-hardened 17-4PH stainless steel fabricated by the laser powder bed fusion process. Initially, six specimens were manufactured by varying the thickness and the fabrication angle, followed by tensile testing to obtain the modulus and yield strength. The results showed that the mechanical properties were low; therefore, further research is needed to change the manufacturing parameters to improve the mechanical properties

Material Health of NiCrBSi Alloy Parts Produced via the Laser Powder Bed Fusion Process

Laser powder bed fusion (L-PBF) is a novel process representing a possible solution for producing resistant parts using NiCrBSi hard-facing nickel alloys with complex geometry. Process parameters for more common alloys are explored with a standard Renishaw AM400 device (Renishaw, Wotton-under-Edge, UK) and an SLM Solution 250 device (SLM Solutions Group AG, Lübeck, Germany) modified with a baseplate preheated at high temperatures (300 °C and 500 °C). Laser remelting is also investigated in hopes of further improving material health. The origin of the main defects is studied. A lack of fusion is likely to be generated by spatters ejected from the melting pool while cracks are induced by the alloy’s lack of toughness. Using image analyses, those defects are quantified and correlated with processing parameters. Lack of fusion and total crack length decrease with an increase in baseplate’s preheating temperature. However, crack width increases with preheating temperature. Therefore, via a careful optimization of process parameters, samples with a surface density of 99% and narrow cracks are obtained.