Laser-based Powder Bed Fusion Process Research Articles

Efficient Simulation of the Laser-Based Powder Bed Fusion Process Demonstrated on Open Lattice Materials Fabrication

Strut-based or open lattice materials are a category of advanced materials used in medical and aerospace applications due to their properties, such as high strength-to-weight ratio and energy absorption capability. The most prominent method for the fabrication of lattice materials is the Laser-based Powder Bed Fusion (L-PBF) additive manufacturing (AM) process, due to its ability to produce parts of complex geometries. The current work presents an efficient meso-scale finite element (FE) modeling methodology of the L-PBF process demonstrated in the fabrication of body-centered cubic (BCC) lattice materials. The modeling efficiency is gained through an adaptive mesh refinement technique, which results in accurate and efficient prediction of the temperature field during the process evolution. To examine the efficiency of the modeling method, the computational time is compared with that of a conventional FE simulation, based on the element and birth technique. The temperature history difference between the two approaches is minor but the adaptive mesh modeling requires only a small portion of the simulation time of the conventional model. In addition, the computational results present a good correlation with the available experimental measurements for various process parameters validating the presented efficient method.

Experimental study of convective heat transfer in additive manufactured minichannels: the impact of the roughness and Prandtl number

Additive Manufacturing, especially Laser-based Powder Bed Fusion (L-PBF), can fabricate internal channels with enhanced cooling properties. Roughness is a natural consequence of the L-PBF process that can increase flow friction and also influence heat transfer in these cooling channels. While existing literature predominantly explores the impact of roughness on flow friction, less attention has been given to the effects on heat transfer. In this study, a novel experimental setup employing Joule heating was developed to investigate water flow in minichannels fabricated by L-PBF. The impact of roughness and different Prandtl numbers on flow friction and heat transfer was studied. The results indicated that the Nusselt number in rough channels scales with the Prandtl number to the power of 0.8 (Pr 0.8), suggesting greater heat transfer with higher Prandtl numbers for rough channels compared to those of smooth channels. At a specific combination of relative roughness and Reynolds number, the enhancement of heat transfer due to roughness is maximized.

Pure niobium manufactured by Laser-Based Powder Bed Fusion: influence of process parameters and supports on as-built surface quality

Niobium (Nb) is a transition metal commonly used as an alloying element for increasing strength, toughness, corrosion resistance, and other properties of steel and superalloys. Pure Nb, however, is a very interesting metal for its excellent superconductivity. This makes it suitable for producing superconducting magnets and devices for particle acceleration systems and particle physics research (e.g., superconducting resonant cavities). In this work, the production of Nb by the Laser-Based Powder Bed Fusion (PBF-LB/M, also known as Laser Powder Bed Fusion or LPBF) process was examined. Manufacturing parameters were investigated to achieve additively manufactured parts with a relative density higher than 99.5% and showing a down-skin surface roughness in the range of 20–70 μm, depending on the inclination angle. Studies related to the limiting angle of self-supported Nb parts were also conducted, and innovative non-contact supporting structures were successfully developed. These allowed to creation of parts with very small overhang angles, without compromising the downward-facing surfaces; indeed at the same time, the as-built surface finish was improved.

Defect detection in laser-based powder bed fusion process using machine learning classification methods

The aim of this study is to deploy machine learning (ML) classification methods to detect defective regions in additive manufacturing, colloquially known as 3D printing, particularly for the laser-based powder bed fusion process. A custom-designed test specimen composed of 316L was manufactured using EOS M 290 machine. Multinomial logistic regression (MLR), artificial neural network (ANN), and convolutional neural network (CNN) classification techniques were applied to train the ML models using optical tomography infrared images of each additively manufactured layer of test specimen. Based on the trained MLR, ANN, and CNN classifiers, the ML models predict whether the manufactured layer is standard or defective, yielding five classes. Defective layers were classified into two classes for lack of fusion and two classes for keyhole porosity. The supervised approach yielded impeccable accuracy (>99%) for all three classification methods, however CNN inherited the highest degree of performance with 100% accuracy for independent test dataset unfamiliar to the model for unbiased evaluation. The high performance and low cost of computing observed in this work can have the potential to detect and eliminate defective regions by tuning the processing parameters in real time resulting in significantly decreased costs, lead-time, and waste. The proposed quality control can enable mass adoption of additive manufacturing technologies in a vast number of industries for critical components that are design- and shape- agnostic.

Developing Four Powder Recycling Strategy for Sustainable Additive Manufacturing 3D Printing Process

This paper presents a detailed analysis of four different powder recycling strategies for the metal additive manufacturing process focused on examining the efficacy of recycling Ti6Al4V alloy powder for the Laser-Based Powder Bed Fusion (LBPF) process. The study evaluates four distinct recycling strategies (A, B, C, D) and their impact on powder-saving mechanisms. The experimental methodology involves careful leftover powder sampling, implementation of recycling strategies and print cycle analysis from each recycling strategy. The results show that Strategies C and D exhibit the lowest powder waste, allowing 6 times the reuse of leftover powder and producing 384 parts with minimal powder waste at the end of the process. Strategy B also performs well, enabling 8 cycles and building 512 parts with only a small amount of powder wasted. Conversely, Strategy A yields the highest powder waste despite achieving the same number of cycles and prints. This proposed approach requires a total volume of 224,000 mm3 of powder for each build. This entails using 500g of gas-atomized Ti-6Al-4V powder, with an average particle size D90 of approximately 50 μm. This features a continuous mixing technique, wherein each build combines 50% virgin powder with 50% of the previously collected and sieved powder.

Nitrogen-assisted laser-based powder bed fusion fabricated NiTi with high hardness and low modulus

The role of the protective gases in laser-based powder bed fusion (L-PBF) is mainly to avoid the oxidation of metals during the melting and solidification process. However, the gaseous environment can also interact with metals, and result in changes in microstructure and properties. In a NiTi alloy, we found the L-PBF processes in the N2 gaseous environment can induce TiN particles and facilitate the formation of a precipitate-dislocation network and therefore improve mechanical properties. Nitrogen segregation also led to grain refinement and the generation of high-angle grain boundaries. The as-printed NiTi in N2 loses its shape memory effect, however, an aging process at 500 °C resulted in unchanged grain size, parallel arrays of Ni4Ti3 precipitates around TiN, and a recovery of shape memory effect. The optimization of microstructure finally resulted in a high hardness of ∼4.0 GPa and a low reduced elastic modulus of ∼57.5 GPa. The manufacturing process presented in this study opens a new way for making biomedical devices with low elastic constant, high strength to weight, and high resistance to wear.

Modeling the Evolution of Grain Texture during Solidification of Laser-Based Powder Bed Fusion Manufactured Alloy 625 Using a Cellular Automata Finite Element Model

The grain texture of the as-printed material evolves during the laser-based powder bed fusion (PBF-LB) process. The resulting mechanical properties are dependent on the obtained grain texture and the properties vary depending on the chosen process parameters such as scan velocity and laser power. A coupled 2D Cellular Automata and Finite Element model (2D CA-FE) is developed to predict the evolution of the grain texture during solidification of the nickel-based superalloy 625 produced by PBF-LB. The FE model predicts the temperature history of the build, and the CA model makes predictions of nucleation and grain growth based on the temperature history. The 2D CA-FE model captures the solidification behavior observed in PBF-LB such as competitive grain growth plus equiaxed and columnar grain growth. Three different nucleation densities for heterogeneous nucleation were studied, 1 × 1011, 3 × 1011, and 5 × 1011. It was found that the nucleation density 3 × 1011 gave the best result compared to existing EBSD data in the literature. With the selected nucleation density, the aspect ratio and grain size distribution of the simulated grain texture also agrees well with the observed textures from EBSD in the literature.

Comprehensive review on residual stress control strategies in laser-based powder bed fusion process– Challenges and opportunities

Comprehensive review on residual stress control strategies in laser-based powder bed fusion process– Challenges and opportunities

Laser-based ultrasonic measurement of the influence of PBF-LB/M-typical thermal cycles on sheet metal components of hybrid parts

In order to cope with the upcoming challenges from industrial trends, such as mass customization, hybrid parts made out of sheet metal and additively manufactured components offer a possible approach. Hybrid parts can be used for instance in the field of medical and aerospace industries. The combination of the two technologies forming and additive manufacturing allows to use the advantages of each while at the same time avoiding the disadvantages. During the additive manufacturing process, however, the sheet metal substrates, which are a component of the later part, are subjected to heat input such as substrate heating and laser radiation. Each heat input has a different temperature and duration, which are simulated to detect changes in the material properties. Experiments are performed for 316L and Ti-6Al-4 V, which are both commonly used in additive manufacturing. The investigations aim at proving that the substrate heating has no influence on either material. To investigate the influence of substrate heating during additive manufacturing, tensile tests in heat-treated state as well as laser-based ultrasonic measurements are used to detect changes in mechanical or microstructural properties. However, the heat input of the laser is expected to lead to a phase transformation for the titanium alloy due to the high cooling rate. Therefore, the feasibility of using a laser based ultrasonic measurement system to detect microstructural changes during heat input similar to the laser radiation during laser-based powder bed fusion processes is tested. Based on the results, the substrate heating does not influence the sheet material, despite long holding times. However, the short laser beam-like heat inputs lead to phase transformations for the titanium alloy, which can be detected temperature and time dependent via laser-based ultrasonic measurements.

Hierarchical Analysis of Phase Constituent and Mechanical Properties of AlSi10Mg/SiC Composite Produced by Laser-Based Powder Bed Fusion

Aluminum matrix composites reinforced with ceramic particles have recently received considerable research interest owing to their light weight, high modulus, and high strength. This study fabricated SiC reinforced Al–10 wt%Si–0.35 wt% Mg (AlSi10Mg) alloy matrix composites with high relative density using a laser-based powder bed fusion (LPBF) process. The reaction phase of Al4SiC4 from the interface of the SiC particles occurred under LPBF and its fraction increased with increasing energy density. A high compressive stress of >600 MPa was obtained for the AlSi10Mg/SiC composite produced via LPBF at an optimum energy density (250 J/mm3). This study analyzed the mechanical properties of the LPBF composite hierarchically from micro to macro levels. A higher Young’s modulus (evaluated using a compression test) was obtained for the AlSi10Mg/SiC composite fabricated using LPBF than the AlSi10Mg alloy. From the micro level analysis on mechanical properties, the increased Al4SiC4 formation with an increase in energy density improved the hardness and Young’s modulus compared to those of the AlSi10Mg alloy. However, SiC in the composite is more effective for being high strength and Young’s modulus than Al4SiC4.

Machine learning based prediction of melt pool morphology in a laser-based powder bed fusion additive manufacturing process

Laser-based powder bed fusion (L-PBF) has become the de facto choice for metal additive manufacturing (AM) processes. Even after considerable research investments, components manufactured using L-PBF lack consistency in their quality. Realizing the crucial role of the melt pool in controlling the final build quality, we predict the morphology of the melt pool directly from the build commands in an L-PBF process. We leverage machine learning techniques to predict quantitative attributes like the size as well as qualitative attributes like the shape of the melt pool. The area of the melt pool is predicted using an LSTM network. The outlined LSTM-based approach estimates the area with 90.7 % accuracy. The shape is inferred by synthesising the images of the melt pool by using a Melt Pool Generative Adversarial Network (MP-GAN). The synthetic images attain a structural similarity score of 0.91. The precision and accuracy of the results showcase the efficacy of the outlined approach and pave the way for real-time monitoring and control of the melt pool to build products with consistently better quality.

Effects of Stress-Relieving Temperature on Residual Stresses, Microstructure and Mechanical Behaviour of Inconel 625 Processed by PBF-LB/M

Inconel 625 (IN625) superalloys can be easily fabricated by the laser-based powder bed fusion (PBF-LB/M) process, allowing the production of components with a high level of design freedom. However, one of the main drawbacks of the PBF-LB/M process is the control over thermally induced stresses and their mitigation. A standard approach to prevent distortion caused by residual stress is performing a stress-relieving (SR) heat treatment before cutting the parts from the building platform. Differently from the cast or wrought alloy, in additively manufactured IN625, the standard SR at 870 °C provokes the early formation of the undesirable δ phase. Therefore, this unsuitable precipitation observed in the PBF-LB/M material drives the attention to develop a tailored SR treatment to minimise the presence of undesirable phases. This work investigates SR at lower temperatures by simultaneously considering their effects on residual stress mitigation, microstructural evolution, and mechanical properties. A multiscale approach with cantilever and X-ray technologies was used to investigate how the residual stress level is affected by SR temperature. Moreover, microstructural analyses and phase identifications were performed by SEM, XRD, EBSD, and DSC analyses. Finally, mechanical investigations through microhardness and tensile tests were performed as well. The results revealed that for the additively manufactured IN625 parts, an alternative SR treatment able to mitigate the residual stresses without a massive formation of δ phase could be performed in a temperature range between 750 and 800 °C.

Surface morphology and dimensional accuracy of additively manufactured Ti-6Al-4V alloy: Effect of variable scanning speed with constant laser power in widespread energy density

The present study aims at analysing the combined role of laser power (LP) and scanning speed (SS) on the surface topography and dimensional accuracy of Titanium (Ti–6Al–4V) alloy fabricated through a laser-based powder bed fusion process. The widespread volumetric energy densities (VEDs) ranging from 25 J/mm3 (S1) to 120 J/mm3 (S10) were chosen with five different combinations of LP with varying SS, such as 150 W (276 and 850 mm/s), 250 W (630 and 990 mm/s), 300 W (395 and 465 mm/s), 350 W (405 and 1100 mm/s), and 370 W (850 and 1270 mm/s). Sample S1 (25 J/mm3) revealed the presence of partially melted track and VED beyond 60 J/mm3 producing a detrimental surface quality due to over-burning. At low LPs of 150 and 250 W, a reduction in SS led to a decrease in surface roughness, owing to sufficient melting of the powder layers. Meanwhile, at high LPs of 300 and 350 W, a reduction in SS caused the burning of the layer which led to an increase in the roughness. In contrast, at a high LP of 370 W, a reduction in SS caused a decrease in the roughness parameters. Both LP and SS were found to have strong interdependence in the achievement of the as-built surface quality. Further, the samples fabricated with excessive VED S7 (75 J/mm3), S8 (90 J/mm3), S9 (105 J/mm3) and S10 (120 J/mm3) caused delamination of layers and resulted in severe angular distortion.

Evaluation of the role of hatch-spacing variation in a lack-of-fusion defect prediction criterion for laser-based powder bed fusion processes

Lack of fusion (LOF) defects impact adversely on the mechanical properties of additively manufactured components produced via laser-based powder bed fusion. Following a stress-relieving heat treatment, the tensile properties and hardness of Ti6Al4V components were found to be negatively impacted by the presence of LOF defects. This work considers a geometrical-based inequality for the prediction of LOF defects. We critically evaluate an LOF criterion using both the experimentally and analytically obtained melt pool geometries. Experimentally, we determined melt pool dimensions by analysing a single-layer, multi-track deposition with oversized hatch spacing in order to establish depth and width from non-overlapping melt pools. Analytically, Rosenthal-based predictions of melt pool size (width and depth) are applied. To investigate LOF defects, we used hatch spacing as the main parameter variation to investigate defects while keeping all other controllable parameters unchanged. An original LOF criterion from the literature was found to be an adequate predictor of LOF defects when experimentally obtained melt pool geometry was used. Critically, however, the analytical expressions for melt pool geometry were found to be in error and this caused the LOF criterion to fail in predicting LOF defects in all cases where defects were observed experimentally. However, an adaptation to the LOF prediction criterion is proposed whereby it is recommended that a correction factor {R}_{c}^{2}=0.7 (or {R}_{c}=0.83) is used with the analytically derived melt pool geometry. Furthermore, this correction is extended into the laser power versus scanning speed operating space to give minimum (corrected) line energy for LOF avoidance in Ti6Al4V components.

Density-Based Optimization of the Laser Powder Bed Fusion Process Based on a Modelling Framework

One of the main challenges encountered in the Laser-based Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) process is the fabrication of defect-free parts. The presence of defects severely degrades the mechanical performance of AM parts and especially their fatigue strength. The most popular and reliable method to assess the ability of the employed process parameters for the fabrication of full-density parts is the process windows map, also known as printability map. However, the experimental procedure for the design of the printability maps and the identification of the optimum-density process parameters is usually time-consuming and expensive. In the present work, a modelling framework is presented for the determination of a printability map and the optimization of the L-PBF process based on the prediction and characterization of melt-pool geometric features and the prediction of porosity of small samples of 316L SS and Ti-6Al-4V metal alloys. The results are compared with available experimental data and present a good correlation, verifying the modelling methodology. The suitability of the employed defect criteria for each material and the effect of the hatch-spacing process parameter on the optimum-density parameters are also presented.

Impact of additive manufacturing on titanium supply chain: Case of titanium alloys in automotive and aerospace industries

Additive manufacturing (AM) is a promising technology for designing complex metallic pieces for different sectors with resource and time effectiveness. Titanium (Ti) is an essential critical material for AM development. AM can produce intricate and cost-effective components with Ti alloys for the transportation sector which would not be possible with conventional manufacturing (CM) technologies. This study assesses the impact of AM on the life cycle of Ti and its alloys by using review (numerical data, case examples) and dynamics simulation modelling. This article quantifies potential environmental benefits and examines aspects related to using Ti alloys in the automotive and aerospace industries. Mass flow, energy consumption and related greenhouse gas (GHG) emissions are assessed by making a comparison between subcategories of AM including binder jetting (BJT), directed energy deposition (DED), electron beam-based powder bed fusion (EB-PBF), and laser-based powder bed fusion (L-PBF) and CM processes including forging, milling, machining, and die casting. The results show that the AM subcategories considered potentially reduce manufacturing phase energy consumption and GHG emissions except for L-PBF. The findings highlight that an inclusive consideration of all life cycle phases is needed to fully identify potential benefits of AM for industries. Also, the scenario analysis in this study proposes the opportunity for saving mass and minimizing energy consumption and GHG emissions by optimizing the structural design and manufacturing processes for Ti components.

A conceptual framework for layerwise energy prediction in laser-based powder bed fusion process using machine learning

Additive Manufacturing (AM) is the process of successively joining materials layer by layer to produce a component from a digital 3D model. One of the widely adopted AM processes for producing metal components is laser-based powder bed fusion of metals (PBF-LB/M), in which lasers are used to selectively melt and fuse metallic powder in successive steps. An essential step towards economic industrial application is to minimize the energy consumption of the technology. Therefore, it is important to understand the relation between the process parameters (e.g., scanning speed, layer thickness, build time, laser power), part design, working environment, and the energy required to manufacture a component. In this work, we propose a conceptual framework of multimodal regression (MMR) for layerwise energy and quality prediction in AM process. The MMR model can be implemented using neural networks and used to analyze 2D images and process information of a layer to predict the layer quality and energy required to manufacture the layer. This model can further be used to optimize the part geometry, process design, and machine level parameters to utilize less energy without compromising on quality. Additionally, a PBF-LB/M use case is provided to discuss the feasibility of the proposed framework.

Solute-induced near-isotropic performance of laser powder bed fusion manufactured pure titanium

Materials produced by laser-based powder bed fusion process ( L -PBF) undergo rapid solidification and several cycles of remelting. The resulting unique microstructure imparts anisotropy to the material and leads to its non-uniform deformation under load that can cause limitations in some applications. In this study, we explored the ability of nitrogen (solute) to reduce the degree of anisotropy of L -PBF fabricated Ti materials. The correlation among microstructural changes, texture evolution, and deformation behavior was investigated. L -PBF fabricated Ti with low N content (0.01 wt%) exhibits mainly continuous epitaxial growth of α grains with a strong crystallographic texture, leading to anisotropic tensile properties. A high N content (0.31 wt%) significantly changes the structure and texture of L -PBF fabricated Ti through the introduction of refined martensite grains with random crystallographic orientations, which reduces the degree of anisotropy and enhances strength (1066 MPa) and ductility (25%). This study will facilitate the manufacture of Ti with superior near-isotropic tensile properties, which has not been realized through traditional manufacturing processes.

Correlation of powder degradation, energy absorption and gas pore formation in laser-based powder bed fusion process of AlSi10Mg0.4

Gas pore formation observed during laser-based powder bed fusion is yet not fully understood. This study is a comprehensive investigation of the mechanisms of hydrogen pore formation in the powder bed fusion process of AlSi10Mg0.4 via laser beam. The influence of varying process parameters such as hatch distance, layer thickness and build plate temperature as well as the impact of virgin and long-term reused powder on part porosity and melt pool characteristics is investigated in detail. A novel, very efficient method to characterize powder aging by the correlation of powder gray value measurements with the oxygen content is presented. The relation between powder aging and the change of the absorption coefficient reveals the origin of gas formation as being intimately related to the energy input. Vacuum heat treatment of as-built parts indicates the potential of hydrogen removal in a postprocessing step with the prospect of welding additively manufactured aluminum parts.

Analysis of element loss, densification, and defects in laser-based powder-bed fusion of magnesium alloy WE43

It is well known that laser-based powder-bed fusion (L-PBF) additive manufacturing of magnesium (Mg) and its alloys is associated with high Mg loss due to vaporization (Mg Loss ) and high incidence of many types of defects in the manufactured parts/samples. Despite this, Mg Loss , densification, and defect characteristics have not been holistically considered in the determination of the optimal values of L-PBF processing parameters for Mg and its alloys. This study presents a combined modeling and experimental approach applied for a widely used Mg alloy (WE43) to address this shortcoming in the literature. First, an experimentally calibrated model is proposed to determine Mg Loss as a function of the L-PBF processing parameters. The model couples the temperature profile using a double ellipsoidal heat source with a Langmuir vaporization model and is calibrated using the width of the single-track L-PBF process and the measured Mg loss using inductively coupled plasma mass spectrometry (ICP-MS). Second, the densification of the samples is determined using a modification of the Archimedes method that considers the amount of Mg Loss in the calculation of the relative density. Third, a comprehensive and quantitative study is conducted on the relationships between the characteristics of porosity defects and the L-PBF processing parameters. Finally, the optimized L-PBF processing parameters are determined by considering the Mg Loss , densification, and the characteristics of defects. The present study yields 0.23 wt.% Mg Loss compared to 2 wt.% Mg Loss that was reported in the previous studies. Furthermore, more than 99.5% densification is achieved while only ∼2% and ∼0.5% of the total defects are characterized as keyhole and lack of fusion defects, respectively.