Metal Additive Manufacturing Processes Research Articles

Characterizing process window for microscale selective laser sintering

This paper outlines the experimental approach to create a sintering window for a microscale metal additive manufacturing (AM) process. The experimental framework discussed in the paper involves fabricating sintered features by varying pattern area and laser irradiance. The sintered features are then imaged under a microscope and the processed images are compared against an ideal image to quantify the uncertainty in near-net shape of the feature. Evaluating this data defines the process window within which good sintering and near-net shaped features can be expected in the microscale AM process.

Influence of layer thickness on the printability of nickel alloy 718:A systematic process optimization framework

Laser-powder bed fusion (L-PBF) is a metal additive manufacturing (AM) process involving layer-by-layer metal parts fabrication. It has been successfully used in the literature to manufacture nickel alloy 718 parts. However, the process parameter selection has generally been based on either intensive experimental methods or expensive computational simulations. As such, these methods test a limited region of the parameter space. Thus, hindering the evaluation of possible parameter combinations that can prove superior to those in the tested space. Moreover, most of the previous studies combine the process parameters into single metrics, e.g., linear energy density (LED) or volumetric energy density (VED). Both metrics have been shown in the literature to provide an incomplete understanding of the produced parts and inconsistent mechanical properties and microstructures. This paper uses a systematic framework developed by the authors that covers the entire scanning speed-laser power (v-P) parameter space, estimates the maximum hatch spacing at each v-P combination, and detects the good printability region. This framework defines the good printability region as the region that is free of the main possible porosity defects, namely, lack of fusion, keyholing, and balling. Furthermore, to cover the effect of the fourth process parameter (layer thickness), the framework is applied to 60 and 90μm layer thicknesses. This extension of the framework is performed to test the possibility of increasing the volumetric build rate (VBR) by increasing the layer thickness. Finally, the printability is validated using density and porosity measurements, while performance is evaluated using tensile tests. Using the framework, parts with 99.99% densification were fabricated successfully for both layer thicknesses. Moreover, tensile strength values of 1059.1 and 1063.5 MPa and volumetric build rates of 7.10 and 6.92 mm3/s were achieved for the as-printed parts for the 60 and 90μm layer thicknesses, respectively. However, these VBR values are constrained by the conservative hatch spacing criterion used in the framework. Further optimization of the hatch spacing can provide higher VBR values for the 90μm layer thickness.

Recent advances of metallic bio-materials in additive manufacturing in biomedical implants–A review

During the last three decades, metal additive manufacturing (metal-AM) has evolved significantly. It has become possible to produce prototypes of products using metal-AM in combination with various manufacturing processes since the technology has matured enough to be used for complex geometries. Recent advancements in biomedical implants have attracted increasing attention to additive manufacturing (AM). The use of titanium and its alloys in biomedical implants, cobalt alloys, stainless steel 316L, nickel-titanium, and others is explored. A number of metal additive manufacturing processes are available, including Selective Laser Melting (SLM), Selective Laser Sintering (SLS), and Electron Beam Melting (EBM). Moreover, the paper discusses the latest developments in metal-AM via direct metal laser sintering and the fabrication and characterization of a three-dimensional scaffold. There is increasing use of laser or electron beam melting techniques in the manufacture of metal implants, among all methods of additive manufacturing (AM). In the future, metal-AM is expected to provide various benefits to the biomedical industry.

Thermocycling-guided cracking and deformation control in directed energy deposition

In single-step metal Additive Manufacturing (AM) processes, thermal cycling resulting from inappropriate scanning length and deposition height will eventually cause the cracking and deformation of the deposits, which will significantly damage object performance and intended applications. In this work, a unique altering thermal cycling pathway is proposed to efficiently control the cracking and deformation of AMed CuSi3Mn alloys without either using any auxiliary equipment or adjusting the alloy composition. With the help of the Intrinsic Heat Treatment (IHT) caused by secondary or multiple heating and cooling between the subsequent layers and the previous layers, a crack-free cylindric part fabricated by the Gas Metal Arc assisted Directed Energy Deposition (GMA-DED) based on Surface Tension Transfer (STT) is finally obtained, at the same time, the experimental results were verified by the numerical simulation. This further contributes to a superior deposition strategy to control the crack and deformation behavior on the metal AM.

Hybrid manufacturing of steel construction parts via arc welding of LPBF-produced and hot-rolled stainless steels

AbstractThe demand for free-form steel structures having improved performances, reducing labour and resource usage is increasing in the construction sector. Structural nodes are some of the most critical regions for steel structures characterised often by large dimensions. These nodes can exploit the geometrical freedom of metal additive manufacturing (MAM) processes. Laser powder bed fusion (LPBF) is arguably the most developed MAM process, which has limitations regarding the size of the parts to be produced. A way to overcome the size limits of LPBF for producing structural nodes while still exploiting its geometrical capacity is producing hybrid components by welding them to traditionally manufactured beams. Such hybrid joints would constitute a complex system from a mechanical design perspective requiring a systematic analysis in order to be certified for structural use. Accordingly, this work studies the mechanical behaviour of hybrid steel components generated by welding LPBF plates and quarto plates made of AISI 316L stainless steel. The work was guided by a case study based on a large steel node, which helped defining the requirements to fill the gap of the international standards. The mechanical characterisation of LPBF-produced plates and quarto plates, as well as the welded hybrid components revealed a maximum of 10% difference between the properties of the differently manufactured plates. Through the digital image correlation (DIC) analyses, the anisotropic deformation behaviour along the LPBF, weld seam, and quarto plate regions have been identified, and the properties after welding did not show relevant modifications. The tests allowed to define that the failure behaviour is mainly governed by interlayer bounds, and a 0.9 safety reduction parameter for considering the reduction of ductility induced by arc welding to LPBF. Finally, design and production suggestions have been provided for a correct evaluation of gross and effective sections of the designed nodes.

Long-short term memory networks for modeling track geometry in laser metal deposition.

Modeling metal additive manufacturing processes is of great importance because it allows for the production of objects that are closer to the desired geometry and mechanical properties. Over-deposition often takes place during laser metal deposition, especially when the deposition head changes its direction and results in more material being melted onto the substrate. Modeling over-deposition is one of the necessary steps toward online process control, as a good model can be used in a closed-loop system to adjust the deposition parameters in real-time to reduce this phenomenon. In this study, we present a long-short memory neural network to model over-deposition. The model has been trained on simple geometries such as straight tracks, spiral and V-tracks made of Inconel 718. The model shows good generalization capabilities and can predict the height of more complex and previously unseen random tracks with limited performance loss. After the addition to the training dataset of a small amount of data coming from the random tracks, the performance of the model for such additional shapes improves significantly, making this approach feasible for more general applications as well.

SIMULATION TOOL FOR MATERIAL BEHAVIOUR PREDICTION IN ADDITIVE MANUFACTURING

Simulation tools play an important role not only in terms of understanding the processes taking place in the production, and the possibility to prevent failures, but ultimately, and most importantly, to optimize the production process. Thus, simulation tools should be able to work with many input parameters. The processes in which many parameters influence the final quality of the part, and its properties is metal additive manufacturing (AM). The volume of support material used in the metal AM process is also of importance, as its reduction plays an important role in terms of cost-effectiveness, which can be increased by minimizing support structures. The paper deals with the numerical simulation of the metal AM process. The simulation analyses with the same input parameters were performed, comparing each other the support material distribution, volume fraction or shape deviation with respect to the conventional support generation method and the function allowing the support generation by means of the optimization mode. Less variation in the shape of the part, variation in its volume fraction, variation in the density of the generated support structures as well as variation in the spacing of the generated support structure was achieved by using the support generation optimization mode.

A method for geometric features equivalence to assess the thermal behavior in the selective laser melting process

Selective laser melting (SLM) is a common technique categorized as one of the metal Additive Manufacturing (AM) processes to efficiently manufacture complex geometries. However, due to the diversity and difficulty in quantification of part geometry, it is hard to investigate different geometry, which is highly vital to the part quality, on the thermal behavior (TB) in the SLM process to ensure the part forming rate. To tackle this problem, a geometry feature equivalence (GFE) method was proposed to divide the geometric features into permutations and combinations of different GF elements. Firstly, common elements and uncommon elements were defined, where the former represents the element with the same track length in each track and the letter means the element has various track length in each track. Then, a finite element model was established and verified based on experiments to investigate the influence of GF elements on all kinds of TB including temperature variation, temperature changing rate, molten pool dimensions and lifetime. Next, for common elements, single-track model was analyzed to obtain the influence of the track length on the TB, which showed that the peak value of temperature, heating rate and cooling rate are at the starting point, and then gradually become stable with the track length increasing. After that, multi-track model was established to investigate the effect of the track number on the TB, which represented that the maximum heating rate keeps declining, the maximum cooling rate of each track are almost same, and the molten pool width and depth increase with the track number increasing. Moreover, two uncommon elements were designed and studied to obtain its TB, which demonstrates that even though the track length is the same in each track, different arrangement will bring out various temperature evolution. Finally, a thin-wall ring was built and measured based on experiments to validate the effectiveness of the proposed method. The proposed method can contribute to the part designer to obtain the TB of arbitrary geometric feature in design phase and guide the part geometry design and optimization.

Binder jetting in gears

Due to near netshape production, powder metallurgically manufactured (PM) gears have a high potential to increase cost and resource efficiency. Compared to conventionally machined gears, the advantages of PM gears are the different local density distribution in the component and the potential integration of secondary design elements. These advantages are accompanied by the reduction of weight and the possibility of optimizing the NVH-behavior of a gear. PM gears may be manufactured by both, die pressing and additive manufacturing (AM). The powder bed-based additive manufacturing processes for metals can be classified into binder-based (e.g. Binder Jetting, BJT) and beam-based thermal processes (e.g. Laser Powder Bed Fusion, LPBF). Due to the specific process technology, the manufacturing of PM gears by die pressing is only economical for large batch sizes in series production. For small batches, AM offers an approach to manufacture gears that meet the requirements in terms of quality, strength, acoustics and economic efficiency of the manufacturing process.This report describes the potential of BJT gears made of stainless steel 17-4PH (X5CrN-CuNb16-4) with a relative density of ρ0,rel ≈ 99% regarding the tooth bending strength. The reliability of the process and thus the tooth bending strength can be increased further by a specific adjustment of process parameters. Subsequently, the gears are tested with regard to the tooth bending strength on the pulsator test rig. The results are summarized in a SN-curve.

Surface morphologies of intra-layer printing process in electron beam powder bed fusion: A high-fidelity modeling study with experimental validation

The surface morphology of intra-layer printing process is crucial in powder bed fusion. It not only determines the top-surface quality but also indicates the formation evolution during a building process. But the formation mechanisms of rich surface morphologies and the correlation with build quality are still unclear. This paper developed a high-fidelity model of electron beam powder bed fusion to depict the intra-layer printing process. Four typical surface morphologies, namely the porous surface, even surface, swelling surface, and the labyrinth like surface, were reproduced in powder-scale, and studied on the formation process from a scope of heat and mass and transfer. Three formation mechanisms considering track interaction were figured out, including morphology coalescence, pre-heating and re-melting, and the lateral extension of melt pool. It reveals that the morphology coalescence effect could make the current track an overall bias toward the prior track, which might make textures along the hatch direction. The pre-heating effect due to heat accumulation improves the formation quality by making the melt pool develop adequately, while the re-melting effect could further improve the formation quality except for the contour tracks. Particularly, the lateral-extended melt pool across several adjacent tracks, once occurs, could make significantly unique surface morphologies due to the lateral convection or possible central break-up. Finally, the intra-layer printed top surface morphologies were further correlated with the internal build quality via an efficient model with inter-layer printing, where the even or slightly swelling surfaces were found to correspond to a good build quality. This could enhance the understanding of build mechanisms and help to improve the metal additive manufacturing process.

Evaluating gas-driven flow mechanics of non-spherical powders for directed energy deposition

The size and shape of metal powder particles play a central role in the performance and utility of directed energy deposition (DED) based metal additive manufacturing processes. Given their significant economic advantage, unconventional non-spherical metal powders have recently emerged as a potential, economically viable, alternative for use as DED feedstock. In this work, we investigate the mechanics of carrier-gas driven powder flows for non-spherical metal powders using coupled experiments and numerical simulations. The experiments are performed on a custom-built DED system that allows us to individually vary both gas and powder flows systematically. By suitably adjusting the flow conditions to ensure equal mass flow rates, we show that non-spherical powders compare favorably against gas atomized spherical particles vis-á-vis final concentration distribution and flow mechanics. The differences between these two types of powder particles are quantified with flat, angled and curved substrate geometries, allowing predictions for many practical deposition situations. The simulations use a two-way discrete phase model and predict best (perfectly elastic) and worst (perfectly inelastic) case concentrations on the substrate by varying the particle restitution coefficient. Our results suggest suitable process modifications (gas flow rate, feed rate, axis traverse speed) to facilitate the use of non-spherical powders alongside conventional spherical powders in DED processes.

Gaussian process classification of melt pool motion for laser powder bed fusion process monitoring

Laser powder bed fusion (L-PBF) is a metal additive manufacturing (AM) process with great potential in producing high performance metal components. Due to lack of stability and repeatability of the building process, its wide application in industry is limited. The process monitoring and control are import to ensure product quality. The size and shape of the melt pool are continuously changing during the L-PBF process, which may lead to the generation of defects. To represent the melt pool variations more accurately, a new motion feature is extracted and a classification model is constructed to identify the melting state. Firstly, a 36-dimensional motion feature is obtained by contour unwrapping with respect to the melt pool centroid. Subsequently, a sample dataset of melt pool image including four categories of melting states is established. Finally, a Gaussian process classification (GPC) model is constructed to identify the melting state based on motion feature. To verify the performance of GPC, it is also given that the recognition results based on support vector machine (SVM) model, multilayer perceptron (MLP) and long short-term memory (LSTM) neural network. The research results show that under the advantages of automatically optimizing hyperparameters and providing probability distribution information of melting state, the GPC model can still achieve a better recognition result. The overall recognition rate reaches 87.1%, and the melting state can be better identified. A novel in-situ monitoring idea is provided for the L-PBF in this research.

Temporally continuous thermofluidic–thermomechanical modeling framework for metal additive manufacturing

Additive manufacturing (AM) is known to generate large magnitudes of residual stresses (RS) within builds due to steep and localized thermal gradients. In the current state of commercial AM technology, manufacturers generally perform heat treatments in effort to reduce the generated RS and its detrimental effects on part distortion and in-service failure. Computational models that effectively simulate the deposition process can provide valuable insights to improve RS distributions. Accordingly, it is common to employ Computational fluid dynamics (CFD) models or finite element (FE) models. While CFD can predict geometric and thermal-fluid behavior, it cannot predict the structural response (e.g., stress–strain) behavior. On the other hand, an FE model can predict mechanical behavior, but it lacks the ability to predict geometric and fluid behavior. Thus, an effectively integrated thermofluidic–thermomechanical modeling framework that exploits the benefits of both techniques while avoiding their respective limitations can offer valuable predictive capability for AM processes. In contrast to previously published efforts, the work herein describes a one-way coupled CFD-FEA framework that abandons major simplifying assumptions, such as geometric steady-state conditions, the absence of material plasticity, and the lack of detailed RS evolution/accumulation during deposition, as well as insufficient validation of results. The presented framework is demonstrated for a directed energy deposition (DED) process, and experiments are performed to validate the predicted geometry and RS profile. Both single- and double-layer stainless steel 316L builds are considered. Geometric data is acquired via 3D optical surface scans and X-ray micro-computed tomography, and residual stress is measured using neutron diffraction (ND). Comparisons between the simulations and measurements reveal that the described CFD-FEA framework is effective in capturing the coupled thermomechanical and thermofluidic behaviors of the DED process. The methodology presented is extensible to other metal AM processes, including power bed fusion and wire-feed-based AM.

Physics-Informed Machine Learning for Accurate Prediction of Temperature and Melt Pool Dimension in Metal Additive Manufacturing.

The temperature distribution and melt pool size have a great influence on the microstructure and mechanical behavior of metal additive manufacturing process. The numerical method can give relatively accurate results but is time-consuming and, therefore, unsuitable for in-process prediction. Owing to its remarkable capabilities, machine learning methods have been applied to predict melt pool size and temperature distribution. However, the success of traditional data-driven machine learning methods is highly dependent on the amount and quality of the training data, which is not always convenient to access. This article proposes a physics-informed machine learning (PIML) method, which integrates data and physics laws in the training parts, overcoming the problems of low speed and data availability. An artificial neural network constrained by the heat transfer equation and a small amount of labeled data is developed to predict the melt pool size and temperature distribution. Besides, the locally adaptive activation function is utilized to improve the prediction performance. The result shows that the developed PIML model can accurately predict the temperature and melt pool dimension under different scanning speeds with a small amount of labeled data, which shows significant potential in practical application.

Additive manufacturing of TiC-based cermets: A detailed comparison with spark plasma sintered samples

The present work is a comparative study on the TiC-430 L ferritic stainless steel (FSS) cermets manufactured via two powder metallurgical processes, namely, conventional spark plasma sintering (SPS) and metal additive manufacturing (AM) process (laser powder-bed fusion process (LPBF)/selective laser melting (SLM)). The rescanning strategy has been used to preheat and melt the powder bed with different laser parameters during the SLM process to suppress the presence of residual thermal stress leading to the fabrication of cermets without cracks. The as-fabricated SPS samples (95 %) show a relatively lower density than the SLM-built parts (~98 %). A study of their mechanical properties such as hardness, compressive strength, and fracture toughness was conducted and discussed in detail. Further, the corrosion behavior of the fabricated cermets parts was evaluated in 3.5 wt% NaCl. The SLM-prepared specimens reveal finer microstructures and better mechanical properties (compressive strength and fracture toughness) due to the presence of fine microstructure. Furthermore, the corrosion current density of TiC-430 L fss-based cermets fabricated by SLM is approximately 270 times lower than that of cermets parts fabricated by SPS, indicating excellent corrosion resistance. On the other hand, the hardness shows an opposite trend, where the SPS samples show the maximum hardness as compared to the SLM counterparts due to the presence of hard and coarse TiC particles along with some metallic carbides formed during the SPS process. The results reveal that AM processes not only can fabricate cermets with intricate shapes but can also fabricate them with improved mechanical and corrosion properties.

SLM Additive Manufacturing of Oscillating Heat Pipe

This study employed metal additive manufacturing technology to fabricate oscillating heat pipes using SUS316L as the material and conducted related printing parameter experiments and thermal performance tests. The initial experimentation involved testing the relative density and size error of the metal additive manufacturing process. Density measurement was performed using the Archimedes method, and further X-ray CT scanning was utilized to observe the internal structure and compactness. The outcomes indicate that suitable laser parameters yield favorable results in producing oscillating heat pipes, achieving good compactness and minimal dimensional error with proper parameter adjustments. Following relevant pre-processing and post-processing on the oscillating heat pipe, leakage experiments were conducted to ensure experimental accuracy. The oscillating heat pipe had dimensions of 120 mm in length and 51 mm in width, with five turns of 2 mm × 2 mm cross-sectional channels inside. Interval design was employed to address inter-channel thermal interaction commonly encountered in flat heat pipes for comparison. Methanol was selected as the working fluid to investigate the oscillating characteristics and thermal performance under different input powers (20 W, 30 W, 40 W, 60 W, 80 W). The results indicated that the inter-channel spacing can significantly decrease the lateral thermal interaction and enhance the oscillation effect during the operation of the oscillating heat pipe, resulting in improved thermal performance. The experiments demonstrated that at 20 W, the equivalent thermal conductivity of the heat pipes with and without inter-channel spacing was 2428 and 1743 (W/mK), respectively, and at 80 W, it was 2663 and 2511 (W/mK), respectively. These results indicate that reducing thermal interaction can significantly improve the oscillation effect, leading to higher equivalent thermal conductivity at low power.

Microstructure evolution and mechanical properties of multi-layer deposition of Ti-6Al-4V-5Ni alloy developed by μ-plasma-based metal additive manufacturing process

Microstructure evolution and mechanical properties of multi-layer deposition of Ti-6Al-4V-5Ni alloy developed by μ-plasma-based metal additive manufacturing process

Influence of oxygen content on melt pool dynamics in metal additive manufacturing: High-fidelity modeling with experimental validation

In the metal additive manufacturing process, the exposure to oxygen and its incorporation into the melt pool are usually deemed unfavorable, but cannot be completely eliminated. Yet, the understanding of this inevitable process remains limited. This work aims to shed light on the effect of oxygen content on melt pool dynamics through multiphysics thermal-fluid flow simulations of the laser powder bed fusion process. Our simulations reveal that oxygen sources from the powder, base plate and oxygen absorption from the atmosphere influences the melt pool dynamics. Although changes in oxygen content barely affect melt pool dimensions, they induce huge differences in the melt pool dynamics and the corresponding material composition distribution within the melt pool. Moreover, our model further clarifies and explains observed experimental phenomena. We demonstrate that the melt pool flow characteristics are responsible for the formation of oxygen-rich streaks observed in experiments regardless of inward or outward Marangoni circulation, while previous experimental studies attributed that to the outward circulation. Additionally, we show that sulfur content minimizes the effect of oxygen on Marangoni flow in iron alloys, and thus leads to the apparent consistency of surface roughness for additively manufactured iron alloys. This work is a fundamental development towards modeling for additive manufacturing under reactive atmospheres and provides unprecedented details on the effects of oxygen on melt pool dynamics. Consequently, this work can further offer practical guidance on powder reuse and adjusting manufacturing parameters for reused powders, thereby improving the sustainability of additive manufacturing.

Computationally efficient model to predict the evolution in the thermal field in the EB-PBF and LP-DED processes

Understanding the development of a number of defects found in components fabricated by the metal Additive Manufacturing (AM) processes requires an understanding of the evolution in the thermal field within the component at both the macro- and meso-scales. As a first step, in this work, the agglomeration method was used in combination with a time-averaged input of energy to simulate the macro-scale evolution in temperature. Two example processes: 1) laser-based powder-fed directed energy deposition; and 2) electron beam powder bed fusion, are used to demonstrate the modelling methodology. The approach employed focuses on ensuring the conservation of heat and is applied using ABAQUS. The two applications have been validated by comparing the predicted thermal behaviour with process-derived data. The results indicate that this method is an efficient strategy to predict the thermal field at the scale of the component being fabricated.

Powder Bed Monitoring Using Semantic Image Segmentation to Detect Failures during 3D Metal Printing.

Monitoring the metal Additive Manufacturing (AM) process is an important task within the scope of quality assurance. This article presents a method to gain insights into process quality by comparing the actual and target layers. Images of the powder bed were captured and segmented using an Xception-style neural network to predict the powder and part areas. The segmentation result of every layer is compared to the reference layer regarding the area, centroids, and normalized area difference of each part. To evaluate the method, a print job with three parts was chosen where one of them broke off and another one had thermal deformations. The calculated metrics are useful for detecting if a part is damaged or for identifying thermal distortions. The method introduced by this work can be used to monitor the metal AM process for quality assurance. Due to the limited camera resolutions and inconsistent lighting conditions, the approach has some limitations, which are discussed at the end.