Powder Bed Fusion Additive Manufacturing Research Articles

Surface analysis in additive manufacturing: A systematic literature review regarding powder bed fusion processes

Abstract The analysis of surface effects in powder bed fusion additive manufacturing is the subject of intensive research activities. The aim of this paper is to provide an overview of the current state of knowledge and to gain a comprehensive understanding of this subject area. The paper is intended to enable researchers to select specific articles for their own further research context. In addition, a bibliometric analysis validates the data base. A discussion of the findings suggests that the criticality of the surface should be considered as a quality factor in the field of additive manufacturing by powder bed fusion processes. An accurate and reliable measurement is crucial for predicting component quality. There is a clear trend from two-dimensional measurements to three-dimensional measurements. Conducting comprehensive research is essential to improve the reliability and comparability of measurement results and to promote broad acceptance and application of this technology in the industry.

Spatter detection and tracking in high-speed video observations of laser powder bed fusion

Purpose In laser powder bed fusion (L-PBF) additive manufacturing, spatter particles are ejected from the melt pool and can be detrimental to material performance and powder recycling. Quantifying spatter generation with respect to processing conditions is a step toward mitigating spatter and better understanding the phenomenon. This paper reveals process insights of spatter phenomena by automatically annotating spatter particles in high-speed video observations using machine learning. Design/methodology/approach A high-speed camera was used to observe the L-PBF process while varying laser power, laser scan speed and scan strategy on a constant geometry on an EOSM290 using Ti-6Al-4V powder. Two separate convolutional neural networks were trained to segment and track spatter particles in captured high-speed videos for spatter characterization. Findings Spatter generation and ejection angle significantly differ between keyhole and conduction mode melting. High laser powers lead to large ejections at the beginning of scan lines. Slow and fast build rates produce more spatter than moderate build rates at constant energy density. Scan strategies with more scan vectors lead to more spatter. The presence of powder significantly increases the amount of spatter generated during the process. Originality/value With the ability to automatically annotate a large volume of high-speed video data sets with high accuracy, an experimental design of observed parameter changes reveals quantitively stark changes in spatter morphology that can aid process development to mitigate spatter occurrence and impacts on material performance.

A Machine Learning-Based Model for Multiple Material Density Prediction Developed by Powder Bed Fusion Additive Manufacturing

A Machine Learning-Based Model for Multiple Material Density Prediction Developed by Powder Bed Fusion Additive Manufacturing

An automated computational framework to construct printability maps for additively manufactured metal alloys

In metal additive manufacturing (AM), processing parameters can affect the probability of macroscopic defect formation (lack-of-fusion, keyholing, balling), which can, in turn, jeopardize the final product’s integrity. A printability map classifies regions in the processing space where an alloy can be printed with or without porosity defects. However, the creation of these printability maps is resource-intensive. Previous efforts to generate printability maps have required single-track experiments on pre-alloyed powder, limiting the utilization of these printability maps for the high-throughput design of printable alloys. We address these challenges in the case of Laser Powder Bed Fusion AM (L-PBF-AM) by introducing a fully computational, predictive approach to create printability maps for arbitrary alloys. Our framework uses physics-based thermal models and a variety of defect formation criteria. We benchmark the predictive ability of the proposed framework against literature data for the following commonly printed alloys: 316 Stainless Steel, Inconel 718, Ti-6Al-4V, AF96, and Ni-5Nb. Furthermore, we deploy the framework on NiTi-based Shape Memory Alloys (SMAs) as a case study. We scrutinize the accuracy of various sets of defect criteria and use these accuracy measurements to create an uncertainty-aware probabilistic framework capable of predicting the printability maps of arbitrary alloys. This framework has the potential to guide alloy designers to potentially easy-to-print alloys, enabling the co-design of high-performing printable alloys.

A novel age-hardenable austenitic stainless steel with superb printability

Precipitation-hardened high strength alloys, such as nickel-based alloys, aluminum alloys and stainless steels, are susceptible to hot cracking during 3D printing. This issue is typically mitigated by reducing solute segregation or promoting columnar-to-equiaxed transition. Here, we demonstrate an alternative approach by increasing segregation solutes, especially Ti element, to reduce hot cracking during laser powder bed fusion (L-PBF) additive manufacturing of a new austenitic stainless steel (ASS). Enhanced segregation triggers peritectic-like reactions at cell/grain boundaries, forming multiple phases that bridge FCC dendrites. As a result, the new ASS exhibited excellent printability across a broad range of processing parameters. The as-built (AB) steel displayed a heterogeneous columnar grain microstructure with fine L21/BCC/C14 precipitates partially decorating cell structures, achieving a yield strength (σ0.2) above 690 MPa and uniform elongation (εu) beyond 17.5%. The epitaxial growth of the columnar grains was frequently interrupted by puddles of fine grains, leading to near-isotropic tensile properties. Following isochronal annealing at temperatures between 600 to 1150 °C for two hours, the AB steel underwent varying degrees of microstructure evolution, resulting in a broad range of mechanical properties (σ0.2 from 300 to 1460 MPa and εu from 59.5% to 7.6%). This high strength is attributed to the formation of the L21/σ/η multiple phases at cell and grain boundaries, in combination with coherent L12-ordered (γ') nanoparticles precipitated within cell interiors during aging. This study explored that compositional design leveraging the unique solidification behavior of the L-PBF process can produce hierarchical-structured stainless steels with excellent printability and tunable mechanical performance.

Laser powder bed fusion of high-strength crack-free Al7075 alloy with the in-situ formation of TiB2/Al3Ti-reinforced phases and nucleation agents

The existence of solidification cracks caused by columnar grains in precipitation-hardened aluminium alloys limit the applicability of Al7075 components manufactured via laser powder bed fusion (LPBF) additive manufacturing. A novel approach was developed to co-incorporate submicron-sized B and micron-grade Ti6Al4V to eliminate hot cracks and to effectively transform coarse columnar grains into fine equiaxed grains, thus improving the mechanical performance of LPBF-fabricated modified Al7075 material. The grain refinement was mainly attributable to the heterogeneous nucleation promoted by the combination of in-situ-formed L12-Al3Ti and TiB2 nano-sized phases. After an optimised T6 heat treatment, excellent comprehensive mechanical properties were achieved, with a tensile strength of 460 MPa and an elongation of 13 %. This research provides an efficient and cost-effective path for addressing crack-sensitive metallic materials used for LPBF additive manufacturing processes.

Synchronized Multi-Laser Powder Bed Fusion (M-LPBF) Additive Manufacturing: A Technique for Controlling the Microstructure of Ti–6Al–4V

One of the technological hurdles in the widespread application of additive manufacturing is the formation of undesired microstructure and defects, e.g., the formation of columnar grains in Ti-6Al-4V—the columnar microstructure results in anisotropic mechanical properties, a reduction in ductility, and a decrease in the endurance limit. Here, we present the potential implementation of a hexagonal array of synchronized lasers to alter the microstructure of Ti–6Al–4V toward the formation of preferable equiaxed grains. An anisotropic heat transfer model is employed to obtain the temporal/spatial temperature distributions and construct the solidification map for various process parameters, i.e., laser power, scanning speed, and the internal distance among lasers in the array. Approximately 55% of the volume fraction of equiaxed grains is obtained using a laser power of P = 500 W and a scanning speed of v = 100 mm/s. The volume fraction of the equiaxed grains decreases with increasing scanning velocity; it drops to 38% for v = 1000 mm/s. This reduction is attributed to the decrease in absorbed heat and thermal crosstalk among lasers, i.e., the absorbed heat is higher at low scanning speeds, promoting thermal crosstalk between melt pools and subsequently forming a large volume fraction of equiaxed grains. Additionally, a degree of overlap between lasers in the array is required for high scanning speeds (v = 1000 mm/s) to form a coherent melt pool, although this is unnecessary for low scanning speeds (v = 100 mm/s).

Evaluation of stiffness-matched, 3D-printed, NiTi mandibular graft fixation in an ovine model

BackgroundManually bent, standard-of-care, Ti–6Al–4V, mandibular graft fixation devices are associated with a significant post-operative failure rate. These failures require the patient to endure stressful and expensive re-operation. The approach recommended in this report demonstrates the optimization of graft fixation device mechanical properties via “stiffness-matching” by varying the fixation device’s location, shape, and material composition through simulation of the device’s post-operative performance. This provides information during pre-operative planning that may avoid future device failure. Optimized performance may combine translation of all loading into compression of the bone graft with the adjacent bone segments and elimination or minimization of post-healing interruption of normal stress–strain (loading) trajectories.ResultsThis study reports a sheep mandibular graft model where four animals received virtually optimized, experimental nickel–titanium (NiTi) fixation plates fabricated using laser beam powder bed fusion (LPBF) additive manufacturing (AM). The last animal, our control, received a standard-of-care, manually bent, Ti–6Al–4V (aka Ti64) fixation plate. A 17.5-mm mandibular graft healed completely in all four animals receiving the experimental device. Experimental NiTi-implanted sheep experienced mandibular bone healing and restoration. The Ti64 plate, in the control animal, fractured and dislocated shortly after being implanted.ConclusionThe use of stiffness-matched implants, by means of plate material (NiTi) and geometry (porosity) enhanced bone healing and promoted better load transfer to the healed bone when compared to the bulk Ti64 found in the fixation plate that the Control animal received. The design technique and screw orientation and depth planning improved throughout the study leading to more rapid healing. The large animal model reported here provides data useful for a follow-on clinical trial.Graphical

Online Quality Control of Powder Bed Fusion with High-Resolution Eddy Current Testing Inductive Sensor Arrays.

This paper presents the development of a novel eddy current array (ECA) system for real-time, layer-by-layer quality control in powder bed fusion (PBF) additive manufacturing. The system is integrated into the recoater of a PBF machine to provide spatially resolved electrical conductivity imaging of the manufactured part. The system features an array of 40 inductive sensors spaced at 1 mm pitch and is capable of performing a full array readout every 0.192 mm at 100 mm/s recoater speed. Array scalability was achieved through the careful selection of the electromagnetic configuration, miniaturized and seamlessly integrated sensor elements, and the use of advanced mixed signal processing techniques. Experimental validation was performed on stainless steel 316L parts, successfully detecting metallic structures and confirming system performance in both laboratory and real-time PBF environments. The prototype achieved a signal-to-noise ratio (SNR) of 26.5 dB, discriminating metal from air and thus demonstrating its potential for improving PBF part design, process optimization, and defect detection.

Predicting the fatigue life of additively manufactured AlSi10Mg alloy using a physics-informed neural network incorporating continuous damage mechanics

The material characteristics of additively manufactured AlSi10Mg alloy, including the random distribution of process-induced micro defects, microstructual anisotropy, and grain morphologies with complex diversity, poses a significant challenge in accurately predicting its fatigue life, limiting its application in the aircraft field. This work herein introduces a novel approach incorporating a physics-informed neural network (PINN), in which an artificial neural network (ANN) is embedded with a damage mechanics model (CDM) and the critical process parameters of laser powder bed fusion (L-PBF) additive manufacturing. The partial differential equations of the updated CDM model are introduced into the training procedures of the PINN to building a loss function, effectively “teaching” the PINN to learn the physical knowledge. A comparison of the fatigue life prediction results of ANN and the proposed PINN models shows that the PINN model outperforms its counterparts with a 38.71% higher prediction accuracy. The effects of L-PBF process parameters on the fatigue life of as-built AlSi10Mg is examined using both ANN and PINN, proving the better predictive performance and data-physics consistency of the PINN model. The scheme of this work can inform the efficient prediction of fatigue life of additively manufactured alloys and also reverse prediction or fast finding of optimized process parameters for additive manufacturing.

An alloy-agnostic machine learning framework for process mapping in laser powder bed fusion

PurposeThis study aims to introduce an image-based method to determine the processing window for a given alloy system using laser powder bed fusion equipment based on achieving the desired melting mode across multiple materials for powder-free specimens. The method uses a convolutional neural network trained to classify different track morphologies across different alloy systems to select appropriate printing settings. This method is intended for the development of new alloy systems, where the powder feedstock may be unavailable, or prohibitively expensive to manufacture.Design/methodology/approachA convolutional neural network is designed from scratch to identify the 4 key melting modes that are observed in laser powder bed fusion additive manufacturing across different alloy systems. To increase the prediction accuracy and generalisation accuracy across different materials, the network is trained using a novel hybrid data set that combines fully unsupervised learning with semi-supervised learning.FindingsThis study demonstrates that our convolutional network with a novel hybrid training approach can be generalised across different materials, and k-fold validation shows that the model retains good accuracy with changing training conditions. The model can predict the processing maps for the different alloys with an accuracy of up to 96% in some cases. It is also shown that powder-free single-track experiments are a useful indicator for predicting the final print quality of a component.Originality/valueThe “invariant information clustering” (IIC) approach is applied to process optimisation for additive manufacturing, and a novel hybrid data set construction approach that accounts for uncertainty in the ground truth data, enables the trained convolutional model to perform across a range of different materials and most importantly, generalise to materials outside of the training data set. Compared to the traditional cross-sectioning approach, this method considers the whole length of the single track when determining the melting mode.

A bimodal and heterogeneous laminate structure with alternately distributed copper-base and nickel-base alloys via multi-material laser powder bed fusion (MM-LPBF) additive manufacturing

A micro-laminated bimodal heterostructure with alternately distributed copper-base and nickel-base alloys was additively manufactured via the multi-material laser powder bed fusion (MM-LPBF) technique. The high melting point elements such as nickel act as the heterogeneous nucleation sites for the copper melt, promoting the generation of ultrafine grains and forming a multi-layered structure with alternating coarse and fine grains. The differentiated grain size, shape and orientation in the bimodal heterostructure lead to the diversity of deformation mechanisms in different localized areas, which helps to achieve the internal stress gradient distribution and plastic deformation harmonization, thereby overcoming the strength-ductility trade-off for the overall structure. This work provides a novel fabrication path and experimental basis for understanding bimetallic laminated heterostructures.

Best practices for machine learning strategies aimed at process parameter development in powder bed fusion additive manufacturing

AbstractThe process parameters used for building a part utilizing the powder-bed fusion (PBF) additive manufacturing (AM) system have a direct influence on the quality—and therefore performance—of the final object. These parameters are commonly chosen based on experience or, in many cases, iteratively through experimentation. Discovering the optimal set of parameters via trial and error can be time-consuming and costly, as it often requires examining numerous permutations and combinations of parameters which commonly have complex interactions. However, machine learning (ML) methods can recommend suitable processing windows using models trained on data. They achieve this by efficiently identifying the optimal parameters through analyzing and recognizing patterns in data described by a multi-dimensional parameter space. We reviewed ML-based forward and inverse models that have been proposed to unlock the process–structure–property–performance relationships in both directions and assessed them in relation to data (quality, quantity, and diversity), ML method (mismatches and neglect of history), and model evaluation. To address the common shortcomings inherent in the published works, we propose strategies that embrace best practices. We point out the need for consistency in the reporting of details relevant to ML models and advocate for the development of relevant international standards. Significantly, our recommendations can be adopted for ML applications outside of AM where an optimum combination of process parameters (or other inputs) must be found with only a limited amount of training data.

On morphology and roughness of upskin surfaces in laser powder-bed fusion additive manufacturing – Contouring strategy effects

The surface quality of inclined parts made by Laser powder-bed fusion (L-PBF) additive manufacturing is inferior to those made by conventional formative and subtractive manufacturing techniques. The inherited step edges formed during such layer-by-layer fabrications and partially melted or non-melted particles attached to the newly formed surface are the major causes of poor surface finish of L-PBF parts. This study is a part of a research framework, in which the effects of different contouring strategies and scan parameters on the surface morphology of inclined parts are investigated by both experimental and numerical methods. In particular, the effect of two different contour scan strategies, namely pre-contouring and post-contouring, on the upskin surface roughness of 30⁰ inclined parts is analyzed. The inclined specimens were fabricated with Ti6Al4V powder using an EOS M270 system and the upskin surfaces were measured by a white light interferometer. In addition, a multi-track multi-layer numerical approach using coupled discrete element method and thermo-fluid simulation was also performed to understand the inclined surface formation in L-PBF.The findings show that the pre-contouring strategy with high linear energy density (LED) (0.4 J/mm) results in average surface roughness (Sa) as low as 7.43 µm, whereas the lowest LED (0.05 J/mm) resulted in the highest Sa of 33.74 µm. Larger melt pool dimensions were obtained in the high LED pre-contouring simulations, which indicates a better material fusion resulting in a good surface finish. The numerical simulation showed that the upskin surface roughness is also affected by the raster scan, if the raster scanning LED is higher than that of pre-contour scans. For the post-contouring strategy studied, higher and lower LEDs were used in inner and outer contour scanning, respectively. It is indicated that the inner contour scanning with a high LED reduces the surface roughness because of a larger melt pool size, irrespective of outer contour scanning. The lowest Sa obtained was 8.24 µm for the highest LED inner contour (0.05 J/mm) case and the highest Sa was 19.95 µm, for the parts made with the lowest LED inner contour scan (0.075 J/mm) strategy. The coupled discrete-element and thermo-fluid-based simulations offer insights into the sloped surface formation and the results supported the experimental findings in qualitative manner.

Investigating temperature, stress, and residual stresses in laser powder bed fusion additive manufacturing of Inconel 625

Laser powder bed fusion (LPBF) is a pivotal method in metal additive manufacturing, enabling the intricate fabrication of complex components. However, the rapid thermal transitions inherent in LPBF can induce residual stresses, potentially leading to defects like distortions, cracks, and delamination. This research aims to investigate the distribution of temperature and stress during the LPBF manufacturing of Inconel 625, as well as the generation of residual stresses. For this purpose, a three-dimensional finite element (FE) model of the LPBF process was developed to explore the influence of various factors, including the number of layers, deposition region dimensions, and layer thicknesses, on temperature and stress distribution. Additionally, the study thoroughly examined the residual stress occurred on the part related to post-cooling and their variations after substrate separation. The outcomes reveal that the dimension of the deposition region significantly impacts both temperature and the size of the melt pool. The melt pool depth of 91 μm was calculated from the FE models, closely aligning with the experimentally measured value of 84.7 μm. Furthermore, the separation of the substrate has a notable effect on the distribution of residual stresses in the LPBF specimen. For instance, the residual stress at the center of the first layer decreased from 818 MPa after the cooling process to 108 MPa following substrate separation.

Solidification and crystallographic texture modeling of laser powder bed fusion Ti-6Al-4V using finite difference-monte carlo method

Laser powder bed fusion (LPBF) additive manufacturing makes near-net-shaped parts with reduced material cost and time, rising as a promising technology to fabricate Ti-6Al-4 V, a widely used titanium alloy in aerospace and medical industries. However, LPBF Ti-6Al-4 V parts produced with 67° rotation between layers, a scan strategy commonly used to reduce microstructure and property inhomogeneity, have varying grain morphologies and weak crystallographic textures that change depending on processing parameters. This study predicts LPBF Ti-6Al-4 V solidification at three energy levels using a finite difference-Monte Carlo method and validates the simulations with large-area electron backscatter diffraction (EBSD) scans. The developed model accurately shows that a 〈001〉 texture forms at low energy and a 〈111〉 texture occurs at higher energies parallel to the build direction but with a lower strength than the textures observed from EBSD. A validated and well-established method of combining spatial correlation and general spherical harmonics representation of texture is developed to calculate a difference score between simulations and experiments. The quantitative comparison enables effective fine-tuning of nucleation density (N0) input, which shows a nonlinear relationship with increasing energy level. Future improvements in texture prediction code and a more comprehensive study of N0 with different energy levels will further advance the optimization of LPBF Ti-6Al-4 V components. These developments contribute a novel understanding of crystallographic texture formation in LPBF Ti-6Al-4 V, the development of robust model validation and calibration pipeline methodologies, and provide a platform for mechanical property prediction and process parameter optimization.

Machine learning-based layer-wise detection of overheating anomaly in LPBF using photodiode data

Overheating anomaly detection is essential for the quality and reliability of parts produced by laser powder bed fusion (LPBF) additive manufacturing (AM). In this research, we focus on the detection of overheating anomalies which can lead to various defects in the part including geometric distortion, and poor surface roughness, among others, using photodiode sensor data. Photodiode sensors can collect high-frequency data from the melt pool, reflecting the process dynamics and thermal history. Hence, the proposed method offers a machine learning (ML) framework to utilize photodiode sensor data for layer-wise detection of overheating anomalies. In doing so, three sets of features are extracted from the raw photodiode data: MSMM (mean, standard deviation, median, maximum), MSQ (mean, standard deviation, quartiles), and MSD (mean, standard deviation, deciles). These three datasets are used to train several ML classifiers. Cost-sensitive learning is used to handle the class imbalance between the “anomalous” layers (affected by overheating) and “nominal” layers in the benchmark dataset. To boost detection accuracy, our proposed ML framework involves utilizing the majority voting ensemble (MVE) approach. First, the top three ML classifiers are identified from an initial pool of classifiers, based on their performance in k-fold cross-validation. Next, final predictions are generated using majority voting from the individual predictions of the top three classifiers. We performed 100 iterations to generate statistically reliable results. This proposed method is demonstrated using a case study including an open benchmark dataset of photodiode measurements from an LPBF specimen with deliberate overheating anomalies at some layers. The results from the case study demonstrate that the MSD features yield the best performance for all classifiers, and the MVE classifier (with a mean F1-score of 0.8654) surpasses the individual ML classifiers. Moreover, our machine learning methodology achieves superior results (9.66% improvement in mean F1-score) in detecting layer-wise overheating anomalies, surpassing the existing methods in the literature that use the same benchmark dataset. Finally, based on our results, we provide useful insights and recommendations for future research on applying machine learning techniques for defect detection in AM.

Achieving uniform distribution of nanoparticles in oxide dispersion strengthened (ODS) SS316L through laser powder bed fusion (L-PBF)

Oxide dispersion strengthened (ODS) SS316L is a promising candidate for the nuclear industry for its enhanced irradiation resistance and high temperature strength. Additionally, additive manufacturing enables the design flexibility of components. Achieving a uniform distribution of oxides in ODS SS316L is one of the remaining challenges in additive manufacturing. In this paper, we investigated the effects of printing parameters on the microstructure and mechanical properties of ODS SS316L (SS316L + 0.5 wt% Y2O3) through laser powder bed fusion (L-PBF) additive manufacturing. The results showed that plate-like Y-Si-rich oxides (∼50 μm) were observed along the molten pool boundary in the ODS SS316L printed with nominal parameters (48.5 J/mm3) for pure SS 316L, resulting from inadequate heat input in molten pool due to the reduced laser absorption rate of powder feedstock. Through higher volumetric energy density (76.2 J/mm3) and remelting, a bimodal distribution of oxides, including nanoparticles and fine spherical oxide (∼2.5 μm), was achieved. Consequently, this contributed to increased ultimate tensile strength (UTS) and strain of ODS SS316L from 685.2.6 ± 31.4 MPa and 27.8 ± 6.2 % to 706.6 ± 36.2 MPa and 33.0 ± 6.1 %, respectively. The exploration of parameters optimization provides valuable insights into the additive manufacturing of ODS alloys with uniformly distributed nanoparticles.

Integrated modeling to control vaporization-induced composition change during additive manufacturing of nickel-based superalloys

A critical issue in laser powder bed fusion (LPBF) additive manufacturing is the selective vaporization of alloying elements resulting in poor mechanical properties and corrosion resistance of parts. The process also alters the part’s chemical composition compared to the feedstock. Here we present a novel multi-physics modeling framework, integrating heat and fluid flow simulations, thermodynamic calculations, and evaporation modeling to estimate and control the composition change during LPBF of nickel-based superalloys. Experimental validation confirms the accuracy of our model. Moreover, we quantify the relative vulnerabilities of different nickel-based superalloys to composition change quantitatively and we examine the effect of remelting due to the layer-by-layer deposition during the LPBF process. Spatial variations in evaporative flux and compositions for each element were determined, providing valuable insights into the LPBF process and product attributes. The results of this study can be used to optimize the LPBF process parameters such as laser power, scanning speed, and powder layer thickness to ensure the production of high-quality components with desired chemical compositions.

An improved thermomechanical model for the prediction of stress and strain evolution in proximity to the melt pool in powder bed fusion additive manufacturing

An improved thermomechanical model for the prediction of stress and strain evolution in proximity to the melt pool in powder bed fusion additive manufacturing