Geometry-Dependent Mechanical Performance of Additively Manufactured Metal–Polymer Hybrid Joints with Lattice-Based Transition Zones

A multiscale model for efficiently simulating laser powder bed fusion process with detailed microstructure and mechanical performance results

Widespread adoption of additive manufacturing (AM) is hindered by challenges in achieving part quality using metrics such as geometry accuracy and porosity. These metrics are affected by the microstructure and mechanical properties of fabricated parts which can be controlled by the AM’s process parameters. Fine-tuning these parameters can enable control over the part quality. In this study, an optimization-based approach for selecting the AM process parameters is proposed for achieving part quality. The proposed approach integrates design of experiments, AM process simulation, surrogate modeling, and multi-objective optimization. While the proposed approach is general and applicable to any AM process, the applicability of the proposed approach is demonstrated through a laser powder bed fusion (LPBF) process. Three LPBF process parameters, namely layer thickness, laser power, and scanning speed are considered for obtaining optimized part quality considering geometric accuracy and porosity. A cylinder and a heat exchanger example are used to demonstrate the effectiveness of the proposed approach with the LPBF process. For these examples, it is shown that with the optimized process parameters, the part has about 17% better geometric accuracy when compared to the unoptimized part while satisfying a porosity requirement. The results also reveal that laser power is the most influential process parameter affecting both the geometric accuracy and porosity.

Laser powder bed fusion (L-PBF) process represents a form of metal additive manufacturing (AM) where micron-level powdered material is selectively melted and fused layer by layer to create intricate three-dimensional parts. This process involves rapid melting and solidification, leading to intense thermocapillary convection within the molten pool. The melt pool is a crucial element of the L-PBF process and refers to the localized region where the powder particles are melted and solidified to form each layer of the printed part. The shape and dimensions of the melt pool directly influence the accuracy and surface finish of the printed part. Precise control of the melt pool geometry is essential for achieving the desired dimensions and avoiding defects in the final part. Optimizing process parameters and achieving high-quality printed parts require a deep understanding of the dynamics governing the melt pool. In this regard, both experimental and simulation methods were employed to study the melt pool geometry and its variations, considering various parameter combinations and different length scales. The printed parts were also examined for defects like porosity and to analyze their surface characteristics. The study started with an initial implementation of a simplified three-dimensional model of a powder bed using ANSYS Fluent. The simulation setup was based on a custom user defined function that integrated a volumetric heat source, temperature-dependent material properties, and volume of fluid method for identifying the free surface. The simulation setup was then employed to investigate the impact of varying powder size distributions on the formation of a melt pool. The results show that the size and distribution of particles in the powder mixture play a crucial role in shaping the evolution and geometry of the molten pool. Smaller particles encourage a consistent and uninterrupted flow within the molten pool. However, the presence of voids promotes fluid convection in the downward direction, leading to a temporary increase in the depth of the molten pool. This finding highlights the importance of understanding the role of particle size and distribution in shaping the characteristics of the melt pool during the L-PBF process. The evolution of the melt pool in the L-PBF process is closely related to pore formation in the final printed parts. Porosity refers to the presence of voids or empty spaces within the printed parts during the AM processes. Several factors related to melt pool dynamics, such as insufficient energy input, balling phenomenon, insufficient overlapping, gas entrapment,

Recently, additive manufacturing (AM) became a promising technology to manufacture complex structures with acceptable mechanical properties. The laser powder-bed fusion (L-PBF) process is one of the most common AM processes that has been used for producing a wide variety of metals and composites. Invar 36 is an austenite iron-nickel alloy that has a very low coefficient of thermal expansion; therefore, it is a good candidate for the L-PBF process. This chapter covers the state-of-the-art for producing Invar 36 using the L-PBF process. The chapter aims at describing research insights of using metal AM techniques in producing Invar 36 components. Like most of nickel-based alloys, Invar 36 is weldable but hard-to-machine. However, there are some challenges while processing these alloys by laser. This chapter also covers the challenges of using the L-PBF process for producing nickel-based alloys. In addition, it reports the L-PBF conditions that could be used to produce fully dense Invar 36 components with mechanical properties comparable to the wrought Invar 36.

Additive manufacturing (AM) has advanced the manufacturing industry and has been employed in a wide range of industrial applications, including aerospace, automotive, medical, and die-casting equipment. To ensure the cost-effectiveness of the AM process, unfused powder must be recycled even if its characteristics may change after each cycle, making essential the validation of powder quality and component mechanical performances. Despite the research published to date, predicting the mechanical performance of printed parts issued from reused powder remains challenging since it is dependent on many AM process variables. Until now, no research has looked at the impact of powder recycling on the fatigue behavior of maraging steel components. This study investigates the impact of maraging steel powder reuse on powder characteristics, as well as on the tensile and fatigue properties of printed components. Our results indicate that the powder particle size distribution increased after eight powder reuses, particle morphology showed the presence of aggregates, broken particles, and shattered and deformed particles, while powder apparent density remained constant. Powder reusing had no significant impact on the surface roughness of as-built specimens. Although there was a slight decrease in mechanical properties over reuse cycles, tensile and fatigue performance remained globally stable, while the standard deviation of fatigue stress became narrower after eight cycles. Finally, fractography revealed that the fatigue fracture surfaces of components manufactured from an eight-time recycled powder have more fusion defects and carbon inclusions than the parts made from virgin powder.

Additive manufacturing (AM) metal parts offers opportunities for various industrial applications. From individual production of spare parts for unique mechanical components to prototyping of complex structures, the possibilities of production using the additive manufacturing process are manifold. One common AM technique is the Laser Powder Bed Fusion (PBF-LB/M) process, where a laser is used to selectively melt metal powder and create the parts layer wise as designed in a model. During manufacturing certain defects like pores, cracks and lack of fusion may be created in the built parts. As AM parts often have complex geometries, a postprocess non-destructive testing is difficult or even not possible. Thus, different optical monitoring techniques are applied to detect flaws during the build process with the aim to detect and repair defects right away during the manufacturing process. However, optical monitoring system require a clear view of the melt pool and quite expensive equipment. Acoustic monitoring by AE sensors attached to the built plate would be a cheaper alternative which also can be used without visual contact to the building chamber. This paper shows a first approach of an AE based process monitoring. The build plate of a PBF-LB/M machine therefore was adapted to hold four AE sensors that are then used to monitor the process. The build process itself creates AE signals caused by the melting and cooling and the mechanical application of powder. The formation of pores and cracks is expected to create additional acoustic emissions that will be distinct from the AE pattern from the printing process. This AE signals can be linked to the different layers of the printed part or in the long run to the laser position. Results from the first tests in a customized LPBF machine will be shown as well as the implementation of AE into the PBF-LB/M machine.

Additive manufacturing (AM) enables the production of customized and complex metal parts, providing greater flexibility than traditional mass production. However, maintaining consistent product quality for AM processes remains challenging due to process variability and heat emission fluctuations, which can cause defects such as overheating. Overheating occurs when non-uniform temperature distributes across the printed parts, leading to issues such as structural weaknesses. Predicting heat emissions accurately is essential to preventing such issues, but AM facilities, particularly small- and medium-sized enterprises (SMEs), generate limited and heterogeneous datasets due to unique configurations and privacy concerns, making traditional centralized predictive model training paradigms impractical. Federated learning (FL) offers a privacy-preserving solution, it alleviates data privacy and data scarcity concerns by enabling collaborative learning across decentralized datasets while keeping data local. However, its performance suffers under heterogeneous client data and varied computational resources. This creates a need to address data heterogeneity while maintaining both global model performance and individual client personalization. We propose FedScope-KD, a novel heterogeneity-aware FL framework designed to balance the scope of global and personalized learning. Client heterogeneity level is quantified prior to the FL training, to improve the model weight aggregation during training. This is achieved through component decomposition and knowledge distillation. The framework decomposes client data into process-invariant representations, which capture the fundamental nature of the laser powder bed fusion (LPBF) process, and process-variant representations, which reflect local, client-specific configuration settings. This decomposition ensures that global models benefit from collaborative learning on the homogeneous data representations, while personalized predictions remain tailored to client-specific heterogeneous data representations. A knowledge distillation (KD) mechanism further enhances the framework by efficiently transferring process-invariant knowledge from a global teacher model to local student models. We compare the heterogeneity index generation and calculate the Dynamic Time Warping (DTW) distance for both inter-client variant and invariant components. The variant components exhibit greater distances between clients, reflecting their inherent heterogeneity, while the invariant components are more closely aligned across clients. This pattern demonstrates the effectiveness of the decomposition process in separating heterogeneous and homogeneous information. The heterogeneity index is further used for weight aggregation during FL training. The framework’s performance is validated through a comparison with baseline FL methods such as FedAvg, as well as centralized and individual learning paradigms. Performance metrics include personalized performance and global accuracy with MSE. Through experiments on heat emission datasets in the LPBF process, we show that FedScope-KD successfully addresses the challenges of data heterogeneity and resource constraints, providing a robust solution of predictive modeling for SMEs.

A fully transient discrete-source 3D Additive Manufacturing (AM) process model was coupled with a 3D stochastic solidification structure model to simulate the grain structure evolution quickly and efficiently in metallic alloys processed through Electron Beam Powder Bed Fusion (EBPBF) and Laser Powder Bed Fusion (LPBF) processes. The stochastic model was adapted to rapid solidification conditions of multicomponent alloys processed via multi-layer multi-track AM processes. The capabilities of the coupled model include studying the effects of process parameters (power input, speed, beam shape) and part geometry on solidification conditions and their impact on the resulting solidification structure and on the formation of inter layer/track voids. The multi-scale model assumes that the complex combination of the crystallographic requirements, isomorphism, epitaxy, changing direction of the melt pool motion and thermal gradient direction will produce the observed texture and grain morphology. Thus, grain size, morphology, and crystallographic orientation can be assessed, and the model can assist in achieving better control of the solidification microstructures and to establish trends in the solidification behavior in AM components. The coupled model was previously validated against single-layer laser remelting IN625 experiments performed and analyzed at National Institute of Standards and Technology (NIST) using LPBF systems. In this study, the model was applied to predict the solidification structure and inter layer/track voids formation in IN718 alloys processed by LPBF processes. This 3D modeling approach can also be used to predict the solidification structure of Ti-based alloys processes by EBPBF.

The structural design and additive manufacturing (AM) of cross-flow heat exchangers (HXs) are studied. A unit-based design framework is proposed to optimize the channel configuration in order to improve heat exchange performance (HXP) and meanwhile control pressure drop (PD) between the fluid inlet and outlet. A gradient-based optimization methodology is employed to drive the iterative design process. Both shape and topology changes are observed during the channel configuration evolution. Moreover, AM printability evaluation is considered and some re-design work is proposed with respect to metal laser powder bed fusion (LPBF) process. For an original optimized structure from the unit-based design, corner rounding operation is adopted first, specifically to avoid sharp features. Then the building process of the entire cross-flow HX containing the top, bottom caps, surrounding walls and the optimized thin-walled channels is simulated, and residual deformation is predicted through the sequential layer-by-layer analysis. Based on residual deformation profile, geometrical compensation is implemented for the 3D reconstructed model to reduce geometrical inaccuracy of the printed HX. Finally, a mature design scheme for cross-flow HX can be achieved as the solution that leads to largely improved HXP (e.g., nearly 200% increase), well controlled PD and enhanced printability with respect to the LPBF AM process.

The use of gas-atomized powder as the feedstock material for the laser powder bed fusion (LPBF) process is common in the additive manufacturing (AM) community. Although gas-atomization produces powder with high sphericity, its relatively expensive production cost is a downside for application in AM processes. Water atomization of powder may overcome this limitation due to its low cost relative to the gas-atomization process. In this work, gas- and water-atomized 304L stainless steel powders were morphologically characterized through scanning electron microscopy (SEM). The water-atomized powder had a wider particle size distribution and exhibited less sphericity. Measuring powder flowability using the Revolution Powder Analyzer (RPA) indicated that the water-atomized powder had less flowability than the gas-atomized powder. Through examining the mechanical properties of LPBF fabricated parts using tensile tests, the gas-atomized powder had significantly higher yield tensile strength and elongation than the water-atomized powder; however, their ultimate tensile strengths were not significantly different.

Hybrid additive manufacturing (AM) and subtractive manufacturing (SM) processes utilize the combination of AM (e.g., LPBF and DED) and SM (e.g., milling and turning operations) to produce the final part. Due to the poor surface roughness resulting from the uneven melting of powders in AM, the subtractive process is a necessary finishing operation to improve the surface roughness of the AM part. The hybrid AM/SM technology combines the benefits of AM and SM processes to create complex geometry while introducing good surface finish and compressive stress to prevent crack initiation. However, the relationship between large process parameter space and the residual stress/distortion in the part is not well understood, which impedes the adoption of hybrid AM/SM to minimize the residual stress in the final product. To expedite the process optimization, we establish a pipeline for the sequential modeling of additive manufacturing (AM) and subtractive manufacturing (SM) processes. Key accomplishments achieved under this study include (1) development of thermal abstraction technique for the AM process to speed up the macroscale level heat transfer analysis based on the manufacturing factors including scanning vector, laser power, dwelling time, etc.; (2) development of the sequentially coupled thermal-mechanical model to predict the residual stress and distortion after AM process by passing the temperature history obtained from heat transfer analysis to the mechanical analysis at each time point; (3) validation of the thermal-mechanical model for AM using thin-wall structure from literature and cantilever beam structure from UNT’s experiments data; (4) conduction of the parametric study on the chamber temperature and part design in the AM process to demonstrate how the temperature gradient and supporting structure affect the residual stress and distortion; (5) exploration of macro and micro scale models to predict the bulk and surface residual stress after cutting; (6) applying the developed modeling framework to tailoring the hybrid AM/SM process. To support model verification and demonstration, we print cantilever beam structure with different supporting structure designs and cutting strategies to study how these factors affect the final part residual stress and distortion. The data collected in the printing and cutting process is used to examine the applicability of the developed simulation tool.

Hybrid additive manufacturing (AM) and subtractive manufacturing (SM) processes utilize the combination of AM (e.g., LPBF and DED) and SM (e.g., milling and turning operations) to produce the final part. Due to the poor surface roughness resulting from the uneven melting of powders in AM, the subtractive process is a necessary finishing operation to improve the surface roughness of the AM part. The hybrid AM/SM technology combines the benefits of AM and SM processes to create complex geometry while introducing good surface finish and compressive stress to prevent crack initiation. However, the relationship between large process parameter space and the residual stress/distortion in the part is not well understood, which impedes the adoption of hybrid AM/SM to minimize the residual stress in the final product. To expedite the process optimization, we establish a pipeline for the sequential modeling of additive manufacturing (AM) and subtractive manufacturing (SM) processes. Key accomplishments achieved under this study include (1) development of thermal abstraction technique for the AM process to speed up the macroscale level heat transfer analysis based on the manufacturing factors including scanning vector, laser power, dwelling time, etc.; (2) development of the sequentially coupled thermal-mechanical model to predict the residual stress and distortion after AM process by passing the temperature history obtained from heat transfer analysis to the mechanical analysis at each time point; (3) validation of the thermal-mechanical model for AM using thin-wall structure from literature and cantilever beam structure from UNT’s experiments data; (4) conduction of the parametric study on the chamber temperature and part design in the AM process to demonstrate how the temperature gradient and supporting structure affect the residual stress and distortion; (5) exploration of macro and micro scale models to predict the bulk and surface residual stress after cutting; (6) applying the developed modeling framework to tailoring the hybrid AM/SM process. To support model verification and demonstration, we print cantilever beam structure with different supporting structure designs and cutting strategies to study how these factors affect the final part residual stress and distortion. The data collected in the printing and cutting process is used to examine the applicability of the developed simulation tool.

The laser powder bed fusion (L-PBF) process is a powder-based additive manufacturing process that can manufacture complex metallic components. However, when the metallic components are fabricated with the L-PBF process, they frequently encounter the residual stress and distortion that occurs due to the cyclic of rapid heating and cooling. The distortion detrimentally impacts the dimensional and geometrical accuracy of final built parts in the L-PBF process. The purpose of this research was to explore and predict the distortion of Ti-6Al-4V components manufactured using the L-PBF process by using numerical modeling in Simufact Additive 2020 FP1 software. Firstly, the numerical model validation was conducted with the twin-cantilever beam part. Later, studies were carried out to examine the effect of component sizes and support-structure designs on the distortion of tibial component produced by the L-PBF process. The results of this research revealed a good agreement between the numerical model and experiment data. In addition, the platform was extended to predict the distortion in the tibial component. Large distortion arose near the interface between the tibial tray and support structure due to the different stiffness between the solid bulk and support structure. The distortion of the tibial component increased with increasing component size according to the surface area of the tibial tray, and with increasing thickness of the tibial tray. Furthermore, the support-structure design plays an important role in distortion reduction in the L-PBF process. For example, the maximum distortion of the tibial component was minimized up to 44% when a block support-structure design with a height of 2.5 mm was used instead of the lattice-based support. The present study provides useful information to help the medical sector to manufacture effective medical components and reduce the chance of part failure from cracking in the L-PBF process.

Effect of build interruption during laser powder bed fusion process on structural integrity of Ti-6Al-4V

The effect of thermal cycle on microstructure evolution and mechanical properties of Co-free maraging steel produced by wire arc additive manufacturing