Laser Powder Bed Fusion Parts Research Articles

A generalized machine learning framework for data-driven prediction of relative density in laser powder bed fusion parts

A generalized machine learning framework for data-driven prediction of relative density in laser powder bed fusion parts

Resource-efficient add-on structures for the mechanical postprocessing of laser powder bed fusion parts using five-axis machining

Resource-efficient add-on structures for the mechanical postprocessing of laser powder bed fusion parts using five-axis machining

Prediction of residual stresses in additively manufactured parts using lumped capacitance and classical lamination theory

Several industries are interested in Laser Powder Bed Fusion (L-PBF) Additively Manufactured (AM) metal parts because their designs can be made arbitrarily complex while retaining bulk-type material properties. However, the residual stresses (RS) and distortions caused by the heat gradients inherent to L-PBF processes are detrimental to the structural integrity of the parts and must be taken into consideration during the part design cycle. Predicting the state of stresses in as-built 3D printed parts is a difficult problem that is typically approached with the use of transient thermomechanical Finite Element Models (FEMs). However, the nonlinearities associated with AM processes are difficult to capture in these FEMs without increasing the computational cost of the simulation, limiting their ability to be incorporated into practical design cycles. This work presents a novel analytical framework that combines lumped capacitance nonlinear heat transfer with time dependent classical lamination theory to efficiently and accurately predict RS in as-built L-PBF parts without the need of FEMs. The simulation was compared to Neutron Diffraction (ND) residual strain measurements taken at Oak Ridge National Laboratories (ORNL) as well as Synchrotron X-ray Diffraction (XRD) strain data published by the National Institute of Standards and Technology (NIST). The simulation predictions and the experimental data showed excellent agreement for the in-plane strain directions, and general agreement for the out of plane strain component, highlighting an area where further development can be implemented.

Data-driven roughness estimation of additively manufactured samples using build angles

Achieving control of Laser Powder Bed Fusion (L-PBF) over the quality of the print is the main motivation for finding an optimum set of parameters in the process. Surface roughness is one of the characteristics of the print that impacts the performance of the desired functionality. This research focus is to relate the build angle with the surface roughness on the L-PBF printed specimens and utilize machine learning methods for roughness estimation of geometric features with varying build angles. The EOS M290 L-PBF printer was used to print Inconel-718 coupons using standard process parameters while varying build angles from 20 to 90 degrees at fixed 5-degree intervals. The specimens’ surface was analyzed using metrology tools and the data obtained was used for training the machine learning models. Machine learning methods are used to create regression models for estimating the roughness of the specimens using the build angle and location of the sample on the substrate. The findings of this study provide build angle-based predictive estimation of surface roughness of printed L-PBF parts. The machine learning model will help to make reliable decisions on choosing the build angle of a complex part based on the desired surface roughness of its geometric features.

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.

Mechanical, tribological, and corrosion behavior of laser powder-bed fusion 316L stainless steel parts: Effect of build orientation

In this study, 316L stainless steel Laser Powder Bed Fusion (LPBF) samples were manufactured under three different building orientations (0°, 45°, and 90°). Mechanical, microstructural, tribological and corrosion resistance properties were analyzed for all samples. The results demonstrate that the build orientation significantly influences the microstructure, resulting in variations in grain size, texture and defect distribution. Specifically, 0° (Horizontal) samples exhibited excellent mechanical properties, including ultimate tensile strength of 784 MPa and a hardness of 292 HV, while the vertical (90°) samples showed enhanced wear resistance, characterized by reduction in the coefficient of friction. Corrosion resistance was found to be highest in the 0° samples, with a corrosion current density of 0.650 μA/cm2, compared to 1.580 μA/cm2 in the 90° samples. The results from this study show the non-linear effects of build orientation for certain properties and indicates that individual studies are not sufficient to predict the performance of LPBF parts. Therefore, combined studies are required for orientation-based optimization of the mechanical, tribological and corrosion properties of LPBF parts. This study offers valuable insights into the relationship between build orientation and material properties, providing a pathway to tailor the properties of LPBF parts for specific applications.

Nondestructive fatigue life prediction for additively manufactured metal parts through a multimodal transfer learning framework

Understanding the fatigue behavior and accurately predicting the fatigue life of laser powder bed fusion (L-PBF) parts remain a pressing challenge due to complex failure mechanisms, time-consuming tests, and limited fatigue data. This study proposes a physics-informed data-driven framework, a multimodal transfer learning (MMTL) framework, to understand process-defect-fatigue relationships in L-PBF by integrating various modalities of fatigue performance, including process parameters, XCT-inspected defects, and fatigue test conditions. It aims to leverage a pre-trained model with abundant process and defect data in the source task to predict fatigue life nondestructively with limited fatigue test data in the target task. MMTL employs a hierarchical graph convolutional network (HGCN) to classify defects in the source task by representing process parameters and defect features in graphs, thereby enhancing its interpretability. The feature embedding learned from HGCN is then transferred to fatigue life modeling in neural network layers, enabling fatigue life prediction for L-PBF parts with limited data. MMTL validation through a numerical simulation and real-case study demonstrates its effectiveness, achieving an F1-score of 0.9593 in defect classification and a mean absolute percentage log error of 0.0425 in fatigue life prediction. MMTL can be extended to other applications with multiple modalities and limited data.

Investigating the process parameter window for laser powder bed fusion of copper chrome zirconium

ABSTRACT Copper-based alloy UNS C18150 has become an attractive material for laser powder bed fusion (LPBF) processing. However, the complexity of LPBF mandates that unique process parameters be devised for each alloy as these are highly sensitive to material chemistry. This work aims to clarify the general boundaries of the process window for the most influential parameters alongside an exploration into the effects of scan strategy on the density. A series of statistical design of experiments were utilised to model density as a response and predict process parameters that would result in minimised residual porosity. Using optimised parameters, specimen with up to 99.1% of theoretical density were produced, and a window of process parameters was established that yielded similarly dense products. Post-build aging of the LPBF parts was also explored. Here, direct aging of the as-built product at 390°C for 100 h resulted in a peak hardness of 83.3 ± 0.6 HRB.

Digital twins for rapid in-situ qualification of part quality in laser powder bed fusion additive manufacturing

This work concerns the laser powder bed fusion (LPBF) additive manufacturing process. Currently, LPBF parts are inspected post-process using such techniques as X-ray computed tomography, optical and scanning electron microscopy, among others. This empirical build-and-test approach for qualification of part quality is prohibitively expensive and cumbersome. To enable rapid and accurate in-situ qualification of LPBF part quality, in this work, we developed a physics and data-integrated digital twin approach. To demonstrate the approach, Inconel 718 parts of various shapes were manufactured under differing LPBF processing conditions. The process was continuously monitored using in-situ thermal and optical tomography imaging cameras. The part-scale thermal history was predicted using an experimentally validated computational thermal simulation. The simulation-derived thermal history and sensor signatures were used as inputs to a k-nearest neighbor machine learning model. The machine learning model was trained with ground truth porosity and microstructure data obtained from post-process characterization. The approach predicted the onset of porosity, meltpool depth, grain size, and microhardness with an accuracy exceeding 90 % (R2). This work thus takes a critical step towards realizing an in-situ Born Qualified part quality assessment paradigm in LPBF.

Research on microstructure and properties enhancement of TiN/GH5188 Co-superalloy composite fabricated by laser powder bed fusion

Improving mechanical properties effectively in Laser Powder Bed Fusion (LPBF) is a substantial challenge. Utilizing metal matrix composites with customizable TiN nanoparticle proportions presents an efficient strategy for fabricating high-strength LPBF parts. This study employed LPBF to fabricate TiN/GH5188 Co-based superalloy composites with alternative TiN nanoparticle contents (0, 0.5, 1, and 1.5 wt%). The effect of nano-TiN particles on the microstructure, texture evolution, and mechanical properties of TiN/GH5188 composites was investigated. Notably, the presence of nano-TiN acts as a reinforcing particle, facilitating the precipitation of the BCC strengthening phase, resulting in substantial strength enhancement. As the TiN content increases, the yield strength and microhardness consistently rise. At a 1.5 wt% TiN addition, a significant enhancement is observed, with yield strength rising by 22.0 % and microhardness increasing by 16.9 %. Although the aggregation of TiN precipitates into larger particles reduces ductility, the appropriate addition of TiN can effectively mitigate the microstructural anisotropy and induce a transition in the texture toward a non-preferred orientation, while maintaining high strength and ductility. A TiN/GH5188 composite containing 1 wt% TiN exhibits a yield strength and tensile strength of 822.4 MPa and 1131.8 MPa, respectively, accompanied by a microhardness of 334.3 HV. The elongation remains at 29.3 %, while exhibiting a non-preferred orientation (111) texture. Compared to GH5188, the yield strength, tensile strength and microhardness are increased by 17.6 %, 7.2 % and 12.1 %, respectively. This study demonstrates the significance of LPBF in producing high-performance TiN/GH5188 Co-superalloy composites and elucidates the potential mechanisms of TiN in improving microstructure and enhancing properties.

Fatigue life prediction of rough Hastelloy X specimens fabricated using laser powder bed fusion

Additive manufacturing, especially laser powder bed fusion (L-PBF), is extensively used for fabricating metal parts with intricate geometries. However, parts produced via L-PBF suffer from varied surface roughness, which affects the fatigue properties. Accurate prediction of fatigue properties as a function of surface roughness is a critical requirement for qualifying L-PBF parts. In this work, an analytical methodology was put forth to predict the fatigue life of L-PBF components having heterogeneous surface roughness. Thirty-six Hastelloy X specimens were printed using L-PBF followed by industry-standard heat treatment procedures. Half of these specimens had as-printed gauge sections and the other half were printed as cylinders from which fatigue specimens were extracted via machining. Specimens were printed in a vertical orientation and an orientation of 30° from the vertical axis. The surface roughness of the specimens was measured using computed tomography and parameters such as the maximum valley depth were used to build an extreme value distribution. Fatigue testing was conducted at an isothermal condition of 500 °F. It was observed that the rough specimens failed much earlier than the machined specimens due to the deep valleys present on the surfaces of the former ones. The valleys behaved as notches leading to high strain localization. Based on this observation, an analytical functional relationship was formulated that treated surface valleys as notches and correlated the strain localization around these notches with fatigue life, using the Coffin-Manson-Basquin and Ramberg-Osgood equations. The functional relationship was generated with the average of the extreme value distribution. The mean life curve from the functional relationship showed a maximum difference of 2 % from the experimental mean fatigue life observations for vertically built rough specimens and 10 % for 30⁰-built rough specimens. In conclusion, the proposed analytical model successfully predicted the fatigue life of L-PBF specimens at an elevated temperature undergoing different strain loadings.

Spatter transport in a laser powder-bed fusion build chamber

The adverse effects of spatter particles are well known in laser powder-bed fusion (LPBF) additive manufacturing. To prevent the deposition of spatter particles, an inert gas flow is commonly used to transport these spatters away from the build plate. However, the inert gas flow does not remove all spatters due to varying spatter sizes and ejection angles. Therefore, it is essential to understand and predict spatter trajectories to achieve superior LPBF parts. The present study focuses on numerical modelling of spatter trajectories in Renishaw AM250 using an Eulerian-Lagrangian discrete phase model. The argon velocity profile and spatter trajectories with and against the flow are computed for various materials, sizes and ejection angles. The simulation results are validated with experimental results and show a presence of uneven flow due to inlet geometry along with varying flow profiles across the build height due to inlet location. Spatter analysis shows three methods which result in spatter deposition. Spatter particles either fall directly on the build plate, are transported by the airflow or are re-directed in the recirculation zone. The findings presented in this work indicate the importance of build chamber design along with material-based parameter optimization that results in maximum spatter removal.

Interpenetrating microstructure in laser powder-bed fusion parts using selective rescanning

In-situ microstructural control is desirable in additively manufactured metal parts due to limited post-processing options for net-shaped components. Here, we introduce a novel selective rescanning approach to control the local solidification conditions and the microstructure in metal parts produced by laser powder-bed fusion (LPBF). We show that the melt pool dimensions, grain size, and sub-grain cell structure can be selectively varied in three dimensions to engineer the mechanical response of LPBF parts. The lattice-based rescanning strategy enables the formation of an interpenetrating microstructure comprised of fine and coarse grains. The localized heating and cooling-induced thermal stresses increase the hardness and tensile strength of rescanned specimens. The study shows the potential of selective rescanning strategy as a promising avenue for achieving precise control of microstructure and properties in as-printed LPBF parts without subsequent processing.

Multi-objective optimization of parts construction direction by scaling-enumeration method in L-PBF process

PurposePart building orientation (PBO) is an important factor affecting the quality of laser powder bed fusion (L-PBF), which can affect the surface quality and manufacturing cost. The purpose of this paper is to propose a PBO optimization method to optimize the surface roughness and molding time of parts at the same time on the premise of small calculation scale and arbitrary resolution.Design/methodology/approachEfficient and accurate evaluation is an important index of PBO optimization method. In this paper, a PBO optimization method based on scaling enumeration method is proposed, and the surface roughness and molding time of L-PBF parts are modeled as the objective evaluation function of PBO optimization process. To realize multi-objective optimization, an expert system is established, and the fuzzy multiple-attribute group decision-making theory is used to provide weights for each objective evaluation function.FindingsResearch shows that the scaling-enumeration method can optimize the surface roughness and molding time at the same time and get the best PBO. Compared with the traditional method, the surface roughness and molding time are reduced by 1.1% and 0.58%, respectively, and the operation scale of the scaling-enumeration method is reduced by 99% compared with the traditional method. PBO with arbitrary angular resolution can be achieved.Originality/valueThis paper presents a new method to optimize the forming direction of L-PBF parts. This method has small operation scale and accurate results, so it is meaningful for industrial application.

Correlative spatter and vapour depression dynamics during laser powder bed fusion of an Al-Fe-Zr alloy

Spatter during laser powder bed fusion (LPBF) can induce surface defects, impacting the fatigue performance of the fabricated components. Here, we reveal and explain the links between vapour depression shape and spatter dynamics during LPBF of an Al-Fe-Zr aluminium alloy using high-speed synchrotron x-ray imaging. We quantify the number, trajectory angle, velocity, and kinetic energy of the spatter as a function of vapour depression zone/keyhole morphology under industry-relevant processing conditions. The depression zone/keyhole morphology was found to influence the spatter ejection angle in keyhole versus conduction melting modes: (i) the vapour-pressure driven plume in conduction mode with a quasi-semi-circular depression zone leads to backward spatter whereas; and (ii) the keyhole rear wall redirects the gas/vapour flow to cause vertical spatter ejection and rear rim droplet spatter. Increasing the opening of the keyhole or vapour depression zone can reduce entrainment of solid spatter. We discover a spatter-induced cavity mechanism in which small spatter particles are accelerated towards the powder bed after laser-spatter interaction, inducing powder denudation and cavities on the printed surface. By quantifying these laser-spatter interactions, we suggest a printing strategy for minimising defects and improving the surface quality of LPBF parts.

Influence of Pre- and Post-Contouring Strategies to Downskin Sloped Surfaces in Laser Powder-Bed Fusion (L-PBF) Additive Manufacturing.

Among various metal additive manufacturing (AM) technologies, L-PBF is known for fabricating intricate components. However, due to step edges and powder particle attachments, attaining a good surface finish is challenging, especially on downskin surfaces. Contour scanning has potential to improve surface quality because such scanning may dominate the surface formation of sloped features. This study evaluates the effects of pre- and post-contouring strategies on the sloped downskin surfaces fabricated using a commercial L-PBF system with Ti6Al4V powder. L-PBF parts printed at inclination angles 30°, 45° and 60° were investigated. A double-contouring approach with varying processing conditions was employed and surface characteristics were analyzed using data acquired by white light interferometry. The average surface roughness, Sa, surface skewness, Ssk, and percentage area of powder particles attached onto surfaces were statistically evaluated. The lowest Sa obtained for pre- and post-contoured samples is 14.08 µm and 18.88 µm, respectively. For both strategies, the combination of a low laser power and a high scan speed on the interface of downskin surface and underneath powder results in smoother surfaces. However, while comparing both strategies, pre-contouring gives better surface finish for samples built at similar processing conditions, with a difference of nearly 5 µm in Sa.

Effect of heat treatment on microstructure and mechanical characteristics of laser powder bed fusion (LPBF) produced CuCrZr alloy

In this work, the CuCrZr alloy was fabricated by employing the laser powder bed fusion (LPBF) process. For elevating the mechanical characteristics offered by the LPBF parts, heat treatment was performed and three cooling pathways such as water quenching, air cooling and furnace cooling were adapted. The obtained results showed that the water quenching leads to the formation of more craters in its microstructure compared to the air cooled and furnace cooled samples. The nanoindentation analysis revealed that the furnace cooled sample had higher hardness (1.56 GPa) which is attributed to the precipitation of Cu-Zr intermetallic precipitates. Moreover, this sample tends to have a low wear rate and higher tensile characteristics due to the formation of intermetallic precipitates.

Effects of process parameters and geometry on dimensional accuracy and surface quality of thin strut heart valve frames manufactured by laser powder bed fusion

Laser powder bed fusion (LPBF) is one of the most popular metal additive manufacturing technologies, which has found its applications in high-value sectors such as aerospace and biomedical devices. Some recent studies on the LPBF of stents have demonstrated its feasibility in the fabrication in thin strut structures including heart valve frames, as used in transcatheter aortic valve implantation (TAVI) for the treatment of severe aortic stenosis. The state of the art method of laser cutting TAVI frame limits the scope for novel concepts which are made possible by additive manufacturing. However, the surface quality and dimensional accuracy of LPBF parts are lower than that produced by laser cutting. To start the development of new TAVI concepts, the feasibility of manufacturing thin frames by LPBF has been investigated based on the SAPIEN 3 frame by Edwards Lifesciences. In this study, simplified frames with strut size from 0.3 to 0.5 mm have been successfully manufactured. The effects of strut size, strut angle, laser power and scan speed on the dimensional accuracy and surface quality were systemically studied. In addition, a representative SAPIEN 3 frame was manufactured and assessed with high-resolution X-ray CT scans. Good overall dimensional accuracy and low porosity were obtained for the SAPIEN 3 frame. However, inclined struts were found to have relatively low dimensional accuracy and poor surface quality.

On the role of flat-top beam in pore elimination and fatigue performance of Ti-6Al-4V fabricated by laser powder bed fusion

It's unavoidable to produce defects in the repeated layer-by-layer manufacturing process of laser powder bed fusion (LPBF). This limits the application of LPBF for safety–critical parts. The formation of defects is closely related to the interaction between the laser beam and material in the molten pool. In this paper, beam shaping was used to improve the building quality of LPBF. Process parameter optimization was performed to obtain samples with the fewest defects. Heat treatment was also employed to further improve the mechanical properties. The results showed that the obtained flat-top beam significantly reduced the propensity for keyhole pore formation and enlarged the process windows. In-situ heat treatment induced by high heat input led to the decomposition of martensite and improved the fatigue crack growth (FCG) threshold (∼3.7 MPa m0.5) of the as-built samples. After heat treatment (annealed and solution/aged), the FCG threshold further increased to 5.5/6.3 MPa m0.5, respectively. Reduced defects and high FCG resistance resulted in a fatigue limit above 600 MPa (N = 1e7, R = 0.1), which is comparable to wrought and aged Ti-6Al-4V parts. This study demonstrates the feasibility of improving molten pool stability and reducing defects through beam shaping, providing a new approach to manufacturing high-quality LPBF parts.

Incorporating surface roughness into numerical modeling for predicting fatigue properties of L-PBF AlSi10Mg specimens

Laser-powder bed fusion (L-PBF) is a popular additive manufacturing method for fabricating metal parts with complex geometries. However, one of the primary setbacks lies in the surface quality of the as-built components. Poor surface quality influences the dynamic fatigue properties of L-PBF parts. Experimental investigation of fatigue properties is time-intensive and expensive. Hence, computational modeling can be implemented to critically evaluate the impact of surface roughness on fatigue. In this work, a numerical modeling framework is developed that considers the influence of part-scale surface roughness on fatigue properties. To calibrate and validate the numerical modeling framework, mechanical investigations of miniature AlSi10Mg specimens are conducted in a custom-built tensile-fatigue testing apparatus. First, the tensile properties are evaluated to find out the reliability of the apparatus. Thereafter, the fatigue investigations of two sets of specimens are conducted: five specimens (FS1-FS5) for calibrating the numerical model and three specimens (VFS1-VFS3) for model validation. The numerical modeling framework involves the 3D reconstructed specimen geometries from computed tomography (CT) imaging of the VFS1-VFS3 specimens. The resultant 3D geometries are discretized using Ansys finite element software and analyzed to simulate the fatigue behavior. The numerical results of fatigue life and failure locations of the VFS1-VFS3 specimens are compared with the experimental observations. The comparison shows that the numerical results are within 5 % of the experimental observations. Additionally, the location of maximum strain localization in numerical modeling matches the crack initiation location. In conclusion, the numerical modeling can predict low cycle fatigue life and failure location of L-PBF AM specimens.