Commercial Laser Powder Bed Fusion Research Articles

Intelligent Additive Manufacturing Architecture for Enhancing Uniformity of Surface Roughness and Mechanical Properties of Laser Powder Bed Fusion Components

In the Laser Powder Bed Fusion (L-PBF) process, 3D components with complex geometries are fabricated in a layer-by-layer fashion by using a controlled laser beam to selectively melt particular regions of the metal powder bed. However, due to the stochastic nature of the L-PBF process, the top surface roughness of each solidified layer tends to be different even when the optimal processing conditions for the different positions on the build plate are employed. As a result, the mechanical properties of the built components frequently vary from one component to the next. Accordingly, this study proposes an Intelligent Additive Manufacturing Architecture (IAMA) for controlling the surface roughness of each build layer through an appropriate adjustment of the laser re-melting parameters. The IAMA architecture comprises five modules, namely In-Situ Metrology (ISM), Ex-Situ Metrology (ESM), Automatic Virtual Metrology (AVM), Additive Manufacturing Simulation (AMS) and Intelligent Compensator (IC). The feasibility of the proposed architecture is demonstrated by comparing the top surface roughness of cubic and mechanical strengths of tensile test samples built using the proposed method with those built using a traditional L-PBF approach without surface roughness control. It is found that the samples fabricated using the IAMA approach have an average top surface roughness of 1.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu$</tex-math> </inline-formula> m and a standard deviation is 0.7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu$</tex-math> </inline-formula> m. By contrast, the samples produced using the traditional L-PBF approach have an average surface roughness of 13.45 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu$</tex-math> </inline-formula> m and a standard deviation of 2.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu$</tex-math> </inline-formula> m. In addition, the specimens produced with the assistance of IAMA architecture have an average tensile strength of 1013 MPa with a standard deviation of 69.5 MPa, while those printed without surface roughness control have an average tensile strength of 903 MPa with a standard deviation of 101.4 MPa <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —As L-PBF produce part in a layer-by-layer manner, therefore, the roughness on the top surface of previous layer have a strong influence on the printing quality of current layer. The variations of surface roughness will lead to the fluctuation of mechanical properties of the fabricated components. Additionally, to the best of author’s knowledge, current commercial L-PBF machines can not actively control the surface roughness of samples during the process. The proposed IAMA in this work can predict and control the roughness on the top surface of each layer during L-PBF process. Therefore, the constructed architecture has strong potential for integrating into the commercial L-PBF machine for controlling the surface roughness on the top of the parts during the manufacturing process. As a result, the quality of the fabricated components is expected to be in consistent. Accordingly, L-PBF machine equipped with IAMA will have a strong potential in applying for mass production of the components in aerospace and automobile industries.

Additive manufacturing process parameter determination for a new Fe-C-Cu alloy

As a potential replacement for stainless steel alloys commonly used to print parts with laser powder bed fusion, a new Cu precipitation strengthened ferrous alloy, with composition Fe-0.2C-6Cu (wt%), was recently developed. This material is Co- and Ni-free, printable, has mechanical properties comparable to that of high strength stainless steels (approximately 1300 MPa UTS), and is cost-advantaged relative to existing low-alloy steels for additive manufacturing of parts with complex shapes. To gain traction for broader application, optimal laser powder bed fusion (LPBF) processing conditions to produce nearly defect-free parts without compromising mechanical strength are needed. Here, an optimal processing window based upon laser speeds that achieve minimal porosity was quantified via comparisons of measured melt pool penetration, scan speeds, printed material density and laser beam energy density on a commercial LPBF unit operating at 350 W, 80 μm hatch spacing, and 50 μm layer thickness based upon optimal parameters for 17-4PH. The window is 300 to 500 mm/s to achieve >99.8 % dense Fe-0.2C-6Cu (wt%) specimens. Other defects such as burning, spatter, and lack of fusion were also avoided in this window. The window was validated with in-situ synchrotron X-ray imaging that enabled visualization of the vapor cavity, melting, and solidification during a single laser track scan on a miniature powder bed sample. In addition, in-situ infrared imaging provided temperature fields, cooling rate and solidification range, and confirmed minimal printing defects and spatter within the processing window.

Convolutional neural network assisted infrared imaging technology: An enhanced online processing state monitoring method for laser powder bed fusion

Laser powder bed fusion (LPBF) manufacturing technology is regarded as one of the most promising additive manufacturing technologies. However, the quality and properties of parts manufactured by LPBF are usually affected by different processing states in the LPBF process, which seriously limits the application of this technology. In this present study, an enhanced online processing state monitoring method for LPBF is proposed, which combines convolutional neural network (CNN) and infrared imaging technology. The LPBF process of 316L stainless steel samples with different parameters was recorded by an infrared thermal imager integrated into a commercial LPBF system. An improved YOLOV5 target recognition method for macro defect monitoring in LPBF manufacturing process is proposed. This method has the ability to enhance the small target detection capability by adding a transformer encoder structure and small target monitoring framework in the global scope. Double FPN structure is used to shape the neck network to obtain more effective multi-scale fusion. The CBAM structure block was inserted into the neck network to improve the fuzzy feature recognition capability. The experimental results demonstrated that compared with the original algorithm, the Mean Average Precision (mAP) of the improved model increased from 0.351 to 0.856, indicating that the improved model has a strong ability to recognize the macro processing state in LPBF process.

Numerical and experimental investigation of the geometry dependent layer-wise evolution of temperature during laser powder bed fusion of Ti–6Al–4V

Laser powder bed fusion (L-PBF) is currently the additive manufacturing process with the widest industrial use for metal parts. Yet some hurdles persist on the way to a widespread industrial serial production, with reproducibility of the process and the resulting part properties being a major concern. As the geometry changes, so do the local boundary conditions for heat dissipation. Consequently, the use of global, geometry-independent processing parameters, which are today’s state of the art, may result in varying part properties or even defects. This paper presents a numerical simulation as a method to predict the geometry-dependent temperature evolution during the build. For demonstration, an overhang structure with varying angles towards the build platform was manufactured using Ti–6Al–4V. A calibrated infrared camera was integrated into a commercial L-PBF system to measure the temperature evolution over time for a total build height of 10 mm, and the results are used for validation of the simulation. It is shown that the simulation is capable of predicting the temperature between layers. The deviations between simulation and measurement remain in single digit range for smaller overhang structures (90°, 60° and 45°). For large overhang structures (30°), the simulation tends to over-predict the temperatures up to 15 °C. Experiments with varying process parameters showed the feasibility of energy reduction as compensation of the heat accumulation produced by overhang structures.

Impacts of microsecond control in laser powder bed fusion processing

Lasers used in standard metal Laser Powder Bed Fusion (LPBF) machines are focused at the build plane with typical beam diameters less than 100 µm. This results in millions of individual laser beam scanning vectors when fabricating complex metal parts, and in most build scenarios, the laser turns on and off at the beginning and end of each vector, respectively. The standard configuration for LPBF machines includes a laser beam steering system with two mirrors attached to galvanometers, called the scanner. Most commercial lasers have response times for beam power control below 10 µs yet acceleration/deceleration times of commercial scanners are an order of magnitude slower (due to the dynamics of the mechanical system). A common strategy for allowing the scanner to “catch up” with the commanded location and speed is to employ “skywriting” with additional built-in time delays to allow for the scanner to maintain constant velocity while the laser is on. These delays along with skywriting are intended to increase resolution and reduce defects, although the actions and times are typically not accessible by the user. In fact, the millions of laser on/off events result in additional sources for defect generation to include lack of fusion voids, keyhole voids, surface roughness, and spatter. Ultimately, this results in differences in part performance within builds (build location) and between LPBF platforms. Recent work has identified methods to eliminate some forms of laser on/off defects by controlling the laser power ramp rate. To the authors knowledge, laser power ramping is not currently implemented on any commercial LPBF machine, and the use of laser power ramping has yet to be evaluated on realistic, complex parts. As a result, the current work implemented laser power ramping on a commercial LPBF machine using a complicated geometry with 1,309,338 laser on/off events (vectors) to analyze the holistic impact of fabricating real parts on defects, mechanical properties, and spatter. Ramping the laser power over 300µs eliminated the formation of back spatter, and a demonstration build of a complex qualification test artifact using IN718 reduced total spatter by nearly 5%. However, this laser power ramp change over 300µs (i.e., linearly varying laser power from 0 W to the desired power set point over 300µs) increased lack of fusion defects near part edges that reduced the tensile ductility from 17% to 12% without affecting tensile strength. The research highlighted the financial challenges imposed when scrutinizing microsecond level interactions, and as a result, a low-cost method to identify minimum ramp rates to prevent back spatter using a smart phone and an optical microscope was demonstrated. Microsecond control over the laser must be accompanied by microsecond control of the scanner and the impacts must be quantified holistically over a variety of response variables to broadly apply the developments to industry applications.1

Registration and fusion of large-scale melt pool temperature and morphology monitoring data demonstrated for surface topography prediction in LPBF

In-situ monitoring technologies for laser powder bed fusion (LPBF) additive manufacturing often face one key challenge, extracting the ultrafast melt pool (MP) signatures for understanding the localized part properties. Further, the spatial information of each monitored MP signature is essential for correlating the MP – part property. This spatial information is often unavailable especially from commercial LPBF printers. Many MP monitoring methods have been reported and utilized. However, very few of these have the MP’s spatial information. To overcome this challenge, in this work we report a method for spatially registering the key MP signatures (MP intensity, temperature, and area) to the monitored print parts. The MP signatures are obtained from our coaxial high-speed single-camera based two-wavelength imaging pyrometry (STWIP) system and the MP spatial information is obtained from an off-axis camera system. A machine learning aided image analysis method is employed to retrieve the spatial distribution of MPs within the corresponding part’s coordinates system. Then, the MP signature maps (MPSMs) are reconstructed by mapping the STWIP measured MP signatures to the registered MP coordinates. Further, a long short-term memory (LSTM) neural network is developed for estimating the layer surface topography from the registered MPSMs. The obtained results indicate that the layer surface topography can be more accurately estimated by using MP temperature signature rather than MP intensity and/or area signatures as in common practice. Our developed methods for MP monitoring, registration, and MP-surface topography prediction offer advanced capabilities for the online detection of process anomalies and part defects. • Multi-modal in-situ melt pool (MP) monitoring. • Data registration framework for large scale multilayer MP data. • In-situ MP signature significance, quantification, and analysis. • Accurate surface topography prediction using the registered MP data. • Applicability in in-situ part qualification.

Predicting melt pool depth and grain length using multiple signatures from in-situ single camera two-wavelength imaging pyrometry for laser powder bed fusion

In laser powder bed fusion (LPBF), the in-situ process signatures are known to have a direct correlation with the microstructural properties of the solidified melt pool (MP). It is known that the MP cooling and heating rates, and laser processing parameters can critically determine the grain structure and thereby affect the part properties. The objective of this work is to study the feasibility of using in-process, high-speed imaging pyrometry for evaluating the solidified MP properties “below” the surface, such as depth and microstructural properties. To accomplish this, we employ an in-house single camera-based two-wavelength imaging pyrometry (STWIP) system for monitoring the printing of single-scan tracks with Inconel 718 on a commercial LPBF printer (EOS M290). The lab designed STWIP system is a coaxial high-speed (>10,000 fps) imaging system capable of monitoring MP temperature, morphology, and intensity profiles. The temperature measurements from STWIP are emissivity independent. The STWIP measured MP signatures of the printed tracks are correlated with the ex-situ microscopy characterized MP depth and the average grain lengths. From the data analysis, using support vector machine (SVM)-based regression models, we found that the MP temperature signatures are crucial for an accurate prediction of MP depth and the grain length, thus validating the novelty and necessity of the developed in-situ monitoring methods and analysis. • In-situ melt pool (MP) monitoring using a single camera based pyrometry system. • In-situ MP temperature and width measurements of single track prints. • Correlating in process MP signatures to ex-situ characterized MP depth and grain lengths. • Evaluation of solidified MP properties “below” the surface, using in-situ MP signatures. • MP temperature signatures are crucial for predicting MP depth and the grain lengths.

Data processing techniques for in-situ monitoring in L-PBF process

Quality assurance is a major concern driving machine manufacturers to introduce in-situ monitoring modules in commercial Laser-Powder Bed Fusion (L-PBF) machines. The post-treatment of the humongous amount of in-situ data and the extraction of key process indicators (KPIs) are a topic of research. This study proposes a methodology to extract critical characteristics from the melt pool monitoring (MPM) and layer control system (LCS) data using statistical, machine learning, and computer vision techniques. The variabilities in MPM data are monitored at local (melt pool level) and global scales (layer level). A qualitative comparison between the MPM data and Computed Tomography scan is made for Ti6Al4V alloy. Alongside, a case study to investigate the link between LCS and MPM data is presented. The proposed methodology can be used to assess the parts qualitatively.

Melt pool temperature measurement and monitoring during laser powder bed fusion based additive manufacturing via single-camera two-wavelength imaging pyrometry (STWIP)

Melt pool (MP) temperature is one of the determining factors and a key signature for evaluating the properties of printed components in metal additive manufacturing (AM). The state-of-the-art measurement systems are hindered, primarily by the large-scale data acquisition and processing demands. In this work, we introduce a novel coaxial, high-speed, single-camera two-wavelength imaging pyrometer (STWIP) system as opposed to the typical utilization of multiple cameras for measuring MP temperature profiles in laser powder bed fusion (LPBF) processes. Developed on a commercial LPBF machine (EOS M290), the STWIP system demonstrated its ability to quantitatively monitor the MP temperature and its variation for 50 layers at high framerates (>30,000 fps) for a real-world application (standard fatigue specimens) print. High performance computing is employed to analyze the acquired big data (MP images), for determining each MP's average temperature and 2D temperature profile. The MP temperature evolution in the gage section of a fatigue specimen is also examined at a temporal resolution of 1 ms, by evaluating the MP temperatures in the samples' first, middle, and last layers. This paper is first of its kind on monitoring MP temperature distribution and evolution at such a large, detailed scale for longer durations in practical applications.

Thermal history and high-speed optical imaging of overhang structures during laser powder bed fusion: A computational and experimental analysis

Laser powder bed fusion (LPBF) is a powerful tool for additive manufacturing (AM) of metal components. However, fabricating components with overhanging features using LPBF remains a challenge. Overhangs suffer from dimensional inaccuracies, high surface roughness, and agglomerated material or dross. These parts often do not meet engineering requirements and are discarded and reprinted until requirements are met, or redesigned to avoid overhangs. Printing a flat overhang is especially challenging due to the long, unsupported powder bed that fosters material agglomeration and inhibits heat flux. Commercial LPBF machines use distinct laser parameters for the downskin layers in overhang regions. Downskin parameters for a given material are often selected by trial and error: printing many parts, changing the laser power and velocity parameters, as well as the number of layers to apply them. The impact of changing the laser parameters on the thermal history of a given layer and multiple layers is not well characterized or understood. This research uses thermal simulations, infrared (IR) imaging, and high-speed optical imaging to study the fabrication of flat overhang structures in 316L stainless steel. The thermal evolution of different downskin strategies on multiple layers is simulated with high fidelity thermal simulations and experimentally measured as the part is printed with carefully calibrated forward looking infrared (FLIR) imaging. Emissivity measurements are performed in a custom chamber under a range of conditions to accurately convert FLIR measurements to temperatures. The layer construction is also recorded with high-speed video to observe the powder bed, melt pool formation, and effects of material agglomeration. Parameter strategies that minimize layer-to-layer variations of the peak temperature, heating rate, and cooling rate are investigated to inform print conditions for overhang features.

A differential evaporation model to predict chemistry change of additively manufactured metals

The desire for increased performance and functionality has introduced additional complexities to the design and fabrication of additively manufactured (AM) parts. However, addressing these needs would require improved control over local properties using in-line feedback from fast-acting low-fidelity models during the fabrication process. In this regard, differential evaporation is an inherent characteristic in metal AM processes, directly influencing local chemistry, material properties, functionality, and performance. In the present work, a differential evaporation model (DEM) is presented for laser powder bed fusion (LPBF) AM to predict and control the effect of evaporation on chemistry and properties on local and part-wide scales. The DEM model is coupled with an analytical thermal model that is calibrated against 51.2 Ni [at%] nickel titanium shape memory alloy (NiTi SMA) single-track experiments and a multi-layer model that accounts for the AM part’s multi-layer design and the inherent melt pool overlap and chemistry propagation. The combined hierarchical model, consisting of the thermal, evaporation, and multi-layer components, is used to predict location-specific chemistry for LBPF AM fabrication of Ni50.8Ti49.2 [at%] SMAs. Model predictions are validated with values obtained from multi-layer experiments on a commercial LPBF system, resulting in a root mean square error (RMSE) of 0.25 Ni [at%] for predicted Ni content. Additionally, martensitic transformation temperature, Ms, is calculated and compared with empirical data, resulting in an RMSE of 18.6 K. A practical account of the cumulative and propagative thermal-induced evaporation effect on location-specific chemistry is made through this linkage of models. Fundamentally, this model chain has also provided a solution to the forward modeling problem, enabling steps to be taken towards resolving the inverse design problem of determining processing parameters based on desired location-specific properties.

Feasibility of a laser powder bed fusion process for additive manufacturing of hybrid structures using aluminum-titanium powder-substrate pairings

The state of the art in the thermal joining of aluminum and titanium sheets in butt or overlap joint configuration specifies that the titanium should remain in the solid state to limit the formation of a brittle intermetallic compound layer in the interface. In this study, aluminum samples were additively manufactured onto titanium substrates and vice versa titanium samples onto aluminum substrates using a standard commercial laser powder bed fusion machine. The influences of the energy density during the process on the sample porosity, the characteristics of the interface between sample and substrate, as well as the tensile strength are analyzed. Despite the melting of both materials, a great potential for high interfacial tensile strength has been found for laser fusion of titanium powder on aluminum substrates.

Spatial inhomogeneity of build defects across the build plate in laser powder bed fusion

The population of build defects as a function of position on the build plate was examined in Ti-6Al-4V specimens fabricated on two common commercial laser powder bed fusion (LPBF) systems. Using standard build parameters, X-ray computed tomography revealed that spatial heterogeneity of the defect population can be substantial, relative to the variations in defect population that can occur due to other parameters, e.g. part geometry, build orientation, and laser power schedule. To understand the potential importance of such variability, its impact on fatigue performance is considered. Asymmetries inherent to LPBF fabrication are investigated as potential sources of the spatial heterogeneity, with shielding gas flow simulations providing an explanation.

Digitally twinned additive manufacturing: Detecting flaws in laser powder bed fusion by combining thermal simulations with in-situ meltpool sensor data

The goal of this research is the in-situ detection of flaw formation in metal parts made using the laser powder bed fusion (LPBF) additive manufacturing process. This is an important area of research, because, despite the considerable cost and time savings achieved, precision-driven industries, such as aerospace and biomedical, are reticent in using LPBF to make safety–critical parts due to tendency of the process to create flaws. Another emerging concern in LPBF, and additive manufacturing in general, is related to cyber security – malicious actors may tamper with the process or plant flaws inside a part to compromise its performance. Accordingly, the objective of this work is to develop and apply a physics and data integrated strategy for online monitoring and detection of flaw formation in LPBF parts. The approach used to realize this objective is based on combining (twinning) in-situ meltpool temperature measurements with a graph theory-based thermal simulation model that rapidly predicts the temperature distribution in the part (thermal history). The novelty of the approach is that the temperature distribution predictions provided by the computational thermal model were updated layer-by-layer with in-situ meltpool temperature measurements. This digital twin approach is applied to detect flaw formation in stainless steel (316L) impeller-shaped parts made using a commercial LPBF system. Four such impellers are produced emulating three pathways of flaw formation in LPBF parts, these are: changes in the processing parameters (process drifts); machine-related malfunctions (lens delamination), and deliberate tampering with the process to plant flaws inside the part (cyber intrusions). The severity and nature of the resulting flaws, such as porosity and microstructure heterogeneity, are characterized ex-situ using X-ray computed tomography, optical and scanning electron microscopy, and electron backscatter diffraction. The digital twin approach is shown to be effective for detection of the three types of flaw formation causes studied in this work.

A printability assessment framework for fabricating low variability nickel-niobium parts using laser powder bed fusion additive manufacturing

PurposeThere is recent emphasis on designing new materials and alloys specifically for metal additive manufacturing (AM) processes, in contrast to AM of existing alloys that were developed for other traditional manufacturing methods involving considerably different physics. Process optimization to determine processing recipes for newly developed materials is expensive and time-consuming. The purpose of the current work is to use a systematic printability assessment framework developed by the co-authors to determine windows of processing parameters to print defect-free parts from a binary nickel-niobium alloy (NiNb5) using laser powder bed fusion (LPBF) metal AM.Design/methodology/approachThe printability assessment framework integrates analytical thermal modeling, uncertainty quantification and experimental characterization to determine processing windows for NiNb5 in an accelerated fashion. Test coupons and mechanical test samples were fabricated on a ProX 200 commercial LPBF system. A series of density, microstructure and mechanical property characterization was conducted to validate the proposed framework.FindingsNear fully-dense parts with more than 99% density were successfully printed using the proposed framework. Furthermore, the mechanical properties of as-printed parts showed low variability, good tensile strength of up to 662 MPa and tensile ductility 51% higher than what has been reported in the literature.Originality/valueAlthough many literature studies investigate process optimization for metal AM, there is a lack of a systematic printability assessment framework to determine manufacturing process parameters for newly designed AM materials in an accelerated fashion. Moreover, the majority of existing process optimization approaches involve either time- and cost-intensive experimental campaigns or require the use of proprietary computational materials codes. Through the use of a readily accessible analytical thermal model coupled with statistical calibration and uncertainty quantification techniques, the proposed framework achieves both efficiency and accessibility to the user. Furthermore, this study demonstrates that following this framework results in printed parts with low degrees of variability in their mechanical properties.

Demonstration of a laser powder bed fusion combinatorial sample for high-throughput microstructure and indentation characterization

High-throughput experiments that use combinatorial samples with rapid measurements can be used to provide process-structure-property information at reduced time, cost, and effort. Developing these tools and methods is essential in additive manufacturing where new process-structure-property information is required on a frequent basis as advances are made in feedstock materials, additive machines, and post-processing. Here we demonstrate the design and use of combinatorial samples produced on a commercial laser powder bed fusion system to study 60 distinct process conditions of nickel superalloy 625: five laser powers and four laser scan speeds in three different conditions. Combinatorial samples were characterized using optical and electron microscopy, x-ray diffraction, and indentation to estimate the porosity, grain size, crystallographic texture, secondary phase precipitation, and hardness. Indentation and porosity results were compared against a regular sample. The smaller-sized regions (3 mm × 4 mm) in the combinatorial sample have a lower hardness compared to a larger regular sample (20 mm × 20 mm) with similar porosity (<0.03%). Despite this difference, meaningful trends were identified with the combinatorial sample for grain size, crystallographic texture, and porosity versus laser power and scan speed as well as trends with hardness versus stress-relief condition.

Frequency domain measurements of melt pool recoil force using modal analysis

Recoil pressure is a critical factor affecting the melt pool dynamics during Laser Powder Bed Fusion (LPBF) processes. Recoil pressure depresses the melt pool. When the recoil pressure is low, thermal conduction and capillary forces may be inadequate to provide proper fusion between layers. However, excessive recoil pressure can produce a keyhole inside the melt pool, which is associated with gas porosity. Direct recoil pressure measurements are challenging because it is localized over an area proportionate to the laser spot size producing a force in the mN range. This paper reports a vibration-based approach to quantify the recoil force exerted on a part in a commercial LPBF machine. The measured recoil force is consistent with estimates from high speed synchrotron imaging of entrained particles, and the results show that the recoil force scales with applied laser power and is inversely related to the laser scan speed. These results facilitate further studies of melt pool dynamics and have the potential to aid process development for new materials.

Roughness and Near-Surface Porosity of Unsupported Overhangs Produced by High-Speed Laser Powder Bed Fusion.

Laser powder bed fusion (LPBF) is a promising technology that requires further work to improve productivity to be adopted more widely. One possible approach is to increase the laser power and scan speed. A customized high-speed and high-power LPBF system has been developed for this purpose. The current study investigated the surface roughness and near-surface porosity as a result of unsupported overhangs at varying inclination angles and orientations during the manufacturing of Ti6Al4V parts with this custom high-speed and high-power LPBF system. It is known that surface roughness and porosity are among the main drawbacks for parts manufactured by LPBF, and that supports are required for overhang regions with low inclination angles relative to the powder bed, typically in commercial LPBF systems requiring supports for regions with inclination angles less than 45°. However, the appropriate inclination angles for this custom system with high power and speed requires investigation. In this article, a simple benchmark test artefact with different inclination angles was manufactured in different orientations on the build platform and characterized by X-ray tomography, touch probe roughness meter, optical microscopy, and scanning electron microscopy. The analysis of surface roughness and near-surface porosity at upskin and downskin regions was performed as a function of inclination angle. The results indicate that the high-speed LPBF process produces relatively high roughness in all cases, with different porosity distributions at upskin and downskin areas. Both roughness and porosity vary as a function of inclination angle. Significant warping was observed, depending on build orientation relative to laser scanning direction. These are the first reported results of the detailed surface roughness and porosity characterization of part quality from such a high-speed, high-power LPBF process.

Process variation in Laser Powder Bed Fusion of Ti-6Al-4V

In this work, a concept of using surface roughness data as an evaluation tool of the process variation in a commercial Laser Powder Bed Fusion (L-PBF) machine is demonstrated. The interactive effects of powder recoating, spatter generation, gas flow and heat transfer are responsible for the intra-build quality inconsistency of the L-PBF process. Novel specimens and experiments were designed to investigate how surface roughness varies across the build volume and with the progression of a build. The variation in roughness has a clear and repeatable pattern due to the strong impact of the orientation of inclined surface to the laser origin. The effects of other factors such as exposure sequence of specimens, build height, and recoating process are less prominent and are difficult to isolate. A neural network regression model was built upon the large dataset in measured Ra values. The neural network model was applied to predict distribution of roughness within the build volume under hypothetical processing conditions. Connections between the predicted variation in roughness and underlying physical mechanisms are discussed. The present work has value for machine qualification and modifications which lead to the manufacturing of parts with better consistency in quality. The detailed variation observed in surface roughness can be used as a reference for designing experiments to optimise processing parameters in order to minimise the roughness of inclined surfaces.

A Differential Evaporation Model to Predict Chemistry Change of Additively Manufactured Metals

The desire for increased performance and functionality has introduced additional complexities to the design and fabrication of additively manufactured (AM) parts. However, addressing these needs would require improved control over local properties during the fabrication process. In this regard, differential evaporation is an inherent characteristic in metal AM processes, directly influencing local chemistry, material properties, functionality, and performance. In the present work, a differential evaporation model (DEM) is presented for laser powder bed fusion (LPBF) AM to predict and control the effect of evaporation on chemistry and properties on local and part-wide scales. The DEM model is coupled with an analytical thermal model that is calibrated against 51.2 Ni [at.%] nickel titanium SMA single-track experiments and a multi-layer model that accounts for the AM part’s multi-layer design and the inherent melt pool overlap and chemistry propagation. The combined hierarchical model, consisting of the thermal, evaporation, and multi-layer components, is used to predict location-specific chemistry for LBPF AM fabrication of 50.8 Ni [at.%] nickel titanium shape memory alloys(NiTi SMAs). Model predictions are validated with values obtained from multi-layer experiments on a commercial LPBF system, resulting in a root mean square error (RMSE) of 0.25 Ni [at.%] for predicted Ni content. Additionally, martensitic transformation temperature, Ms, is calculated and compared with empirical data, resulting in an RMSE of 18.6 K. A practical account of the cumulative and propagative thermal-induced evaporation effect on location-specific chemistry is made through this linkage of models. Fundamentally, this model chain has also provided a solution to the forward modeling problem, enabling steps to be taken towards resolving the inverse design problem of deter-mining processing parameters based on desired location-specific properties.