Commercial Laser Powder Bed Fusion Research Articles

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.

In Situ Thermography During Laser Powder Bed Fusion of a Nickel Superalloy 625 Artifact with Various Overhangs and Supports

This document provides details on the experiment and associated measurement files available fordownload in the dataset “In Situ Thermography During Laser Powder Bed Fusion of a Nickel Superalloy 625 Artifact with Various Overhangs and Supports.” The measurements were acquired during the fabrication of a small nickel superalloy 625 (IN625) artifact using a commercial laser powder bed fusion (LPBF) system. The artifact consists of two half-arch features with increasing slopes for overhangs. These overhangs range from 5° from vertical to 85° from vertical in increments of 10°. The artifact geometry and process are controlled to ensure consistent processing along the overhang geometry. This control enables the effect of overhang geometry and support structures to be isolated from effects of inter-layer scan strategy variations. The measurements include high-speed thermography of each layer, from which radiance temperature, cooling rate, and melt pool length are calculated.

Transient Laser Energy Absorption, Co-axial Melt Pool Monitoring, and Relationship to Melt Pool Morphology

Melt pool monitoring (MPM) is a technique used in laser powder bed fusion (LPBF) to extract features from in-situ sensor signals that correlate to defect formation or general part fabrication quality. Various melt pool phenomena have been shown to relate to measured transient absorption of the laser energy, which in turn, can be relatable to the melt pool emission measured in MPM systems. This paper describes use of a reflectometer-based instrument to measure the dynamic laser energy absorption during single-line laser scans. Scans are conducted on bare metal and single powder layer of nickel alloy 625 (IN625) at a range of laser powers. In addition, a photodetector aligned co-axially with the laser, often found in commercial LPBF monitoring systems, synchronously measured of the incandescent emission from the melt pool with the dynamic laser absorption. Relationships between the dynamic laser absorption, co-axial MPM, and surface features on the tracks are observed, providing illustration of the melt pool dynamics that formed these features. Time-integrated measurements of laser absorption are shown to correlate well with MPM signal, as well as indicate the transition between conduction and keyhole mode. This transition is corroborated by metallographic cross-section measurement, as well as topographic measurements of the solidified tracks. Ultimately, this paper exemplifies the utility of dynamic laser absorption measurements to inform both the physical nature of the melt pool dynamics, as well as interpretation of process monitoring signals.

Productivity optimization of laser powder bed fusion by hot isostatic pressing

Laser Powder Bed Fusion is today used for the serial production of parts, e.g. in the medical and aerospace markets. One of the major limitations is the comparatively low build rate of the process, which leads to low productivity and high costs when compared to conventional processes. Current approaches such as the use of multi-laser systems help increasing the build rate but come at higher investment costs. Overall, the low productivity limits the number of business cases for Laser Powder Bed Fusion and hinders the market uptake in more cost-sensitive industries.This paper suggests a combined approach of Laser Powder Bed Fusion and subsequent Hot Isostatic Pressing as a method to improve productivity. Hot Isostatic Pressing is often used as a post-process to eliminate any remnant porosity. It is shown that the process, however, is able to densify specimens that come out of Laser Powder Bed Fusion with an as-build density as low as 95 %. This opens up a larger process window for the initial Laser Powder Bed Fusion step. Experimental investigations are presented using two commercial Laser Powder Bed Fusion systems with the widely used titanium alloy Ti-6Al-4V. Instead of optimizing the process for the highest possible density, the parameters were optimized to yield the highest possible speed while maintaining a density above 95 %. A scan speed increase of 67 % was achieved and the specimens were then successfully compacted to above 99.8 % density in the Hot Isostatic Processing step. The high-speed parameter set was then applied to a demonstrator build job, where it leads to an overall saving of 26 % of build time.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

In Situ Measurements of Melt-Pool Length and Cooling Rate During 3D Builds of the Metal AM-Bench Artifacts

This work provides results and analysis of the in situ thermal measurement acquired during the 3D builds performed for the 2018 additive manufacturing benchmark tests. The objective is to provide context for post-process characterization of distortion, residual strain, and microstructure, which are reported elsewhere in this Journal, and to provide validation data for thermal models of the build process. Four bridge artifacts (75 mm long, 5 mm wide, 12.5 mm tall) are created in each of three builds using a commercial laser powder bed fusion system. The builds are performed using nickel super alloy 625 (IN625). High-speed infrared thermography performed during four of the builds is used to measure the melt-pool length and cooling rate within a select region. The temperature of the substrate and build volume is measured during the fifth build to provide data to establish the boundary conditions for thermal models.

In-process monitoring of porosity in additive manufacturing using optical emission spectroscopy

A key challenge in metal additive manufacturing is the prevalence of defects, such as discontinuities within the part (e.g., porosity). The objective of this work is to monitor porosity in Laser Powder Bed Fusion (L-PBF) additive manufacturing of nickel alloy 718 (popularly called Inconel 718) test parts using in-process optical emission spectroscopy. To realize this objective, cylinder-shaped test parts are built under different processing conditions on a commercial L-PBF machine instrumented with an in-situ multispectral photodetector sensor. Optical emission signatures are captured continuously during the build by the multispectral sensor. Following processing, the porosity-level within each layer of a test part is quantified using X-ray Computed Tomography (CT). The graph Fourier transform coefficients are derived layer-by-layer from signatures acquired from the multispectral photodetector sensor. These graph Fourier transform coefficients are subsequently invoked as input features within various machine learning models to predict the percentage porosity-level in each layer with CT data taken as ground truth. This approach is found to predict the porosity on a layer-by-layer basis with an accuracy of ∼90% (F-score) in a computation time less than 0.5 seconds. In comparison, statistical moments, such as mean, variation, etc., are less accurate (F-score ≈ 80%) and require a computation time exceeding 5 seconds.

Layerwise Anomaly Detection in Laser Powder-Bed Fusion Metal Additive Manufacturing

A growing research trend in additive manufacturing (AM) calls for layerwise anomaly detection as a step toward enabling real-time process control, in contrast to ex situ or postprocess testing and characterization. We propose a method for layerwise anomaly detection during laser powder-bed fusion (L-PBF) metal AM. The method uses high-speed thermal imaging to capture melt pool temperature and is composed of the following four-step anomaly detection procedure: (1) using the captured thermal images, a process signature of a just-fabricated layer is generated. Next, a signature difference is obtained by subtracting the process signature of that particular layer from a prespecified reference signature, (2) a screening step selects potential regions of interests (ROIs) within the layer that are likely to contain process anomalies, hence reducing the computational burden associated with analyzing the full layer data, (3) the spatial dependence of these ROIs is modeled using a Gaussian process model, and then pixels with statistically significant deviations are flagged, and (4) using the quantity and the spatial pattern of the flagged pixels as predictors, a classifier is trained and implemented to determine whether the process is in- or out-of-control. We validate the proposed method using a case study on a commercial L-PBF system custom-instrumented with a dual-wavelength imaging pyrometer for capturing the thermal images during fabrication.

Visualization of the shielding gas flow in SLM machines by space-resolved thermal anemometry

PurposeThe purpose of this paper is to introduce an approach in measuring the shielding gas flow within laser powder bed fusion (L-PBF) machines under near-process conditions (regarding oxygen content and shielding gas flow).Design/methodology/approachThe measurements are made sequentially using a hot-wire anemometer. After a short introduction into the measurement technique, the system which places the measurement probe within the machine is described. Finally, the measured shielding gas flow of a commercial L-PBF machine is presented.FindingsAn approach to measure the shielding gas flow within SLM machines has been developed and successfully tested. The use of a thermal anemometer along with an automated probe-placement system enables the space-resolved measurement of the flow speed and its turbulence.Research limitations/implicationsThe used single-normal (SN) hot-wire anemometer does not provide the flow vectors’ orientation. Using a probe with two or three hot-films and an improved placement system will provide more information about the flow and less disturbance to it.Originality/valueA measurement system which allows the measurement of the shielding gas flow within commercial L-PBF machines is presented. This enables the correlation of the shielding gas flow with the resulting parts’ quality.

Process Mapping and In-Process Monitoring of Porosity in Laser Powder Bed Fusion Using Layerwise Optical Imaging

The goal of this work is to understand the effect of process conditions on lack of fusion porosity in parts made using laser powder bed fusion (LPBF) additive manufacturing (AM) process, and subsequently, to detect the onset of process conditions that lead to lack of fusion-related porosity from in-process sensor data. In pursuit of this goal, the objectives of this work are twofold: (1) quantify the count (number), size and location of pores as a function of three LPBF process parameters, namely, the hatch spacing (H), laser velocity (V), and laser power (P); and (2) monitor and identify process conditions that are liable to cause porosity through analysis of in-process layer-by-layer optical images of the build invoking multifractal and spectral graph theoretic features. These objectives are important because porosity has a significant impact on the functional integrity of LPBF parts, such as fatigue life. Furthermore, linking process conditions to defects via sensor signatures is the first step toward in-process quality assurance in LPBF. To achieve the first objective, titanium alloy (Ti–6Al–4V) test cylinders of 10 mm diameter × 25 mm height were built under differing H, V, and P settings on a commercial LPBF machine (EOS M280). The effect of these process parameters on count, size, and location of pores was quantified based on X-ray computed tomography (XCT) images. To achieve the second objective, layerwise optical images of the powder bed were acquired as the parts were being built. Spectral graph theoretic and multifractal features were extracted from the layer-by-layer images for each test part. Subsequently, these features were linked to the process parameters using machine learning approaches. Through these image-based features, process conditions under which the parts were built were identified with the statistical fidelity over 80% (F-score).

Sensor-Based Build Condition Monitoring in Laser Powder Bed Fusion Additive Manufacturing Process Using a Spectral Graph Theoretic Approach

The goal of this work is to monitor the laser powder bed fusion (LPBF) process using an array of sensors so that a record may be made of those temporal and spatial build locations where there is a high probability of defect formation. In pursuit of this goal, a commercial LPBF machine at the National Institute of Standards and Technology (NIST) was integrated with three types of sensors, namely, a photodetector, high-speed visible camera, and short wave infrared (SWIR) thermal camera with the following objectives: (1) to develop and apply a spectral graph theoretic approach to monitor the LPBF build condition from the data acquired by the three sensors; (2) to compare results from the three different sensors in terms of their statistical fidelity in distinguishing between different build conditions. The first objective will lead to early identification of incipient defects from in-process sensor data. The second objective will ascertain the monitoring fidelity tradeoff involved in replacing an expensive sensor, such as a thermal camera, with a relatively inexpensive, low resolution sensor, e.g., a photodetector. As a first-step toward detection of defects and process irregularities that occur in practical LPBF scenarios, this work focuses on capturing and differentiating the distinctive thermal signatures that manifest in parts with overhang features. Overhang features can significantly decrease the ability of laser heat to diffuse from the heat source. This constrained heat flux may lead to issues such as poor surface finish, distortion, and microstructure inhomogeneity. In this work, experimental sensor data are acquired during LPBF of a simple test part having an overhang angle of 40.5 deg. Extracting and detecting the difference in sensor signatures for such a simple case is the first-step toward in situ defect detection in additive manufacturing (AM). The proposed approach uses the Eigen spectrum of the spectral graph Laplacian matrix as a derived signature from the three different sensors to discriminate the thermal history of overhang features from that of the bulk areas of the part. The statistical accuracy for isolating the thermal patterns belonging to bulk and overhang features in terms of the F-score is as follows: (a) F-score of 95% from the SWIR thermal camera signatures; (b) 83% with the high-speed visible camera; (c) 79% with the photodetector. In comparison, conventional signal analysis techniques—e.g., neural networks, support vector machines, linear discriminant analysis were evaluated with F-score in the range of 40–60%.

Measurement of the Melt Pool Length During Single Scan Tracks in a Commercial Laser Powder Bed Fusion Process

This work presents high-speed thermographic measurements of the melt pool length during single track laser scans on nickel alloy 625 substrates. Scans are made using a commercial laser powder bed fusion (PBF) machine while measurements of the radiation from the surface are made using a high speed (1800 frames per second) infrared camera. The melt pool length measurement is based on the detection of the liquidus–solidus transition that is evident in the temperature profile. Seven different combinations of programmed laser power (49–195 W) and scan speed (200–800 mm/s) are investigated, and numerous replications using a variety of scan lengths (4–12 mm) are performed. Results show that the melt pool length reaches steady-state within 2 mm of the start of each scan. Melt pool length increases with laser power, but its relationship with scan speed is less obvious because there is no significant difference between cases performed at the highest laser power of 195 W. Although keyholing appears to affect the anticipated trends in melt pool length, further research is required.

Thermographic Measurements of the Commercial Laser Powder Bed Fusion Process at NIST.

Measurement of the high-temperature melt pool region in the laser powder bed fusion (L-PBF) process is a primary focus of researchers to further understand the dynamic physics of the heating, melting, adhesion, and cooling which define this commercially popular additive manufacturing process. This paper will detail the design, execution, and results of high speed, high magnification in-situ thermographic measurements conducted at the National Institute of Standards and Technology (NIST) focusing on the melt pool region of a commercial L-PBF process. Multiple phenomena are observed including plasma plume and hot particle ejection from the melt region. The thermographic measurement process will be detailed with emphasis on the 'measurability' of observed phenomena and the sources of measurement uncertainty. Further discussion will relate these thermographic results to other efforts at NIST towards L-PBF process finite element simulation and development of in-situ sensing and control methodologies.

Multiple Sensor Detection of Process Phenomena in Laser Powder Bed Fusion.

Laser powder bed fusion (LPBF) is an additive manufacturing (AM) process in which a high power laser melts metal powder layers into complex, three-dimensional shapes. LPBF parts are known to exhibit relatively high residual stresses, anisotropic microstructure, and a variety of defects. To mitigate these issues, in-situ measurements of the melt-pool phenomena may illustrate relationships between part quality and process signatures. However, phenomena such as spatter, plume formation, laser modulation, and melt-pool oscillations may require data acquisition rates exceeding 10 kHz. This hinders use of relatively data-intensive, streaming imaging sensors in a real-time monitoring and feedback control system. Single-point sensors such as photodiodes provide the temporal bandwidth to capture process signatures, while providing little spatial information. This paper presents results from experiments conducted on a commercial LPBF machine which incorporated synchronized, in-situ acquisition of a thermal camera, high-speed visible camera, photodiode, and laser modulation signal during fabrication of a nickel alloy 625 AM part with an overhang geometry. Data from the thermal camera provides temperature information, the visible camera provides observation of spatter, and the photodiode signal provides high temporal bandwidth relative brightness stemming from the melt pool region. In addition, joint-time frequency analysis (JTFA) was performed on the photodiode signal. JTFA results indicate what digital filtering and signal processing are required to highlight particular signatures. Image fusion of the synchronized data obtained over multiple build layers allows visual comparison between the photodiode signal and relating phenomena observed in the imaging detectors.