Effect of laser-plume interaction on part quality in multi-scanner Laser Powder Bed Fusion

Tenbrock, Christian; Kelliger, Tobias; Praetzsch, Niklas; Ronge, Marcel; Jauer, Lucas; Schleifenbaum, Johannes Henrich

doi:10.1016/j.addma.2020.101810

Cited by 27 publications

(21 citation statements)

References 15 publications

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“…Qualitative/Quantitative References Schlieren Imaging Qualitative Bidare et al [17] Vane anemometer Quantitative Shen et al [10] Particle image velocimetry Quantitative Chen and Wang [8] Hot wire or hot film anemometer Quantitative Schniedenharn et al [18]; Reijonen et al [11]; Wang and Chang [19]; Philo et al [7]; Tenbrock et al [16] Smoke visualization Qualitative Wang and Chang [19] Methods and materials…”

Section: Methodsmentioning

confidence: 99%

“…The hypothesis to explain the increase in spatter was that the gas flow trapped rather than removed spatter in this region. Lastly Tenbrok et al [16] studied the interference of the laser plume from one laser affecting another laser in order to establish multi-laser LPBF scan strategies that avoid this type of interference. They noted a correlation between plume propagation and the occurrence of defects in parts.…”

Section: Introductionmentioning

confidence: 99%

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Inert gas flow speed measurements in laser powder bed fusion additive manufacturing

Weaver¹,

Schlenoff²,

Deisenroth³

et al. 2021

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Laser powder bed fusion (LPBF) is an additive manufacturing technology that uses a laser to selectively melt powder feedstock to build parts in a layer-by-layer process. For metals-based LBPF additive manufacturing, the interaction of the laser and powder feedstock creates byproducts such as a plume, spatter, and powder ejecta. Directional gas flow, typically nitrogen or argon, is used to remove or mitigate the negative effects of these byproducts. This report documents and presents gas flow measurements using hot-wire anemometers (HWA) for two different nozzles on a commercial LPBF machine and two different gasses at the NIST Additive Manufacturing Metrology Testbed (AMMT). The AMMT gas flow system generates comparable volumetric flow rates with argon and with nitrogen, which result in comparable flow speed profiles for both gasses. There are significant differences in the gas speed profiles along the gas flow direction (Y-position) and minimal differences perpendicular to the gas flow direction (X-position) with both machines. The speed differences with Y-position are in part due to the elevated inlet and outlet nozzles from the build platform. The average speed only decreases slightly from the inlet to the outlet despite these differences in the speed profiles. The grid nozzle on the commercial machine with a downward facing row of channels at the base of the nozzle increases the gas speed close to the build platform; however, non-uniform speed profiles remain. Gas speed and therefore gas speed measurements with HWAs are highly dependent on Z and Y position. This should be considered when prescribing machine performance protocols. Additional suggestions for measuring and reporting gas flow are made as well as recommended future experiments and simulations to assist in machine performance standards.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inert gas flow speed measurements in laser powder bed fusion additive manufacturing

Weaver¹,

Schlenoff²,

Deisenroth³

et al. 2021

View full text Add to dashboard Cite

show abstract

“…[ 24,25 ] Furthermore, nonadjustable parameters such as the shielding gas flow or plume generation might influence the repeatability of parts built with lower energy parameter sets. [ 26,27 ]…”

Section: Introductionmentioning

confidence: 99%

Investigation on Process Stability and Part Positioning Influence on the Relative Density of Designed Materials via Laser‐Based Powder Bed Fusion of Metals on a Multi‐Laser Machine

et al. 2021

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Component production via laser‐based powder bed fusion of metals (PBF‐LB/M) is becoming reality for multiple applications. Especially for sophisticated parts, as in case of gas turbines, assurance of repeatable properties involves control of process and machine parameters. PBF‐LB/M is used to build configurable open‐porous structures—designed materials (DMs)—out of the high temperature superalloy Haynes 282 with relative densities from 35 % to 65 % . The objective is investigating positioning influence and process stability based on their relative densities in discrete scan fields and build jobs. As DMs made from Haynes 282 are not yet scientifically investigated, first the process boundaries are identified. The used PBF‐LB/M machine—EOS M 400‐4—works with four lasers in four quadrants—a quadrant benchmarking is carried out. The experiment is repeated in a second build job. Scatter is identified in quadrant and build job benchmarking. A further aspect under investigation is positioning within laser quadrants. However, keeping laser and parameter combinations constant, sample locations are assessed. A relative density dependency on the laser deflection angle—as a positional measure—is observed. The results of positioning influence and scatter are used to formulate a tolerance equation for relative densities of DMs.

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“…The metal partially evaporates and might alter the laser beam profile and affect the quality of the manufactured part [35]. It is also reported that as plume condenses, it might form nanoparticles ejected out of the meltpool [36]. Spatter is known to have a negative impact on the performance of metallic parts manufactured using L-PBF processes.…”

Section: Process Signaturesmentioning

confidence: 99%

Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

et al. 2021

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One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.

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Effect of laser-plume interaction on part quality in multi-scanner Laser Powder Bed Fusion

Cited by 27 publications

References 15 publications

Inert gas flow speed measurements in laser powder bed fusion additive manufacturing

Inert gas flow speed measurements in laser powder bed fusion additive manufacturing

Investigation on Process Stability and Part Positioning Influence on the Relative Density of Designed Materials via Laser‐Based Powder Bed Fusion of Metals on a Multi‐Laser Machine

Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

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