scholarly journals Effect of bimodal powder blends on part density and melt pool fluctuation in laser powder bed fusion

2021 ◽  
Author(s):  
Lukas Haferkamp ◽  
Simon Liechti ◽  
Adriaan Spierings ◽  
Konrad Wegener
Keyword(s):  
Volume Fraction ◽  
Final Part ◽  
Powder Particle Size ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Coarse Powder ◽  
Powder Bed ◽  
Melt Pool ◽  
Powder Blends ◽  
Part Density

AbstractThe final part density in laser powder bed fusion is influenced by the powder particle size distribution. Too fine powders are not spreadable, and too coarse powders cause porosity. Powder blends, especially bimodal ones, can exhibit higher packing densities and changes in flowability compared to their monomodal constituents. These properties can influence final part density. Therefore, the influence of bimodal powder on final part density was investigated. Two gas atomized 316L (1.4404) powders with a D50 of 20.3 µm and 60.3 µm were blended at weight ratios of 3:1, 1:1, and 1:3, and the original and blended powders were processed. The results show that the final part porosity increases almost linearly with an increasing volume fraction of coarse powder. Furthermore, the final part density is independent of powder bulk density and flowability. Measurements of the top surface show that an increase of part porosity by coarse powder is caused by an increase in melt pool fluctuation, which in turn causes irregular solidified scan tracks. Additionally, the results show that the powder segregation during coating is stronger for the bimodal powder; however, no influence of the segregation on the part density could be found.

scholarly journals The Influence of Particle Shape, Powder Flowability, and Powder Layer Density on Part Density in Laser Powder Bed Fusion

Metals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 418
Author(s):  
Lukas Haferkamp ◽  
Livia Haudenschild ◽  
Adriaan Spierings ◽  
Konrad Wegener ◽  
Kirstin Riener ◽  
...  
Keyword(s):  
Particle Shape ◽  
Final Part ◽  
Powder Layer ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Layer Density ◽  
Hausner Ratio ◽  
Powder Flowability ◽  
Part Density

The particle shape influences the part properties in laser powder bed fusion, and powder flowability and powder layer density (PLD) are considered the link between the powder and part properties. Therefore, this study investigates the relationship between these properties and their influence on final part density for six 1.4404 (316L) powders and eight AlSi10Mg powders. The results show a correlation of the powder properties with a Pearson correlation coefficient (PCC) of −0.89 for the PLD and the Hausner ratio, a PCC of −0.67 for the Hausner ratio and circularity, and a PCC of 0.72 for circularity and PLD. Furthermore, the results show that beyond a threshold, improvement of circularity, PLD, or Hausner ratio have no positive influence on the final part density. While the water-atomized, least-spherical powder yielded parts with high porosity, no improvement of part density was achieved by feedstock with higher circularities than gas-atomized powder.

scholarly journals Optimization of Laser Powder Bed Fusion Processing Using a Combination of Melt Pool Modeling and Design of Experiment Approaches: Density Control

2019 ◽  
Vol 3 (1) ◽  
pp. 21 ◽  
Author(s):  
Morgan Letenneur ◽  
Alena Kreitcberg ◽  
Vladimir Brailovski
Keyword(s):  
Scanning Speed ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Density Prediction ◽  
Melt Pool ◽  
Density Control ◽  
Pool Model ◽  
Conventional Material ◽  
Prediction Approach

A simplified analytical model of the laser powder bed fusion (LPBF) process was used to develop a novel density prediction approach that can be adapted for any given powder feedstock and LPBF system. First, calibration coupons were built using IN625, Ti64 and Fe powders and a specific LPBF system. These coupons were manufactured using the predetermined ranges of laser power, scanning speed, hatching space, and layer thickness, and their densities were measured using conventional material characterization techniques. Next, a simplified melt pool model was used to calculate the melt pool dimensions for the selected sets of printing parameters. Both sets of data were then combined to predict the density of printed parts. This approach was additionally validated using the literature data on AlSi10Mg and 316L alloys, thus demonstrating that it can reliably be used to optimize the laser powder bed metal fusion process.

A Physics-Informed Two-Level Machine Learning Model for Predicting Melt-Pool Size in Laser Powder Bed Fusion

2021 ◽  
Author(s):  
Yong Ren ◽  
Qian Wang ◽  
Panagiotis (Pan) Michaleris
Keyword(s):  
Machine Learning ◽  
Initial Temperature ◽  
Pool Size ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool ◽  
Input Feature ◽  
Melt Pool Size ◽  
Melt Pool Characteristics

Abstract Laser powder bed fusion (L-PBF) additive manufacturing (AM) is one type of metal-based AM process that is capable of producing high-value complex components with a fine geometric resolution. As melt-pool characteristics such as melt-pool size and dimensions are highly correlated with porosity and defects in the fabricated parts, it is crucial to predict how process parameters would affect the melt-pool size and dimensions during the build process to ensure the build quality. This paper presents a two-level machine learning (ML) model to predict the melt-pool size during the scanning of a multi-track build. To account for the effect of thermal history on melt-pool size, a so-called (pre-scan) initial temperature is predicted at the lower-level of the modeling architecture, and then used as a physics-informed input feature at the upper-level for the prediction of melt-pool size. Simulated data sets generated from the Autodesk's Netfabb Simulation are used for model training and validation. Through numerical simulations, the proposed two-level ML model has demonstrated a high prediction performance and its prediction accuracy improves significantly compared to a naive one-level ML without using the initial temperature as an input feature.

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

2017 ◽  
Author(s):  
J. C. Heigel ◽  
B. M. Lane
Keyword(s):  
Laser Power ◽  
High Speed ◽  
Infrared Camera ◽  
Powder Bed Fusion ◽  
Single Track ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool ◽  
Significant Difference ◽  
Scan Speed

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 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 W to 195 W) and scan speed (200 mm/s to 800 mm/s) are investigated and numerous replications using a variety of scan lengths (4 mm to 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.

Camera signal dependencies within coaxial melt pool monitoring in laser powder bed fusion

2020 ◽  
Vol 26 (1) ◽  
pp. 100-106 ◽  
Author(s):  
Tobias Kolb ◽  
Reza Elahi ◽  
Jan Seeger ◽  
Mathews Soris ◽  
Christian Scheitler ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Layer Thickness ◽  
Powder Layer ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Content Type ◽  
Powder Bed ◽  
Sensory Signals ◽  
Melt Pool ◽  
Research Activities

Purpose The purpose of this paper is to analyse the signal dependency of the camera-based coaxial monitoring system QMMeltpool 3D (Concept Laser GmbH, Lichtenfels, Germany) for laser powder bed fusion (LPBF) under the variation of process parameters, position, direction and layer thickness to determine the capability of the system. Because such and similar monitoring systems are designed and presented for quality assurance in series production, it is important to present the dominant signal influences and limitations. Design/methodology/approach Hardware of the commercially available coaxial monitoring QMMeltpool 3D is used to investigate the thermal emission of the interaction zone during LPBF. The raw images of the camera are analysed by means of image processing to bypass the software of QMMeltpool 3D and to gain a high level of signal understanding. Laser power, scan speed, laser spot diameter and powder layer thickness were varied for single-melt tracks to determine the influence of a parameter variation on the measured sensory signals. The effects of the scan direction and position were also analysed in detail. The influence of surface roughness on the detected sensory signals was simulated by a machined substrate plate. Findings Parameter variations are confirmed to be detectable. Because of strong directional and positional dependencies of the melt-pool monitoring signal a calibration algorithm is necessary. A decreasing signal is detected for increasing layer thickness. Surface roughness is identified as a dominating factor with major influence on the melt-pool monitoring signal exceeding other process flaws. Research limitations/implications This work was performed with the hardware of a commercially available QMMeltpool 3D system of an LPBF machine M2 of the company Concept Laser GmbH. The results are relevant for all melt-pool monitoring research activities connected to LPBF, as well as for end users and serial production. Originality/value Surface roughness has not yet been revealed as being one of the most important origins for signal deviations in coaxial melt-pool monitoring. To the best of the authors’ knowledge, the direct comparison of influences because of parameters and environment has not been published to this extent. The detection, evaluation and remelting of surface roughness constitute a plausible workflow for closed-loop control in LPBF.

scholarly journals Toward determining melt pool quality metrics via coaxial monitoring in laser powder bed fusion

2018 ◽  
Vol 15 ◽  
pp. 119-121 ◽  
Author(s):  
Brian A. Fisher ◽  
Brandon Lane ◽  
Ho Yeung ◽  
Jack Beuth
Keyword(s):  
Quality Metrics ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool
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