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 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.

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 Keyhole Formation by Laser Drilling in Laser Powder Bed Fusion of Ti6Al4V Biomedical Alloy: Mesoscopic Computational Fluid Dynamics Simulation versus Mathematical Modelling Using Empirical Validation

Nanomaterials ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 3284
Author(s):  
Asif Ur Rehman ◽  
Muhammad Arif Mahmood ◽  
Fatih Pitir ◽  
Metin Uymaz Salamci ◽  
Andrei C. Popescu ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Energy Density ◽  
Operating Conditions ◽  
Dynamics Simulation ◽  
Powder Layer ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool

In the laser powder bed fusion (LPBF) process, the operating conditions are essential in determining laser-induced keyhole regimes based on the thermal distribution. These regimes, classified into shallow and deep keyholes, control the probability and defects formation intensity in the LPBF process. To study and control the keyhole in the LPBF process, mathematical and computational fluid dynamics (CFD) models are presented. For CFD, the volume of fluid method with the discrete element modeling technique was used, while a mathematical model was developed by including the laser beam absorption by the powder bed voids and surface. The dynamic melt pool behavior is explored in detail. Quantitative comparisons are made among experimental, CFD simulation and analytical computing results leading to a good correspondence. In LPBF, the temperature around the laser irradiation zone rises rapidly compared to the surroundings in the powder layer due to the high thermal resistance and the air between the powder particles, resulting in a slow travel of laser transverse heat waves. In LPBF, the keyhole can be classified into shallow and deep keyhole mode, controlled by the energy density. Increasing the energy density, the shallow keyhole mode transforms into the deep keyhole mode. The energy density in a deep keyhole is higher due to the multiple reflections and concentrations of secondary reflected beams within the keyhole, causing the material to vaporize quickly. Due to an elevated temperature distribution in deep keyhole mode, the probability of pores forming is much higher than in a shallow keyhole as the liquid material is close to the vaporization temperature. When the temperature increases rapidly, the material density drops quickly, thus, raising the fluid volume due to the specific heat and fusion latent heat. In return, this lowers the surface tension and affects the melt pool uniformity.

A Combined Experimental-Numerical Method to Evaluate Powder Thermal Properties in Laser Powder Bed Fusion

2018 ◽  
Author(s):  
Bo Cheng ◽  
Brandon Lane ◽  
Justin Whiting ◽  
Kevin Chou
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Thermal Properties ◽  
Thermal Diffusivity ◽  
Laser Flash ◽  
Powder Layer ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Powder Particles

Powder bed metal additive manufacturing (AM) utilizes a high-energy heat source scanning at the surface of a powder layer in a pre-defined area to be melted and solidified to fabricate parts layer by layer. It is known that powder bed metal AM is primarily a thermal process and further, heat conduction is the dominant heat transfer mode in the process. Hence, understanding the powder bed thermal conductivity is crucial to process temperature predictions, because powder thermal conductivity could be substantially different from its solid counterpart. On the other hand, measuring the powder thermal conductivity is a challenging task. The objective of this study is to investigate the powder thermal conductivity using a method that combines a thermal diffusivity measurement technique and a numerical heat transfer model. In the experimental aspect, disk-shaped samples, with powder inside, made by a laser powder bed fusion (LPBF) system, are measured using a laser flash system to obtain the thermal diffusivity and the normalized temperature history during testing. In parallel, a finite element model is developed to simulate the transient heat transfer of the laser flash process. The numerical model was first validated using reference material testing. Then, the model is extended to incorporate powder enclosed in an LPBF sample with thermal properties to be determined using an inverse method to approximate the simulation results to the thermal data from the experiments. In order to include the powder particles’ contribution in the measurement, an improved model geometry, which improves the contact condition between powder particles and the sample solid shell, has been tested. A multi-point optimization inverse heat transfer method is used to calculate the powder thermal conductivity. From this study, the thermal conductivity of a nickel alloy 625 powder in powder bed conditions is estimated to be 1.01 W/m·K at 500 °C.

scholarly journals Influence of particle morphology and size distribution on the powder flowability and laser powder bed fusion manufacturability of Ti-6Al-4V alloy

2020 ◽  
Vol 31 ◽  
pp. 100929 ◽  
Author(s):  
Salah Eddine Brika ◽  
Morgan Letenneur ◽  
Christopher Alex Dion ◽  
Vladimir Brailovski
Keyword(s):  
Size Distribution ◽  
Particle Morphology ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Powder Flowability

scholarly journals Automatic Quality Assessments of Laser Powder Bed Fusion Builds from Photodiode Sensor Measurements

2020 ◽  
Author(s):  
Sarini Jayasinghe ◽  
Paolo Paoletti ◽  
Chris Sutcliffe ◽  
John Dardis ◽  
Nick Jones ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Learning Algorithms ◽  
Gaussian Process Regression ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Part Density

This study evaluates whether a combination of photodiode sensor measurements, taken during laser powder bed fusion (L-PBF) builds, can be used to predict the resulting build quality via a purely data-based approach. We analyse the relationship between build density and features that are extracted from sensor data collected from three different photodiodes. The study uses a Singular Value Decomposition to extract lower-dimensional features from photodiode measurements, which are then fed into machine learning algorithms. Several unsupervised learning methods are then employed to classify low density (< 99% part density) and high density (≥ 99% part density) specimens. Subsequently, a supervised learning method (Gaussian Process regression) is used to directly predict build density. Using the unsupervised clustering approaches, applied to features extracted from both photodiode sensor data as well as observations relating to the energy transferred to the material, build density was predicted with up to 93.54% accuracy. With regard to the supervised regression approach, a Gaussian Process algorithm was capable of predicting the build density with a RMS error of 3.65%. The study shows, therefore, that there is potential for machine learning algorithms to predict indicators of L-PBF build quality from photodiode build-measurements. Moreover, the work herein describes approaches that are predominantly probabilistic, thus facilitating uncertainty quantification in machine-learnt predictions of L-PBF build quality.

On the measurement of effective powder layer thickness in laser powder-bed fusion additive manufacturing of metals

2018 ◽  
Vol 4 (2) ◽  
pp. 109-116 ◽  
Author(s):  
Yahya Mahmoodkhani ◽  
Usman Ali ◽  
Shahriar Imani Shahabad ◽  
Adhitan Rani Kasinathan ◽  
Reza Esmaeilizadeh ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Layer Thickness ◽  
Powder Layer ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed

scholarly journals Increasing the Productivity of Laser Powder Bed Fusion for Stainless Steel 316L through Increased Layer Thickness

2020 ◽  
Author(s):  
Alexander Leicht ◽  
Marie Fischer ◽  
Uta Klement ◽  
Lars Nyborg ◽  
Eduard Hryha
Keyword(s):  
Stainless Steel ◽  
Layer Thickness ◽  
Electron Backscatter Diffraction ◽  
Powder Layer ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Stainless Steel 316L ◽  
Powder Bed ◽  
Backscatter Diffraction ◽  
Am Processes

AbstractAdditive manufacturing (AM) is able to generate parts of a quality comparable to those produced through conventional manufacturing, but most of the AM processes are associated with low build speeds, which reduce the overall productivity. This paper evaluates how increasing the powder layer thickness from 20 µm to 80 µm affects the build speed, microstructure and mechanical properties of stainless steel 316L parts that are produced using laser powder bed fusion. A detailed microstructure characterization was performed using scanning electron microscopy, electron backscatter diffraction, and x-ray powder diffraction in conjunction with tensile testing. The results suggest that parts can be fabricated four times faster with tensile strengths comparable to those obtained using standard process parameters. In either case, nominal relative density of > 99.9% is obtained but with the 80 µm layer thickness presenting some lack of fusion defects, which resulted in a reduced elongation to fracture. Still, acceptable yield strength and ultimate tensile strength values of 464 MPa and 605 MPa were obtained, and the average elongation to fracture was 44%, indicating that desirable properties can be achieved.

scholarly journals Influence of the particle size distribution of monomodal 316L powder on its flowability and processability in powder bed fusion

2021 ◽  
Author(s):  
Marvin A. Spurek ◽  
Lukas Haferkamp ◽  
Christian Weiss ◽  
Adriaan B. Spierings ◽  
Johannes H. Schleifenbaum ◽  
...  
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Powder Bed Fusion ◽  
Median Particle Size ◽  
Distribution Width ◽  
Powder Bed ◽  
Melt Pool ◽  
Powder Flowability ◽  
Median Particle ◽  
Part Density

AbstractPowder bed fusion (PBF) is the most commonly adopted additive manufacturing process for fabricating complex metal parts via the layer-wise melting of a powder bed using a laser beam. However, the qualification of PBF-manufactured parts remains challenging and expensive, thereby limiting the broader industrialization of the technology. Powder characteristics significantly influence part properties, and understanding the influencing factors contributes to effective quality standards for PBF. In this study, the influence of the particle size distribution (PSD) median and width on powder flowability and part properties is investigated. Seven gas-atomized SS316L powders with monomodal PSDs, a median particle size ranging from 10 μm to 60 μm, and a distribution width of 15 μm and 30 μm were analyzed and subsequently processed. The PBF-manufactured parts were analyzed in terms of density and melt pool dimensions. Although powder flowability was inversely related to the median particle size, it was unrelated to the distribution width. An inverse relationship between the median particle size and the part density was observed; however, no link was found to the distribution width. Likely, the melt pool depth and width fluctuation significantly influence the part density. The melt pool depth decreases and the width fluctuation increases with an increasing median particle size.

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