Evaluation of Powder Layer Density for the Selective Laser Melting (SLM) Process

Choi, Jin Nam; Shin, Gi-Hun; Lee, Hak Sung; Yang, Dong‐Yeol; Yang, Sangsun; Lee, Chang-Woo; Brochu, Mathieu; Yu, Juan

doi:10.2320/matertrans.m2016364

Cited by 25 publications

(11 citation statements)

References 14 publications

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“…Regarding the elastic modulus, the values determined are in concordance with the literature [11][12][13]. The 12-13% elongation at fracture of SLM samples is similar to what has been reported [13,33,35]. However, the elongation is not satisfactory and is still below the standard, which requires a minimum of 40% (details in Table 5).…”

Section: Physical-mechanical Propertiessupporting

confidence: 87%

“…The hall flow rate of this 316L powder was 18.2 s per 50 g. This is a high flowability compared with other 316L powders (e.g., LPW 316 [35]). This can be attributed to the fact that the powder did not have a wide particle size distribution with a large amount of small particles and it also has a high convexity parameter (described in Table 3).…”

Section: Powder Examinationmentioning

confidence: 80%

“…Figure 8 summarizes the effort undertaken to identify some sets of SLM parameters for stable processability on a preheated platform at 250 • C. As it was reported previously [13], this study also confirms that a uniform preheating significantly decreased the manufacturing defects and it can provide a relatively stable or stable processability. The hall flow rate of this 316L powder was 18.2 s per 50 g. This is a high flowability compared with other 316L powders (e.g., LPW 316 [35]). This can be attributed to the fact that the powder did not have a wide particle size distribution with a large amount of small particles and it also has a high convexity parameter (described in Table 3).…”

Section: Manufacturing Evaluationmentioning

confidence: 80%

“…The main steps to design the dental bridge are specific to the restorative dentistry field [30,31]. Initially, an intraoral digital scan of a prepared mandibular premolar (35) and molar (37) was conducted. The Dental Wings IntraOral (DWIO, Montréal, QC, Canada) system was use to capture multi-scan imaging with a continuous blue light projection.…”

Section: Dental Bride and Lattice Scaffolds Designmentioning

confidence: 99%

“…The ability of a powder to flow is a function of interparticle friction. As a general rule in the SLM process, a powder with high flowability tends to guarantee reproducible deposition of single powder layers, higher powder packing density and better mechanical properties [35].…”

Section: Powder Examinationmentioning

confidence: 99%

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Improving the Mechanical Strength of Dental Applications and Lattice Structures SLM Processed

Cosma

Kessler²,

Gebhardt

et al. 2020

Materials

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To manufacture custom medical parts or scaffolds with reduced defects and high mechanical characteristics, new research on optimizing the selective laser melting (SLM) parameters are needed. In this work, a biocompatible powder, 316L stainless steel, is characterized to understand the particle size, distribution, shape and flowability. Examination revealed that the 316L particles are smooth, nearly spherical, their mean diameter is 39.09 μm and just 10% of them hold a diameter less than 21.18 μm. SLM parameters under consideration include laser power up to 200 W, 250–1500 mm/s scanning speed, 80 μm hatch spacing, 35 μm layer thickness and a preheated platform. The effect of these on processability is evaluated. More than 100 samples are SLM-manufactured with different process parameters. The tensile results show that is possible to raise the ultimate tensile strength up to 840 MPa, adapting the SLM parameters for a stable processability, avoiding the technological defects caused by residual stress. Correlating with other recent studies on SLM technology, the tensile strength is 20% improved. To validate the SLM parameters and conditions established, complex bioengineering applications such as dental bridges and macro-porous grafts are SLM-processed, demonstrating the potential to manufacture medical products with increased mechanical resistance made of 316L.

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Section: Physical-mechanical Propertiessupporting

confidence: 87%

Section: Powder Examinationmentioning

confidence: 80%

Section: Manufacturing Evaluationmentioning

confidence: 80%

Section: Dental Bride and Lattice Scaffolds Designmentioning

confidence: 99%

Section: Powder Examinationmentioning

confidence: 99%

See 3 more Smart Citations

Improving the Mechanical Strength of Dental Applications and Lattice Structures SLM Processed

Cosma

Kessler²,

Gebhardt

et al. 2020

Materials

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Machine‐Learning‐Based Monitoring of Laser Powder Bed Fusion

Yuan

Guss

Wilson

et al. 2018

Adv Materials Technologies

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variability in the powder properties, [9] bed thickness nonuniformity, [10] and laser parameters and scan paths that result in improper power melting. [11] Thus, even after optimizing LPBF operating para meters and identifying suitable processing windows, [12] rapid build qualification, improved quality, and higher production yields require methods of monitoring the melt pool and/or powder bed in situ, i.e., during a build, that enable realtime process feedback and automated quality detection. [13,14] The majority of LPBF process moni toring approaches rely on noncontact sensing [15] from optical, thermal, [16] and/ or acoustic [17,18] sensors. These sen sors provide assessments of spatial and spectral features of the melt pool, [19,20] process plume, [21] degree of spatter, [22][23][24][25] overhang layers, [26] or print bed. High speed image sequences of the melting process, [27] scans of the powder bed, [10,28] beam quality, [29] and/or thermal monitoring [30] are all routinely collected forms of in situ monitoring data. Making use of this data requires methods that can extract rel evant diagnostic information. For instance, before initiating laser melting, automated computer vision algorithms can characterize metal powder feedstocks, [31] and image analysis of newly spread powder can reveal nonuniformities in the powder bed thickness. [32] Aminzadeh et al. demonstrated layerbylayer detection of fusion defects from images using a Bayesian clas sifier. [33] Realtime events such as material ejecta are detectable by applying manually set thresholds to highspeed nearinfrared images of the melt pool. Also, increasing the µm pixel −1 image resolution relative to the standard deviation of measured track width, σ measured , may result in improved predictions of the final track width, δ predicted , and topography. [34] Reducing laser power proportionally to an integrated signal from a photodiode cali brated against a camera results in smoother overhang struc tures. [35] Using images of the print bed taken after laser melting, a level sets method can detect intentionally created defects, [36] machine vision algorithms can identify pore defects, [37] and multifractal image analysis can characterize layers with balling, cracks and pores, and no defects. [38] Visual imaging equipment is appealing to LPBF monitoring systems because it is relatively inexpensive and provides noncontact sensing. [13] As with most additive manufacturing systems, analysis of LPBF sensor data currently occurs postbuild, rendering A two-step machine learning approach to monitoring laser powder bed fusion (LPBF) additive manufacturing is demonstrated that enables on-the-fly assessments of laser track welds. First, in situ video melt pool data acquired during LPBF is labeled according to the (1) average and (2) standard deviation of individual track width and also (3) whether or not the track is continuous, measured postbuild through an ex situ height map analysis algorithm. This procedure generates three ground truth labeled datasets for supervised...

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Effect of hard and soft re-coater blade on porosity and processability of thin walls and overhangs in laser powder bed fusion additive manufacturing

Reijonen,

Revuelta,

Metsä-Kortelainen

et al. 2023

Int J Adv Manuf Technol

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Spreading powder into thin layers is a fundamental step in the laser powder bed fusion (PBF-LB) additive manufacturing process. This step is called re-coating and it is typically performed using either a hard, soft or brush-type re-coater blade or a rotating roller, depending on the machine brand and model. With such variety in powder spreading approaches, the question arises whether the used re-coater type has a significant effect on the quality of parts produced? In this study, an industrial contact image sensor integrated to the re-coater of a PBF-LB system was used for powder bed quality monitoring. Powder bed images at 21 µm/pixel resolution, 184 mm scanning width and 95 mm/s re-coating speed were acquired. With this, the effect of using either soft (rubber) or hard (steel) re-coater blade on the processability of challenging features such as thin walls and steep overhangs was studied. In addition, porosity and dimensional accuracy of parts produced using either the soft or hard blade was analyzed with X-ray computed tomography. It is shown that when building bulk material without any complex features, both the hard and soft re-coating blade results in extremely low porosity ≤ 0.001% without any issues in the processability. However, when thin walls and overhangs are produced, differences in processability, porosity and dimensional accuracy are observed as a function of re-coater blade and part orientation. This is an important factor in understanding all the significant sources contributing to the variability on quality of parts produced using different PBF-LB machines.

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Evaluation of Powder Layer Density for the Selective Laser Melting (SLM) Process

Cited by 25 publications

References 14 publications

Improving the Mechanical Strength of Dental Applications and Lattice Structures SLM Processed

Improving the Mechanical Strength of Dental Applications and Lattice Structures SLM Processed

Machine‐Learning‐Based Monitoring of Laser Powder Bed Fusion

Effect of hard and soft re-coater blade on porosity and processability of thin walls and overhangs in laser powder bed fusion additive manufacturing

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