The Influence of a Ceramic Recoater Blade on 3D Printing using Direct Metal Laser Sintering

Daňa, Milan; Zetková, Ivana; Hanzl, Pavel

doi:10.21062/ujep/239.2019/a/1213-2489/mt/19/1/23

Cited by 9 publications

(3 citation statements)

References 5 publications

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“…The following are the main components of the apparatus (Figure 1): the powder dispensing module, consisting of the dispensing platform (1), the actuator (2) and the dispensing duct (3); the powder spreading module, consisting of the spreading platform (4) controlled by the actuator (5) and equipped with the load cells (6); the spreading mechanism, consisting of the spreading arm (7), the actuator (8) monitored by a torque sensor (9), and two limit switches (10); the powder bed uniformity control module, consisting of the CCD camera (11) and the 3D scanner (12). In addition to the other main components, the apparatus includes the powder collector (13), the control panel (14), and the main platform (15).…”

Section: Design and Description Of The Powder Spreading Apparatusmentioning

confidence: 99%

“…Nevertheless, wear and tear, particularly of polymeric blades, can generate less uniform spreading and include contamination in the powder bed. In addition, interference between flexible blades and surface imperfections can generate temporary jamming followed by abrupt movements of the spreading tool that can cause disturbances to the spread powder bed [11][12][13]. Other systems feature spreading mechanisms including mobile powder supply tanks.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Apparatus for the Simulation of Powder Spreading Procedures in Powder-Bed-Based Additive Manufacturing Processes: Design, Calibration, and Case Study

Brika,

Brailovski

2023

JMMP

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Powder-bed-based additive manufacturing processes (PBAM) are sensitive to variations in powder feedstock characteristics, and yet the link between the powder properties and process performance is still not well established, which complicates the powder selection, quality control, and process improvement processes. An accurate assessment of the powder characteristics and behavior during recoating is important and must include the flow and packing properties of the powders, which are dependent on the application conditions. To fulfill the need for suitable powder testing techniques, a novel apparatus is developed to reproduce the generic PBAM powder spreading procedure and allow the measurements of the powder bed density, surface uniformity, and spreading forces as functions of the powder characteristics and spreading conditions, including the spreading speed and the type of spreading mechanism. This equipment could be used for research and development purposes as well as for the quality control of the PBAM powder feedstock, as showcased in this paper using a gas-atomized Ti-6Al-4V powder (D10 = 25.3 µm, D50 = 35.8 µm and D90 = 46.4 µm) spread using a rigid blade by varying the recoating speed from 100 to 500 mm/s and the layer thickness from 30 to 100 µm.

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Section: Design and Description Of The Powder Spreading Apparatusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Apparatus for the Simulation of Powder Spreading Procedures in Powder-Bed-Based Additive Manufacturing Processes: Design, Calibration, and Case Study

Brika,

Brailovski

2023

JMMP

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“…Then, the building platform is heated if required, and the first layer of powder is spread on the building platform using a recoater. This recoater can have different geometries (flat, round, or sharp) [30] and materials (ceramic, high-speed steel, or carbon fiber brush) [31]. Then, the laser starts scanning specific locations in the powder bed according to the part's geometry.…”

Section: Overview Of the L-pbf Processmentioning

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 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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The Influence of a Ceramic Recoater Blade on 3D Printing using Direct Metal Laser Sintering

Cited by 9 publications

References 5 publications

A Novel Apparatus for the Simulation of Powder Spreading Procedures in Powder-Bed-Based Additive Manufacturing Processes: Design, Calibration, and Case Study

A Novel Apparatus for the Simulation of Powder Spreading Procedures in Powder-Bed-Based Additive Manufacturing Processes: Design, Calibration, and Case Study

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

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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