Hatch pattern based inherent strain prediction using neural networks for powder bed fusion additive manufacturing

2020 ◽  
Vol 56 ◽  
pp. 1344-1352 ◽  
Author(s):  
Lun Li ◽  
Sam Anand
Keyword(s):  
Neural Networks ◽  
Additive Manufacturing ◽  
Powder Bed Fusion ◽  
Powder Bed ◽  
Inherent Strain

Multi-material model for the simulation of powder bed fusion additive manufacturing

2021 ◽  
Vol 194 ◽  
pp. 110415
Author(s):  
Vera E. Küng ◽  
Robert Scherr ◽  
Matthias Markl ◽  
Carolin Körner
Keyword(s):  
Additive Manufacturing ◽  
Material Model ◽  
Powder Bed Fusion ◽  
Powder Bed

scholarly journals GEOMETRICAL BENCHMARKING OF LASER POWDER BED FUSION SYSTEMS BASED ON DESIGNER NEEDS

2021 ◽  
Vol 1 ◽  
pp. 1657-1666
Author(s):  
Joaquin Montero ◽  
Sebastian Weber ◽  
Christoph Petroll ◽  
Stefan Brenner ◽  
Matthias Bleckmann ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Use Case ◽  
Powder Bed Fusion ◽  
Design For Additive Manufacturing ◽  
Laser Powder Bed Fusion ◽  
Measuring Systems ◽  
Powder Bed ◽  
Geometric Dimensioning And Tolerancing ◽  
Uncertainty Map ◽  
New System

AbstractCommercially available metal Laser Powder Bed Fusion (L-PBF) systems are steadily evolving. Thus, design limitations narrow and the diversity of achievable geometries widens. This progress leads researchers to create innovative benchmarks to understand the new system capabilities. Thereby, designers can update their knowledge base in design for additive manufacturing (DfAM). To date, there are plenty of geometrical benchmarks that seek to develop generic test artefacts. Still, they are often complex to measure, and the information they deliver may not be relevant to some designers. This article proposes a geometrical benchmarking approach for metal L-PBF systems based on the designer needs. Furthermore, Geometric Dimensioning and Tolerancing (GD&T) characteristics enhance the approach. A practical use-case is presented, consisting of developing, manufacturing, and measuring a meaningful and straightforward geometric test artefact. Moreover, optical measuring systems are used to create a tailored uncertainty map for benchmarking two different L-PBF systems.

Establishing Specimen Property to Part Performance Relationships for Laser Beam Powder Bed Fusion Additive Manufacturing

2021 ◽  
pp. 106384
Author(s):  
Arash Soltani-Tehrani ◽  
Rakish Shrestha ◽  
Nam Phan ◽  
Mohsen Seifi ◽  
Nima Shamsaei
Keyword(s):  
Additive Manufacturing ◽  
Laser Beam ◽  
Powder Bed Fusion ◽  
Powder Bed
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