Abstract
Metal powder-based Additive Manufacturing (AM) processes are increasingly used in industry and science due to their unique capability of building complex geometries. However, the immense computational cost associated with AM predictive models hinders the further industrial adoption of these technologies for time-sensitive applications, process design with uncertainties or real-time process control. In this work, a novel approach to accelerate the explicit finite element analysis of the transient heat transfer of AM processes is proposed using Graphical Processing Units. The challenges associated with this approach are enumerated and multiple strategies to overcome each challenge are discussed. The performance of the proposed algorithms is evaluated on multiple test cases. Speed-ups of about 100 ×–150 × compared to an optimized single CPU core implementation for the best strategy were achieved.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 879-894 |
| Number of pages | 16 |
| Journal | Computational Mechanics |
| Volume | 64 |
| Issue number | 3 |
| DOIs | |
| State | Published - Sep 15 2019 |
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Keywords
- Additive manufacturing
- Directed energy deposition
- Finite element methods
- GPU acceleration
- High performance computing
ASJC Scopus subject areas
- Computational Mechanics
- Ocean Engineering
- Mechanical Engineering
- Computational Theory and Mathematics
- Computational Mathematics
- Applied Mathematics
Cite this
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Acceleration strategies for explicit finite element analysis of metal powder-based additive manufacturing processes using graphical processing units. / Mozaffar, Mojtaba; Ndip-Agbor, Ebot; Lin, Stephen; Wagner, Gregory J; Ehmann, Kornel; Cao, Jian.
In: Computational Mechanics, Vol. 64, No. 3, 15.09.2019, p. 879-894.Research output: Contribution to journal › Article
TY - JOUR
T1 - Acceleration strategies for explicit finite element analysis of metal powder-based additive manufacturing processes using graphical processing units
AU - Mozaffar, Mojtaba
AU - Ndip-Agbor, Ebot
AU - Lin, Stephen
AU - Wagner, Gregory J
AU - Ehmann, Kornel
AU - Cao, Jian
PY - 2019/9/15
Y1 - 2019/9/15
N2 - Metal powder-based Additive Manufacturing (AM) processes are increasingly used in industry and science due to their unique capability of building complex geometries. However, the immense computational cost associated with AM predictive models hinders the further industrial adoption of these technologies for time-sensitive applications, process design with uncertainties or real-time process control. In this work, a novel approach to accelerate the explicit finite element analysis of the transient heat transfer of AM processes is proposed using Graphical Processing Units. The challenges associated with this approach are enumerated and multiple strategies to overcome each challenge are discussed. The performance of the proposed algorithms is evaluated on multiple test cases. Speed-ups of about 100 ×–150 × compared to an optimized single CPU core implementation for the best strategy were achieved.
AB - Metal powder-based Additive Manufacturing (AM) processes are increasingly used in industry and science due to their unique capability of building complex geometries. However, the immense computational cost associated with AM predictive models hinders the further industrial adoption of these technologies for time-sensitive applications, process design with uncertainties or real-time process control. In this work, a novel approach to accelerate the explicit finite element analysis of the transient heat transfer of AM processes is proposed using Graphical Processing Units. The challenges associated with this approach are enumerated and multiple strategies to overcome each challenge are discussed. The performance of the proposed algorithms is evaluated on multiple test cases. Speed-ups of about 100 ×–150 × compared to an optimized single CPU core implementation for the best strategy were achieved.
KW - Additive manufacturing
KW - Directed energy deposition
KW - Finite element methods
KW - GPU acceleration
KW - High performance computing
UR - http://www.scopus.com/inward/record.url?scp=85062774543&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062774543&partnerID=8YFLogxK
U2 - 10.1007/s00466-019-01685-4
DO - 10.1007/s00466-019-01685-4
M3 - Article
VL - 64
SP - 879
EP - 894
JO - Computational Mechanics
JF - Computational Mechanics
SN - 0178-7675
IS - 3
ER -