Macroscale finite element (FE) models, with their ability to simulate additive manufacturing (AM) processes of metal parts and accurately predict residual stress distribution, are potentially powerful design tools. However, these simulations require enormous computational cost, even for a small part only a few orders larger than the melt pool size. The existing adaptive meshing techniques to reduce computational cost substantially by selectively coarsening are not well suited for AM process simulations due to the continuous modification of model geometry as material is added to the system. To address this limitation, a new FE framework is developed. The new FE framework is based on introducing updated discretized geometries at regular intervals during the simulation process, allowing greater flexibility to control the degree of mesh coarsening than a technique based on element merging recently reported in the literature. The new framework is evaluated by simulating direct metal deposition (DMD) of a thin-walled rectangular and a thin-walled cylindrical part, and comparing the computational speed and predicted results with those predicted by simulations using the conventional framework. The comparison shows excellent agreement in the predicted stress and plastic strain fields, with substantial savings in the simulation time. The method is then validated by comparing the predicted residual elastic strain with that measured experimentally by neutron diffraction of the thin-walled rectangular part. Finally, the new framework's capability to substantially reduce the simulation time for large-scale AM parts is demonstrated by simulating a one-half foot thin-walled cylindrical part.

References

References
1.
Gibson
,
I.
,
Rosen
,
D.
, and
Stucker
,
B.
,
2015
,
Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing
, Vol. 498, Springer-Verlag, New York.
2.
Sames
,
W. J.
,
List
,
F. A.
,
Pannala
,
S.
,
Dehoff
,
R. R.
, and
Babu
,
S. S.
,
2016
, “
The Metallurgy and Processing Science of Metal Additive Manufacturing
,”
Int. Mater. Rev.
,
61
(5), pp.
315
360
.
3.
Gu
,
D. D.
,
Meiners
,
W.
,
Wissenbach
,
K.
, and
Poprawe
,
R.
,
2012
, “
Laser Additive Manufacturing of Metallic Components: Materials, Processes and Mechanisms
,”
Int. Mater. Rev.
,
57
(
3
), pp.
133
164
.
4.
Oropallo
,
W.
, and
Piegl
,
L. A.
,
2016
, “
Ten Challenges in 3D Printing
,”
Eng. Comput.
,
32
(
1
), pp.
135
148
.
5.
Frazier
,
W. E.
,
2014
, “
Metal Additive Manufacturing: A Review
,”
J. Mater. Eng. Perform.
,
23
(
6
), pp.
1917
1928
.
6.
Atkinson
,
H.
, and
Davies
,
S.
,
2000
, “
Fundamental Aspects of Hot Isostatic Pressing: An Overview
,”
Metall. Mater. Trans. A
,
31
(
12
), pp.
2981
3000
.
7.
Denlinger
,
E. R.
,
Heigel
,
J. C.
,
Michaleris
,
P.
, and
Palmer
,
T.
,
2015
, “
Effect of Inter-Layer Dwell Time on Distortion and Residual Stress in Additive Manufacturing of Titanium and Nickel Alloys
,”
J. Mater. Process. Technol.
,
215
, pp.
123
131
.
8.
Bikas
,
H.
,
Stavropoulos
,
P.
, and
Chryssolouris
,
G.
,
2016
, “
Additive Manufacturing Methods and Modelling Approaches: A Critical Review
,”
Int. J. Adv. Manuf. Technol.
,
83
(
1–4
), pp.
389
405
.
9.
Megahed
,
M.
,
Mindt
,
H.-W.
,
N'Dri
,
N.
,
Duan
,
H.
, and
Desmaison
,
O.
,
2016
, “
Metal Additive-Manufacturing Process and Residual Stress Modeling
,”
Integr. Mater. Manuf. Innovation
,
5
(
1
), p.
4
.
10.
Denlinger
,
E. R.
,
Heigel
,
J. C.
, and
Michaleris
,
P.
,
2014
, “
Residual Stress and Distortion Modeling of Electron Beam Direct Manufacturing Ti-6Al-4V
,”
Proc. Inst. Mech. Eng., Part B
,
229
(
10
), pp. 1803–1813.
11.
Denlinger
,
E. R.
,
Irwin
,
J.
, and
Michaleris
,
P.
,
2014
, “
Thermomechanical Modeling of Additive Manufacturing Large Parts
,”
ASME J. Manuf. Sci. Eng.
,
136
(
6
), p.
061007
.
12.
Hodge
,
N. E.
,
Ferencz
,
R. M.
, and
Solberg
,
J. M.
,
2014
, “
Implementation of a Thermomechanical Model for the Simulation of Selective Laser Melting
,”
Comput. Mech.
,
54
(
1
), pp.
33
51
.
13.
Hussein
,
A.
,
Hao
,
L.
,
Yan
,
C.
, and
Everson
,
R.
,
2013
, “
Finite Element Simulation of the Temperature and Stress Fields in Single Layers Built Without-Support in Selective Laser Melting
,”
Mater. Des.
,
52
, pp.
638
647
.
14.
Li
,
C.
,
Liu
,
J. F.
, and
Guo
,
Y. B.
,
2016
, “
Prediction of Residual Stress and Part Distortion in Selective Laser Melting
,”
Procedia CIRP
,
45
, pp.
171
174
.
15.
Mukherjee
,
T.
,
Zhang
,
W.
, and
DebRoy
,
T.
,
2017
, “
An Improved Prediction of Residual Stresses and Distortion in Additive Manufacturing
,”
Comput. Mater. Sci.
,
126
, pp.
360
372
.
16.
Papadakis
,
L.
,
Loizou
,
A.
,
Risse
,
J.
, and
Schrage
,
J.
,
2014
, “
Numerical Computation of Component Shape Distortion Manufactured by Selective Laser Melting
,”
Procedia CIRP
,
18
, pp.
90
95
.
17.
Zaeh
,
M. F.
, and
Branner
,
G.
,
2010
, “
Investigations on Residual Stresses and Deformations in Selective Laser Melting
,”
Prod. Eng.
,
4
(
1
), pp.
35
45
.
18.
Ding
,
J.
,
Colegrove
,
P.
,
Mehnen
,
J.
,
Ganguly
,
S.
,
Almeida
,
P. M. S.
,
Wang
,
F.
, and
Williams
,
S.
,
2011
, “
Thermo-Mechanical Analysis of Wire and Arc Additive Layer Manufacturing Process on Large Multi-Layer Parts
,”
Comput. Mater. Sci.
,
50
(
12
), pp.
3315
3322
.
19.
Amine
,
T.
,
Newkirk
,
J. W.
, and
Liou
,
F.
,
2014
, “
An Investigation of the Effect of Direct Metal Deposition Parameters on the Characteristics of the Deposited Layers
,”
Case Stud. Therm. Eng.
,
3
, pp.
21
34
.
20.
Michaleris
,
P.
,
2014
, “
Modeling Metal Deposition in Heat Transfer Analyses of Additive Manufacturing Processes
,”
Finite Elem. Anal. Des.
,
86
, pp.
51
60
.
21.
ABAQUS
,
2014
, “
ABAQUS User's Manual: Version 6.14
,” ABAQUS, Pawtucket, RI.
22.
Kubiak
,
M.
,
Piekarska
,
W.
, and
Parkitny
,
R.
,
2015
, “
Theoretical Study on Thermal and Structural Phenomena in Thin Elements Heated by a Laser Beam
,”
Arch. Mech.
,
67
(
1
), pp.
3
24
.http://am.ippt.pan.pl/am/article/view/v67p3
You do not currently have access to this content.