Abstract

Additive manufacturing (AM) processes such as direct metal laser sintering (DMLS) are highly attractive manufacturing processes due to the ability to create certain geometries which would be prohibitive or even impossible to manufacture by other means. However, with such high thermal gradients which are usually present in these processes, manufacturing distortions may result in the creation of unacceptable parts. This paper presents an approach to compensate input STL files based on registration of the point cloud from sacrificial part builds. A novel strain energy based non-rigid registration algorithm has been developed for robust registration of data points to the original computer-aided design (CAD) model. A neural network based approach is used to learn the deformation of the geometry based on the deviation of the scan geometry. This network is subsequently used to modify the STL file to generate a new compensated STL file. The compensated STL file was validated by building parts and comparing the change in the part distortion.

References

1.
Paul
,
R.
,
Anand
,
S.
, and
Gerner
,
F.
,
2014
, “
Effect of Thermal Deformation on Part Errors in Metal Powder Based Additive Manufacturing Processes
,”
ASME J. Manuf. Sci. Eng.
,
136
(
3
), pp.
1
12
. 10.1115/1.4026524
2.
Mukherjee
,
T.
,
Zuback
,
J. S.
,
De
,
A.
, and
DebRoy
,
T.
,
2016
, “
Printability of Alloys for Additive Manufacturing
,”
Sci. Rep.
,
6
(
19717
), pp.
1
8
. 10.1038/srep19717
3.
Cheng
,
B.
,
Shrestha
,
S.
, and
Chou
,
K.
,
2016
, “
Stress and Deformation Evaluations of Scanning Strategy Effect in Selective Laser Melting
,”
Addit. Manuf.
,
12
(
B
), pp.
240
251
. 10.1016/j.addma.2016.05.007
4.
Ingrassia
,
T.
,
Nigrelli
,
V.
,
Ricotta
,
V.
, and
Tartamella
,
C.
,
2016
, “
Process Parameters Influence in Additive Manufacturing
,”
International Joint Conference on Mechanics, Design Engineering & Advanced Manufacturing
,
Catania, Italy
,
Sept. 14–16
.
5.
Gao
,
W.
,
Zhang
,
Y.
,
Ramanujan
,
D.
,
Ramani
,
K.
,
Chen
,
Y.
,
Williams
,
C.
,
Wnag
,
C.
,
Shin
,
Y.
,
Zhang
,
S.
, and
Zavattieri
,
P.
,
2015
, “
The Status, Challenges, and Future of Additive Manufacturing in Engineering
,”
Comput.-Aided Des.
,
69
(
1
), pp.
65
89
. 10.1016/j.cad.2015.04.001
6.
Mukherjee
,
V.
,
Manvatkar
,
V.
,
De
,
A.
, and
DebRoy
,
T.
,
2017
, “
Mitigation of Thermal Distortion During Additive Manufacturing
,”
Scr. Mater.
,
127
(
1
), pp.
79
83
. 10.1016/j.scriptamat.2016.09.001
7.
Wang
,
X.
,
Gong
,
X.
, and
Chou
,
K.
,
2017
, “
Review on Powder-Bed Laser Additive Manufacturing of Inconel 718 Parts
,”
Proc IMechE Part B: J. Eng. Manuf.
,
231
(
11
), pp.
1890
1903
. 10.1177/0954405415619883
8.
Das
,
P.
,
Mhapsekar
,
K.
,
Chowdhury
,
S.
,
Samant
,
R.
, and
Anand
,
S.
,
2017
, “
Selection of Build Orientation for Optimal Support Structures and Minimum Part Errors in Additive Manufacturing
,”
Comput.-Aided Des. Appl.
,
14
(
sup1
), pp.
1
13
. 10.1080/16864360.2017.1308074
9.
Schoinochoritis
,
B.
,
Chantzis
,
D.
, and
Salonitis
,
K.
,
2017
, “
Simulation of Metallic Powder Bed Additive Manufacturing Processes With the Finite Element Method: A Critical Review
,”
Proc IMechE Part B: J Eng. Manuf.
,
231
(
1
), pp.
96
117
. 10.1177/0954405414567522
10.
Keller
,
N.
, and
Ploshikhin
,
V.
,
2014
, “
New Method for Fast Prediction of Residual Stress and Distortion of AM Parts
,”
Solid Freeform Fabrication Symposium
,
Austin, TX
,
Aug. 4–6
, Vol.
25
.
11.
Afazov
,
S.
,
Denmark
,
W.
,
Toralles
,
B.
,
Holloway
,
A.
, and
Yaghi
,
A.
,
2017
, “
Distortion Prediction and Compensation in Selective Laser Melting
,”
Addit. Manuf.
,
17
(
1
), pp.
15
22
. 10.1016/j.addma.2017.07.005
12.
Chowdhury
,
S.
,
2016
, “
Artificial Neural Network Based Geometric Compensation for Thermal Deformation in Additive Manufacturing Processes
,”
M.S. thesis
,
University of Cincinnati
,
Cincinnati, Ohio
.
13.
Tong
,
K.
,
Joshi
,
S.
, and
Lehtihet
,
E.
,
2008
, “
Error Compensation for Fused Deposition Modeling (FDM) Machine by Correcting Slice Files
,”
Rapid Prototyp. J.
,
14
(
1
), pp.
4
14
. 10.1108/13552540810841517
14.
Huang
,
Q.
,
Zhang
,
J.
,
Sabbaghi
,
A.
, and
Dasgupta
,
T.
,
2015
, “
Optimal Offline Compensation of Shape Shrinkage for 3D Printing Processes
,”
IIE Trans.
,
47
(
5
), pp.
431
441
. 10.1080/0740817X.2014.955599
15.
Huang
,
Q.
,
Nouri
,
H.
,
Zu
,
K.
,
Chen
,
Y.
,
Sosina
,
S.
, and
Dasgupta
,
T.
,
2014
, “
Statistical Predictive Modeling and Compensation of Geometric Deviations of Three-Dimensional Printed Products
,”
ASME J. Manuf. Sci. Eng.
,
136
(
6
), p.
061008
. 10.1115/1.4028510
16.
Huang
,
Q.
,
2015
, “
An Analytical Foundation for Optimal Compensation of Three-Dimensional Shape Deviations in Additive Manufacturing
,”
ASME J. Manuf. Sci. Eng.
,
138
(
6
), p.
061010
. 10.1115/1.4032220
17.
Cheng
,
L.
,
Wang
,
A.
, and
Tsung
,
F.
,
2018
, “
A Prediction and Compensation Scheme for In Plane Shape Deviation of Additive Manufacturing With Information on Process Parameters
,”
IISE Trans.
,
50
(
5
), pp.
394
406
. 10.1080/24725854.2017.1402224
18.
Zhu
,
Z.
,
Anwer
,
N.
, and
Mathieu
,
L.
,
2017
, “
Deviation Modeling and Shape Transformation in Design for Additive Manufacturing
,”
Procedia CIRP
,
60
(
1
), pp.
211
216
. 10.1016/j.procir.2017.01.023
19.
Afazov
,
S.
,
Okioga
,
A.
,
Holloway
,
A.
,
Denmark
,
W.
,
Triantaphyllou
,
A.
,
Smith
,
S. A.
, and
Bradley-Smith
,
L.
,
2017
, “
A Methodology for Precision Additive Manufacturing Through Compensation
,”
Precis. Eng.
,
50
(
1
), pp.
269
274
. 10.1016/j.precisioneng.2017.05.014
20.
Xu
,
K.
,
Kwok
,
T. H.
,
Zhao
,
Z.
, and
Chen
,
Y.
,
2017
, “
A Reverse Compensation Framework for Shape Deformation Control in Additive Manufacturing
,”
ASME J. Comput. Inf. Sci. Eng.
,
17
(
2
), p.
021012
. 10.1115/1.4034874
21.
Chowdhury
,
S.
, and
Anand
,
S.
,
2016
, “
Artificial Neural Network Based Geometric Compensation for Thermal Deformation in Additive Manufacturing Processes
,”
MSEC-NAMRC Symposia. MSEC2016-8784
,
Blacksburg, VA
,
June 27–July 1
, p.
V003T08A006
.
22.
Chowdhury
,
S.
,
Mhapsekar
,
K.
, and
Anand
,
S.
,
2017
, “
Part Build Orientation Optimization and Neural Network-Based Geometry Compensation for Additive Manufacturing Process
,”
ASME J. Manuf. Sci. Eng.
,
140
(
3
), p.
031009
. 10.1115/1.4038293
23.
Paul
,
R.
,
Anand
,
S.
, and
Gerner
,
F.
,
2014
, “
Effect of Thermal Deformation on Part Errors in Metal Powder Based Additive Manufacturing Processes
,”
ASME J. Manuf. Sci. Eng.
,
136
(
3
), p.
031009
. 10.1115/1.4026524
24.
Paul
,
R.
,
2013
, “
Modeling and Optimization of Powder Based Additive Manufacturing (AM) Processes
,”
Ph.D. dissertation
,
University of Cincinnati
,
Cincinnati, OH
.
25.
Roberts
,
I. A.
,
2012
, “
Investigation of Residual Stresses in the Laser Melting of Metal Powders in Additive Layer Manufacturing
,”
Ph.D. dissertation
,
University of Wolverhampton
,
Wolverhampton, England
.
26.
Kushan
,
M.
,
Poyraz
,
O.
,
Uzunonat
,
Y.
, and
Orak
,
S.
,
2018
, “
Systematical Review on the Numerical Simulations of Laser Powder Bed Additive Manufacturing
,”
Sigma J. Eng. Nat. Sci.
,
36
(
4
), pp.
1197
1214
.
27.
Besl
,
P.
, and
McKay
,
N.
,
1992
, “
A Method for Registration of 3D Shapes
,”
IEEE Trans. Pattern Anal. Mac. Intell.
,
14
(
2
), pp.
239
256
. 10.1109/34.121791
28.
Li
,
H.
,
Sumner
,
R.
, and
Pauly
,
M.
,
2008
, “
Global Correspondence Optimization for Non-Rigid Registration of Depth Scans
,”
Eurographics Symposium on Geometry Processing
,
Copenhagen, Denmark
,
July 2–4
, Vol.
27
, No.
5
.
29.
Hahnel
,
D.
,
Thrun
,
S.
, and
Burgard
,
W.
,
2003
, “
An Extension of the ICP Algorithm for Modeling Nonrigid Objects With Mobile Robots
,”
International Joint Conference on Artificial Intelligence
,
Acapulco, Mexico
,
Aug. 9–15
.
30.
Amberg
,
B.
,
Romdhani
,
S.
, and
Vetter
,
T.
,
2007
, “
Optimal Step Nonrigid ICP Algorithms for Surface Registration
,”
IEEE Conference on Computer Vision and Pattern Recognition
,
Minneapolis, MN
,
June 17–22
.
31.
Pauly
,
M.
,
Mitra
,
N. J.
,
Giesen
,
J.
,
Guibas
,
L.
, and
Gross
,
M.
,
2005
, “
Example-Based 3D Scan Completion
,”
Eurographics Symposium on Geometry Processing
,
Vienna, Austria
,
July 4–6
.
32.
Guyan
,
R.
,
1965
, “
Reduction of Stiffness and Mass Matrices
,”
AIAA J.
,
3
(
2
), pp.
380
380
. 10.2514/3.2874
33.
Rusinkiewicz
,
S.
, and
Levoy
,
M.
,
2001
, “
Efficient Variants of the ICP Algorithm
,”
Third International Conference on 3D Digital Imaging and Modeling
,
Quebec City, Canada
,
May 28–June 1
, pp.
145
152
.
34.
Matsuda
,
T.
,
Sakaue
,
K.
, and
Yokoya
,
N.
,
1996
, “
Registration and Integration of Multiple Range Images for 3D Model Construction
,”
International Conference on Pattern Recognition
,
Vienna, Austria
,
Aug. 25–29
, pp.
879
883
.
35.
Turk
,
G.
, and
Levoy
,
M.
,
1994
, “
Zippered Polygon Meshes From Range Images
,”
SIGGRAPH
,
Orlando, FL
,
July 24–29
, pp.
311
318
. 10.1145/192161.192241
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