Abstract

The powder bed fusion-based additive manufacturing process uses a laser to melt and fuse powder metal material together and creates parts with intricate surface topography that are often influenced by laser path, layer-to-layer scanning strategies, and energy density. Surface topography investigations of as-built, nickel alloy (625) surfaces were performed by obtaining areal height maps using focus variation microscopy for samples produced at various energy density settings and two different scan strategies. Surface areal height maps and measured surface texture parameters revealed the highly irregular nature of surface topography created by laser powder bed fusion (LPBF). Effects of process parameters and energy density on the areal surface texture have been identified. Machine learning methods were applied to measured data to establish input and output relationships between process parameters and measured surface texture parameters with predictive capabilities. The advantages of utilizing such predictive models for process planning purposes are highlighted.

References

References
1.
Bourell
,
D. L.
,
Rosen
,
D. W.
, and
Leu
,
M. C.
,
2014
, “
The Roadmap for Additive Manufacturing and Its Impact, 3D Print
,”
Addit. Manuf.
,
1
(
1
), pp.
6
9
. 10.1089/3dp.2013.0002
2.
Townsend
,
A.
,
Senin
,
N.
,
Blunt
,
L.
,
Leach
,
R. K.
, and
Taylor
,
J. S.
,
2016
, “
Surface Texture Metrology for Metal Additive Manufacturing: a Review
,”
Precis. Eng.
,
46
, pp.
34
47
. 10.1016/j.precisioneng.2016.06.001
3.
Khairallah
,
S. A.
,
Anderson
,
A. T.
,
Rubenchik
,
A.
, and
King
,
W. E.
,
2016
, “
Laser Powder-Bed Fusion Additive Manufacturing: Physics of Complex Melt Flow and Formation Mechanisms of Pores, Spatter, and Denudation Zones
,”
Acta Mater.
,
108
, pp.
36
45
. 10.1016/j.actamat.2016.02.014
4.
Denlinger
,
E. R.
,
Jagdale
,
V.
,
Srinivasan
,
G. V.
,
El-Wardany
,
T.
, and
Michaleris
,
P.
,
2016
, “
Thermal Modeling of Inconel 718 Processed With Powder Bed Fusion and Experimental Validation Using In Situ Measurements
,”
Addit. Manuf.
,
11
, pp.
7
15
. 10.1016/j.addma.2016.03.003
5.
Criales
,
L. E.
,
Arisoy
,
Y. M.
,
Lane
,
B.
,
Moylan
,
S.
,
Donmez
,
A.
, and
Özel
,
T.
,
2017
, “
Predictive Modeling and Optimization of Multi-Track Processing for Laser Powder Bed Fusion of Nickel Alloy 625
,”
Addit. Manuf.
,
13
, pp.
14
36
. 10.1016/j.addma.2016.11.004
6.
Dunbar
,
A. J.
,
Denlinger
,
E. R.
,
Gouge
,
M. F.
,
Simpson
,
T. W.
, and
Michaleris
,
P.
,
2017
, “
Comparisons of Laser Powder Bed Fusion Additive Manufacturing Builds Through Experimental In Situ Distortion and Temperature Measurements
,”
Addit. Manuf.
,
15
, pp.
57
65
. 10.1016/j.addma.2017.03.003
7.
Everton
,
S. K.
,
Hirsch
,
M.
,
Stravroulakis
,
P.
,
Leach
,
R. K.
, and
Clare
,
A. T.
,
2016
, “
Review of In-Situ Process Monitoring and In-Situ Metrology for Metal Additive Manufacturing
,”
Mater. Des.
,
95
, pp.
431
445
. 10.1016/j.matdes.2016.01.099
8.
Grasso
,
M.
,
Laguzza
,
V.
,
Semeraro
,
Q.
, and
Colosimo
,
B. M.
,
2016
, “
In-Process Monitoring of Selective Laser Melting: Spatial Detection of Defects Via Image Data Analysis
,”
ASME J. Manuf. Sci. Eng.
,
139
(
5
), p.
051001
. 10.1115/1.4034715
9.
Grasso
,
M.
, and
Colosimo
,
B. M.
,
2017
, “
Process Defects and In Situ Monitoring Methods in Metal Powder Bed Fusion: A Review
,”
Meas. Sci. Technol.
,
28
(
4
), p.
044005
. 10.1088/1361-6501/aa5c4f
10.
Repossini
,
G.
,
Laguzza
,
V.
,
Grasso
,
M.
, and
Colosimo
,
B. M.
,
2017
, “
On the Use of Spatter Signature for In-Situ Monitoring of Laser Powder Bed Fusion
,”
Addit. Manuf.
,
16
, pp.
35
48
. 10.1016/j.addma.2017.05.004
11.
Zhang
,
B.
,
Ziegert
,
J.
,
Farahi
,
F.
, and
Davies
,
A.
,
2016
, “
In Situ Surface Topography of Laser Powder Bed Fusion Using Fringe Projection
,”
Addit. Manuf.
,
12
, pp.
100
1007
. 10.1016/j.addma.2016.08.001
12.
Dickins
,
A.
,
Widjanarko
,
T.
,
Lawes
,
S.
, and
Leach
,
R. K.
,
2018
, “
Design of a Multi-Sensor In-Situ Inspection System for Additive Manufacturing
,”
Proceedings of ASPE/Euspen Advancing Precision in Additive Manufacturing
,
Berkeley, CA
,
July 22–25
, pp.
225
245
.
13.
Thompson
,
A.
,
Senin
,
N.
,
Giusca
,
C.
, and
Leach
,
R.
,
2017
, “
Topography of Selectively Laser Melted Surfaces: A Comparison of Different Measurement Methods
,”
CIRP Ann.
,
66
(
1
), pp.
543
546
. 10.1016/j.cirp.2017.04.075
14.
Townsend
,
A.
,
Pagani
,
L.
,
Scott
,
P.
, and
Blunt
,
L.
,
2018
, “
Areal Surface Texture Data Extraction From X-Ray Computed Tomography Reconstructions of Metal Additively Manufactured Parts
,”
Precis. Eng.
,
48
, pp.
254
264
. 10.1016/j.precisioneng.2016.12.008
15.
Criales
,
L. E.
,
Arisoy
,
Y. M.
,
Lane
,
B.
,
Moylan
,
S.
,
Donmez
,
A.
, and
Özel
,
T.
,
2017
, “
Laser Powder Bed Fusion of Nickel Alloy 625: Experimental Investigations of Effects of Process Parameters on Melt Pool Size and Shape With Spatter Analysis
,”
Int. J. Mach. Tools Manuf.
,
121
, pp.
22
36
. 10.1016/j.ijmachtools.2017.03.004
16.
Arisoy
,
Y. M.
,
Criales
,
L. E.
,
Özel
,
T.
,
Lane
,
B.
,
Moylan
,
S.
, and
Donmez
,
A.
,
2017
, “
Influence of Scan Strategy and Process Parameters on Microstructure and Its Optimization in Additively Manufactured Nickel Alloy 625 Via Laser Powder Bed Fusion
,”
Int. J. Adv. Manuf. Technol.
,
90
(
5–8
), pp.
1393
1417
. 10.1007/s00170-016-9429-z
17.
Özel
,
T.
,
Altay
,
A.
,
Donmez
,
A.
, and
Leach
,
R.
,
2018
, “
Surface Topography Investigations on Nickel Alloy 625 Fabricated Via Laser Powder Bed Fusion
,”
Int. J. Adv. Manuf. Technol.
,
94
(
9–12
), pp.
4451
4458
. 10.1007/s00170-017-1187-z
18.
Gobert
,
C.
,
Reutzel
,
E. W.
,
Petrich
,
J.
,
Nassar
,
A. R.
, and
Phoha
,
S.
,
2018
, “
Application of Supervised Machine Learning for Defect Detection During Metallic Powder Bed Fusion Additive Manufacturing Using High Resolution Imaging
,”
Addit. Manuf.
,
21
, pp.
517
528
. 10.1016/j.addma.2018.04.005
19.
EMPA
, Swiss Federal Laboratories for Materials Science and Technology, https://www.empa.ch/web/coating-competence-center/selective-laser-melting, Accessed November 4, 2018.
20.
Leach
,
R. K.
,
2011
,
Optical Measurement of Surface Topography
,
Springer
,
Berlin, Heidelberg
.
21.
ISO 25178-3 B.E.
,
2012
, Geometrical Product Specifications (GPS) Surface Texture: Areal Part 3: Specification Operators. British Standards Institute, BS EN ISO 25178-3.
22.
ISO 25178-2 B.E.
,
2012
, Geometrical Product Specifications (GPS) Surface Texture: Areal 2: Terms, Definitions and Surface Texture Parameters. British Standards Institute, BS EN ISO 25178-2.
23.
Koza
,
J.
,
1994
, “Introduction to Genetic Programming,”
Advances in Genetic Programming
,
K. E.
Kinnear
, Jr.
, ed.,
The MIT Press
,
Cambridge, MA
, pp.
21
45
.
24.
Kayzoglu
,
T.
,
1999
, “
Determining Optimum Structure for Artificial Neural Networks
,”
25th Annual Technical Conference and Exhibition of the Remote Sensing Society
,
Cardiff
,
Sept. 8–10
, pp.
675
682
.
25.
Haykin
,
S.
,
1998
,
Neural Networks: A Comprehensive Foundation
,
2nd ed
,
Prentice Hall
,
Upper Saddle River, NJ
.
26.
Hyndman
,
R.
, and
Koehler
,
A. B.
,
2006
, “
Another Look at Measures of Forecast Accuracy
,”
Int. J. Forecast.
,
22
(
4
), pp.
679
688
. 10.1016/j.ijforecast.2006.03.001
27.
Ju
,
M. Y.
,
Wang
,
S. E.
, and
Guo
,
J. H.
,
2014
, “
Path Planning Using a Hybrid Evolutionary Algorithm Based on Tree Structure Encoding
,”
Sci. World J.
, p.
8
. 10.1155/2014/746260
28.
Demuth
,
H.
, and
Beale
,
M.
,
2017
,
Matlab User’s Guide
,
The MathWorks
,
Natick, MA
.
29.
Puheim
,
M.
, and
Madarasz
,
L.
,
2014
, “
Normalization of Inputs and Outputs of Neural Network Based Robotic Arm Controller in Role of Inverse Kinematic Model
,”
IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI)
,
Slovakia
,
Jan. 23–25
, pp.
35
38
.
30.
Dummett
,
M.
,
1998
, “
The Borda Count and Agenda Manipulation
,”
Soc. Choice Welf.
,
15
(
2
), pp.
289
296
. 10.1007/s003550050105
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