Coaxial imaging of melt pool dynamics provides several advantages over other monitoring methods in selective laser melting (SLM). The ability to track the processing zone ensures the possibility to observe defect formation dynamics mainly related to melting and solidification. Commonly, the melt pool dynamics are observed by means of process emission. In process emission images, geometrical information of the melt pool is not directly available and their extraction would require the use of a calibrated sensor in order to measure the temperature levels; as a consequence, commonly an arbitrary threshold is applied to the image. The use of external illumination for monitoring purposes allows for suppressing the process emission and observing the melt pool geometry by means of the reflected light. On the other hand, the obtained images show lower contrast and can be difficult to process by means of image processing algorithms. Accordingly, this work proposes the complementary use of external illumination to calibrate the melt pool geometry. Afterward, the process emission and melt pool dynamics in SLM are characterized. For the purpose, an open SLM platform with an in-house designed coaxial monitoring module is used. Images with external illumination were used to estimate the melt pool size for AISI 316L stainless steel. The information was used to set a threshold value for determining the melt pool size observed at the near-infrared emission band. The proposed strategy proved promising for real time monitoring and control applications and can represent a feasible solution for industrial systems.

1.
T.
Toeppel
,
P.
Schumann
,
M. C.
Ebert
,
T.
Bokkes
,
K.
Funke
,
M.
Werner
,
F.
Zeulner
,
F.
Bechmann
, and
F.
Herzog
, “
3d Analysis in laser beam melting based on real-time process monitoring
,” in
Materials Science & Technology 2016, Salt Lake City, UT, USA, October 23–27, 2016
(Program Master,
2016
).
2.
B.
Regaard
,
S.
Kaierle
,
W.
Schulz
, and
A.
Moalem
, “
Advantages of coaxial external illumination for monitoring and control of laser material processing
,”
Proceedings of ICALEO, Miami, FL, USA, October 31–November 03, 2005
(Laser Institute of America, Orlando, FL,
2005
), pp.
915
919
.
3.
L.
Caprio
,
A. G.
Demir
, and
B.
Previtali
, “
Comparative study between cw and pw emissions in selective laser melting
,”
Proceedings of ICALEO, Atlanta, GA, USA, October 22–26, 2017
(Laser Institute of America, Orlando, FL,
2017
), p.
1304
.
4.
U.
Thombansen
and
P.
Abels
, “
Process observation in selective laser melting (SLM)
,” in
High-Power Laser Materials Processing: Lasers, Beam Delivery, Diagnostics, and Applications IV, San Francisco, CA, USA, February 10–12, 2015
(
International Society for Optics and Photonics
(SPIE),
2015
), Vol. 9356, p.
93560R
.
5.
T.
Craeghs
,
S.
Clijsters
,
E.
Yasa
,
F.
Bechmann
,
S.
Berumen
, and
J. P.
Kruth
, “
Determination of geometrical factors in layerwise laser melting using optical process monitoring
,”
Opt. Lasers Eng.
49
,
1440
1446
(
2011
).
6.
F.
Dorsch
,
H.
Braun
,
S.
Keßler
,
D.
Pfitzner
, and
V.
Rominger
, “
Online characterization of laser beam welds by NIR-camera observation
,”
Proc. SPIE
8603
,
86030R
(
2013
).
7.
C. H.
Kim
and
D. C.
Ahn
, “
Coaxial monitoring of keyhole during Yb:YAG laser welding
,”
Optic Laser Technol.
44
,
1874
1880
(
2012
).
8.
F.
Dorsch
,
H.
Braun
,
S.
Keßler
,
D.
Pfitzner
, and
V.
Rominger
, “
Detection of faults in laser beam welds by near-infrared camera observation
,”
Proc. ICALEO
,
2012
,
212
(
2012
).
9.
A. G.
Demir
,
L.
Monguzzi
, and
B.
Previtali
, “
Selective laser melting of pure Zn with high density for biodegradable implant manufacturing
,”
Add. Manuf.
15
,
20
28
(
2017
).
10.
A.
Gökhan Demir
,
C.
De Giorgi
, and
B.
Previtali
, “
Design and implementation of a multisensor coaxial monitoring system with correction strategies for selective laser melting of a maraging steel
,”
J. Manuf. Sci. Eng.
140
,
041003-1
041003-14
(
2018
).
11.
K.
Kempen
,
B.
Vrancken
,
S.
Buls
,
L.
Thijs
,
J.
Van Humbeeck
, and
J.
Kruth
, “
Selective laser melting of crack-free high density m2 high speed steel parts by baseplate preheating
,”
J. Manuf. Sci. Eng.
136
,
61026
(
2014
).
12.
T. G.
Spears
and
S. A.
Gold
, “
In-process sensing in selective laser melting (SLM) additive manufacturing
,”
Integr. Mater. Manuf. Innov.
1
,
2
(
2016
).
13.
M.
Ester
,
H.-P.
Kriegel
,
J.
Sander
, and
X.
Xu
, “
A density-based algorithm for discovering clusters in large spatial databases with noise
,” in
Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD’96), Portland, Oregon, August 2–4, 1996
(AAAI Press,
1996
), pp.
226
231
.
14.
C.
Sammut
and
G. I.
Webb
,
Encyclopedia of Machine Learning and Data Mining
(Springer Berlin Heidelberg,
2011
), pp.
349
353
.
15.
A.
Dhua
,
D. N.
Sarma
,
S.
Singh
, and
B.
Roy
, “
Segmentation of images using density-based algorithms
,”
Int. J. Adv. Res. Comput. Commun. Eng.
4
,
273
277
(
2015
).
16.
R. D.
Kumar
,
K.
Ramareddy
, and
B.
Rao
, “
A simple region descriptor based on object area per scan line
,”
Int. J. Comput. Appl.
3
,
24
27
(
2010
).
17.
S.
Clijsters
,
T.
Craeghs
,
S.
Buls
,
K.
Kempen
, and
J.-P.
Kruth
, “
In situ quality control of the selective laser melting process using a high-speed, real-time melt pool monitoring system
,”
Int. J. Adv. Manuf. Technol.
75
,
1089
1101
(
2014
).
18.
S.
Fujinaga
,
H.
Takenaka
,
T.
Narikiyo
,
S.
Katayama
, and
A.
Matsunawa
, “
Direct observation of keyhole behaviour during pulse modulated high-power Nd:YAG laser
,”
J. Phys. D: Appl. Phys.
33
,
492
497
(
2000
).
19.
C.
Kägeler
and
M.
Schmidt
, “
Frequency-based analysis of weld pool dynamics and keyhole oscillations at laser beam welding of galvanized steel sheet
,”
Phys. Proc.
5
,
447
453
(
2010
).
20.
T.
Furumoto
,
T.
Ueda
,
M. R.
Alkahari
, and
A.
Hosokawa
, “
Investigation of laser consolidation process for metal powder by two-color pyrometer and high-speed video camera
,”
CIRP Ann.
62
,
223
226
(
2013
).
21.
T.
Craeghs
,
S.
Clijsters
,
J. P.
Kruth
,
F.
Bechmann
, and
M. C.
Ebert
, “
Detection of process failures in layerwise laser melting with optical process monitoring
,”
Phys. Proc.
39
,
753
759
(
2012
).
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