This paper presents a numerical model able to control the temperature distribution along a 4340 steel cylinder heat-treated with laser. The numerical model developed using the numerical finite element method (FEM) was based on a study of surface temperature variation and the adjustment of this temperature by a control of the heat treatment laser power. The proposed analytical approach was built gradually by (i) the development of a numerical model of laser heat treatment of the cylindrical workpiece, (ii) an analysis of the results of simulations and experimental tests, (iii) development of a laser power adjustment approach, and (iv) proposal of a laser power control predictor using neural networks. This approach was made possible by highlighting the influence of the fixed (nonvariable) parameters of the laser heat treatment on the case depth and has shown that it is possible by controlling the laser parameters to homogenize the distribution of the maximum temperature reached on the surface for a uniform case depth. The feasibility and effectiveness of the proposed approach lead to a reliable and accurate model able to guarantee a uniform surface temperature and a regular case depth for a cylindrical workpiece of a length of 50 mm and with a diameter of between 16 and 22 mm.

References

1.
Ion
,
J. C.
,
2002
, “
Laser Transformation Hardening
,”
Surf. Eng.
,
18
(
1
), pp.
14
31
.
2.
Gujba
,
A. K.
, and
Medraj
,
M.
,
2014
, “
Laser Peening Process Its Impact Materials Properties Comparison Shot Peening Ultrasonic Impact Peening
,”
Materials
,
7
(
12
), pp.
7925
7974
.
3.
Kennedy
,
E.
,
Byrne
,
G.
, and
Collins
,
D. N.
,
2004
, “
A Review of the Use of High Power Diode Lasers in Surface Hardening
,”
J. Mater. Process. Technol.
,
155
, pp.
1855
1860
.
4.
DeGaspari
,
J.
,
2000
, “
Post-Heat Treatment Involving Cryogenics May Greatly Extend the Wear Life of Mechanical Components
,”
Compon. Mech. Eng.
,
122
(
11
), pp.
94
97
.
5.
Elijah
, and
K.-A.
, Jr.
,
2009
,
Principles of Laser Materials Processing
, Vol.
4
,
Wiley
, Hoboken, NJ.
6.
Mazumder
,
J.
,
1983
, “
Laser Heat Treatment: The State of the Art
,”
J. Met.
,
35
(
5
), pp.
18
26
.
7.
Mackerle
,
J.
,
2003
, “
Finite Element Analysis and Simulation of Quenching and Other Heat Treatment Processes a Bibliography (1976–2001)
,”
Comput. Mater. Sci.
,
27
(
3
), pp.
313
332
.
8.
Lakhkar
,
R. S.
,
Shin
,
Y. C.
, and
Krane
,
M. J. M.
,
2008
, “
Predictive Modeling of Multi-Track Laser Hardening of AISI 4140 Steel
,”
Mater. Sci. Eng. A
,
480
(
1–2
), pp.
209
217
.
9.
Barka
,
N.
, and
El Ouafi
,
A.
,
2015
, “
Effects of Laser Hardening Process Parameters on Case Depth of 4340 Steel Cylindrical Specimen—A Statistical Analysis
,”
J. Surf. Eng. Mater. Adv. Technol.
,
5
(
3
), p.
124
.
10.
Fakir
,
R.
,
Barka
,
N.
, and
Brousseau
,
J.
,
2018
, “
Case Study of Laser Hardening Process Applied to 4340 Steel Cylindrical Specimens Using Simulation and Experimental Validation
,”
Case Stud. Therm. Eng.
,
11
, pp.
15
25
.
11.
Fakir
,
R.
,
Barka
,
N.
,
Brousseau
,
J.
, and
Caron-Guillemette
,
G.
,
2018
, “
Numerical Investigation by the Finite Difference Method of the Laser Hardening Process Applied to AISI-4340
,”
J. Appl. Math. Phys.
,
6
(
10
), pp.
2087
2106
.
12.
Jerniti
,
A. G.
,
El Ouafi
,
A.
, and
Barka
,
N.
,
2016
, “
A Predictive Modeling Based on Regression and Artificial Neural Network Analysis of Laser Transformation Hardening for Cylindrical Steel Workpieces
,”
J. Surf. Eng. Mater. Adv. Technol.
,
6
(
4
), pp.
149
163
.
13.
Hadhri
,
M.
,
El Ouafi
,
A.
, and
Barka
,
N.
,
2017
, “
Prediction of the Hardness Profile of an AISI 4340 Steel Cylinder Heat-Treated by Laser-3D and Artificial Neural Networks Modelling and Experimental Validation
,”
J. Mech. Sci. Technol.
,
31
(
2
), pp.
615
623
.
14.
Deng
,
X.
, and
Ju
,
D.
,
2013
, “
Modeling and Simulation of Quenching and Tempering Process in Steels
,”
Phys. Procedia
,
50
, pp.
368
374
.
15.
Boyer
,
H. E
.
, and
Gall
,
T. L.
,
1985
,
Metals Handbook
,
American Society for Metals
,
Materials Park, OH
.
16.
Harvey
,
P. D.
,
1982
,
Engineering Properties of Steels
,
American Society for Metals
,
Metals Park, OH
.
17.
Douglas
,
C. G.
,
1989
,
Physics for Scientists and Engineers With Modern Physics
, 2nd ed.,
Prentice Hall Publishers
,
Englewood Cliffs, NJ
.
18.
Fakir
,
R.
,
Barka
,
N.
, and
Brousseau
,
J.
,
2018
, “
Optimization of the Case Depth of a Cylinder Made With 4340 Steel by a Control of the Laser Heat Treatment Parameters
,”
ASME
Paper No. DETC2018-85574.
19.
Zimmerman, W. J., 2008,
Multiphysics Modelling with Finite Element Methods
, World Scientific, Danvers, MA.
20.
Carslaw
,
H. S.
, and
Jaeger
,
J. C.
,
1959
,
Conduction of Heat in Solids
, 2nd ed.,
Clarendon Press
,
Oxford, UK
, pp.
1907
1979
.
21.
Ion
,
J.
,
2005
,
Laser Processing of Engineering Materials: Principles, Procedure and Industrial Application
,
Elsevier
, Oxford, UK.
22.
Teen
,
W. M.
, and
Courtney
,
C. H. G.
,
1979
, “
Surface Heat Treatment of EnS 8 Steel Using a 2 kW Continuous-Wave CO2 Laser
,”
Met. Technol.
,
6
(
1
), pp.
456
462
.
23.
Bradley
,
J. R.
,
1988
, “
A Simplified Correlation Between Laser Processing Parameters and Case Depth in Steels
,”
J. Phys. D: Appl. Phys.
,
21
(
5
), p.
834
.
24.
Dausinger
,
F.
, and
Shen
,
J.
,
1993
, “
Energy Coupling Efficiency in Laser Surface Treatment
,”
ISIJ Int.
,
33
(
9
), pp.
925
933
.
25.
Pantsar
,
H.
, and
Kujanpää
,
V.
,
2004
, “
Diode Laser Beam Absorption in Laser Transformation Hardening of Low Alloy Steel
,”
J. Laser Appl.
,
16
(
3
), pp.
147
153
.
26.
Tobar
,
M. J.
,
Álvarez
,
C.
,
Amado
,
J. M.
,
Ramil
,
A.
,
Saavedra
,
E.
, and
Yáñez
,
A.
,
2006
, “
Laser Transformation Hardening of a Tool Steel: Simulation-Based Parameter Optimization and Experimental Results
,”
Surf. Coat. Technol.
,
200
(
22-23
), pp.
6362
6367
.
27.
Bathe
,
K-JÜ.
, and
Wilson
,
E. L.
,
1976
,
Numerical Methods in Finite Element Analysis
, Vol.
197
,
Prentice Hall
,
Englewood Cliffs, NJ
.
28.
Fakir
,
R.
,
Barka
,
N.
, and
Brousseau
,
J.
,
2018
, “
Mechanical Properties Analysis of 4340 Steel Specimen Heat Treated in Oven and Quenching in Three Different Fluids
,”
Met. Mater. Int.
,
24
(
5
), pp.
981
991
.
29.
Roy
,
R. K.
,
2010
,
A Primer on the Taguchi Method
,
Society of Manufacturing Engineers
, New York.
30.
Montgomery
,
D. C.
,
Runger
,
G. C.
, and
Hubele
,
N. F.
,
2009
,
Engineering Statistics
,
Wiley
, New York.
31.
Fowlkes
,
W. Y.
,
Creveling
,
C. M.
, and
Derimiggio
,
J.
,
1995
,
Engineering Methods for Robust Product Design: Using Taguchi Methods in Technology and Product Development
,
Addison-Wesley
,
Reading, MA
, pp.
121
123
.
32.
Myers
,
R. H.
,
1971
,
Response Surface Methodology
,
Allyn and Bacon
,
Boston, MA
.
33.
Marquardt
,
D. W.
,
1963
, “
An Algorithm for Least-Squares Estimation of Nonlinear Parameters
,”
J. Soc. Ind. Appl. Math.
,
11
(
2
), pp.
431
441
.
34.
Hagan
,
M. T.
,
Demuth
,
H. B.
,
Beale
,
M. H.
, and
De Jesús
,
O.
,
1996
,
Neural Network Design
,
Pws Pub
,
Boston, MA
.
35.
Beale
,
M. H.
,
Hagan
,
M. T.
, and
Demuth
,
H. B.
, 2018, “
Deep Learning Toolbox
,” User's Guide, The MathWorks, Inc., Natick, MA.
You do not currently have access to this content.