TY - BOOK
T1 - Automatic Optimization of Focal Point Position in CO2 Laser
Welding with Neural Network in A Focus Control System
AU - Gong, Hui
AU - Olsen, Flemming Ove
PY - 1997
Y1 - 1997
N2 - CO2 lasers are increasingly being utilized for quality welding in
production. Considering the high cost of equipment, the start-up
time and the set-up time should be minimized. Ideally the
parameters should be set up and optimized more or less
automatically. In this paper a control system is designed and
built to automatically optimize the focal point position, one of
the most important parameters in CO2 laser welding, in order to
perform a desired deep/full penetration welding. The control
system mainly consists of a multi-axis motion controller - PMAC, a
light sensor - Photo Diode, a data acquisition card - DAQCard-700,
and a self-learning mechanism - Neural Network. The optimization
procedure starts with the welding process being carried out by
continuously moving the focal point position from above a welding
plate to below the plate, thus the process is ensured to be
shifted from initially surface welding to deep/full penetration
welding and back to surface welding again. A clear change on
plasma brightness from the process is monitored by the photo diode
on the front side of the plate with a viewing angle of 45o. The
photo diode signal is acquired with the A/D converter card and
installed in a computer hard disk for later data processing.
Thereafter the optimum focal point position (OFPP) is output by
the self-learning mechanism - the neural network. The optimization
procedure is completed with the welding process being carried out
by adjusting the focus of the laser beam to the OFPP.A
self-learning mechanism - neural network as the essence of the
control system is trained with the photo diode signals extracted
from various welding processes with the changes on the laser
power, translation speed, material and thickness of the plate,
shielding gas type and flow rate, and welding configuration. The
results of the self-learning focus control system show that the
neural network is capable of optimizing the focal point position
with good accuracy in CW CO2 laser welding.
AB - CO2 lasers are increasingly being utilized for quality welding in
production. Considering the high cost of equipment, the start-up
time and the set-up time should be minimized. Ideally the
parameters should be set up and optimized more or less
automatically. In this paper a control system is designed and
built to automatically optimize the focal point position, one of
the most important parameters in CO2 laser welding, in order to
perform a desired deep/full penetration welding. The control
system mainly consists of a multi-axis motion controller - PMAC, a
light sensor - Photo Diode, a data acquisition card - DAQCard-700,
and a self-learning mechanism - Neural Network. The optimization
procedure starts with the welding process being carried out by
continuously moving the focal point position from above a welding
plate to below the plate, thus the process is ensured to be
shifted from initially surface welding to deep/full penetration
welding and back to surface welding again. A clear change on
plasma brightness from the process is monitored by the photo diode
on the front side of the plate with a viewing angle of 45o. The
photo diode signal is acquired with the A/D converter card and
installed in a computer hard disk for later data processing.
Thereafter the optimum focal point position (OFPP) is output by
the self-learning mechanism - the neural network. The optimization
procedure is completed with the welding process being carried out
by adjusting the focus of the laser beam to the OFPP.A
self-learning mechanism - neural network as the essence of the
control system is trained with the photo diode signals extracted
from various welding processes with the changes on the laser
power, translation speed, material and thickness of the plate,
shielding gas type and flow rate, and welding configuration. The
results of the self-learning focus control system show that the
neural network is capable of optimizing the focal point position
with good accuracy in CW CO2 laser welding.
M3 - Book
BT - Automatic Optimization of Focal Point Position in CO2 Laser
Welding with Neural Network in A Focus Control System
PB - Laser Institute of America
CY - Orlando, FL, USA
T2 - International Congress on Applications of Lasers and Electro-Optics
Y2 - 17 November 1997 through 20 November 1997
ER -