TY - BOOK
T1 - Pre-Industry-Optimisation of the Laser Welding Process
AU - Gong, Hui
PY - 1998
Y1 - 1998
N2 - This dissertation documents the investigations into on-line
monitoring the CO2 laser welding process and optimising the
process parameters for achieving high quality welds. The
requirements for realisation of an on-line control system are,
first of all, a clear understanding of the dynamic phenomena of
the laser welding process including the behaviour of the keyhole
and plume, and the correlation between the adjustable process
parameters: laser power, welding speed, focal point position, gas
parameters etc. and the characteristics describing the quality of
the weld: seam depth and width, porosity etc. Secondly, a reliable
monitoring system for sensing the laser-induced plasma and plume
emission and detecting weld defects and process parameter
deviations from the optimum conditions. Finally, an efficient
control system with a fast signal processor and a precise
feed-back controller.With the purpose of optimising welding
process parameters automatically or semi-automatically, the
fundamental principle of the laser welding process and the
correlation between process parameters: i.e. the gas parameters
and the focal point position, weld quality characteristics: i.e.
the seam width and depth, and monitoring signals are
systematically studied and investigated. For gas parameter
optimisation, there are 5 gas variables optimised when applying
the Design of Experiment (DOE). One photo diode is set to monitor
the welding process in order to characterise the consistence of
the discrimination features of the welding process by applying FFT
analysis of the signals. DOE is proven to be a useful tool for
manual parameter optimisations in laser welding. The frequency of
the photo diode signal could be related to weld quality measures,
however, the application possibility of the signal in a control
system has not yet been discernible.For focal point position
optimisation, a focus control system is designed and built up with
the controller, sensor, and signal processor. The optimisation
procedure starts with the welding process being carried out by
continuously moving the focus 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. Thereafter the
optimum focal point position (OFPP) is generated by the
self-learning mechanism - the neural network. The optimisation
procedure is completed with the welding process being carried out
by adjusting the focus of the laser beam to the OFPP. The
self-learning focus control system, employing the off-line trained
neural network to generate the OFPPs from on-line monitored photo
diode signals, has proved to be capable of optimising the focal
point position automatically with good accuracy in CW CO2 laser
welding.
AB - This dissertation documents the investigations into on-line
monitoring the CO2 laser welding process and optimising the
process parameters for achieving high quality welds. The
requirements for realisation of an on-line control system are,
first of all, a clear understanding of the dynamic phenomena of
the laser welding process including the behaviour of the keyhole
and plume, and the correlation between the adjustable process
parameters: laser power, welding speed, focal point position, gas
parameters etc. and the characteristics describing the quality of
the weld: seam depth and width, porosity etc. Secondly, a reliable
monitoring system for sensing the laser-induced plasma and plume
emission and detecting weld defects and process parameter
deviations from the optimum conditions. Finally, an efficient
control system with a fast signal processor and a precise
feed-back controller.With the purpose of optimising welding
process parameters automatically or semi-automatically, the
fundamental principle of the laser welding process and the
correlation between process parameters: i.e. the gas parameters
and the focal point position, weld quality characteristics: i.e.
the seam width and depth, and monitoring signals are
systematically studied and investigated. For gas parameter
optimisation, there are 5 gas variables optimised when applying
the Design of Experiment (DOE). One photo diode is set to monitor
the welding process in order to characterise the consistence of
the discrimination features of the welding process by applying FFT
analysis of the signals. DOE is proven to be a useful tool for
manual parameter optimisations in laser welding. The frequency of
the photo diode signal could be related to weld quality measures,
however, the application possibility of the signal in a control
system has not yet been discernible.For focal point position
optimisation, a focus control system is designed and built up with
the controller, sensor, and signal processor. The optimisation
procedure starts with the welding process being carried out by
continuously moving the focus 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. Thereafter the
optimum focal point position (OFPP) is generated by the
self-learning mechanism - the neural network. The optimisation
procedure is completed with the welding process being carried out
by adjusting the focus of the laser beam to the OFPP. The
self-learning focus control system, employing the off-line trained
neural network to generate the OFPPs from on-line monitored photo
diode signals, has proved to be capable of optimising the focal
point position automatically with good accuracy in CW CO2 laser
welding.
M3 - Book
BT - Pre-Industry-Optimisation of the Laser Welding Process
PB - IPT, Technical University of Denmark
CY - Lyngby, Denmark
ER -