Abstract:
Welding engineering has undergone with a various changes
in processes and materials to meet the requirement of customers and
low welding cost in a present global competitive time. Large scale
and high rate of production of components using sheet metal has been
possible only due to resistance welding processes which have the
dual advantage of making joints quickly and with minimum
distortion. It is working on the principle that heat is generated by the
resistance offered to the flow of current. Resistance welding
processes include spot welding, seam welding, projection welding,
flash welding, percussion welding, upset butt welding, high
frequency resistance welding and high frequency induction welding.
The process parameters affect upon Resistance Spot Welding (RSW)
are welding current, resistance, time, electrode force, cooling water
flow, electrode shape, sheet metal surface condition, etc. There is a
various effects of process parameters interaction on final weld
quality. There are different methods to get optimum weld quality by
changing process parameters and their values. Artificial intelligence
and neural network is also used to predict the resistance spot welded
weldment quality.
