  Quality assurance in production line demands reliable weld joints. Human made
errors is a major cause of faulty production. Promptly Identifying errors in
the weld while welding is in progress will decrease the post inspection cost
spent on the welding process. Electrical parameters generated during welding,
could able to characterize the process efficiently. Parameter values are
collected using high speed data acquisition system. Time series analysis tasks
such as filtering, pattern recognition etc. are performed over the collected
data. Filtering removes the unwanted noisy signal components and pattern
recognition task segregate error patterns in the time series based upon
similarity, which is performed by Self Organized mapping clustering algorithm.
Welder quality is thus compared by detecting and counting number of error
patterns appeared in his parametric time series. Moreover, Self Organized
mapping algorithm provides the database in which patterns are segregated into
two classes either desirable or undesirable. Database thus generated is used to
train the classification algorithms, and thereby automating the real time error
detection task. Multi Layer Perceptron and Radial basis function are the two
classification algorithms used, and their performance has been compared based
on metrics such as specificity, sensitivity, accuracy and time required in
training.
