Prashant Sharma1 , Dr. Shaju K. Albert2 and S. Rajeswari3
¹Computer Division, Indira Gandhi Centre For Atomic Research, Kalpakkam²Material Technology Division, Indira Gandhi centre for Atomic Research, Kalpakkam ³Computer Division, Indira Gandhi Centre for Atomic Research, Kalpakkam

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’s quality is thus compared by detecting and counting number of error patterns appeared in his parametric time series. Moreover, Self Organized mapping algorithmprovides 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

Manual Metal Arc welding Process, Neural networks, Self Organized Mapping, Multi Layer Perceptron, Radial Basis Functions