Real time error detection in metal arc welding process using Artificial Neural Netwroks

by   Prashant Sharma, et al.

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.


page 1

page 2

page 3

page 4


Bag of Recurrence Patterns Representation for Time-Series Classification

Time-Series Classification (TSC) has attracted a lot of attention in pat...

A Periodicity-based Parallel Time Series Prediction Algorithm in Cloud Computing Environments

In the era of big data, practical applications in various domains contin...

Robust Parameter-Free Season Length Detection in Time Series

The in-depth analysis of time series has gained a lot of research intere...

A Parallel Framework for Multilayer Perceptron for Human Face Recognition

Artificial neural networks have already shown their success in face reco...

SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations

There is an increasing demand for scalable algorithms capable of cluster...

Spatial Pattern Recognition with Adjacency-Clustering: Improved Diagnostics for Semiconductor Wafer Bin Maps

In semiconductor manufacturing, statistical quality control hinges on an...

Pattern Matching for Self- Tuning of MapReduce Jobs

In this paper, we study CPU utilization time patterns of several MapRedu...

Please sign up or login with your details

Forgot password? Click here to reset