Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/121743
Title: INTELLIGENT SPOT WELDING QUALITY MONITORING USING ADVANCED SIGNAL PROCESSING TECHNIQUES
Authors: WANG XINGJUE
Keywords: Spot welding,SOM, Feature extraction,RNN,nugget size,expulsion
Issue Date: 4-Aug-2015
Source: WANG XINGJUE (2015-08-04). INTELLIGENT SPOT WELDING QUALITY MONITORING USING ADVANCED SIGNAL PROCESSING TECHNIQUES. ScholarBank@NUS Repository.
Abstract: Resistance Spot welding is one of the most important metal joining techniques nowadays. The research on quality evaluation is one of the most important aspects. Traditional quality testing method is destructive, time-consuming and expensive. Many online non-destructive test schemes have been proposed using neural networks, finite element modeling, etc. However, these online monitoring methods require measurement of many physical parameters and are vulnerable to experiment condition change. In this thesis, only the easily-obtainable electrical signals were used and two schemes were proposed. The first scheme applied windowed feature extraction and SOM network to realize fast and accurate quality classification. The second scheme used a recurrent neural network for feature extraction, a sliding window recurrent neural network for HAZ size estimation and a SOM-type classifier for expulsion checking. The scheme considered more aspects of quality and was able to deal with weld of varying time with satisfying accuracy.
URI: http://scholarbank.nus.edu.sg/handle/10635/121743
Appears in Collections:Master's Theses (Open)

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