Performance Evaluation of Hot Spot Extraction and Quantification Algorithms for On-Line Weld Monitoring from Weld Thermographs

The quality of welded steel structures play an important role in determining the reliability of a building. Weld quality is affected by Incomplete Penetration, which is a most commonly occurring defect in welds. An automated adaptive welding system if developed can correct the deviation in the welding current on-line so as to adjust the depth of Penetration to provide defect free welds. This system requires an on-line weld-monitoring sensor, efficient image processing algorithm for defect identification and neurofuzzy control software for correlating the defect characteristics with deviations in physical parameters. Infrared Thermography is the best-suited sensor for on-line weld monitoring and continuous assessment of welds. Incomplete penetration affects the hot spot of the thermograph and hence hot spot quantification describes the defect effectively. Three different algorithms namely conventional algorithm, region-growing algorithm and Euclidean distance based color image segmentation algorithm are developed for hot spot quantification. The paper compares the effectiveness and suitability of these algorithms for on-line weld monitoring.