Design the Feature Extraction for Real Time Inspection of Welding Quality

Advances in Robotic Welding for Metallic Materials: Application of Inspection, Modeling, Monitoring and Automation Techniques

“…This is achieved by optimizing welding parameters through real-time sensing and feedback control [66], all of which depend on process properties/capabilities, process innovations, predictive models, numerical models for fluid dynamics, numerical models for structures, real-time sensing, and dynamic controls [67]. Throughout this section, examples have been described that include the detection of defects in real time, but they can also be completed with the following examples: parameter monitoring [68], radiographic inspection [69], and spectroscopy signal [70], depending on the frequency band [71].…”

“…Detection of defects [87] in the weld: an image transformation algorithm for defect extraction that creates a pattern recognition method for automatic defect identification [74], and a sub-pixel algorithm to detect defects, oxidation, and the lack of penetration [48], detect in-line faults such as porosity, undercuts, lack of fusion, and spatter through temperature signal based algorithm [72], defect classification [31], detect defects in bead geometry, poor penetration, and porosity according to frequency band [71].…”

“…• Assessing weld quality through voltage [88]: an optimization algorithm through plasma spectroscopy analysis technique [75], an artificial intelligence algorithm based on current and voltage parameters [76], correlating weld quality with current and voltage [31], and on-line weld quality through voltage variance parameter algorithm [89], and algorithms for real-time inspection of quality [71].…”