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Two examples of this occurrence are arc data monitoring and laser vision sensing. Arc data monitoring has evinced the ability to predict such defects as gross porosity and lack of fusion, but struggles with identifying bead surface related issues such as excessive reinforcement or insufficient leg length. The complete opposite tendencies are exhibited through laser vision sensing. This technique provides no information of subsurface characteristics of the weld, but its inherent characteristic as a direct quality indicating process has resulted in a strong ability to detect weld surface quality issues.
This work looked to study the feasibility and benefit of fusing the arc data monitoring and laser vision techniques into one integrated system, with the hope taking advantage of the core competencies of each technique while negating their deficiencies. A monitoring system was built which synchronized the measurements from both monitors. Quantitative correlations were developed between each sensor reading with respect to measured quality through means of regression analysis. These correlations were then built into quality differentiating algorithms which exhibited the capability to identify a multitude of defective occurrences in the real-time.