Learning and online prediction of weld quality in robotic GMAW
We have come a long way in the automation of robotic welding. But with the decline in qualified welders, there has been a steadily growing demand for robotic solutions with capabilities that match that of human welders. Among these capabilities, a major missing element is the ability of the robotic systems to extract actionable information about the quality of their welds, in real time. While this is a challenging problem, we have been able to show that we can extract this information through-the-arc that captures the controlled dynamics leading to defect occurrence and at the same time ...
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