Semi-Supervised Training for Positioning of Welding Seams

Abstract

Supervised deep neural networks have been successfully applied to many real-world measurement applications. However, their success relies on labeled data which is expensive and time-consuming to obtain, especially when domain expertise is required. For this reason, researchers have turned to semi-supervised learning for image classification tasks. Semi-supervised learning uses structural assumptions to automatically leverage unlabeled data, dramatically reducing manual labeling efforts. We conduct our research based on images from Enclosures Direct Inc. (EDI) which is a manufacturer of enclosures used to house and protect electronic devices. Their industrial robotics utilizes a computer vision system to guide a robot in a welding application employing a laser and a camera. The laser is combined with an optical line generator to cast a line of structured light across a joint to be welded. An image of the structured light is captured by the camera which needs to be located in the image in order to find the desired coordinate for the weld seam. The existing system failed due to the fact that the traditional machine vision algorithm cannot analyze the image correctly in unexpected imaging conditions or during variations in the manufacturing process. In this thesis, we purpose a novel algorithm for semi-supervised key-point detection for seam placement by a welding robot. Our deep learning based algorithm overcomes unfavorable imaging conditions providing faster and more precise predictions. Moreover, we demonstrate that our approach can work with as few as ten labeled images accepting a reduction of detection accuracy. In addition, we also purpose a method that can utilize full image resolution to enhance the accuracy of the key-point detection.