A Deep Semantic Segmentation Approach to Accurately Detect Seam Gap in Fixtured Workpiece Laser Welding

The recent technological advancements in today’s manufacturing industry have extended the quality control operations for welding processes. However, the realm of pre-welding inspection, which significantly influences the quality of the final products, remains relatively uncharted. To this end, this study proposes an innovative vision system designed to extract the seam gap width and centre between two components before welding and make informed decisions regarding the initiation of the welding process. The system incorporates a deep learning semantic segmentation network for identifying and isolating the desired gap area within an acquired image from the vision sensor. Then, additional processing is performed, with conventional computer vision techniques and fundamental Euclidean geometry operations for acquiring the desired width and the centre of that area with a precision of 0.1 mm. Additionally, a control graphical interface has been implemented that allows the operator to initiate and monitor the entire inspection procedure. The overall framework is applied and tested on a manufacturing case study involving the laser welding operations of sheet metal parts, and although it is designed to handle gaps of different shapes and sizes, it is mainly focused on obtaining the characteristics of butt weld gaps.