RESUMO
The application of industrial technologies is undergoing significant changes. Finding the level at which to use efficient cyberphysical systems is perhaps one of the most important technical preparatory tasks in implementing digital manufacturing. Welding technology systems are investigated, and a framework for capturing the data sets required for data-driven manufacturing is developed. To make full autonomy in a manufacturing environment meaningful, formerly isolated groups of equipment need to be organized into a production information system. In our research, a test system is created that can implement a digital virtual interface and achieve new levels of efficiency with a future digital twin system. In the discourse of the study, the technological parameters of welding test pieces were investigated, namely the available measurement data sets of current, and voltage data. In the summary section, most of the tasks and research directions are presented, which can be envisaged as a continuation of the present study. Our study will be followed by further research, already testing a complete digital twin system, thus reaching another milestone on the way to autonomous manufacturing.
RESUMO
This paper describes a framework for detecting welding errors using 3D scanner data. The proposed approach employs density-based clustering to compare point clouds and identify deviations. The discovered clusters are then classified according to standard welding fault classes. Six welding deviations defined in the ISO 5817:2014 standard were evaluated. All defects were represented through CAD models, and the method was able to detect five of these deviations. The results demonstrate that the errors can be effectively identified and grouped according to the location of the different points in the error clusters. However, the method cannot separate crack-related defects as a distinct cluster.
