This paper describes a framework for detecting welding errors using 3D scanner data. The proposed... more 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.
The inspection of welded structures requires particular attention due to many aspects that define... more The inspection of welded structures requires particular attention due to many aspects that define the quality of the product. Deciding on the suitability of welds is a complex process. This work aims to propose a method that can support this qualification. This paper presents a state-of-the-art data-driven evaluation method and its application in the quality assessment of welds. Image processing and CAD modelling software was applied to generate a reference using the Iterative Closest Point algorithm that can be used to generate datasets which represent the model errors. The results demonstrate that the distribution of these variables characterises the typical welding defects. Based on the automated analysis of these distributions, it is possible to reduce the turnaround time of testing, thereby improving the productivity of welding processes.
This paper describes a framework for detecting welding errors using 3D scanner data. The proposed... more 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.
The inspection of welded structures requires particular attention due to many aspects that define... more The inspection of welded structures requires particular attention due to many aspects that define the quality of the product. Deciding on the suitability of welds is a complex process. This work aims to propose a method that can support this qualification. This paper presents a state-of-the-art data-driven evaluation method and its application in the quality assessment of welds. Image processing and CAD modelling software was applied to generate a reference using the Iterative Closest Point algorithm that can be used to generate datasets which represent the model errors. The results demonstrate that the distribution of these variables characterises the typical welding defects. Based on the automated analysis of these distributions, it is possible to reduce the turnaround time of testing, thereby improving the productivity of welding processes.
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Papers by József Szőlősi