Publikationen

Refuse separation and sorting is currently done by recycling plants that are manually optimised for a fixed refuse composition. Since the refuse compositions constantly change, these plants deliver either suboptimal sorting performances or require constant monitoring and adjustments by the plant operators. Image recognition offers the possibility to continuously monitor the refuse composition on the conveyor belts in a sorting facility. When information about the refuse composition is combined with parameters and measurements of the sorting machinery, the sorting performance of a plant can be continuously monitored, problems detected, optimisations suggested and trends predicted. This article describes solutions for multispectral and 3D image capturing of refuse streams and evaluates the performance of image segmentation models. The image segmentation models are trained with synthetic training data to reduce the manual labelling effort thus reducing the costs of the image recognition introduction. Furthermore, an outlook on the combination of image recognition data with parameters and measurements of the sorting machinery in a combined time series analysis is provided.

Painting a modern car involves applying many coats during a highly complex and automated process. The individual coats not only serve a decoration purpose but are also curial for protection from damage due to environmental influences, such as rust. For an optimal paint job, many parameters have to be optimised simultaneously. A forecasting model was created, which predicts the paint flaw probability for a given set of process parameters, to help the production managers modify the process parameters to achieve an optimal result. The mathematical model was based on historical process and quality observations. Production managers who are not familiar with the mathematical concept of the model can use it via an intuitive Web-based Graphical User Interface (Web-GUI). The Web-GUI offers production managers the ability to test process parameters and forecast the expected quality. The model can be used for optimising the process parameters in terms of quality and costs.