https://fordatis.fraunhofer.de/handle/fordatis/15 Thu, 30 Apr 2026 22:24:08 GMT 2026-04-30T22:24:08Z https://fordatis.fraunhofer.de/handle/fordatis/490 Titel: Digitale Anlagendaten in AutomationML für das industrielle Energiemanagement Datenautorinnen und Datenautoren: Petrichenko, Valentyn; Thiele, Gregor Zusammenfassung: Digitale Beschreibung einer Roboterzelle mit zwei Förderbändern in AutomationML, ergänzt um das Aufwand-Nutzen-Einflussgrößen (ANEG)-Konzept zur Einbindung von für das Energiemanagement relevanten Daten. Thu, 01 Jan 2026 00:00:00 GMT https://fordatis.fraunhofer.de/handle/fordatis/490 2026-01-01T00:00:00Z https://fordatis.fraunhofer.de/handle/fordatis/445 Titel: Start and end poses for energy modeling of a collaborative robot Datenautorinnen und Datenautoren: Lokstein, Lisa; Haninger, Kevin; Petrichenko, Valentyn; Thiele, Gregor Zusammenfassung: The data set contains the coordinates of start and end points of a point-to-point trajectory of a collaborative robot. Wed, 01 Jan 2025 00:00:00 GMT https://fordatis.fraunhofer.de/handle/fordatis/445 2025-01-01T00:00:00Z https://fordatis.fraunhofer.de/handle/fordatis/363.2 Titel: VIADUCT: Multisector data set for Visual Industrial Anomaly Detection Datenautorinnen und Datenautoren: Lehr, Jan; Philipps, Jan; Sargsyan, Alik; Botschen, Maximilian; Gevorgyan, Shoghik; Paust, Anna-Maria; Pape, Martin; Nguyen Hoang, Viet Zusammenfassung: VIADUCT data set provides 49 categories of industrial objects with corresponding 135 defect categories. Mon, 15 Jan 2024 00:00:00 GMT https://fordatis.fraunhofer.de/handle/fordatis/363.2 2024-01-15T00:00:00Z https://fordatis.fraunhofer.de/handle/fordatis/329.3 Titel: InVar-100: Industrial Objects in Varied Contexts Dataset Datenautorinnen und Datenautoren: Lehr, Jan; Chavan, Vivek; Koch, Paul; Schlüter, Marian; Briese, Clemens Zusammenfassung: The Industrial Objects in Varied Contexts (InVar) dataset was internally produced by our team and contains 100 objects in 20800 total images (208 images per class). The objects consist of common automotive, machine and robotics lab parts. Each class contains 4 sub-categories (52 images each) with different attributes and visual complexities. White background (Dwh): The object is against a clean white background and the object is clear, centred and in focus. Stationary Setup (Dst): These images are also taken against a clean background using a stationary camera setup, with uncentered objects at a constant distance. The images have lower DPI resolution with occasional cropping. Handheld (Dha): These images are taken with the user holding the objects, with occasional occluding. Cluttered background (Dcl): These images are taken with the object placed along with other objects from the lab in the background and with no occlusion. The dataset was produced to simulate the miscellaneous issues in industrial setups as discussed. The dataset was produced by our staff at different workstations and labs in Berlin. More details regarding the objects used for digitisation are available in the metadata file. Sun, 01 Jan 2023 00:00:00 GMT https://fordatis.fraunhofer.de/handle/fordatis/329.3 2023-01-01T00:00:00Z