Artificial Intelligence‐Based Assistance System for Visual Inspection of X‐ray Scatter Grids - Selmaier, Andreas; Kunz, David; Kißkalt, Dominik; Benaziz, Mohamed; Fürst, Jens; Franke, Jörg (Sensors, 2022)
Convolutional neural network (CNN)‐based approaches have recently led to major performance steps in visual recognition tasks. However, only a few industrial applications are described in the literature. In this paper, an object detection application for visual quality evaluation of X‐ray scatter grids is described and evaluated. To detect the small defects on the 4K input images, a sliding window approach is chosen. A special characteristic of the selected approach is the aggregation of overlapping prediction results by applying a 2D scalar field. The final system is able to detect 90% of the relevant defects, taking a precision score of 25% into account. A practical examination of the effectiveness elaborates the potential of the approach, improving the detection results of the inspection process by over 13%.
CRIS-Team Scopus Importer:2022-01-28
Keywords: Assistance system; Industrial artificial intelligence application; Object detection; Visual inspection; X‐ray scatter grid

Automatische Identifikation mittels RFID - Rückverfolgbarkeit von Fertigungshilfsmitteln in der Produktion von Hochleistungsröntgenstrahlern - Herbert, Meike; Stirm, Julian; Hofmann, Christian; Fürst, Jens; Franke, Jörg (Zeitschrift für wirtschaftlichen Fabrikbetrieb, 2022)
Modern technologies are a key enabler for the automatic identification of objects in production to ensure quality-related traceability of components or manufacturing equipment. This article presents a possible solution for the traceability of equipment, tools and measuring devices by applying a RFID system in the production of high-performance X-ray tubes and discusses the results of durability analysis.
CRIS-Team Scopus Importer:2023-03-07
Keywords: Identification Systems; RFID; Lifetime; Traceability; X-Ray

Cross-Process Quality Analysis of X-ray Tubes for Medical Applications Using Machine Learning Techniques - Selmaier, Andreas; Robitzch, Philippe; Mayr, Andreas; Fürst, Jens; Franke, Jörg (Production at the leading edge of technology. Proceedings of the 9th Congress of the German Academic Association for Production Technology (WGP), September 30th - October 2nd, Hamburg 2019, 2019)
X-rays have a large range of medical applications and play an important role in imaging diagnostics. However, the ionized radiation that the patient is exposed to during its application is harmful. In order to keep the radiation exposure as low as possible and at the same time improve image quality, the development of X-ray tubes is being pushed to physical limits. These increased requirements create new challenges not only for product development, but also for the production of X-ray tubes. In addition to the chemical composition of the raw material, the manufacturing processes play a significant role in the quality and service life of the products. In order to obtain updated cause and effect relations and knowledge about the product from the quantity of influencing factors, a cross-process analysis of the manufacturing data is necessary. While classical statistical methods reach their limits in this task, machine learning (ML) algorithms seem promising in the analysis of high-dimensional data. However, according to today's state of the art, such analyses are accompanied by a considerable amount of pre-processing and data cleansing. This paper presents an approach with which the cross-process analysis of product quality can be realized in an efficient manner by creating a standardized procedure. The validation of the approach is based on a prototypical implementation for two product lines.<br
Keywords: Cross-Process Analysis; CRISP-DM; Machine Learning; RAMI 4.0; X-ray Tube Production

Der Digitale Zwilling als echtzeitnahes Fertigungsabbild - Selmaier, Andreas; Sjarov, Martin; Herbert, Meike; Distler, Johannes; Fürst, Jens; Franke, Jörg (Simulation in Produktion und Logistik 2021, 2021)
In the course of the fourth industrial revolution, a multitude of new digital applications are being introduced along the entire life cycle of production systems. The structures of the classic automation pyramid are being softened and replaced by hybrid and decentralised structures in order to allow data and information to flow flexibly directly to where they are needed. For decision making processes in the manufacturing environment, a comprehensive overview of the factory and its current state is crucial. For this, specialised solutions are required to provide people intuitive access to the digital factory. The digital representation including enrichment with actual data falls under the paradigm "digital twin", which in relation to mechatronic manufacturing systems can be systematized by means of the Digital Twin Structure Model (DTSM). The article discusses a corresponding solution in the "proof-of-concept" status. The underlying concept as well as a real-world application are presented accordingly. Furthermore, the added value and future potentials are laid out.<br
Keywords: Digitaler Zwilling; Digitale Fabrik; Industrie 4.0

Fully spray coated organic photodiodes - Tedde, Sandro F.; Kern, Johannes; Sterzl, Tobias; Fürst, Jens; Lugli, Paolo; Hayden, Oliver (Nano Letters, 2009)
Solution-processed organic diodes based on bulk heterojunctlons are attractive for large area photodetectlon. We report a general approach for fully spray-coated organic photodiodes with outstanding characteristics In comparison to bladed or spin-coated devices. Despite the high surface roughness and the less defined morphology of the spray-deposited organic layers, we observe organic photodetectors with responsivities of 0.36 A/W and noise equivalent powers of 0.2 pW/H1/2 in the visible spectrum at high reverse biases of -5 V. Furthermore, we demonstrate device lifetimes beyond 1 year as well as superior yield and reproducibilties for the dark current and photocurrent densities. © 2009 American Chemical Society.
CRIS-Team Scopus Importer:2025-09-22

Near-infrared imaging with quantum-dot-sensitized organic photodiodes - Rauch, Tobias; Böberl, Michaela; Tedde, Sandro F.; Fürst, Jens; Kovalenko, Maksym V.; Hesser, Günter; Lemmer, Uli; Heiss, Wolfgang; Hayden, Oliver (Nature Photonics, 2009)
Solution-processed photodiodes with infrared sensitivities at wavelengths beyond the bandgap of silicon (corresponding to a wavelength of 1,100nm) would be a significant advance towards cost-effective imaging. Colloidal quantum dots are highly suitable as infrared absorbers for photodetection, but high quantum yields have only been reported with photoconductors. For imaging, photodiodes are required to ensure low-power operation and compatibility to active matrix backplanes. Organic bulk heterojunctions are attractive as solution-processable diodes, but are limited to use in the visible spectrum. Here, we report the fabrication and application of hybrid bulk heterojunction photodiodes containing PbS nanocrystalline quantum dots as sensitizers for near-infrared detection up to 1.8νm, with rectification ratios of 6,000, minimum lifetimes of one year and external quantum efficiencies of up to 51%. By integration of the solution-processed devices on amorphous silicon active matrix backplanes, we demonstrate for the first time near-infrared imaging with organic/inorganic hybrid photodiodes. © 2009 Macmillan Publishers Limited.
CRIS-Team Scopus Importer:2025-09-22

Röntgenröhren in der Medizintechnik: High End Technologie für immer bessere Medizin im 21. Jahrhundert - Fürst, Jens (Vakuum in Forschung und Praxis, 2015)
Die Röntgentechnik ist aus der medizinischen Diagnostik nicht mehr wegzudenken. Jedes Krankenhaus und jede etwas größere Arztpraxis nutzen Röntgenstrahlung, um innerhalb kürzester Zeit Bilder vom Inneren des menschlichen Körpers zu erhalten. Egal, ob beim Zahnarzt der Kiefer durchleuchtet oder ob ein Schwerverletzter in einem modernen Computertomographen innerhalb von wenigen Sekunden von Kopf bis Fuß gescannt wird, es kommt dabei stets die gleiche Technologie zum Einsatz. Dieser Artikel beschreibt die Geschichte der Entdeckung der Röntgenstrahlung. Anhand eines einfachen Aufbaus wird die Erzeugung von Röntgenstrahlung erklärt. Die Bedeutung und Funktion der Kathode als Elektronenquelle ist hierbei besonders wichtig. Es wird beschrieben, wie die Entwicklung von den ersten Glühfilamenten zu den Feldemittern fortgeschritten ist. Ebenso wichtig ist die Innovation des Flüssigmetall Gleitlagers, um die in der Anode entstehende Wärme zu entfernen. Nach einem kurzen Exkurs in die medizinischen Anwendungen, wird ein völlig neuartiger Ansatz eines Röntgengerätes beschrieben.
CRIS-Team Scopus Importer:2025-09-22

Two-stage robotic bin picking of small metallic objects - Herbert, Meike; Bach, Paul; Lieret, Markus; Fürst, Jens; Franke, Jörg (Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2022, 2023)
Robotic grasping of small metallic objects such as bolts is a challenging task due to the small dimensions and textureless reflective surfaces. Depth images acquired of such objects are often noisy and error-prone. In addition, overlapping of parts occur as they are provided randomly oriented in a box such as a small load carrier. To overcome the limitations of existing solutions for bolt separation, a flexible and costeffective system is developed using an industrial robot and a magnetic gripper. In a two-stage procedure, the bolts are first grasped blindly from a box and placed on a flat surface. In the second step, object detection and pose estimation is performed and the individual bolts are grasped and inserted into a fixture, so that finally the bolts are in a defined position. Industrial use cases for this system are the automated preparation of bolts for robotic screwing processes or automated commissioning of small objects for assembly tasks. The methodology, implementation and evaluation of the proposed solution is presented in this paper.
CRIS-Team Scopus Importer:2024-12-16
Keywords: Bin picking; Convolutional neural network; Magnetic gripper; Object detection