A multi-perspective deep learning approach for optical inspection of crimp connections - Scheck, Albert; Hofmann, Bernd; Nguyen, Huong Giang; Franke, Jörg (Proceedings of the 2024 10th International Conference on Applied System Innovation, ICASI 2024, 2024)
In the era of digitalization and big data, the evolution of electrical systems and their manufacturing is closely tied to the advancement of machine learning, particularly in the industrial sector. With the rise of electromobility and constant connectivity, there is a growing demand for efficient signal and power networking, where the crimp connection plays a critical role. Against this background, this study introduces an intelligent computer vision pipeline that leverages multiperspective deep learning models for automated and comprehensive quality inspection of crimp connections.
CRIS-Team Scopus Importer:2024-07-12
Keywords: Artificial Intelligence; Deep Learning; Quality Analysis; Wiring Harness

Enhancing Crimp Curve Monitoring in Wiring Harness Production: A Machine Learning Approach with Emphasis on Diverse Data - Hofmann, Bernd; Scheck, Albert; Nguyen, Huong Giang; Meiners, Moritz; Franke, Jörg (Proceedings of the 11th International Conference on Industrial Engineering and Applications, 2024)
With emerging trends toward electromobility and autonomous driving, rigorous standards on the quality, safety, and reliability of underlying signal and power distribution networks are becoming increasingly important. These developments necessitate significant adjustments in manufacturing methodologies, particularly in the production process of wiring harnesses, which are characterized by a high variety of components. Crimping, one of the most complex processes within the wiring harness value chain, is commonly used for joining wire and connecting elements. To ensure elevated quality standards throughout the manufacturing process and to address the complexity associated with the high variety of crimp connections, a deeper investigation of data-driven monitoring approaches during production is necessary. Machine learning-based techniques, which diverge from conventional approaches by not relying on fixed programming rules but being trained on sample data, are gaining prominence. This paper further investigates the capabilities of machine learning for monitoring crimp curves, placing a special emphasis on a diverse dataset. The dataset, encompassing process curves derived from various machines and wire cross-sections, highlights the diverse conditions encountered in manufacturing environments. It is analyzed by evaluating several supervised machine learning techniques, with results compared to the conventional monitoring system. Achieving accuracies up to 95%, the results underline the suitability of this approach for handling diverse process curves in the domain of crimpin

Ganzheitliche und auf maschinellen Lernverfahren basierende Qualitätsüberwachung - Nguyen, Huong Giang; Scheck, Albert; Hofmann, Bernd; Meiners, Moritz; Neubauer, Stefan; Schäfer, Alexander; Franke, Jörg (Zeitschrift für wirtschaftlichen Fabrikbetrieb, 2023)
Die Kontakt- und Leitungsverarbeitung ist charakterisiert durch eine hohe Komponentenvielfalt, kurze Zykluszeiten und steigende Anforderungen hinsichtlich Qualität, Dokumentation und Rückverfolgbarkeit. Mit dem Ziel, diese Rahmenbedingungen zu erfüllen, wird in diesem Beitrag eine holistische und auf maschinellen Lernverfahren basierende Qualitätsüberwachung vorgestellt. Darüber hinaus liegt der Qualitätsüberwachung die intelligente Analyse von Crimpkraftkurven zugrunde. Die Auswertung von Bilddaten und Zeitreihen ermöglicht die Fehlererkennung und Anomaliedetektion an der Maschine, ohne dass es zu Einbußen in der Taktzeit kommt. Für die Visualisierung und Werkerakzeptanz der Qualitätsparameter und Prognosen der Modelle wird das Thema Erklärbarkeit in der vorgestellten Qualitätsüberwachung besonders betrachtet.<br
CRIS-Team Scopus Importer:2023-05-05
Keywords: Automatische optische Inspektion; Crimpkraftkurvenüberwachung; Crimpverbindung; Deep Learning; Kabelsatz; Kontakt-; Leitungsverarbeitung; Maschinelles Lernen; Qualitätsüberwachung; verarbeitung