Data-Driven Fault Detection and Identification in Rubber Injection Molding

Thomas Hutterer

    Research output: ThesisDoctoral Thesis

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    In injection molding, the properties of rubber compounds, and the manufacturing conditions themselves are subject to fluctuations, which impact the quality of the finished rubber parts negatively. To detect such fluctuations and to keep part quality constant, process monitoring systems are used in a wide range of industrial applications. For this purpose, a data-driven statistical monitoring system for rubber injection molding needs to be developed. By employing multivariate statistics, faulty parts can be detected and, once a fault database is present, fault types can be identified. To develop the system, first a dynamic compression testing methodology is presented, which is able to determine the relevant dynamic quality parameters of rubber parts in a way that is fast enough for on-line implementation and 100 % quality control. Second, the temperature of the rubber, the most important factor influencing the final part quality, is determined at every stage of the rubber injection molding process. By employing ultrasound and thermography, the rubber temperature resulting from the processing conditions can be modeled. On this base, the data-driven process monitoring system is built. This process monitoring system detects fluctuations causing faults by using a Principal Component Analysis (PCA) based approach. As a result, all available process signals can be evaluated simultaneously, even linear dependent ones. Additionally, by Fisher Discriminant Analysis (FDA), the type of fault can be automatically identified. Compared to other methods of process monitoring available for rubber injection molding, the presented data-driven system eliminates the need for any preliminary material tests, modelling or data selection. It is therefore much more straightforward and cost-efficient in implementing in smart manufacturing facilities.
    Translated title of the contributionDatenbasierte Fehlererkennung und Fehleridentifizierung beim Kautschukspritzgießen
    Original languageEnglish
    Awarding Institution
    • Montanuniversität
    • Berger-Weber, Gerald, Assessor A (internal)
    • O'Leary, Paul, Co-Supervisor (internal)
    • Friesenbichler, Walter, Supervisor (internal)
    • Steinbichler, Georg, Assessor A (external), External person
    Publication statusPublished - 2021

    Bibliographical note

    embargoed until 20-11-2023


    • Injection molding
    • Rubber
    • Process monitoring
    • Machine Learning
    • Data-driven modelling
    • Process modelling
    • Quality
    • Curing

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