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  • Adaptive process control based on a self-learning mechanism in autonomous manufacturing systems
    Žapčević, Seid ; Butala, Peter, 1953-
    To survive in the highly competitive global economy, manufacturing systems must be able to adapt to new circumstances. An important prerequisite for adaptation is the ability to learn, a process ... based on knowledge discovery and growth. The aim of this research is to uncover knowledge by examining a large volume of real-time manufacturing data collected during manufacturing operations and to use the insights gained to support decision-making and adaptive process control. The paper presents the concept of a self-learning autonomous work system. This concept introduces a learning loop into a manufacturing system composed of data acquisition, data mining (DM), and knowledge-building models. Two methods for DM are applied. A descriptive DM method enables discovery of patterns in data that may contribute to a better understanding of the manufacturing processes. A predictive process provides knowledge in the form of rules, which can then be used for enhanced decision-making. To illustrate the utility of the knowledge models, the concept of adaptive process control is introduced and implemented in a high pressure die-casting domain. A case study based on industrial data collected during die-casting operations provides a demonstration of the concept.
    Source: International journal of advanced manufacturing technology. - ISSN 0268-3768 (Vol. 66, iss. 9/12, Jun. 2013, str. 1725-1743)
    Type of material - article, component part
    Publish date - 2013
    Language - english
    COBISS.SI-ID - 12421403
    DOI

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