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• W3113054716 endingPage "101197" @default.
• W3113054716 startingPage "101197" @default.
• W3113054716 abstract "Machine learning methods are considered a promising approach for improving operations and processes in manufacturing. However, the application of machine learning often requires the expertise of a data scientist combined with thorough knowledge of the manufacturing processes. Small and medium-sized companies that specialize in certain high value-added, variant rich production processes often lack an in-house data scientist and therefore miss out on generating a deeper data-driven insight from their production data streams. This paper proposes a three-step machine learning methodology to empower process experts with limited knowledge in machine learning: 1) data exploration through clustering, 2) representation of the production systems behaviour through specially structured neural networks and 3) querying this representation through evolutionary algorithms to achieve decision support through online optimization or scenario simulation. The chosen algorithms focus on parameter-light, well-established, general use algorithms in order to lower knowledge requirements for their application." @default.
• W3113054716 created "2020-12-21" @default.
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• W3113054716 date "2021-01-01" @default.
• W3113054716 modified "2023-10-01" @default.
• W3113054716 title "Introduction of a time series machine learning methodology for the application in a production system" @default.
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• W3113054716 doi "https://doi.org/10.1016/j.aei.2020.101197" @default.
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