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Context-aware manufacturing system design using machine learning
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DOI：10.1016/j.jmsy.2022.08.012.png)
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Abstract
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With the development of computer, automation and information technology, workers have more challenges to take care of several devices at the same time. Under this situation, context-aware manufacturing system is proposed to help users capture the most relevant information and make the decision timely. Due to the increased demand for small-batch customized products, manufacturing resources and products frequently change, and this leads to variation of context in manufacturing. Traditional rule-based context-aware manufacturing systems need their rules to be modified manually, which is time-consuming and error-prone under the current variability of the market. To create a framework for updating the context-aware logic automatically, this paper presents a novel notion of applying machine learning techniques in the context-aware manufacturing system design. For the proposed context-aware manufacturing system, components comprising a context model for the manufacturing domain, a machine learning based calibration framework and a context extraction module are designed to improve the update efficiency with less costs. Finally, a test manufacturing scenario is simulated to verify the feasibility of applying machine learning algorithms in context awareness.
Keywords:
Context -aware
Machine learning
Manufacturing system
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