Resource-Aware Steel Production Through Data Mining
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Based on a rolling mill case study, this paper discusses how data mining techniques and intelligent machine-to-machine telematics could be used to predict internal quality issues of intermediate products in manufacturing processes. The huge amount of data recorded during processing and the distributed but sequential nature of the manufacturing lead to challenging questions for data mining applications and advanced process control approaches in industries like steel production. Moreover, the discovery for hidden information, knowledge and dependencies in the process data contribute significantly to support avoiding waste of resources and achieving the objectives of zero-defect-production, sustainable and energy-efficient manufacturing processes.
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