Abstract
Massive continuous streaming data are generated over time during production in a multistage manufacturing process. This paper aims to develop a product-oriented synchronization and effective information extraction of continuous streaming data and further model the relationships among variables for knowledge discovery. Take the steel rolling process as an example; this paper proposes a three-step data analytics procedure for product-oriented synchronization of continuous streaming data, effective information extraction, and further conducting relationship mining between the roll gap adjustment operations and product shapes based on the product-oriented data. The developed procedure first converts the continuous streaming data generated over time in a production process to product-oriented data set, then extracts the information related to the causes and effects of roll gap adjustments, and finally fits the model describing the relationship among the roll gap adjustments, the change of rolling torques, and the change of product dimensions. This data analytics procedure facilitates the decision-making in the steel rolling process and illustrates an effective application of massive in-situ sensing data towards intelligent decision-making in data-rich manufacturing processes.
Issue Section:
Research Papers
Keywords:
high-dimensional streaming data analysis,
relationship mining,
process data and product data synchronization,
multistage manufacturing process,
data-driven rolling process modeling,
sensors
Topics:
Dimensions,
Hot rolling,
Manufacturing,
Mining,
Modeling,
Sensors,
Synchronization,
Torque,
Shapes,
Algorithms,
Steel
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