Critical data detection for dynamically adjustable product quality in IIoT-enabled manufacturing

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Authors
Sen, Sachin K.
Karmakar, G. C.
Pang, Shaoning
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Grantor
Date
2023-05-17
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Type
Journal Article
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
wine industry
wine quality
quality control
industrial internet of things (IIoT)
data analytics
Citation
Sen, S. K., Karmakar, G. C., & Pang, S. (2023). Critical data detection for dynamically adjustable product quality in IIoT-enabled manufacturing. IEEE Access, 11, 49464-49480. https://doi.org/10.1109/ACCESS.2023.3276942 https://hdl.handle.net/10652/6078
Abstract
The IIoT technologies, due to the widespread use of sensors, generate massive data that are key in providing innovative and efficient industrial management, operation, and product quality control processes. The significance of data has prompted relevant research communities and application developers how to harness the values of these data in secure manufacturing. Critical data analysis, identification of critical factors to improve the manufacturing process and critical data associated with product quality have been investigated in the current literature. However, the current works on product quality control are mainly based on static data analysis, where data may change, but there is no way to adjust them dynamically. Thus, they are not applicable for product quality control, at which point their adjustment is instantly required. However, many manufacturing systems exist, like beverages and food, where ingredients must be adjusted instantaneously to maintain product quality. To address this research gap, we introduce a method that identifies the critical data based on their ranking by exploiting three criticality assessment criteria that capture the instantaneous product quality change during manufacturing. These three criteria are – (1) correlation, (2) percentage quality change and (3) sensitivity for the assessment of data criticality. The product quality is estimated using polynomial regression (POLY), SVM, and DNN. The proposed method is validated using wine manufacturing data. Our proposed method accurately identifies critical data, where SVM produces the lowest average production quality prediction error (10.40%) compared with that of POLY (11%) and DNN (14.40%) This work was supported by the Defence Science Institute (DSI), Australia, through the DSI-Facilitated Project
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Institute of Electrical and Electronics Engineers (IEEE)
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Attribution-NonCommercial-NoDerivatives 4.0 International
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