- Title
- Critical data detection for dynamically adjustable product quality in IIoT-enabled manufacturing
- Creator
- Sen, Sachin; Karmakar, Gour; Pang, Shaoning
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/192918
- Identifier
- vital:18081
- Identifier
-
https://doi.org/10.1109/ACCESS.2023.3276942
- Identifier
- ISSN:2169-3536 (ISSN)
- 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%). © 2013 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Access Vol. 11, no. (2023), p. 49464-49480
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by-nc-nd/4.0/
- Rights
- Copyright @ IEEE
- Rights
- Open Access
- Subject
- 40 Engineering; 46 Information and Computing Sciences; Criteria; Data criticality; IIoT-enabled manufacturing; Product quality control; Wine-quality
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