Authors:
Khaled Bahloul
1
and
Nejib Moalla
2
Affiliations:
1
EPSF, 80000, Amiens, France
;
2
Universite Lumiere Lyon 2, DISP Laboratory, EA4570, 69676 Bron, France
Keyword(s):
IoT Network, Risk Management, Resilience, Quality Control, Machine Learning, Machining in Plastic Industry.
Abstract:
The definition of defect prediction models in manufacturing emerges as an attractive alternative supported by industry 4.0 concepts and solutions. We propose in this paper an IoT-based approach for a global quality control mechanism in industry. We cover in this work the in-process quality control inspection, the production machines as well as the production environment monitoring. Our framework addresses data analytics algorithms using monitoring data, risk assessment models, resilience parameters and acceptance criteria for prediction models. The proposed concepts are implemented to control the manufacturing processes of a plastic product where the distinction between irregularity and nonconformity needs to be supported by a smart decision system.