LAPSE:2020.0906
Published Article
LAPSE:2020.0906
Quality 4.0 in Action: Smart Hybrid Fault Diagnosis System in Plaster Production
Javaneh Ramezani, Javad Jassbi
August 5, 2020
Industry 4.0 (I4.0) represents the Fourth Industrial Revolution in manufacturing, expressing the digital transformation of industrial companies employing emerging technologies. Factories of the future will enjoy hybrid solutions, while quality is the heart of all manufacturing systems regardless of the type of production and products. Quality 4.0 is a branch of I4.0 with the aim of boosting quality by employing smart solutions and intelligent algorithms. There are many conceptual frameworks and models, while the main challenge is to have the experience of Quality 4.0 in action at the workshop level. In this paper, a hybrid model based on a neural network (NN) and expert system (ES) is proposed for dealing with control chart patterns (CCPs). The idea is to have, instead of a passive descriptive model, a smart predictive model to recommend corrective actions. A construction plaster-producing company was used to present and evaluate the advantages of this novel approach, while the result shows the competency and eligibility of Quality 4.0 in action.
Keywords
construction industry, control chart pattern, decision support systems, discriminant analysis, disruption management, disruptions, expert systems, failure mode and effects analysis (FMEA), fault diagnosis, Industry 4.0, neural networks, plaster production, statistical process control
Suggested Citation
Ramezani J, Jassbi J. Quality 4.0 in Action: Smart Hybrid Fault Diagnosis System in Plaster Production. (2020). LAPSE:2020.0906
Author Affiliations
Ramezani J: Faculty of Sciences and Technology and Uninova CTS, NOVA University of Lisbon, Campus de Caparica, 2829-516 Caparica, Portugal
Jassbi J: Faculty of Sciences and Technology and Uninova CTS, NOVA University of Lisbon, Campus de Caparica, 2829-516 Caparica, Portugal
Journal Name
Processes
Volume
8
Issue
6
Article Number
E634
Year
2020
Publication Date
2020-05-26
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr8060634, Publication Type: Journal Article
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LAPSE:2020.0906
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doi:10.3390/pr8060634
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Aug 5, 2020
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CC BY 4.0
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Calvin Tsay
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