Quality 4.0 in Action: Smart Hybrid Fault Diagnosis System in Plaster Production

Javaneh Ramezani,Javad Jassbi

doi:10.3390/pr8060634

Javaneh Ramezani, Javad Jassbi

Open Access

https://doi.org/10.3390/pr8060634

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Journal: Processes	Publication Date: May 26, 2020
Citations: 21	License type: CC BY 4.0

Affiliation: Uninova, University of Lisbon, Universidade Nova de Lisboa

Abstract

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.

Highlights

In today’s globally complex and competitive business environments, quality is one of the crucial issues for ensuring the success of enterprises [1]
Using the review of the literature and benchmarking on the extraction of intelligent models used in process control, the structure of a hybrid fault diagnosis system using artificial neural networks (ANNs) and expert system (ES) in the process control of plaster production is presented in this research
The innovation associated with digitalization, automation, communication, optimization, and customization of Industry 4.0 concepts and trends allows for real-time analysis and interpretation of production, industries, and service processes to improve quality by detecting failures and justifying possible causes while staying competitive in volatile business environments [30,39]

Summary

Introduction

In today’s globally complex and competitive business environments, quality is one of the crucial issues for ensuring the success of enterprises [1]. There are numerous studies in this field on CCP recognition that used different machine learning algorithms and other intelligent approaches, namely, K-nearest neighbors (KNN), decision trees (DT), NN-based models, ES-based models, support vector machine (SVM), wavelet-based models, and fuzzy logic [14,15,16]. These approaches aim at extracting meaningful information from a large amount of data to detect instabilities in the process with minimal time and cost and maximum accuracy [17].

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