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.

This research provides a comparison between the performances of TSK (Takagi, Sugeno, Kang)-type versus Mandani-type fuzzy inference systems. The main motivation behind this research was to assess which approach provides the best performance for a gyroscope fault-detection application, developed in 2002 for the European Space Agency (ESA) satellite ENVISAT. Due to the importance of performance in online systems we compare the application, developed with Mamdani model, with a TSK formulation using three types of tests: processing time for both systems, robustness in the presence of randomly generated noise; and sensitivity analysis of the systems' behaviors to changes in input data. The results show that the TSK model perform better in all three tests, hence we may conclude that replacing a Mamdani system with an equivalent TSK system could be a good option to improve the overall performance of a fuzzy inference system.

In many decision support applications, it is important to guarantee the expressive power, easy formalization and interpretability of Mamdani-type fuzzy inference systems (FIS), while ensuring the computational efficiency and accuracy of Sugeno-type FIS. Hence, in this paper we present an approach to transform a Mamdani-type FIS into a Sugeno-type FIS. We consider the problem of mapping Mamdani FIS to Sugeno FIS as an optimization problem and by determining the first order Sugeno parameters, the transformation is achieved. To solve this optimization problem we compare three methods: least squares, genetic algorithms and an adaptive neuro-fuzzy inference system. An illustrative example is presented to discuss the approaches.

The augmentation of physical devices and resources with electronics, software, sensing elements and network connectivity is a “hot topic” as confirmed also by the several research projects and activities on internet-of-things (IoT) and cyber-physical systems (CPS) research streams. It is obvious that intelligent products are taking more responsibility in future collaborative networks. Recent products are becoming more and more intelligent and connected by using the existing network infrastructure, meaning that products are becoming active agents in networks and valuable data sources that are capable to provide data continuously during their operation. This is leading to a massive amount of data that can be used by product manufacturers to be and remain competitive in market sharing. In this scenario, the application of collaborative data mining techniques, supported by machine learning algorithms, is aimed to enable the analysis of the data provided from multiple and above all distributed data sources in order to discover and extract useful knowledge about the behavior of the users along with the usage patterns of their devices and appliances.