# Extended Fuzzy-Based Models of Production Data Analysis within AI-Based Industry 4.0 Paradigm
## 4 Discussion  
From the perspective of previous research and working hypotheses, this study extends existing experiments mainly using decision trees and random forests and traditional artificial neural networks with fuzzy logic [42; 43; 44; 45].The use of fuzzy logic for classification of machining tools has so far been rare. This is because fuzzy classification usually means grouping elements into fuzzy sets, where each observation is assigned a degree of membership to a particular fuzzy set. This allows for a linguistic description of a set of numerical data, and the understanding of implicit classification patterns becomes easier to understand than with traditional logic. The use of fuzzy logic seems to increase with the complexity of the problem and patterns. In the medical and health sciences alone, we found 1124 publications addressing the problem of classification using fuzzy logic. These include the classification of rare diseases or the grouping of patients [46; 47; 48], but also geological [49; 50], climatic phenomena [51; 52], human behavior [53], or crop maturity classifications [54].  
The computerization, automation, and robotization of solutions associated with the growing implementation of the 4.0 paradigm is becoming increasingly complex, requiring the use of increasingly sophisticated methods, tools, and entire fuzzy systems based on fuzzy classifiers using not only data, but also expert opinion or a combination of both.  
Fuzzy systems provide greater robustness against inaccurate, distorted, or incomplete input data, including when feeding data from several types of input simultaneously in which precise input data is not required. This also applies to emergency situations (e.g., excessive inaccuracies), which can be anticipated in the system and signaled with alerts to human operators or technologists, preventing material loss or machine damage (Figure 3).  
There are many phenomena and mechanisms that require the use of fuzzy logic to be considered in the production process; their number will increase as the complexity of processes, machines, equipment, technologies (e.g., 3D printing), and materials (e.g., shape memory or multi-material products) increases.  
Previous approaches on a similar dataset have shown the effectiveness of the dynamic ensemble selection approach based on the value of the score function used in the one-to-one decomposition scheme and its advantages over the static ensemble selection method based on the integration of base classifiers in geometric space (also a one-versus-one decomposition scheme) [44]. Our approach goes one step further, demonstrating effectiveness  
Measurement uncertainty (based on statistical analysis of the problem, e.g., about 2.5 cm or about 10 points).
* Informational uncertainty (based on data mining. e.g., a reliable borrower, a good candidate, i.e., one who collectively meets certain conditions).
* Linguistic uncertainty (based on fuzzy logic, e.g., favorable price, nice weather).  
In doing so, we must bear in mind that thinking, inference, and decision-making is not a universal process and is usually based on patterns that depend on the field of knowledge [55].