# Extended Fuzzy-Based Models of Production Data Analysis within AI-Based Industry 4.0 Paradigm
## 4 Discussion
### Directions for Further Research  
The basic directions of further research within Industry 4.0 paradigm include:  
* Use of directed fuzzy numbers. * Development of hybrid methods, combining the advantages of different solutions (See, for example, the classification of textile products and fiber-reinforced polymer composites using artificial neural networks, genetic algorithm, and fuzzy logic [56], or aerogels combining enhanced physicochemical properties and structural features with sensory and energetic materials [57].) * Development of a framework for the development of various models of industrial processes, adapted on site. * Algorithms based on fuzzy data to reduce subjectivity in estimation [58], including uncertainty in multi-criteria decision analysis [59; 60; 61]. * Assessing the performance of the logistic chain, including the food chain, and preventing threats to supply continuity and product safety [62; 63]. * Simultaneous analysis of product/service quality, price, and consumer satisfaction. * Incorporating this group of solutions into ERP systems. The use of fuzzy logic and other AI methods increases the scope of possibilities for using data contained in technological databases [64; 65]. The experiments carried out have confirmed the usefulness of fuzzy logic in the selection of tools for a technological operation, which is a stage of designing the technological process of a product. AI methods, including fuzzy logic, introduce a new quality of technological process redesign using computer systems. A technologist's knowledge is largely based on his or her years of experience. As a result, the decisions he or she makes are often \"intuitive\"--the expert himself or herself is sometimes unable to concretely express the rule he or she followed when making a particular decision. Gathering knowledge in the form of examples of already-designed technological processes verified during the production of products is, therefore, the best way to discover knowledge from the technologist's experience. This knowledge can then be encapsulated in computer systems using AI methods. In turn, it can be transformed into rules that can be used to design technological processes for new products. We are aiming here at so-called learning systems, which will automatically design the technological process (for example, the use of fuzzy logic here). This is particularly important when developing computer-aided process planning (CAPP) systems for complex real-world systems. However, the disadvantage of this approach is the complexity of the resulting CAPP system. Models for the selection of work-piece tooling, machine tooling, and machining parameters must be developed for each technological operation separately (for milling, grinding, turning, etc.). The neural network learning file contains quantitative and qualitative parameters, so their normalization and coding are necessary. There is also the problem of acquiring new knowledge. The developed models need to be trained periodically, i.e., according to incoming new data. This data include both new machine tools and tools that appear in the company and further examples of developed technological processes. In conclusion, it is clear that the advantages of the selected artificial intelligence methods--neural networks and decision trees--clearly outweigh the disadvantages. In addition, the practical knowledge contained in the computer system that takes into account the experience of technologists and specific data from the company makes it possible to design technological processes also for less experienced technologists. A new employee does not know the capabilities or conditions of the company's machinery stock. If he or she were to attempt to design a technological process using only catalogue values, the end result could prove unsatisfactory. As a result of many years of operation of a machine tool, its parameters specified in the technical and operational documentation are completely different from those in reality.