# Extended Fuzzy-Based Models of Production Data Analysis within AI-Based Industry 4.0 Paradigm
## 1 Introduction
[20] proposed an optimal selection of tools and machining parameters using rules generated from decision trees. The system works on the basis of an updated machining database and a microprocess planning algorithm. The result of the decision-making process, i.e., the cutting conditions, is determined from a feasible regression equation [20]. [PERSON] and [PERSON] [21] proposed a fast and tailored decision-making method based on an improved support vector machine (ISVM) and production environment, one that is more responsive to dynamic changes and the uncertainty and fuzziness of the production environment. The SVM model, based on the triangular fuzzy number theory, is allowed here to represent uncertain input data or unbalanced samples [21]. The solution uses not only triangular fuzzy numbers representing uncertain inputs, but also independent penalty factors for different categories of unbalanced samples. This makes it possible to better respond to certainty, fuzziness, and randomness, but also to develop a new type of production guidance systems based on self-adaptation, self-learning, self-evolution, self-reconfiguration, and self-service. [PERSON] et al. demonstrated efforts to reduce human intervention in cutting tool selection and metal cutting process parameters using AI methods and techniques. The selection of a suitable cutting tool, as well as the optimization of process parameters, is realized by artificial neural networks, fuzzy logic, and a genetic algorithm. This is conducted based on multi-objective optimization criteria, including material removal rate, tool life, and tool cost [22]. [PERSON] et al. developed an expert system model based on response surface methodology (RSM) designed to predict the force generated during the cutting process when machining the Ti-6 Al-4V alloy with minimum quantity lubrication (MQL) [23]. We should pay particular attention to the application of a fuzzy approach to modeling, optimization, and the control of continuous production [24; 25]. In the area of complex process design, [PERSON] et al. [26], [PERSON] et al. [27], [PERSON] et al. [28], and [PERSON] [29] used an agent-based technique for process design. In addition, [PERSON] et al. [30] presented the concept of a process planning system together with the selection of machine tools, machining parameters, toolpath and numerical control (NC) code using neural networks, genetic algorithm, and intelligent search [31]. [PERSON] [32] presented the use of decision trees for the selection of machine tools and machining parameters. Publications by [PERSON] and [PERSON] [33], as well as [PERSON] et al. [34], presented the concept of intelligent, interactive, automated systems for designing machine components and assemblies based on their features described in natural language (linguistically) using artificial neural networks and fuzzy inference. This achieves the normalization of design features as input quantities to an automatic technological classification system. The analysis of the research carried out so far indicates that a significant range of problems related to making optimization decisions for machines remains to be solved. These include: the selection of a machine for processing parts (one or more), an analysis of kinematic and dynamic parameters of the machine, tool selection (in the case of many: selection of the order of their use), selection of the optimal tool path, and the optimization of the selection of control and reinforcement strategies, including tool wear, energy, and costs in general [35]. Intelligent networked AI systems enable a faster and more efficient use of the knowledge in the data, making much more effective decisions. This is based on web-based tools, performance analysis, shared knowledge adaptively tailored to specific organizations, and their business scenarios, AI/machine learning (ML), robotics, chatbots and IoT [36; 37].