# Extended Fuzzy-Based Models of Production Data Analysis within AI-Based Industry 4.0 Paradigm
## 1 Introduction  
Industry 4.0, i.e., the Fourth Industrial Revolution, involves the widespread implementation of artificial intelligence (AI), robotics, and the Internet of Things (IoT) to make processes more flexible, reduce the workforce, and make complex processes easier to control and manage. Industry 4.0 is applicable to all aspects of industry: analysis, diagnostics, manufacturing, technical control at all stages of production, packaging, and product/service evaluation throughout the life cycle [1, 2, 3, 4]. Fast, accurate, and efficient analysis of production data is a key element of the Industry 4.0 paradigm. This applies not only to newly built solutions but also to the digitalization, automation, and robotization of existing factories and production or repair lines. In particular, technologists' extensive experience and know-how are necessary to design correct technological processes to minimize losses during production and product costs. Tool selection is the cognitive process required to use a tool and is based on distinct semantic knowledge under different conditions [5]. Thus, a human (operator, technologist) selecting a tool performs the task of matching the tool(s) that achieves the objective of the activity and requires the selection of the best tool from among many candidates. The aforementioned procedure is repetitive, subject to errors due to selection fatigue, and, at the same time, should be subject to automation in order to reduceits duration as well as improve accuracy and repeatability. It also often requires complex manipulation procedures for the selected tool [6; 7], whereas in some cases, the tool can be fed and assembled automatically. This is always conducted in the same way and with the same accuracy, reducing the preparation time of the machine. That is why the proper selection of tools, machine tools, and production parameters during the manufacturing process is so important. Artificial intelligence (AI) has already provided massive improvements in many areas of technology, including in the wider economy. Process modeling, machine operation simulations, and entire production lines are expanding, as the development directions of equipment and its control systems are based on calculations carried out within their virtual twins. This fits within the Industry 4.0 paradigm. It also works the other way: the ever-new challenges posed to the process industry, including within the framework of the green deal, have already generated innovative technologies used to optimize processes and designs from this point of view, primarily in the form of intelligent, flexible, and open applications, including those that are fast enough to operate in near real time. So, the data generated from the sensors must be simple enough to not hinder the process, and the algorithms used must be computationally efficient so as not to be acquisitive in terms of time and energy. An additional requirement here may be the increasing processing of data on mobile devices, which is economical in the use of memory and processor computing capacity [8]. A number of previous studies have indicated numerous possible benefits of this process, requiring its planned implementation. These benefits include reducing costs (especially unit costs) by up to 25%, increasing production by up to 100%, increasing profitability by up to 30%, and accelerating the implementation of a new product by up to 6 months, giving a significant competitive advantage. The inclusion of computing technologies will allow for the improvement of enterprise data processing at all levels: material supply, planning, preparation, control of production processes, equipment load, active technical control at all stages of work in progress, and, hence, the presented benefits [9]. Fuzzy logic is often used to manage complex processes, especially with constraint satisfaction, linguistic description, and an uncertain dataset [10; 11].