International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

A training discipline “Basics if the Artificial Intelligence”, taught in UCTM, Sofia is presented. The discipline is developed according to the requirements of ACM as well the methodises presented by Russel and Norvig in their seminal book “Artificial Intelligence: A modern approach, 3 ed.”. As a tool for illustration of the AI algorithms to be studied, each student is equipped with a typical cyberphysical system – individual training stand, which includes a simplified robot (addressed here as passive agent) constructed of sensors and actuators only) controlled by an AI algorithm, subject to development by the students and housed on a PC. An outline of the lections is
included.

History of gradual increase of elements of artificial intelligence is resulted in the process of creative thought at creation of on principle new structures and charts of clamping cartridges on the example of cangovykh. Thinking process on on the lasts of peat-time of application of artificial intelligence presented as a binary koda and waves of frequency impulses, reminding cerebration.

The Industrial Predictive Analytics for Industry 4.0 is a system that predict and prevent machine failures and breakdown by analyzing time-series data (temperature, pressure, vibration etc.) received from sensors embedded in machines and equipment. The system can analyze machine parameters to identify patterns and predict breakdowns before they happen. The core of the proposed system is based on Artificial Neural Network approach (both Deep and Shallow Neural Networks). Artificial Intelligence and Artificial Neural Networks allow analyses the huge amounts of data collected from the manufacturing process and predict what will go wrong, and when. The proposed system works in the paradigm of Industry 4.0 and provides the abilities in the area of predictive maintenance. The Industrial Predictive Analytics for Industry 4.0 also contains a decision-making system and support system that significantly increases the level of maintenance.

The Artificial Intelligence (AI) has giant possibilities to optimize the fight against crime and strengthen national security. In the conditions of unimaginable accumulation of information and the need for rapid decision-making, only the use of AI can lead to success. Intelligence, counterintelligence, forensic science, counteracting organized crime, rapid processing of available information, drafting of varied decisions, creating plans and multivariate scenarios, performing various analyzes is a time-consuming process. Only its use can significantly shorten this time and thus dramatically increase the possibilities for detection, prevention and curbing crimes.

The paper deals with the development of a new type of production planning and control in a wood-processing company. The production is already highly automated and data from the production processes are gathered and stored in a database. The project picks up these technical basements in order to automatically provide intelligent decisions and make the factory even smarter.

The modern stage of development is associated with increasing requirements to products and need for sustainable growth, as well as with strive for effective application of technical achievements, industrial and information technologies. The new production concepts require fundamentally new ways for technological construction. The application of the global information networks allows intensification of production and significant improvement of manufactured product quality. The present paper follows the development of Concurrent Engineering (CE) and systemizes its development directions as a whole complex integrated process. It attempts to clarify and determine the place of CE at the stage of general digitalization of project and production activities in an industrial enterprise. Special attention is paid to new software products and their compatibility to the requirements of modern production reality, such as digitalization, artificial intelligence and virtual reality.

The place and role of CE in the new world of smart technologies is not only a subject of scientific research but also a huge challenge for its practical utility.

Firms progressively search for new technologies and methods to develop sustainable competitive advantage. This article is aimed to investigate the pros and cons of integrating human RFID chip implants and Artificial Intelligence in the new digital global environment. The paper focuses on the new digital communication and machine culture, which rises between human and machine. It represents the unlimited industrial possibilities and potential of Artificial Intelligence. It also assesses the future risk that the world of human work being run by artificial management and the social impact to the public.

In the past one hundred years, there were more global changes than in the previous one thousand years.

This article presents the results of scientific research performed by scientists at NTUU Igor Sikorsky Kyiv Polytechnic Institute in the creation and genetic forecasting of the development of a new generation of machine tools and associated mechanisms using the latest advances incorporated into an interdisciplinary intellectual field and based on a unified structured and systematic approach. It is suggested that the revival of the domestic machine-tool building can be done by virtue of an innovation breakthrough and implementation of the "Overdo without catching up!" strategic goal.

There are described in the article current applications with the artificial intelligence and value of using it for the road transport efficiency. This paper deals with concept of artificial intelligence, main reasons for successful growing of AI at present and main areas of AI using in transportation. One part of the article aims to define the artificial neural networks and basic elements of them. The article describes the reason of use them in transportation problems solving, the possibility of using neural networks in the road transport, examples of tasks solvable by neural nets, the advantages and disadvantages of using neural networks.

The dynamic characteristics of spindle-holder-tool assembly is one of the most important factors that have considerable influence on cutting process stability, quality of machined surface, tool life, material removal rate, etc. In order to determine the stable cutting conditions it is essential knowledge of the tool point frequency response function (FRF). The objective of this study is development of a two different artificial intelligence methods, namely, artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) as a potential modelling techniques for prediction of natural frequencies of tool controlled mode. First of all, the natural frequencies of the tool controlled mode for limited combinations of tool overhang length and tool diameter were identified experimentally. The results were used to train an ANN and ANFIS models and both models were compared for their prediction capability with the experimentally determined data. Regarding the results, ANN and ANFIS models were found to be capable of very accurate predictions of natural frequencies of the tool controlled mode.