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.
Keyword: artificial intelligence
THEORETICAL PROBLEMS IN INNOVATIONS
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.
INNOVATION POLICY AND INNOVATION MANAGEMENT
WHAT IS MACHINE CULTURE? RFID CHIP IMPLANTS AND ARTIFICIAL INTELLIGENCE ARE THE NEXT LOGICAL STEPS IN THE EVOLUTION OF THE INDUSTRY.
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.
TECHNOLOGICAL BASIS OF “INDUSTRY 4.0”
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.