Monitoring is an important part of manufacturing process control and management. It plays a crucial role in ensuring agility in a manufacturing system, process robustness, responsiveness to client demands, and achievement of a sustainable production environment. Recent developments in information systems and computer technology allows for the implementation of new philosophies that integrate various monitoring applications into one complex system connected through company-wide IT systems and with systems operating throughout the whole supply chain. This paper reviews developments in the area of advanced monitoring and integration. Research on new approaches, standards, developed solutions, and company applications are presented. New directions of research and development in all areas of advanced monitoring and implementation of recent IT solutions are discussed.

Development of modern machines and production equipment, supported by advanced control systems, based on recent achievements of computer and software technology is necessary to fulfil clients requirements. Significant number of manufacturing systems can work automatically with limited contribution of employees.However even in advanced manufacturing systems still one of the most important factors is human being. Usually the whole system performance depends on human decisions, the significance of them is higher than in the past, because of more complex and costly production systems. In such a situation, the importance of efficient utilisation of manufacturing equipment by proper man-machine interaction is necessary. The paper presents the problem of man-machine interactions, as well as the attempt to model human behaviour. The interaction media have been explained and short survey of research in this field has been done. The aim of the paper is to order the knowledge about man-machine interactions.

Manufacturing companies must compete on more and more global market. Continuos quality improvement, reduction of products' price and production series' shortening are necessary to be competitive. In such a situation development of production systems is necessary. It can be done only by development of all manufacturing systems' elements, like production methods, machines, process, control and information systems. An important part of manufacturing systems, very often not appreciated enough is human being. The paper focuses on the changing role of managers and machine operators. The problem of human decision quality, Artificial Intelligence support and Socio-Technical Design approach are discussed as well. The aim of the paper is to present the important factors that significantly influence balanced development of advanced manufacturing systems. The article is the result of the research project focused on co-operation improvement between machine operators and technical systems in manufacturing companies.

The article presents research on industrial quality control system based on AI deep learning method. They are a part of larger project focusing on development of Holonic Shop Floor Control System for integration of machines, machine operators and manufacturing process monitoring with information fl ow in whole production process according to Industry 4.0 requirements. A system connecting together machine operators, machine control, process and machine monitoring with companywide IT systems is developed. It is an answer on manufacture of airplane industry requirements. The main aim of the system is full automation of information fl ow between a management level and manufacturing process level. Intelligent, fl exible quality control system allowing for active manufacturing optimization on the base of achieved results as well as a historical data collection for further Big Data analysis is the main aim of the current research. During research number of selected AI algorithms were tested for assessing their suitability for performing tasks identifi ed in real manufacturing environment. As a result of the conducted analyzes, Convolutional Neural Networks were selected for further study. Number of built Convolutional Neural Networks algorithms were tested using sets of data and photos from the production line. A further step of research will be focused on testing a system in real manufacturing process for able possible construct a fully functional quality control system based on the use of Convolutional Neural Networks.

Accepted: 19 November 2016 Integrated monitoring system for discrete manufacturing processes is presented in the paper. The multilayer hardware and software reference model was developed. Original research are an answer for industry needs of the integration of information flow in production process.Reference model corresponds with proposed data model based on multilayer data tree allowing to describe orders, products, processes and save monitoring data. Elaborated models were implemented in the integrated monitoring system demonstrator developed in the project. It was built on the base of multiagent technology to assure high flexibility and openness on applying intelligent algorithms for data processing. Currently on the base of achieved experience an application integrated monitoring system for real production system is developed. In the article the main problems of monitoring integration are presented, including specificity of discrete production, data processing and future application of Cyber-Physical-Systems. Development of manufacturing systems is based more and more on taking an advantage of applying intelligent solutions into machine and production process control and monitoring. Connection of technical systems, machine tools and manufacturing processes monitoring with advanced information processing seems to be one of the most important areas of near future development. It will play important role in efficient operation and competitiveness of the whole production system. It is also important area of applying in the future CyberPhysical-Systems that can radically improve functionally of monitoring systems and reduce the cost of its implementation.