Manufacturing is moving towards complexity, large integration, digitalization and high flexibility. A combination of these characteristics is a basic for forming a new kind of production system, known as Cyber Physical System (CPS). CPS is a board range of complex, multidisciplinary, physically-aware next generation engineered systems that integrates embedded computing technologies. Those integrated manufacturing systems usually consist of four levels: network, enterprise, production system and workplace. In this article we are concentrated to the workplace level, examining the implementation of the most suitable robot-cell and integration it into the production system and enterprise structure. The problem is actual for the big companies such as automobile industry, but very important is also for small and medium sized enterprises (SMEs) that tend to produce for example; small tractors, air conditioners for high speed trains or even different type of doors for houses. In all cases the best solution to response the situation is the implementation of robot-based manufacturing cell into a production system, which is not only a challenge but also need a lot of specific knowledge. Designing and selecting optimal solutions for robot-based manufacturing systems is suitable to carry out by a computer-based decision support systems (DSS). DSS typically works by ranking, sorting or choosing among the alternatives. This article emphasis to the problem of integration the DSS with the artificial intelligence (AI) tools. For this objective, the study has been focused to development of a conceptual model for assessing robot-based system by means of technical and functional capabilities, which is combined with cell efficiency based on process Key Performance Indicators (KPIs) and enterprise Critical Success Factors (CSFs). The elaborated model takes into consideration system design parameters, product specific indicators, process execution data, production performance parameters and estimates how the production cell objective can be achieved. Ten different types of companies were selected and their robot-based manufacturing systems were mapped by qualitative and quantitative factors based on the model, whereas executives were interviewed to determine companies’ strategic objectives. The study results comprise of an approach that helps SMEs to gain additional economic-technical information for decision making at different levels of a company.
Cost-effective manufacturing and technology-based manufacturing are basic keywords in contemporary manufacturing. Efficiency and productivity require extremely good job management, correlation of resources and competencies to production requirements, as well as continuous monitoring of possible wastes and additional expenditures, i.e. real efficiency of the continuous improvement process. In the current article the methodology of technological resources and competence management evaluation in terms of manufacturing system ontology are analysed in pre-order and post-order fulfilment stages. The expedience of resources operation and order outsourcing, but also corresponding risk management principles are analysed. The elaborated methodology enables enterprises to implement a more rational utilization of manufacturing resources by estimating the influence of existing competencies and technological possibilities into productivity and efficiency.
Processes are very fast in contemporary dynamic, turbulent and continually changing world. Systems supporting these processes become more and more complicated and are in mutual interaction. Design of high performance workplaces becomes more and more important for achieving competitiveness. In the current article workplace capability, formed by capability of technological resources and human resources, as well as lean manufacturing principles are analysed with an aim to design high performance workplaces.
Industrial robots are mainly used stationarily in one working position. SMEs often find themselves in situations where robots don't have enough work to do, and because in general, robots cannot be easily moved to another position, the efficiency of robots will decrease. This study provides a solution for this issue. The solution can be found in a robot work cell where a mobile robot deals with robot arm transportation. However, since the mobile robot is not precise enough in positioning, machine vision is used to overcome this problem, which helps the robot to position itself accurately in relation to the work object. The solution has been developed and tested successfully at an Industry 4.0 testbed.
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