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.
Many companies are already using robots, but many have not found enough applications for the robot and therefor they have not purchased it jet. One robot can be used to perform several different tasks, but it also raises the question of whether the production needs to be reorganized so that these multiple tasks are directed to the robot, or it can be solved differently where the robot moves between different tasks. In this paper different concepts will be discussed and each of its disadvantages and advantages will be highlighted. Paper also includes survey among Estonian manufacturing companies to find out which tasks are robotized and which tasks are desired to give over to robots in future. Paper also include short description about recently opened Industry 4.0 test hub where mobile robot applications are being tested and paper results will be also tested in this test hub. In general, this paper focus on solution how to use robot arm most efficient way if there is not enough job for stationary robot solution.
The successful selection process of industrial robots (IRs) for today's Cyber-Physical Systems is an important topic and there are different possibilities to solve the task. The primary task is to estimate the existing IR selection systems according to the suitability analysis and to highlight the main positive features and problematic areas. The objective of the reverse task is to carry out the sensitivity analysis of the existing robot-based manufacturing systems. The matching of these two approaches helps decision makers to develop the main principles of IR selection in today`s multidimensional and fast-changing economic world.
The continuous need to develop Industry 4.0 branches has led to a position, where highly sophisticated and multi-layer smart robotic systems are conducting the way in future manufacturing. This study aims to build a connectivity and system intelligent layer on top of a Co-bot integrated CNC-based Manufacturing cell. The connectivity layer is used to bypass all the data from machines to the upper intelligent layer vice versa. When raw data is arriving in the intelligent layer it is converted to information and again to knowledge for reflection back to the cell. Machine to Machine Communication and Digital Twin process for optimization is used for data conversions. This study is a down-scale example of the CPS for further development of existing robot cells.
Manufacturing companies must ensure high productivity and low production cost in rapidly changing market conditions. At the same time products and services are evolving permanently. In order to cope with those circumstances, manufacturers should apply the principles of smart manufacturing together with continuous processes improvement. Smart manufacturing is a concept where production is no longer highly labor-intensive and based only on flexible manufacturing systems, but production as a whole process should be monitored and controlled with sophisticated information technology, integrated on all stages of the product life cycle. Process improvements in Smart Manufacturing are heavily reliance on decisions, which can be achieved by using modeling and simulation of systems with different analyzing tools based on Big Data processing and Artificial Intelligence (AI) technologies. This study was performed to automate an estimation process and improve the accuracy for production cell’s performance evaluation. Although there have been researches performed in the same field, the substantial estimation process outcome and accuracy still need to be elaborated further. In this article a robot integrated production cell simulation framework is developed. A developed system is used to simulate production cell parametric models in the real-life situations. A set of rules and constraints are created and inserted into the simulation model. Data for the constraints were acquired by investigating industries’ best production cells performance parameters. Information was gathered in four main fields: company profile and strategy, cell layout and equipment, manufactured products process data and shortcomings of goal achievements or improvement necessary to perform. From those parametric case model, a 3D virtual manufacturing simulation model is built and simulated for achieving accurate results. The integration of manufacturing data into decision making process through advanced prescriptive analytics models is a one of the future tasks of this study. The integration makes it possible to use “best practice” data and obtained Key Performance Indicators (KPIs) results to find the optimal solutions in real manufacturing conditions. The objective is to find the best solution of robot integrated cell for a certain industry using AI enabled simulation model. It also helps to improve situation assessment and deliberated decision-making mechanism.
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