The problem of production flow and evaluation of productivity in the manufacturing line is analysed. Machines can be operated by humans or by robots. Since breakdowns and human factors affect the destabilization of the production processes, robots are preferred. The main problem is a proper methodology—how can we determine the real difference in work efficiency between human and robot at the design stage? Therefore, an analysis of the productivity and reliability of the machining line operated by human operators or industrial robots is presented. Some design variants and simulation models in FlexSim have been developed, taking into consideration the availability and reliability of the machines, operators and robots. Traditional productivity metrics, such as the throughput and utilization rate, are not very helpful for identifying the underlying problems and opportunities for productivity improvement in a manufacturing system, therefore we apply the OEE (overall equipment effectiveness) metric to present how the availability and reliability parameters influence the performance of the workstation, in the short and long terms. The implementation results of a real robotic line from industry are presented with the use of the overall factory efficiency (OFE) metric. The analysis may help factories achieve the level of world class manufacturing.

The problem of production flow in manufacturing systems is analyzed. The machines can be operated by workers or by robots, since breakdowns and human factors destabilize the production processes that robots are preferred to perform. The problem is how to determine the real difference in work efficiency between humans and robots. We present an analysis of the production efficiency and reliability of the press shop lines operated by human operators or industrial robots. This is a problem from the field of Operations Research for which the Discrete Event Simulation (DES) method has been used. Three models have been developed, including the manufacturing line before and after robotization, taking into account stochastic parameters of availability and reliability of the machines, operators, and robots. We apply the OEE (Overall Equipment Effectiveness) indicator to present how the availability, reliability, and quality parameters influence the performance of the workstations, especially in the short run and in the long run. In addition, the stability of the simulation model was analyzed. This approach enables a better representation of real manufacturing processes.

One of the main problems of modern manufacturing systems is the increasing complexity related to modern Industry 4.0 technologies that are fundamentally changing manufacturing and logistics processes and operations. Industry 4.0 includes, e.g., flexible automation and robotization, which make complex manufacturing systems difficult to analyze. Some modeling and simulation methods are being used to solve industrial problems and can serve as an interface between the production level and management level. The new trend of the Digital Twin, creating simulation models as similar as possible to the real system, and a Digital Twin framework for a manufacturing line from the automotive industry, was considered. Simulation models typically start from the empty state and some warmup time is required to achieve the stable state. The Key Performance Indicators were also analyzed for the stable state. However, there are many stochastic parameters such as machinery failures, human errors, quality issues, etc., that make the real processes differ from simulated processes, and cause the instability of production throughput and changes in the Work in Process. To analyze the Work in Process in the model, initialization of the model with proper production data is required, as the Digital Twin uses data synchronization with the production database. In this paper, the digital model of a human-robot-operated manufacturing system with Work-in-Process data synchronization is analyzed, and the results of the statistical analysis of simulation experiments are presented. The obtained results show high variability of finished production, which is related to system instability due to random failures, especially when the system starts from an empty state. However, an increase in initial Work in Process results in better efficiency and stability for the whole system. The DT simulation of the manufacturing system can be very helpful, as it becomes a repository of knowledge about the real system and enables the analysis of its dynamics. However, for proper functionality, the model should include information about the current WIP state, which enables the start of the simulation with exactly the same number of queues as in the real system. The presented method can also be used in similar enterprises from other industries, especially for those with discrete processes or high WIP variability, and for further synchronization of other DT parameters.

The article presents the problems connected with the performance evaluation of a flexible production system in the context of designing and integrating production and logistics subsystems. The goal of the performed analysis was to determine the parameters that have the most significant influence on the productivity of the whole system. The possibilities of using automated machine tools, automatic transport vehicles, as well as automated storage systems were pointed out. Moreover, the exemplary models are described, and the framework of simulation research related to the conceptual design of new production systems are indicated. In order to evaluate the system’s productivity, the use of Overall Equipment Efficiency (OEE) metrics was proposed, which is typically used for stationary resources such as machines. This paper aims to prove the hypothesis that the OEE metric can also be used for transport facilities such as Automated Guided Vehicles (AGVs). The developed models include the parameters regarding availability and failure of AGVs as well as production efficiency and quality, which allows the more accurate mapping of manufacturing processes. As the result, the Overall Factory Efficiency (OFE) and Overall Transport Efficiency (OTE) metrics were obtained. The obtained outcomes can be directly related to similar production systems that belong to World Class Manufacturing (WCM) or World Class Logistics (WCL), leading to the in-depth planning of such systems and their further improvement in the context of the Industry 4.0.

Industrial robots are used for many tasks, mainly for material handling, welding and cutting. Robots can be also equipped with other tools for example drill or mill cutter and used for machining. Compared to conventional machines, robots have some advantages, which are: large range, flexibility and speed. On the other hand the greater disadvantage is small stiffness of robotic arms. Also the precision of robot positioning is smaller than modern CNC machines. Nowadays small number of robots are used mainly for machining of soft materials, such as plastic, wood, foam and aluminium. We have also executed some experiments with robot machining including styrodur milling. This technique is similar to rapid prototyping technics. Obtained parts can be used as prototypes. Robots can be used also for machining of hard materials and steel, but that is related with greater cutting force. Thanks they flexibility robots can be used for tasks that are performed by hand by locksmiths. An example of deburring and chamfering of sharp edges were analysed. The burrs and sharp edges that remains after some machining operations must be removed. In most cases that is done by chamfering the edges with hand tools. That tasks requires skilled workers and is physically exhausting and therefore industrial robots can be used to perform that work. But the first problem is prediction of cutting force and selection of proper robot with adequate payload. A mechanistic model of cutting force during milling a chamfer on the edge is presented in the article. Obtained results are similar with other experimental results that are described in the analysed bibliography. Afterwards a methodology for robot selection is explained. Because robot manufacturers give only data for static payload of robot arm, there must be a way to take into account the dynamic cutting force. Some problems that are possible during robot machining are discussed, and some solution are proposed. Because milling force is not constant and still subsequently changes value and direction, it can be the source of vibration. Small stiffness of robot arm combined with vibrations can caused losing of robot position and improper surface after machining. Other problem can be robot programming for machining of curved surfaces in 3 dimensional space. There are same CAM system that can be used for that purpose. Results obtained with developed model can be used for design of robotic cell for chamfering and milling. Normal 0 21 false false false PL X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:Standardowy; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";}

This paper presents application of MIAS (Manufacturing Information Acquisition System) methodology to develop customized data acquisition system supporting management of the Central Wastewater Treatment Plant (CWWTP) in Gliwice, Poland, being example of production systems leading continuous flow, automated production processes. Access to current data on the state of production system is a key to efficient management of a company, allowing fast reaction or even anticipation of future problems with equipment and reduction of waste. Overview of both analysis and synthesis of organisational solutions, data sources, data pre-processing and communication interfaces, realised according to proposed MIAS methodology, had been presented. The stage of analysis covered i.e.: organisational structure of the company, IT systems used in the company, specifics of technological processes, machines and equipment, structure of control systems, assignments of crew members, materials used in the technological processes. This paper also presents results of the stage of synthesis of technical and organisational solutions of MIAS for CWWTP, including proposed solutions covering MIAS architecture and connections with other IT systems, data sources in production system that are currently available and newly created, data preprocessing procedures, and necessary communication interfaces.