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5G offers the manufacturing industry a wireless, fast and secure transmission technology with high range, low latency and the ability to connect a large number of devices. Existing transmission technologies are reaching their limits due to the increasing number of networked devices and high demands on reliability, data volume, security and latency. 5G fulfills these requirements and also combines the potential and use cases of previous transmission technologies so that unwanted isolated solutions can be merged. Use cases of transmission technologies that previously required a multitude of solutions can now be realized with a single technology. However, the general literature often refers to 5G use cases that can also be realized over cables in particular. In this paper, a literature review presents the current state of research on the various 5G application scenarios in production . Furthermore, concrete characteristics of 5G use cases are identified and assigned to the identified application scenarios. The goal is to verify the identified 5G use cases and to work out their 5G relevance in order to be able to concretely differentiate them from already existing Industrie 4.0 applications.
Nowadays, the market for information and communication technologies used for IOT-applications grows daily. Since companies need technologies to transform their business processes corresponding to the digital revolution, they need to know which technologies are available, and fit the best for their use case. Their inertial issue is the lacking overview of technologies suitable to connect their production or logistics. Hence, this paper presents a methodology to select technologies (and combinations) based on their functions. It differentiates between information and communication technologies, digital technologies and connecting technologies by the physical function and its role in a cyber-physical system. Depending on the use case, the applicability of every technology varies. Due to that reason, the paper illustrates a ranked qualification of the technologies for typical use cases, focussing tracking and tracing issues in the intralogistics of producing companies. The evaluation is performed upon a literature research, a market study to identify suitable technologies, and various expert interviews to assess the applicability of the technologies.
The operation of CNC milling is expensive because of the cost-intensive use of cutting tools. The wear and tear of CNC tools influence the tool lifetime. Today’s machines are not capable of accurately estimating the tool abrasion during the machining process. Therefore, manufacturers rely on reactive maintenance, a tool
change after breakage, or a preventive maintenance approach, a tool change according to predefined tool specifications. In either case, maintenance costs are high due to a loss of machine utilization or premature tool change. To find the optimal point of tool change, it is necessary to monitor CNC process parameters during machining and use advanced data analytics to predict the tool abrasion. However, data science expertise is limited in small-medium sized manufacturing companies. The long operating life of machines often does not justify investments in new machines before the end of operating life. The publication describes a cost-efficient approach to upgrade legacy CNC machines with a Tool Wear Prediction Upgrade Kit. A practical solution is presented with a holistic hardware/software setup, including edge device, and multiple sensors. The prediction of tool wear is based on machine learning. The user interface visualizes the machine condition for the maintenance personnel in the shop floor. The approach is conceptualized and discussed based on industry requirements. Future work is outlined.
The aim of the related research project eCloud is to enable small and medium sized enterprises (SMEs) to implement flexible energy management without in-depth energy knowledge and with little distraction from day-to-day business, which is prepared for current and future challenges in the field of energy use. The overall result is a validated prototype for a plug and automate capable (i.e. without implementation effort) operational energy management, which can be successively set up in SMEs based on a cloud platform. Through its gradual and modular implementation, energy management meets the individual needs of each company and contributes to energy system transformation and climate protection by reducing energy costs and greenhouse gas emissions by up to 25%. In total, three expansion stages are available with the levels of monitoring, load management and grid usage, which consist of various Software as a Service (SaaS) modules from the cloud that can be retrieved as required. Thus, the user only needs a minimal hardware intervention in his production and saves a complex IT infrastructure. The methodology developed has been successfully applied by two user companies so far. This proves the effectiveness of the method.
In road haulage, transports are interrupted by truck drivers to comply with driving and rest times. On long-distance routes, these interruptions lead to a considerable increase in transport time. Transport interruption can be avoided by so-called relay traffic: a vehicle (e. g. semi-trailer) is handed over to a rested driver at the end of the driving time. This type of transport requires a certain company size. In Germany, however, transport companies have 11 employees on average. Intra-company relay traffic is therefore not economically viable for most transport companies. To organize an intermodal transport across forwarding companies, long-distance routes need to be split into partial routes to divide them between freight forwarders and carriers. This paper presents a data concept for an algorithm to find the best possible route sections along a previously defined start and endpoint. The developed data concept includes order-specific data, forwarder-specific data, real-time traffic data, geographical data as well as data from freight forwarding software and telematics to be the basis for the route sectioning algorithm. In this paper, different data sources, external services and logistic systems are analyzed and evaluated. It is shown which data is needed and what the best ways are to select and derive this data from the different data sources.
It is crucial today that economies harness renewable energies and integrate them into the existing grid. Conventionally, energy has been generated based on forecasts of peak and low demands. Renewable energy can neither be produced on demand nor stored efficiently. Thus, the aim of this paper is to evaluate Deep Learning-based forecasts of energy consumption to align energy consumption with renewable energy production. Using a dataset from a use-case related to landfill leachate management, multiple prediction models were used to forecast energy demand.The results were validated based on the same dataset from the recycling industry. Shallow models showed the lowest Mean Absolute Percentage Error (MAPE), significantly outperforming a persistence baseline for both, long-term (30 days), mid-term (7 days) and short-term (1 day) forecasts. A potential decrease of up to 23% in peak energy demand was found that could lead to a reduction of 3,091 kg in CO2-emissions per year. Our approach requires low finanacial investments for energy-management hardware, making it suitable for usage in Small and Medium sized Enterprises (SMEs).
The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.
Organizations, of all sizes, in every domain and in all geographies, are facing growing challenges to comprehend the scope of social media based technologies for their internal process use and for their networks. To assist the CIO’s and executives, FIR has developed a tool based framework to evaluate the impact of social web based collaborative technologies to support knowledge intensive processes. The FSI framework extends organizational spectrum to three categories of Formal, Semi-formal and Informal. The FSI tool places the emphasis on both business process and IT level.
The FSI framework and approach are validated in conjunction with industrial and research clients as test cases. Initial finding, reflected in this article, show a dire mismatch between the process exploitable potential level and organizational ICT profile. At the end, a set of recommendations are included for the organizational management to consider for organizational transformation.
This paper addresses the challenge of modelling individual cyber-physical systems (CPS) for small and medium-sized enterprises (SMEs) in manufacturing industries. CPS are key technology building blocks for the implementation of Industrie 4.0. Especially for SMEs the increase of production efficiency and reduction of manufacturing costs through CPS offer potential to maintain their competitiveness and innovation capacity. Although SMEs perceive the potential of CPS, they often lack financial and human resources to acquire the necessary CPS-competencies as well as an overview of all the currently available technological solutions. To overcome this issue a matching platform will offer SMEs support in finding suitable CPS-components by letting them express their functional and technical requirements. The matching logic is based on a set of morphologies that encompasses the functional and requirement spectrum of CPS-components. The matching algorithm analyses the input for congruence of requirements and available technologies and suggests suitable technology combinations. This paper describes the methodology of the matching platform, and introduces the research work to define and to develop the technology morphologies. The presented results facilitate the selection and configuration of CPS for SMEs.
The digitalization of manufacturing processes is expected to lead to a growing interconnection of production sites, as well as machines, tools and work pieces. In the course of this development, new use-cases arise which have challenging requirements from a communication technology point of view. In this paper we propose a communication network architecture for Industry 4.0 applications, which combines new 5G and non-cellular wireless network technologies with existing (wired) fieldbus technologies on the shop floor. This architecture includes the possibility to use private and public mobile networks together with local networking technologies to achieve a flexible setup that addresses many different industrial use cases. It is embedded into the Industrial Internet Reference Architecture and the RAMI4.0 reference architecture. The paper shows how the advancements introduced around the new 5G mobile technology can fulfill a wide range of industry requirements and thus enable new Industry 4.0 applications. Since 5G standardization is still ongoing, the proposed architecture is in a first step mainly focusing on new advanced features in the core network, but will be developed further later.