Business Case and Technology Analysis for 5G Low Latency Applications

A large number of new consumer and industrial applications are likely to change the classic operator's business models and provide a wide range of new markets to enter. This article analyses the most relevant 5G use cases that require ultra-low latency, from both technical and business perspectives. Low latency services pose challenging requirements to the network, and to fulfill them operators need to invest in costly changes in their network. In this sense, it is not clear whether such investments are going to be amortized with these new business models. In light of this, specific applications and requirements are described and the potential market benefits for operators are analysed. Conclusions show that operators have clear opportunities to add value and position themselves strongly with the increasing number of services to be provided by 5G.Comment: 18 pages, 5 figure

AREUS \u2013 Innovative Hardware and Software for Sustainable Industrial Robotics

Abstract\u2014 Industrial Robotics (IR) may be envisaged as the key technology to keep the manufacturing industry at the leading edge. Unfortunately, at the current state-of-the-art, IR is intrinsically energy intensive, thus compromising factories sustainability in terms of ecological footprint and economic costs. Within this scenario, this paper presents a new framework called AREUS, focusing on eco-design, eco-programming and Life Cycle Assessment (LCA) of robotized factories. The objective is to overcome current IR energetic limitations by providing a set of integrated technologies and engineering platforms. In particular, novel energy-saving hardware is firstly introduced, which aim at exchanging/storing/recovering energy at factory level. In parallel, innovative engineering methods and software tools for energy-focused simulation are developed, as well as energy-optimal scheduling of multi-robot stations. At last, LCA methods are briefly described, which are capable to assess both environmental and economic costs, linked to the flows of Material, Energy and Waste (MEW). A selected list of industrially-driven demonstration case studies is finally presented, along with future directions of improvement

Manufacturing System Energy Modeling and Optimization

World energy consumption has continued increasing in recent years. As a major consumer, industrial activities uses about one third of the energy over the last few decades. In the US, automotive manufacturing plants spends millions of dollars on energy. Meanwhile, due to the high energy price and the high correlation between the energy and environment, manufacturers are facing competing pressure from profit, long term brand image, and environmental policies. Thus, it is critical to understand the energy usage and optimize the operation to achieve the best overall objective. This research will establish systematic energy models, forecast energy demands, and optimize the supply systems in manufacturing plants. A combined temporal and organizational framework for manufacturing is studied to drive energy model establishment. Guided by the framework, an automotive manufacturing plant in the post-process phase is used to implement the systematic modeling approach. By comparing with current studies, the systematic approach is shown to be advantageous in terms of amount of information included, feasibility to be applied, ability to identify the potential conservations, and accuracy. This systematic approach also identifies key influential variables for time series analysis. Comparing with traditional time series models, the models informed by manufacturing features are proved to be more accurate in forecasting and more robust to sudden changes. The 16 step-ahead forecast MSE (mean square error) is improved from 16% to 1.54%. In addition, the time series analysis also detects the increasing trend, weekly, and annual seasonality in the energy consumption. Energy demand forecasting is essential to production management and supply stability. Manufacturing plant on-site energy conversion and transmission systems can schedule the optimal strategy according the demand forecasting and optimization criteria. This research shows that the criteria of energy, monetary cost, and environmental emission are three main optimization criteria that are inconsistent in optimal operations. In the studied case, comparing to cost-oriented optimization, energy optimal operation costs 35% more to run the on-site supply system. While the monetary cost optimal operation uses 17% more energy than the energy-oriented operation. Therefore, the research shows that the optimal operation strategy does not only depends on the high/low level energy price and demand, but also relies on decision makers’ preferences. It provides not a point solution to energy use in manufacturing, but instead valuable information for decision making. This research complements the current knowledge gaps in systematic modeling of manufacturing energy use, consumption forecasting, and supply optimization. It increases the understanding of energy usage in the manufacturing system and improves the awareness of the importance of energy conservation and environmental protection

Robots in Industry. Past,present and future of a growing collaboration with humans

Robots have been part of automation systems for a very long time, and in public perception, they are often synonymous with automation and industrial revolution perse. Fueled by Industry 4.0 and Internet of Things (IoT) concepts as well as by new software technologies, the field of robotics in industry is currently undergoing a revolution on its own. This article gives an overview of the evolution of robotics from its beginnings to recent trends like collaborative robotics, autonomous robots, and human- robot interaction. Particular attention is devoted to the deep changes of the last decades, from the traditional industrial scenario based on isolated robotic cells up to the most recent coworking and collaborative robots. The role of robotics in the Industry 4.0 framework is analyzed, and the relationships with industrial communications and software technologies are also discussed. Some future directions for robotics are envisaged, focusing on the contributions coming from new materials, sensors, actuators, and technologies. Open issues are highlighted as well as the main barriers that currently limit the deployment of industrial robots in the small and medium enterprise (SME) world

Robotic & Smart Manufacturing in Automotive Welding Shop

The slides present an overview of smart manufacturing system including IR 4.0 assessment & adoption, modern automation protocol & system architecture, and robot application in automotive welding shop

The potential of additive manufacturing in the smart factory industrial 4.0: A review

Additive manufacturing (AM) or three-dimensional (3D) printing has introduced a novel production method in design, manufacturing, and distribution to end-users. This technology has provided great freedom in design for creating complex components, highly customizable products, and efficient waste minimization. The last industrial revolution, namely industry 4.0, employs the integration of smart manufacturing systems and developed information technologies. Accordingly, AM plays a principal role in industry 4.0 thanks to numerous benefits, such as time and material saving, rapid prototyping, high efficiency, and decentralized production methods. This review paper is to organize a comprehensive study on AM technology and present the latest achievements and industrial applications. Besides that, this paper investigates the sustainability dimensions of the AM process and the added values in economic, social, and environment sections. Finally, the paper concludes by pointing out the future trend of AM in technology, applications, and materials aspects that have the potential to come up with new ideas for the future of AM explorations

Performance adaptive manufacturing processes

Part of: Seliger, Günther (Ed.): Innovative solutions : proceedings / 11th Global Conference on Sustainable Manufacturing, Berlin, Germany, 23rd - 25th September, 2013. - Berlin: Universitätsverlag der TU Berlin, 2013. - ISBN 978-3-7983-2609-5 (online). - http://nbn-resolving.de/urn:nbn:de:kobv:83-opus4-40276. - pp. 296-301.Energy efficiency is of increasing importance towards sustainable manufacturing in the automotive industry, in particular due to growing environment regulations and rising electricity costs. Approaches within the manufacturing planning phase are insufficient to address dynamic influences during run-time (e.g., electricity tariffs or workload). Additionally, conventional production monitoring and control systems consider the ‘Overall Equipment Effectiveness‘ of manufacturing systems, but do not include related energy efficiency. This paper introduces a novel approach that combines these both aspects and provides more effectiveness based on socalled production variants. The latter are designed during the planning phase and used to adapt manufacturing behavior when facing dynamically changes during run-time. A simulation shows how dynamic adjustments of cycle times lead to a high reduction of energy costs while maintaining high throughputs