• Energy Research

One of the hot topics currently in manufacturing domain is direct digital manufacturing. With introduction of cheap three-dimensional printers, the direct digital manufacturing seems to become a new manufacturing paradigm with an entirely different impact on society; nevertheless how this will impact the society and the differences between the paradigms are unclear. According to this background, this paper presents a comprehensive analysis of direct digital manufacturing from different perspectives in comparison to various traditional manufacturing paradigms. Authors are using a societal viewpoint to see, describe and analyse the subject instead of traditional manufacturing viewpoint. For the better understanding of direct digital manufacturing origins, a classification and historical background about available techniques are described. Furthermore, direct digital manufacturing as a paradigm is analysed and compared with craft production, mass production and mass customisation. Direct digital manufacturing's sustainability aspects related to social, economical and environmental dimensions are gathered and analysed for a better insight of this technique. A detailed case study demonstrates the energy use differences of direct digital manufacturing and mass production in depth. According to the present work, direct digital manufacturing has the possibility of combining the advantages of the other production paradigms and can have a positive impact on sustainable development; yet, there are several challenges to overcome both in technical and sociality aspects. A challenge within the social aspects can be the life style changes which can impact the job market, working environment, waste management and more.

AbstractIndustry releases vast amounts of heat energy as dissipative waste heat to the atmosphere. It is therefore necessary to acquire a better understanding of the waste heat potentials in manufacturing. The paper presents an integrated approach for identifying and quantifying waste heat potentials of different production processes. The identification is based on an estimation procedure followed by a simulative assessment of production processes to quantify and allocate waste heat over time. The approach further elaborates on a potential source and demand matching of heat streams. A case study from the automotive industry demonstrates the applicability of the approach.

The development of lithium-ion batteries (LIBs) is characterized by a unique level of complexity in the manufacturing process. In particular, cause-effect relationships (CERs) between process parameters have a strong influence on the quality of a manufactured cell and thus on the ramp-up time. First approaches for discovery CERs in LIBs were expert-based and thus afflicted with a high degree of uncertainty. Therefore, data from a real battery production line has for the first time been systematically processed and analyzed using CRISP-DM. However, the approach shows shortcomings in the involvement of domain expert knowledge as well as in the accuracy of the applied models. Addressing these shortcomings, an interdisciplinary data analytics framework is presented using human-computer interaction (HCI). Moreover, the framework aims to improve data analysis with the help of expert knowledge and, conversely, sharpen the knowledge of experts through data analysis. Thus, the model provides a basis for automated fault detection, diagnostics, and prognostics. Implementation and validation of the framework was conducted using the data of an assembly line for prismatic LIBs at the BMW Group in Munich.

AbstractValue stream mapping (VSM) has been a widely used method aiming at the elimination of inefficiencies in manufacturing systems. During the last few years VSM was extended towards the consideration of energy demands of processes and supporting services (EVSM), material use, multi-product perspective, as well as the impact of different product characteristics. However, since VSM is a static method, it is not possible to completely analyze the dynamic interrelations between jobs. This paper proposes a simulation tool which allows the analysis of multiple value streams for different products regarding lead times, as well as non-value adding times and energy demands.

A central strategy for climate change mitigation is expanding electricity generation from renewable energy sources, with an increasing share of decentralized generation. Some of these sources are variable renewable energy (VRE) sources, such as wind and solar resources. Measures have to be enacted to integrate VRE into an existing power supply system. One approach is switching from grid electricity supply towards direct demand of VRE generation to reduce grid transportation requirements and variable electricity grid feed-in. Within this context, energy flexibility control of manufacturing systems can be used to match energy demand of manufacturing systems with on-site VRE generation. Nonetheless, due to their inherent dynamic behavior, interlinked manufacturing systems provide additional operational and technical challenges such as maintaining throughput when energy control actions are executed. Further, stochastic influences from, for example, VRE generation and manufacturing system behavior constitute the requirement for a real-time approach on the level of manufacturing execution systems. Consequently, this paper presents a method for real-time control of manufacturing systems with several processes and intermediate buffers to increase utilization of (on-site) generated VRE without compromising system throughput. An initial method for an energy flexibility control logic is presented and a simulation prototype to evaluate its effectiveness is implemented. A case study is used to demonstrate the effectiveness and to test sensitivities to system parameter changes. Impact on direct VRE demand and additional operational indicators is evaluated.

AbstractEnergy value stream analysis is used to quickly evaluate the energetic performance of process chains within continuous improvement processes in production companies. Existing approaches focus mainly on capturing and allocation of direct energy demands induced by production machines. However, most approaches lack the holistic perspective, leading to neglect huge parts of the indirect energy demands, caused by the technical building services which are vital to maintain the production conditions. This paper presents an extended approach, targeting to fully distribute indirect energy demands upon specific entities of the value stream by presenting systematic allocation rules, which cause-dependently break down peripheral energy demands.

Electrode manufacturing requires multiple process steps, e.g., dispersing and coating. In‐between these steps, intermediate products have to be transferred, stored, and handled. Especially for the development of new active materials or electrode formulations, the variety of parameters that need to be screened is enormous. In addition, these materials are initially tested in small batches, and it is not always possible to upscale the used processes. To evaluate the performance of different materials or differently processed materials, test cells are assembled. This usually requires manual work procedures, which are inherently sensitive to variations and untraceable errors. If the stochastic flaws are large enough, the effects of process variations are covered by these. It is therefore important to increase reproducibility in all process steps. Herein, new concepts for electrode production and automated sample preparation for highly reproducible production and more effective electrode development and screening of parameters are presented. A combined grinding and dispersion process for the production of silicon‐based anodes and an automated assembly system for efficient testing is presented. The processes are supported by methods of data mining to collect process data, ensure high reproducibility, and support research on new active materials.

Guaranteeing defined conditions, such as the temperature levels inside the factory's building shell, is often important to produce high-quality products. Heating, ventilation and air conditioning (HVAC) equipment, as part of the technical building services, is energy intensive and accounts for a major share of the factory's energy demand. For an effective utilisation, the HVAC system control has to compensate time dependent variations of building-internal loads and demands as well as changing weather conditions that can cause local temperature differences. In this paper, a computational fluid dynamic model, coupled with a wireless sensor network, is presented that allows the estimation of the temperature and air flows at every position in the factory building, in real-time. This can then be used to improve control strategies of HVAC systems towards a more energy efficient and demand oriented climate conditioning within factory building shells. © 2015 The Authors. Published by Elsevier B.V.