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Alloy 400 is a corrosion-resistant, NiCu-based material which is used in numerous industrial applications, especially in marine environments and the high-temperature chemical industry. As conventional manufacturing limits geometrical complexity, additive manufacturing (AM) of the present alloy system promises great potential. For this purpose, a robust process chain, consisting of powder production via gas atomization and a design of experiment (DoE) approach for laser powder bed fusion (LPBF), was developed. With a narrow particle size distribution, powders were found to be spherical, flowable, consistent in chemical composition, and, hence, generally applicable to the LPBF process. Copper segregations at grain boundaries were clearly detected in powders. For printed parts instead, low-intensity micro-segregations at cell walls were discovered, being correlated with the iterative thermal stress applied to solidified melt-pool-near grains during layer-by-layer manufacturing. For the production of nearly defect-free LPBF structures, DoE suggested a single optimum parameter set instead of a broad energy density range. The latter key figure was found to be misleading in terms of part densities, making it an outdated tool in modern, software-based process parameter optimization. On the microscale, printed parts showed an orientation of melt pools along the build direction with a slight crystallographic [101] texture. Micro-dendritic structures were detected on the nanoscale being intersected by a high number of dislocations. Checked against hot-extruded reference material, the LPBF variant performed better in terms of strength while lacking in ductility, being attributed to a finer grain structure and residual porosity, respectively.
The significance of digital technologies in the context of digitizing production processes, such as Artificial Intelligence (AI) and Digital Twins, is on the rise. A promising avenue of research is the optimization of digital twins through Reinforcement Learning (RL). This necessitates a simulation environment that can be integrated with RL. One is introduced in this paper as the Digital Model Playground (DMPG). The paper outlines the implementation of the DMPG, followed by demonstrating its application in optimizing production scheduling through RL within a sample process. Although there is potential for further development, the DMPG already enables the modeling and optimization of production processes using RL and is comparable to commercial discrete event simulation software regarding the simulation-speed. Furthermore, it is highly flexible and adaptable, as shown by two projects, which distribute the DMPG to a high-performance cluster or generate 2D/3D-Visualization of the simulation model with Unreal. This establishes the DMPG as a valuable tool for advancing the digital transformation of manufacturing systems, affirming its potential impact on the future of production optimization. Currently, planned extensions include the integration of more optimization algorithms and Process Mining techniques, to further enhance the usability of the framework.
In this research work, the calculation of carbon dioxide emissions for trailer traffic based on sensor data is described. The data consist of trailer type, load, trip and route parameters. While the individual trailer parameters can be derived from the order management, the trip and route parameters are collected by frequently data. With this, the transport carbon footprint is calculated based on DIN EN 16258 by means of the ProBas database. The distributions of carbon dioxide emissions in trailer traffic are analysed and discussed. The results of the case study indicate that sensor-based modelling can be a useful tool for an improved transparency and estimation of carbon dioxide emissions.
Enhancing Digital Twins for Production through Process Mining Techniques : A Literature Review
(2023)
A digital twin (DT) plays a vital role in the advancement of manufacturers towards Industry 4.0. However, the creation and maintenance of DTs can be time-consuming. One approach to streamline this process is the utilization of process mining (PM) methods and techniques, which can automatically generate valuable information for DTs. Therefore, this paper aims to examine different approaches that augment DTs with PM and explore their effects. The review categorizes these approaches into three groups: theoretical approaches, approaches with laboratory case studies, and approaches with real-world case studies conducted by manufacturers. The review reveals that the use of PM can enhance the flexibility and sustainability of DTs. However, this improvement comes at the cost of requiring high-quality data and more data preparation efforts.
Action recognition technology has gained significant traction in recent years. This paper focuses on evaluating neural network architectures for action recognition in the production industry. By utilizing datasets tailored for production or assembly tasks, various architectures are assessed for their accuracy and performance. The findings of this study provide some insights and guidance for researchers and practitioners to select an appropriate architecture or pretrained models for action recognition in the production industry.
The initial idea of Robotic Process Automation (RPA) is the automation of business processes through the presentation layer of existing application systems. For this simple emulation of user input and output by software robots, no changes of the systems and architecture is required. However, considering strategic aspects of aligning business and technology on an enterprise level as well as the growing capabilities of RPA driven by artificial intelligence, interrelations between RPA and Enterprise Architecture (EA) become visible and pose new questions. In this paper we discuss the relationship between RPA and EA in terms of perspectives and implications. As work in-progress we focus on EA.