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Additive Manufacturing (AM) of metallic workpieces faces a continuously rising technological relevance and market size. Producing complex or highly strained unique workpieces is a significant field of application, making AM highly relevant for tool components. Its successful economic application requires systematic workpiece based decisions and optimizations. Considering geometric and technological requirements as well as the necessary post-processing makes deciding effortful and requires in-depth knowledge. As design is usually adjusted to established manufacturing, associated technological and strategic potentials are often neglected. To embed AM in a future proof industrial environment, software-based self-learning tools are necessary. Integrated into production planning, they enable companies to unlock the potentials of AM efficiently. This paper presents an appropriate methodology for the analysis of process-specific AM-eligibility and optimization potential, added up by concrete optimization proposals. For an integrated workpiece characterization, proven methods are enlarged by tooling-specific figures.
The first stage of the approach specifies the model’s initialization. A learning set of tooling components is described using the developed key figure system. Based on this, a set of applicable rules for workpiece-specific result determination is generated through clustering and expert evaluation. Within the following application stage, strategic orientation is quantified and workpieces of interest are described using the developed key figures. Subsequently, the retrieved information is used for automatically generating specific recommendations relying on the generated ruleset of stage one. Finally, actual experiences regarding the recommendations are gathered within stage three. Statistic learning transfers those to the generated ruleset leading to a continuously deepening knowledge base. This process enables a steady improvement in output quality.
Direct methods comprising limit and shakedown analysis is a branch of computational mechanics. It plays a significant role in mechanical and civil engineering design. The concept of direct method aims to determinate the ultimate load bearing capacity of structures beyond the elastic range. For practical problems, the direct methods lead to nonlinear convex optimization problems with a large number of variables and onstraints. If strength and loading are random quantities, the problem of shakedown analysis is considered as stochastic programming. This paper presents a method so called chance constrained programming, an effective method of stochastic programming, to solve shakedown analysis problem under random condition of strength. In this our investigation, the loading is deterministic, the strength is distributed as normal or lognormal variables.
In the Laser Powder Bed Fusion (LPBF) process, parts are built out of metal powder material by exposure of a laser beam. During handling operations of the powder material, several influencing factors can affect the properties of the powder material and therefore directly influence the processability during manufacturing. Contamination by moisture due to handling operations is one of the most critical aspects of powder quality. In order to investigate the influences of powder humidity on LPBF processing, four materials (AlSi10Mg, Ti6Al4V, 316L and IN718) are chosen for this study. The powder material is artificially humidified, subsequently characterized, manufactured into cubic samples in a miniaturized process chamber and analyzed for their relative density. The results indicate that the processability and reproducibility of parts made of AlSi10Mg and Ti6Al4V are susceptible to humidity, while IN718 and 316L are barely influenced.
The seismic performance and safety of major European industrial facilities has a global interest for Europe, its citizens and economy. A potential major disaster at an industrial site could affect several countries, probably far beyond the country where it is located. However, the seismic design and safety assessment of these facilities is practically based on national, often outdated seismic hazard assessment studies, due to many reasons, including the absence of a reliable, commonly developed seismic hazard model for whole Europe. This important gap is no more existing, as the 2020 European Seismic Hazard Model ESHM20 was released in December 2021. In this paper we investigate the expected impact of the adoption of ESHM20 on the seismic demand for industrial facilities, through the comparison of the ESHM20 probabilistic hazard at the sites where industrial facilities are located with the respective national and European regulations. The goal of this preliminary work in the framework of Working Group 13 of the European Association for Earthquake Engineering (EAEE), is to identify potential inadequacies in the design and safety control of existing industrial facilities and to highlight the expected impact of the adoption of the new European Seismic Hazard Model on the design of new industrial facilities and the safety assessment of existing ones.
New materials often lead to innovations and advantages in technical applications. This also applies to the particle receiver proposed in this work that deploys high-temperature and scratch resistant transparent ceramics. With this receiver design, particles are heated through direct-contact concentrated solar irradiance while flowing downwards through tubular transparent ceramics from top to bottom. In this paper, the developed particle receiver as well as advantages and disadvantages are described. Investigations on the particle heat-up characteristics from solar irradiance were carried out with DEM simulations which indicate that particle temperatures can reach up to 1200 K. Additionally, a simulation model was set up for investigating the dynamic behavior. A test receiver at laboratory scale has been designed and is currently being built. In upcoming tests, the receiver test rig will be used to validate the simulation results. The design and the measurement equipment is described in this work.
Concerning current efforts to improve operational efficiency and to lower overall costs of concentrating solar power (CSP) plants with prediction-based algorithms, this study investigates the quality and uncertainty of nowcasting data regarding the implications for process predictions. DNI (direct normal irradiation) maps from an all-sky imager-based nowcasting system are applied to a dynamic prediction model coupled with ray tracing. The results underline the need for high-resolution DNI maps in order to predict net yield and receiver outlet temperature realistically. Furthermore, based on a statistical uncertainty analysis, a correlation is developed, which allows for predicting the uncertainty of the net power prediction based on the corresponding DNI forecast uncertainty. However, the study reveals significant prediction errors and the demand for further improvement in the accuracy at which local shadings are forecasted.
The replacement of existing spillway crests or gates with labyrinth weirs is a proven techno-economical means to increase the discharge capacity when rehabilitating existing structures. However, additional information is needed regarding energy dissipation of such weirs, since due to the folded weir crest, a three-dimensional flow field is generated, yielding more complex overflow and energy dissipation processes. In this study, CFD simulations of labyrinth weirs were conducted 1) to analyze the discharge coefficients for different discharges to compare the Cd values to literature data and 2) to analyze and improve energy dissipation downstream of the structure. All tests were performed for a structure at laboratory scale with a height of approx. P = 30.5 cm, a ratio of the total crest length to the total width of 4.7, a sidewall angle of 10° and a quarter-round weir crest shape. Tested headwater ratios were 0.089 ≤ HT/P ≤ 0.817. For numerical simulations, FLOW-3D Hydro was employed, solving the RANS equations with use of finite-volume method and RNG k-ε turbulence closure. In terms of discharge capacity, results were compared to data from physical model tests performed at the Utah Water Research Laboratory (Utah State University), emphasizing higher discharge coefficients from CFD than from the physical model. For upstream heads, some discrepancy in the range of ± 1 cm between literature, CFD and physical model tests was identified with a discussion regarding differences included in the manuscript. For downstream energy dissipation, variable tailwater depths were considered to analyze the formation and sweep-out of a hydraulic jump. It was found that even for high discharges, relatively low downstream Froude numbers were obtained due to high energy dissipation involved by the three-dimensional flow between the sidewalls. The effects of some additional energy dissipation devices, e.g. baffle blocks or end sills, were also analyzed. End sills were found to be non-effective. However, baffle blocks with different locations may improve energy dissipation downstream of labyrinth weirs.
A Gamified Information System (GIS) implements game concepts and elements, such as affordances and game design principles to motivate people. Based on the idea to develop a GIS to increase the motivation of software developers to perform software quality tasks, the research work at hand aims at investigating relevant requirements from that target group. Therefore, 14 interviews with software development experts are conducted and analyzed. According to the results, software developers prefer the affordances points, narrative storytelling in a multiplayer and a round-based setting. Furthermore, six design principles for the development of a GIS are derived.
Useful market simulations are key to the evaluation of diferent market designs existing of multiple market mechanisms or rules. Yet a simulation framework which has a comparison of diferent market mechanisms in mind was not found. The need to create an objective view on different sets of market rules while investigating meaningful agent strategies concludes that such a simulation framework is needed to advance the research on this subject. An overview of diferent existing market simulation models is given which also shows the research gap and the missing capabilities of those systems. Finally, a methodology is outlined how a novel market simulation which can answer the research questions can be developed.
Messenger apps like WhatsApp or Telegram are an integral part of daily communication. Besides the various positive effects, those services extend the operating range of criminals. Open trading groups with many thousand participants emerged on Telegram. Law enforcement agencies monitor suspicious users in such chat rooms. This research shows that text analysis, based on natural language processing, facilitates this through a meaningful domain overview and detailed investigations. We crawled a corpus from such self-proclaimed black markets and annotated five attribute types products, money, payment methods, user names, and locations. Based on each message a user sends, we extract and group these attributes to build profiles. Then, we build features to cluster the profiles. Pretrained word vectors yield better unsupervised clustering results than current
state-of-the-art transformer models. The result is a semantically meaningful high-level overview of the user landscape of black market chatrooms. Additionally, the extracted structured information serves as a foundation for further data exploration, for example, the most active users or preferred payment methods.