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Additive Manufacturing (AM) is a topic that is becoming more relevant to many companies globally. With AM's progressive development and use for series production, integrating the technology into existing production structures is becoming an important criterion for businesses. This study qualitatively examines the actual state and different perspectives on the integration of AM in production structures. Seven semi-structured interviews were conducted and analyzed. The interview partners were high-level experts in Additive Manufacturing and production systems from industry and science. Four main themes were identified. Key findings are the far-reaching interrelationships and implications of AM within production structures. Specific AM-related aspects were identified. Those can be used to increase the knowledge and practical application of the technology in the industry and as a foundation for economic considerations.
The fourth industrial revolution is on its way to reshape manufacturing and value creation in a profound way. The underlying technologies like cyber-physical systems (CPS), big data, collaborative robotics, additive manufacturing or artificial intelligence offer huge potentials for the optimization and evolution of production systems. However, many manufacturing companies struggle to implement these technologies. This can only in part be attributed to the lack of skilled personal within these companies or a missing digitalization strategy. Rather, there is a fundamental incompatibility between the way current production systems and companies (Industry 3.0) are structured across multiple dimensions compared to what is necessary for industry 4.0. This is especially true in manufacturing systems and their transition towards flexible, decentralized and autonomous value creation networks. This paper shows across various dimensions these incompatibilities within manufacturing systems, explores their reasons and discusses a different approach to create a foundation for Industry 4.0 in manufacturing companies.
Establishing high-performance polymers in additive manufacturing opens up new industrial applications. Polyetheretherketone (PEEK) was initially used in aerospace but is now widely applied in automotive, electronics, and medical industries. This study focuses on developing applications using PEEK and Fused Filament Fabrication for cost-efficient vulcanization injection mold production. A proof of concept confirms PEEK’s suitability for AM mold making, withstanding vulcanization conditions. Printing PEEK above its glass transition temperature of 145 °C is preferable due to its narrow process window. A new process strategy at room temperature is discussed, with micrographs showing improved inter-layer bonding at 410°C nozzle temperature and 0.1 mm layer thickness. Minimizing the layer thickness from 0.15 mm to 0.1 mm improves tensile strength by 16%.
In the face of the current trend towards larger and more complex production tasks in the SLM process and the current limitations in terms of maximum build space, the welding of SLM components to each other or to conventionally manufactured parts is becoming increasingly relevant. The fusion welding of SLM components made of 316L has so far been rarely investigated and if so, then for highly specialised laser welding processes. When welding with industrial gas welding processes such as MIG/MAG or TIG welding, distortions occur which are associated with the resulting residual stresses in the components. This paper investigates process-side influencing factors to avoid resulting residual stresses in SLM components made of 316L. The aim is to develop a strategy to build up SLM components as stress-free as possible in order to join them as profitably as possible with a downstream welding process. For this purpose, influencing parameters such as laser power, scan speed, but also scan vector length and different scan patterns are investigated with regard to their influence on residual stresses.
Air–water flows
(2024)
High Froude-number open-channel flows can entrain significant volumes of air, a phenomenon that occurs continuously in spillways, in free-falling jets and in hydraulic jumps, or as localized events, notably at the toe of hydraulic jumps or in plunging jets. Within these flows, turbulence generates millions of bubbles and droplets as well as highly distorted wavy air–water interfaces. This phenomenon is crucial from a design perspective, as it influences the behaviour of high-velocity flows, potentially impairing the safety of dam operations. This review examines recent scientific and engineering progress, highlighting foundational studies and emerging developments. Notable advances have been achieved in the past decades through improved sampling of flows and the development of physics-based models. Current challenges are also identified for instrumentation, numerical modelling and (up)scaling that hinder the formulation of fundamental theories, which are instrumental for improving predictive models, able to offer robust support for the design of large hydraulic structures at prototype scale.
Easy-read and large language models: on the ethical dimensions of LLM-based text simplification
(2024)
The production of easy-read and plain language is a challenging task, requiring well-educated experts to write context-dependent simplifications of texts. Therefore, the domain of easy-read and plain language is currently restricted to the bare minimum of necessary information. Thus, even though there is a tendency to broaden the domain of easy-read and plain language, the inaccessibility of a significant amount of textual information excludes the target audience from partaking or entertainment and restricts their ability to live life autonomously. Large language models can solve a vast variety of natural language tasks, including the simplification of standard language texts to easy-read or plain language. Moreover, with the rise of generative models like GPT, easy-read and plain language may be applicable to all kinds of natural language texts, making formerly inaccessible information accessible to marginalized groups like, a.o., non-native speakers, and people with mental disabilities. In this paper, we argue for the feasibility of text simplification and generation in that context, outline the ethical dimensions, and discuss the implications for researchers in the field of ethics and computer science.
The quest for scientifically advanced and sustainable solutions is driven by growing environmental and economic issues associated with coal mining, processing, and utilization. Consequently, within the coal industry, there is a growing recognition of the potential of microbial applications in fostering innovative technologies. Microbial-based coal solubilization, coal beneficiation, and coal dust suppression are green alternatives to traditional thermochemical and leaching technologies and better meet the need for ecologically sound and economically viable choices. Surfactant-mediated approaches have emerged as powerful tools for modeling, simulation, and optimization of coal-microbial systems and continue to gain prominence in clean coal fuel production, particularly in microbiological co-processing, conversion, and beneficiation. Surfactants (surface-active agents) are amphiphilic compounds that can reduce surface tension and enhance the solubility of hydrophobic molecules. A wide range of surfactant properties can be achieved by either directly influencing microbial growth factors, stimulants, and substrates or indirectly serving as frothers, collectors, and modifiers in the processing and utilization of coal. This review highlights the significant biotechnological potential of surfactants by providing a thorough overview of their involvement in coal biodegradation, bioprocessing, and biobeneficiation, acknowledging their importance as crucial steps in coal consumption.
Several unconnected laboratory experiments are usually offered for students in instrumental analysis lab. To give the students a more rational overview of the most common instrumental techniques, a new laboratory experiment was developed. Marketed pain relief drugs, familiar consumer products with one to three active components, namely, acetaminophen (paracetamol), acetylsalicylic acid (ASA), and caffeine, were selected. Common analytical methods were compared regarding the performance of qualitative and quantitative analysis of unknown tablets: UV–visible (UV–vis), infrared (IR), and nuclear magnetic resonance (NMR) spectroscopies, as well as high-performance liquid chromatography (HPLC). The students successfully uncovered the composition of formulations, which were divided into three difficulty categories. Students were shown that in addition to simple mixtures handled in theoretical classes, the composition of complex drug products can also be uncovered. By comparing the performance of different techniques, students deepen their understanding and compare the efficiency of analytical methods in the context of complex mixtures. The laboratory experiment can be adjusted for graduate level by including extra tasks such as method optimization, validation, and 2D spectroscopic techniques.
Effective government services rely on accurate population numbers to allocate resources. In Colombia and globally, census enumeration is challenging in remote regions and where armed conflict is occurring. During census preparations, the Colombian National Administrative Department of Statistics conducted social cartography workshops, where community representatives estimated numbers of dwellings and people throughout their regions. We repurposed this information, combining it with remotely sensed buildings data and other geospatial data. To estimate building counts and population sizes, we developed hierarchical Bayesian models, trained using nearby full-coverage census enumerations and assessed using 10-fold cross-validation. We compared models to assess the relative contributions of community knowledge, remotely sensed buildings, and their combination to model fit. The Community model was unbiased but imprecise; the Satellite model was more precise but biased; and the Combination model was best for overall accuracy. Results reaffirmed the power of remotely sensed buildings data for population estimation and highlighted the value of incorporating local knowledge.