Institut für Industrieaerodynamik

Computational power continues to increase at a rapid pace and unlike other areas of technology, this trend is not expected to slow down in the foreseeable future. Software GNSS receivers fully exploit these developments to steadily increase performance. Wikipedia lists 21 software receiver projects currently in progress. Mean while, more than 1,000 wideband GNSS channels can be tracked in real-time on a conventional PC. Moreover, smart phones containing multi-core central processing units (CPUs) and powerful many-core graphics processing units (GPUs) bring these super computing technologies into our embedded devices. All these developments can be exploited to produce power-efficient, customized receivers with flexible correlation schemes and more advanced positioning techniques. For example, promising techniques such as the Direct Position Estimation (DPE) paradigm or usage of tracking solutions based on particle filtering, seem to be very appealing in challenging environments but are likewise computationally quite demanding. This article sheds some light on recent processor developments and relates them to use in GNSS software radios.

Abstract Discontinuous processes face common tasks when implementing modeling and optimization techniques for process optimization. While domain data may be unequal, knowledge about approaches for each step toward the solution, e.g., data gathering, model reduction, and model optimization, may be useful across different processes. A joint development of methodologies for machine learning methods, among other things, ultimately supports fast advances in cross-domain production technologies. In this work, an overview of common maturation stages of data-intensive modeling approaches for production efficiency enhancement is given. The stages are analyzed and communal challenges are elaborated. The used approaches include both physically motivated surrogate modeling as well as the advanced use of machine learning technologies. Apt research is depicted for each stage based on demonstrator work for diverse production technologies, among them high-pressure die casting, surface engineering, plastics injection molding, open-die forging, and automated tape placement. Finally, a holistic and general framework is illustrated covering the main concepts regarding the transfer of mature models into production environments on the example of laser technologies. Increasing customer requirements regarding process stability, transparency and product quality as well as desired high production efficiency in diverse manufacturing processes pose high demands on production technologies. The further development of digital support systems for manufacturing technologies can contribute to meet these demands in various production settings. Especially for discontinuous production, such as injection molding and laser cutting, the joint research for different technologies helps to identify common challenges, ranging from problem identification to knowledge perpetuation after successfully installing digital tools. Workstream CRD-B2.II “Discontinuous Production” confronts this research task by use case-based joint development of transferable methods. Based on the joint definition of a standard pipeline to solve problems with digital support, various stages of this pipeline, such as data generation and collection, model training, optimization, and the development and deployment of assistance systems are actively being researched. Regarding data generation, e.g., for the high-pressure die-casting process, data acquisition and extraction approaches for machines and production lines using OPC UA are investigated to get detailed process insights. For diverse discontinuous processes and use cases, relevant production data is not directly available in sufficient quality and needs to be preprocessed. For vision systems, ptychographic methods may improve recorded data by enhancing the picture sharpness to enable the usage of inline or low-cost equipment to detect small defects. Further down the pipeline, several research activities concern the domain-specific model training and optimization tasks. Within the realm of surface technologies, machine learning is applied to predict process behavior, e.g., by predicting the particle properties in plasma spraying process or plasma intensities in the physical vapor deposition process. The injection molding process can also be modeled by data-based approaches. The modeling efficiency based on the used amount of data can furthermore be effectively reduced by using transfer learning to transfer knowledge stored in artificial neural networks from one process to the next. Successful modeling approaches can then be transferred prototypically into production. On the examples of vision-based defect classification in the tape-laying process and a process optimization assistance system in open-die forging, the realization of prototypical support systems is demonstrated. Once mature, research results and consequent digital services must be made available for integrated usage in specific production settings using relevant architecture. By the example of a microservice-based infrastructure for laser technology, a suitable and flexible implementation of a service framework is realized. The connectivity to production assets is guaranteed by state-of-the-art communication protocols. This chapter illustrates the state of research for use-case-driven development of joint approaches.

Using high water-based fluids in a hydraulic system requires the application of other materials than in oil hydraulic facilities. High water-based fluids have a content of water not under 80%. The rest is a HWBF concentrate. The content of water defines the features of the liquid. Because of the low viscosity of a high water-based fluid, the loading of the surfaces in water hydraulic systems is much higher than in a machine operating with oil. The low viscosity produces smaller gaps in tribological contacts. It is possible that the surfaces of a tribological contact will not be separated. The problem of adhesive and abrasive damage is increased. The selection of a suitable material pair for a tribological contact is especially important. To substitute a material in a pump or in a motor directly by another material requires great efforts and may become very lengthy and expensive. IFAS is concerned with the development of a hydrostatic transmission operating with high water-based fluids. The goal of the project is to design and construct a constant displacement motor and a pump with a variable displacement volume based upon the swash plate principle for pressures up to 315 bar. In order to achieve the goal specially coated material pairs and their surface finish have to be tested with the objective of decreasing friction and wear. To reduce the expense of testing, the selection of materials is conducted with a special model. Two ring-shaped test bodies are pressed against each other by a hydraulic cylinder. One of the test bodies is rotated. Both test bodies are flushed by a high water-based fluid. Under different loadings and with different velocities of rotation the friction coefficient and the wear of different material combinations were established. Conventional materials, plastics, PVD coatings, and ceramics were tested. To verify the positive results obtained in the model, one of the materials which was classified as suitable is tested in a swashplate unit.