Fachbereich Maschinenbau und Mechatronik
Refine
Year of publication
Institute
Language
- English (107) (remove)
Document Type
- Conference Proceeding (107) (remove)
Keywords
- Gamification (3)
- Additive manufacturing (2)
- Digital Twin (2)
- IO-Link (2)
- L-PBF (2)
- 10BASE-T1L (1)
- 3D-printing (1)
- Adaptive Systems (1)
- Additive Manufacturing (1)
- Arduino (1)
- Assessment (1)
- Asset Administration Shell (1)
- Augmented Reality (1)
- Binder Jetting (1)
- Business Simulations (1)
- Digital Twins (1)
- Directed Energy Deposition (1)
- Error Recovery (1)
- Ethernet (1)
- Field device (1)
- Glass powder (1)
- Hand-on-training (1)
- Humidity (1)
- Industrial Communication (1)
- Industry 4.0 (1)
- Knowledge Transfer (1)
- LPBF (1)
- Laser processing (1)
- Laser-Powder Bed Fusion (1)
- MEMS ; education and training foundry (1)
- Melting (1)
- Multi-agent Systems (1)
- Powder Material (1)
- Rapid-prototyping (1)
- Sensors (1)
- Serious Games (1)
- Support System (1)
- Technologietransfer (1)
- Training (1)
- Virtual Technology Lab (1)
- Virtuelles Labor (1)
- Virtuelles Laboratorium (1)
- additive manufactureing (1)
- glass (1)
- laser based powder fusion (1)
- technology diffusion (1)
- technology transfer (1)
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.
Selective Laser Melting (SLM) is one of the Additive Manufacturing (AM) technologies applicable for producing complex geometries which are typically expensive or difficult to fabricate using conventional methods. This process has been extensively investigated experimentally for various metals and the fabrication process parameters have been established for different applications; however, fabricating 3D glass objects using SLM technology has remained a challenge so far although it could have many applications. This paper presents a summery on various experimental evaluations of a material database incorporating the build parameters of glass powder using the SLM process for jewelry applications.
The main objective of our ROS Summer School series is to introduce MA level students to program mobile robots with the Robot Operating System (ROS). ROS is a robot middleware that is used my many research institutions world-wide. Therefore, many state-of-the-art algorithms of mobile robotics are available in ROS and can be deployed very easily. As a basic robot platform we deploy a 1/10 RC cart that is wquipped with an Arduino micro-controller to control the servo motors, and an embedded PC that runs ROS. In two weeks, participants get to learn the basics of mobile robotics hands-on. We describe our teaching concepts and our curriculum and report on the learning success of our students.
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.