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Electron beam plasma measurement was realised by means of DIABEAM system invented by ISF RWTH Aachen. The Langmuir probe method is used for measurement. The relative simplicity of the method and the possibility of dispersion of high power on the probe allow its application for the investigation of high-power electron beams. The key element of the method is a rotating thin tungsten wire, which intersects the beam transversely on its axis and collects part of the current by itself. The signals, which are registered in the DIABEAM as a voltage, were taken in the form of amplitude. The conversion of the probe current into the distribution along the beam radius was realised using the Abel’s method. A voltage-current characteristic was built for the beam current. The local electron density as well as the electron temperature, the floating potential and the plasma potential were measured and calculated by means of this characteristic.
Password necessarily. Access only for Students by Prof. Dr. Klaus-Peter Kämper. Winter semester 2008/2009. 488 pages (pdf) Contents 1. Introduction 2. Introduction to Sensors 3. Introduction to Microfabrication 4. Pressure Sensors 5. Acceleration Sensors 6. Angular Rate Sensors 7. Position Sensors 8. Flow Sensors 9. Piezoelectric Actuators 10. Magnetostrictive Actuators 11. Actuators based on Shape Memory Alloys 12. Actuators based on Electrorheological Fluids 13. Actuators based on Magnetorheological Fluids 14. Index
Password necessarily. Access only for Students by Prof. Dr. Klaus-Peter Kämper. Winter semester 2007/2008. Version 2007-08-30. 472 pages (pdf) Contents 1. Introduction 2. Introduction to Sensors 3. Introduction to Microfabrication 4. Pressure Sensors 5. Acceleration Sensors 6. Angular Rate Sensors 7. Position Sensors 8. Flow Sensors 9. Piezoelectric Actuators 10. Magnetostrictive Actuators 11. Actuators based on Shape Memory Alloys 12. Actuators based on Electrorheological Fluids 13. Actuators based on Magnetorheological Fluids
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.
Laserwelding with fillerwire
(2001)
The laser beam-submerged arc hybrid welding method originates from the knowledge that, with increasing penetration depth, the laser beam process has a tendency to pore formation in the lower weld regions. The coupling with the energy-efficient submerged-arc process improves degassing and reduces the tendency to pore formation. The high deposition rate of the SA process in combination with the laser beam process offers, providing the appropriate choice of weld preparation, the possibility of welding plates with a thickness larger than 20° mm in a single pass, and also of welding thicker plates with the double-sided single pass technique.
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.
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 thermal conductivity of components manufactured using Laser Powder Bed Fusion (LPBF), also called Selective Laser Melting (SLM), plays an important role in their processing. Not only does a reduced thermal conductivity cause residual stresses during the process, but it also makes subsequent processes such as the welding of LPBF components more difficult. This article uses 316L stainless steel samples to investigate whether and to what extent the thermal conductivity of specimens can be influenced by different LPBF parameters. To this end, samples are set up using different parameters, orientations, and powder conditions and measured by a heat flow meter using stationary analysis. The heat flow meter set-up used in this study achieves good reproducibility and high measurement accuracy, so that comparative measurements between the various LPBF influencing factors to be tested are possible. In summary, the series of measurements show that the residual porosity of the components has the greatest influence on conductivity. The degradation of the powder due to increased recycling also appears to be detectable. The build-up direction shows no detectable effect in the measurement series.
Additive manufacturing (AM) works by creating objects layer by layer in a manner similar to a 2D printer with the “printed” layers stacked on top of each other. The layer-wise manufacturing nature of AM enables fabrication of freeform geometries which cannot be fabricated using conventional manufacturing methods as a one part. Depending on how each layer is created and bonded to the adjacent layers, different AM methods have been developed. In this chapter, the basic terms, common materials, and different methods of AM are described, and their potential applications are discussed.
Improving the Mechanical Strength of Dental Applications and Lattice Structures SLM Processed
(2020)
To manufacture custom medical parts or scaffolds with reduced defects and high mechanical characteristics, new research on optimizing the selective laser melting (SLM) parameters are needed. In this work, a biocompatible powder, 316L stainless steel, is characterized to understand the particle size, distribution, shape and flowability. Examination revealed that the 316L particles are smooth, nearly spherical, their mean diameter is 39.09 μm and just 10% of them hold a diameter less than 21.18 μm. SLM parameters under consideration include laser power up to 200 W, 250–1500 mm/s scanning speed, 80 μm hatch spacing, 35 μm layer thickness and a preheated platform. The effect of these on processability is evaluated. More than 100 samples are SLM-manufactured with different process parameters. The tensile results show that is possible to raise the ultimate tensile strength up to 840 MPa, adapting the SLM parameters for a stable processability, avoiding the technological defects caused by residual stress. Correlating with other recent studies on SLM technology, the tensile strength is 20% improved. To validate the SLM parameters and conditions established, complex bioengineering applications such as dental bridges and macro-porous grafts are SLM-processed, demonstrating the potential to manufacture medical products with increased mechanical resistance made of 316L.
While bringing new opportunities, the Industry 4.0 movement also imposes new challenges to the manufacturing industry and all its stakeholders. In this competitive environment, a skilled and engaged workforce is a key to success. Gamification can generate valuable feedbacks for improving employees’ engagement and performance. Currently, Gamification in workspaces focuses on computer-based assignments and training, while tasks that require manual labor are rarely considered. This research provides an overview of Enterprise Gamification approaches and evaluates the challenges. Based on that, a skill-based Gamification framework for manual tasks is proposed, and a case study in the Industry 4.0 model factory is shown.
Table of Contents Introduction 1. Generative Manufacturing Processes 2. Classification of Generative Manufacturing Processes 3. Application of Generative Processes on the Fabrication of Ceramic Parts 3.1 Extrusion 3.2 3D-Printing 3.3 Sintering – Laser Sintering 3.4 Layer-Laminate Processes 3.5 Stereolithography (sometimes written: Stereo Lithography) 4. Layer Milling 5. Conclusion - Vision
Virtual Reality (VR) offers novel possibilities for remote training regardless of the availability of the actual equipment, the presence of specialists, and the training locations. Research shows that training environments that adapt to users' preferences and performance can promote more effective learning. However, the observed results can hardly be traced back to specific adaptive measures but the whole new training approach. This study analyzes the effects of a combined point and leveling VR-based gamification system on assembly training targeting specific training outcomes and users' motivations. The Gamified-VR-Group with 26 subjects received the gamified training, and the Non-Gamified-VR-Group with 27 subjects received the alternative without gamified elements. Both groups conducted their VR training at least three times before assembling the actual structure. The study found that a level system that gradually increases the difficulty and error probability in VR can significantly lower real-world error rates, self-corrections, and support usages. According to our study, a high error occurrence at the highest training level reduced the Gamified-VR-Group's feeling of competence compared to the Non-Gamified-VR-Group, but at the same time also led to lower error probabilities in real-life. It is concluded that a level system with a variable task difficulty should be combined with carefully balanced positive and negative feedback messages. This way, better learning results, and an improved self-evaluation can be achieved while not causing significant impacts on the participants' feeling of competence.