6,865 research outputs found
Simulation of metal powder packing behaviour in laser-based powder bed fusion
Laser-based powder bed fusion (L-PBF) is a method of additive manufacturing, in which metal powder is fused into solid parts, layer by layer. L-PBF shows high promise for manufacture of functional Tungsten parts, but the development of Tungsten powder feedstock for L-PBF processing is demanding and expensive. Therefore, computer simulation is explored as a possible tool for Tungsten powder feedstock development at EOS Finland Oy, with whom this thesis was made.
The aim of this thesis was to develop a simulation model of the recoating process of an EOS M 290 L-PBF system, as well as a validation method for the simulation. The validated simulation model can be used to evaluate the applicability of the used simulation software (FLOW-3D DEM) in powder material development, and possibly use the model as a platform for future application with Tungsten powder. In order to reduce complexity and uncertainties, the irregular Tungsten powder is not yet simulated, and a well-known, spherical EOS IN718 powder feedstock was used instead.
The validation experiment is based on building a low, enclosed wall using the M 290 L-PBF system. Recoated powder is trapped inside as the enclosure is being built, making it possible to remove the sampled powder from a known volume. This enables measuring the powder packing density (PD) of the powder bed. The experiment was repeated five times and some sources of error were also quantified. Average PD was found to be 52 % with a standard deviation of 0.2 %.
The simulation was modelled after the IN718 powder and corresponding process used in the M 290 system. Material-related input values were found by dynamic image analysis, pycnometry, rheometry, and from literature. PD was measured with six different methods, and the method considered as most analogous to the practical validation experiment yielded a PD of 52 %. Various particle behavior phenomena were also observed and analyzed.
Many of the powder bed characterization methods found in literature were not applicable to L-PBF processing or were not representative of the simulated conditions. Many simulation studies were also found to use no validation, or used a validation method which is not based on the investigated phenomena. The validation model developed in this thesis accurately represents the simulated conditions and is found to produce reliable and repeatable results. The simulation model was parametrized with values acquired from practical experiments or literature and closely matched the validation experiment, and could therefore be considered a truthful representation of the powder recoating process of an EOS M 290. The model can be used as a platform for future development of Tungsten powder simulation
Neutron scattering studies of heterogeneous catalysis
Understanding the structural dynamics/evolution of catalysts and the related surface chemistry is essential for establishing structure–catalysis relationships, where spectroscopic and scattering tools play a crucial role. Among many such tools, neutron scattering, though less-known, has a unique power for investigating catalytic phenomena. Since neutrons interact with the nuclei of matter, the neutron–nucleon interaction provides unique information on light elements (mainly hydrogen), neighboring elements, and isotopes, which are complementary to X-ray and photon-based techniques. Neutron vibrational spectroscopy has been the most utilized neutron scattering approach for heterogeneous catalysis research by providing chemical information on surface/bulk species (mostly H-containing) and reaction chemistry. Neutron diffraction and quasielastic neutron scattering can also supply important information on catalyst structures and dynamics of surface species. Other neutron approaches, such as small angle neutron scattering and neutron imaging, have been much less used but still give distinctive catalytic information. This review provides a comprehensive overview of recent advances in neutron scattering investigations of heterogeneous catalysis, focusing on surface adsorbates, reaction mechanisms, and catalyst structural changes revealed by neutron spectroscopy, diffraction, quasielastic neutron scattering, and other neutron techniques. Perspectives are also provided on the challenges and future opportunities in neutron scattering studies of heterogeneous catalysis
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Study of soft materials, flexible electronics, and machine learning for fully portable and wireless brain-machine interfaces
Over 300,000 individuals in the United States are afflicted with some form of limited motor function from brainstem or spinal-cord related injury resulting in quadriplegia or some form of locked-in syndrome. Conventional brain-machine interfaces used to allow for communication or movement require heavy, rigid components, uncomfortable headgear, excessive numbers of electrodes, and bulky electronics with long wires that result in greater data artifacts and generally inadequate performance. Wireless, wearable electroencephalograms, along with dry non-invasive electrodes can be utilized to allow recording of brain activity on a mobile subject to allow for unrestricted movement. Additionally, multilayer microfabricated flexible circuits, when combined with a soft materials platform allows for imperceptible wearable data acquisition electronics for long term recording. This dissertation aims to introduce new electronics and training paradigms for brain-machine interfaces to provide remedies in the form of communication and movement for these individuals. Here, training is optimized by generating a virtual environment from which a subject can achieve immersion using a VR headset in order to train and familiarize with the system. Advances in hardware and implementation of convolutional neural networks allow for rapid classification and low-latency target control. Integration of materials, mechanics, circuit and electrode design results in an optimized brain-machine interface allowing for rehabilitation and overall improved quality of life.Ph.D
Stem Cells in Domestic Animals
Stem cells are an attractive tool for cell-based therapies in regenerative medicine, both for humans and animals. The research and review articles published in this first book of the Collection “Stem Cells in Domestic Animals: Applications in Health and Production” are excellent examples of the recent advances made in the field of stem/stromal cell research in veterinary medicine. In this field, sophisticated and new treatments are now required for improving patients’ quality of life; in livestock animals, the goal of regenerative medicine is to improve not only animal welfare but also the quality of production, aiming to preserve human health. The contributions collected in this book concern both laboratory research and clinical applications of mesenchymal stem/stromal cells. The increasing knowledge of cell-based therapies may constitute an opportunity for researchers, veterinary practitioners, and animal owners to contribute to animal and human health and well-being
The Creation of a Biophysical Modeling Universe: The UNIfied and VERSatile bio response Engine
Radiotherapy is a crucial pillar of cancer therapy and ion beams promise superior dose conformity and potentially enhanced biological effectiveness in comparison to conventional radiation modalities. However, several factors are known to modify the biological effect of radiation. The capability to model their impact within a unified description of radiation action in conventional and ion beam fields would greatly enhance the ability to prescribe the optimal treatment and improve the knowledge of underlying mechanisms. To this end, the initial developments of the mechanistic UNIfied and VERSatile bio response Engine (UNIVERSE) are presented in this work. The effects of radiosensitizing drugs and mutations as well as DNA repair kinetics were modeled for each radiation quality. For sparsely ionizing radiation, the sparing effects at ultra-high dose-rates (uHDR) applied in FLASH radiotherapy were introduced based on oxygen depletion rates approaching measured values. Benchmarks against own or literature data are presented for each development. Challenges concerning the transition of oxygen and uHDR effects to ion beams as well as the vision of personalized biomarker-based patient plan adaptation based on UNIVERSE are discussed. UNIVERSE offers clinically relevant insights into radiobiological interdependencies and its versatility will allow it to follow future trends in radiotherapy
GNN-Assisted Phase Space Integration with Application to Atomistics
Overcoming the time scale limitations of atomistics can be achieved by
switching from the state-space representation of Molecular Dynamics (MD) to a
statistical-mechanics-based representation in phase space, where approximations
such as maximum-entropy or Gaussian phase packets (GPP) evolve the atomistic
ensemble in a time-coarsened fashion. In practice, this requires the
computation of expensive high-dimensional integrals over all of phase space of
an atomistic ensemble. This, in turn, is commonly accomplished efficiently by
low-order numerical quadrature. We show that numerical quadrature in this
context, unfortunately, comes with a set of inherent problems, which corrupt
the accuracy of simulations -- especially when dealing with crystal lattices
with imperfections. As a remedy, we demonstrate that Graph Neural Networks,
trained on Monte-Carlo data, can serve as a replacement for commonly used
numerical quadrature rules, overcoming their deficiencies and significantly
improving the accuracy. This is showcased by three benchmarks: the thermal
expansion of copper, the martensitic phase transition of iron, and the energy
of grain boundaries. We illustrate the benefits of the proposed technique over
classically used third- and fifth-order Gaussian quadrature, we highlight the
impact on time-coarsened atomistic predictions, and we discuss the
computational efficiency. The latter is of general importance when performing
frequent evaluation of phase space or other high-dimensional integrals, which
is why the proposed framework promises applications beyond the scope of
atomistics
Systemic Circular Economy Solutions for Fiber Reinforced Composites
This open access book provides an overview of the work undertaken within the FiberEUse project, which developed solutions enhancing the profitability of composite recycling and reuse in value-added products, with a cross-sectorial approach. Glass and carbon fiber reinforced polymers, or composites, are increasingly used as structural materials in many manufacturing sectors like transport, constructions and energy due to their better lightweight and corrosion resistance compared to metals. However, composite recycling is still a challenge since no significant added value in the recycling and reprocessing of composites is demonstrated. FiberEUse developed innovative solutions and business models towards sustainable Circular Economy solutions for post-use composite-made products. Three strategies are presented, namely mechanical recycling of short fibers, thermal recycling of long fibers and modular car parts design for sustainable disassembly and remanufacturing. The validation of the FiberEUse approach within eight industrial demonstrators shows the potentials towards new Circular Economy value-chains for composite materials
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