22,645 research outputs found
Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions
In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request
Towards Advantages of Parameterized Quantum Pulses
The advantages of quantum pulses over quantum gates have attracted increasing
attention from researchers. Quantum pulses offer benefits such as flexibility,
high fidelity, scalability, and real-time tuning. However, while there are
established workflows and processes to evaluate the performance of quantum
gates, there has been limited research on profiling parameterized pulses and
providing guidance for pulse circuit design. To address this gap, our study
proposes a set of design spaces for parameterized pulses, evaluating these
pulses based on metrics such as expressivity, entanglement capability, and
effective parameter dimension. Using these design spaces, we demonstrate the
advantages of parameterized pulses over gate circuits in the aspect of duration
and performance at the same time thus enabling high-performance quantum
computing. Our proposed design space for parameterized pulse circuits has shown
promising results in quantum chemistry benchmarks.Comment: 11 Figures, 4 Table
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
Recommended from our members
Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Exploiting Symmetry and Heuristic Demonstrations in Off-policy Reinforcement Learning for Robotic Manipulation
Reinforcement learning demonstrates significant potential in automatically
building control policies in numerous domains, but shows low efficiency when
applied to robot manipulation tasks due to the curse of dimensionality. To
facilitate the learning of such tasks, prior knowledge or heuristics that
incorporate inherent simplification can effectively improve the learning
performance. This paper aims to define and incorporate the natural symmetry
present in physical robotic environments. Then, sample-efficient policies are
trained by exploiting the expert demonstrations in symmetrical environments
through an amalgamation of reinforcement and behavior cloning, which gives the
off-policy learning process a diverse yet compact initiation. Furthermore, it
presents a rigorous framework for a recent concept and explores its scope for
robot manipulation tasks. The proposed method is validated via two
point-to-point reaching tasks of an industrial arm, with and without an
obstacle, in a simulation experiment study. A PID controller, which tracks the
linear joint-space trajectories with hard-coded temporal logic to produce
interim midpoints, is used to generate demonstrations in the study. The results
of the study present the effect of the number of demonstrations and quantify
the magnitude of behavior cloning to exemplify the possible improvement of
model-free reinforcement learning in common manipulation tasks. A comparison
study between the proposed method and a traditional off-policy reinforcement
learning algorithm indicates its advantage in learning performance and
potential value for applications
In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning
Cracks and keyhole pores are detrimental defects in alloys produced by laser
directed energy deposition (LDED). Laser-material interaction sound may hold
information about underlying complex physical events such as crack propagation
and pores formation. However, due to the noisy environment and intricate signal
content, acoustic-based monitoring in LDED has received little attention. This
paper proposes a novel acoustic-based in-situ defect detection strategy in
LDED. The key contribution of this study is to develop an in-situ acoustic
signal denoising, feature extraction, and sound classification pipeline that
incorporates convolutional neural networks (CNN) for online defect prediction.
Microscope images are used to identify locations of the cracks and keyhole
pores within a part. The defect locations are spatiotemporally registered with
acoustic signal. Various acoustic features corresponding to defect-free
regions, cracks, and keyhole pores are extracted and analysed in time-domain,
frequency-domain, and time-frequency representations. The CNN model is trained
to predict defect occurrences using the Mel-Frequency Cepstral Coefficients
(MFCCs) of the lasermaterial interaction sound. The CNN model is compared to
various classic machine learning models trained on the denoised acoustic
dataset and raw acoustic dataset. The validation results shows that the CNN
model trained on the denoised dataset outperforms others with the highest
overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC
score (98%). Furthermore, the trained CNN model can be deployed into an
in-house developed software platform for online quality monitoring. The
proposed strategy is the first study to use acoustic signals with deep learning
for insitu defect detection in LDED process.Comment: 36 Pages, 16 Figures, accepted at journal Additive Manufacturin
Thread-safe lattice Boltzmann for high-performance computing on GPUs
We present thread-safe, highly-optimized lattice Boltzmann implementations,
specifically aimed at exploiting the high memory bandwidth of GPU-based
architectures. At variance with standard approaches to LB coding, the proposed
strategy, based on the reconstruction of the post-collision distribution via
Hermite projection, enforces data locality and avoids the onset of memory
dependencies, which may arise during the propagation step, with no need to
resort to more complex streaming strategies. The thread-safe lattice Boltzmann
achieves peak performances, both in two and three dimensions and it allows to
sensibly reduce the allocated memory ( tens of GigaBytes for order billions
lattice nodes simulations) by retaining the algorithmic simplicity of standard
LB computing. Our findings open attractive prospects for high-performance
simulations of complex flows on GPU-based architectures
HR Analytics: Concept, Application, and Impact on Talent Management, Branding, and Challenges
Purpose: Making wiser decisions about employees to improve performance at the individual and/or organizational levels is the process of HR analytics. HR analytics is a method for determining the correlation between HR practices and organizational performance outcomes such as sales volume or customer satisfaction. Human Resource Analytics was established in 1978 by Jac Fitz-Enz, the pioneer of human capital strategic analysis and performance benchmarking. In this paper, the researcher wants to discuss the concept of HR analytics, its application, impact on talent management, branding, and challenges in its application.Design/methodology/approach: The researcher examines secondary data and conducts a thorough literature review to understand the concept and its application across industries and nations, as well as to identify any challenges encountered during deployment and any benefits perceived by various industry professionals. Findings: The study's findings indicate that using HR analytics can help businesses build their brand and gain a competitive edge in today's fiercely competitive business environment while also enhancing workforce and employee productivity.Originality/value: This study has significant implications for both literature and HR analytics. Researchers will know more about the factors that contribute to and the mechanisms by which HR analytics improve organisational performance. The author's second claim is that having access to HR technology both facilitates and precedes HR analytics. Finally, concrete data from the literature demonstrates its influence on branding and organisational success. Keywords: Human resource (HR) analytics, People analytics, Branding, Talent Management, Organizational performance. Paper type: Research paper JEL Code: M12, M15 & M51 DOI: 10.7176/EJBM/15-8-06 Publication date: April 30th 202
Changes in soil fertility and microbial communities following cultivation of native grassland in Horqin Sandy Land, China: a 60-year chronosequence
Background: Grassland conversion to cropland is a prevailing change of land use in traditionally nomadic areas, especially in the Mongolian Plateau. We investigated the effects of grassland conversion followed by continuous cultivation on soil properties and microbial community characteristics in Horqin Sandy Land, a typical agro-pastoral transition zone of Northern China. Soil samples were collected from the topsoil (upper 20 cm) across a 60-year cultivation chronosequence (5, 15, 25, 35 and 60 years) and unconverted native grassland. Soil physico-chemical properties were determined and high-throughput sequencing was used to assess microbial community diversity and composition. Results: Grassland cultivation resulted in changes to soil properties in both the short and longer term. Initially, it significantly increased soil bulk density (BD), electrical conductivity (EC), soil total nitrogen (TN), available phosphorus (AP) and available potassium (AK) concentrations, while reducing soil water content (SWC) and soil organic carbon content (SOC). Over the next 35–55 years of continuous cultivation, the trend for most of these characteristics was of reversion towards values nearer to those of native grassland, except for SOC which remained highly depleted. Cultivation of grassland substantially altered soil microbial communities at phylum level but there was no significant difference in microbial α-diversity between native grassland and any cropland. However, soil bacterial and fungal community structures at phylum level in the croplands of all cultivation years were different from those in the native grasslands. Heatmaps further revealed that bacterial and fungal structures in cropland tended to become more similar to native grassland after 15 and 25 years of cultivation, respectively. Redundancy analysis indicated that SOC, EC and BD were primary determinants of microbial community composition and diversity. Conclusions: These findings suggest that agricultural cultivation of grassland has considerable effects on soil fertility and microbial characteristics of Horqin Sandy Land. Intensive high-yield forage grass production is proposed as an alternative to avoid further native grassland reclamation, while meeting the grazing development needs in the ethnic minority settlements of eco-fragile regions
Antenna Arrangement in UWB Helmet Brain Applicators for Deep Microwave Hyperthermia
Deep microwave hyperthermia applicators are typically designed as narrow-band conformal antenna arrays with equally spaced elements, arranged in one or more rings. This solution, while adequate for most body regions, might be sub-optimal for brain treatments. The introduction of ultra-wide-band semi-spherical applicators, with elements arranged around the head and not necessarily aligned, has the potential to enhance the selective thermal dose delivery in this challenging anatomical region. However, the additional degrees of freedom in this design make the problem non-trivial. We address this by treating the antenna arrangement as a global SAR-based optimization process aiming at maximizing target coverage and hot-spot suppression in a given patient. To enable the quick evaluation of a certain arrangement, we propose a novel E-field interpolation technique which calculates the field generated by an antenna at any location around the scalp from a limited number of initial simulations. We evaluate the approximation error against full array simulations. We demonstrate the design technique in the optimization of a helmet applicator for the treatment of a medulloblastoma in a paediatric patient. The optimized applicator achieves 0.3\ua0 (Formula presented.) C higher T90 than a conventional ring applicator with the same number of elements
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