243 research outputs found
Accurate prediction of melt pool shapes in laser powder bed fusion by the non-linear temperature equation including phase changes - isotropic versus anisotropic conductivity
In this contribution, we validate a physical model based on a transient
temperature equation (including latent heat) w.r.t. the experimental set
AMB2018-02 provided within the additive manufacturing benchmark series,
established at the National Institute of Standards and Technology, USA. We aim
at predicting the following quantities of interest: width, depth, and length of
the melt pool by numerical simulation and report also on the obtainable
numerical results of the cooling rate. We first assume the laser to posses a
double ellipsoidal shape and demonstrate that a well calibrated, purely thermal
model based on isotropic thermal conductivity is able to predict all the
quantities of interest, up to a deviation of maximum 7.3\% from the
experimentally measured values.
However, it is interesting to observe that if we directly introduce, whenever
available, the measured laser profile in the model (instead of the double
ellipsoidal shape) the investigated model returns a deviation of 19.3\% from
the experimental values. This motivates a model update by introducing
anisotropic conductivity, which is intended to be a simplistic model for heat
material convection inside the melt pool. Such an anisotropic model enables the
prediction of all quantities of interest mentioned above with a maximum
deviation from the experimental values of 6.5\%.
We note that, although more predictive, the anisotropic model induces only a
marginal increase in computational complexity
Suitably graded THB-spline refinement and coarsening: Towards an adaptive isogeometric analysis of additive manufacturing processes
In the present work we introduce a complete set of algorithms to efficiently
perform adaptive refinement and coarsening by exploiting truncated hierarchical
B-splines (THB-splines) defined on suitably graded isogeometric meshes, that
are called admissible mesh configurations. We apply the proposed algorithms to
two-dimensional linear heat transfer problems with localized moving heat
source, as simplified models for additive manufacturing applications. We first
verify the accuracy of the admissible adaptive scheme with respect to an
overkilled solution, for then comparing our results with similar schemes which
consider different refinement and coarsening algorithms, with or without taking
into account grading parameters. This study shows that the THB-spline
admissible solution delivers an optimal discretization for what concerns not
only the accuracy of the approximation, but also the (reduced) number of
degrees of freedom per time step. In the last example we investigate the
capability of the algorithms to approximate the thermal history of the problem
for a more complicated source path. The comparison with uniform and
non-admissible hierarchical meshes demonstrates that also in this case our
adaptive scheme returns the desired accuracy while strongly improving the
computational efficiency.Comment: 20 pages, 12 figure
An Ontology for Defect Detection in Metal Additive Manufacturing
A key challenge for Industry 4.0 applications is to develop control systems
for automated manufacturing services that are capable of addressing both data
integration and semantic interoperability issues, as well as monitoring and
decision making tasks. To address such an issue in advanced manufacturing
systems, principled knowledge representation approaches based on formal
ontologies have been proposed as a foundation to information management and
maintenance in presence of heterogeneous data sources. In addition, ontologies
provide reasoning and querying capabilities to aid domain experts and end users
in the context of constraint validation and decision making. Finally,
ontology-based approaches to advanced manufacturing services can support the
explainability and interpretability of the behaviour of monitoring, control,
and simulation systems that are based on black-box machine learning algorithms.
In this work, we provide a novel ontology for the classification of
process-induced defects known from the metal additive manufacturing literature.
Together with a formal representation of the characterising features and
sources of defects, we integrate our knowledge base with state-of-the-art
ontologies in the field. Our knowledge base aims at enhancing the modelling
capabilities of additive manufacturing ontologies by adding further defect
analysis terminology and diagnostic inference features
Impact of interaction forces in first order many-agent systems for swarm manufacturing
We study the large time behavior of a system of interacting agents modeling
the relaxation of a large swarm of robots, whose task is to uniformly cover a
portion of the domain by communicating with each other in terms of their
distance. To this end, we generalize a related result for a Fokker-Planck-type
model with a nonlocal discontinuous drift and constant diffusion, recently
introduced by three of the authors, of which the steady distribution is
explicitly computable. For this new nonlocal Fokker-Planck equation, existence,
uniqueness and positivity of a global solution are proven, together with
precise equilibration rates of the solution towards its quasi-stationary
distribution. Numerical experiments are designed to verify the theoretical
findings and explore possible extensions to more complex scenarios
The Effects of Language Background and Foreign Accent on Listening Comprehension
The act of listening to a linguistic signal is an involved process, and, rarely occurs in absolute silence. A person trying to listen and comprehend speech is likely in an environment that has some sort of additional noise: white noise from a fan, passing traffic, construction, or just other talkers. Each of these additional auditory signals creates an unfavorable environment for the listener who is trying to capture the target signal. Research has been able to quantify and describe the effects of noise on the comprehension of linguistic signals, and has also shown that that bilinguals and monolinguals — though their performance is indistinguishable in quiet conditions — are known to be differentially affected by noise: bilinguals perform significantly worse in adverse listening conditions when tasked with comprehend a linguistic signal. What is yet to be established is how a signal with intrinsic, phonological variation differentially affects monolinguals and bilinguals.
This study is a small-scale pilot that investigates this question: what bearing does bilingualism have on the comprehension of foreign-accented speech in quiet and in noise? Stimuli include sentences spoken in English, with five different accents: Neutral English (the English typical of the NYC area), Latin American Spanish English, Mandarin English, Italian English, and Indian English. A true-false verification task is used to assess the participants’ comprehension of the sentences, which are auditorily delivered such that no two sentences with the same accent are heard consecutively. All five accents are heard in both quiet and in noise, in two separate blocks. Accuracy and reaction times are analyzed
Simultaneous Bilinguals’ Comprehension of Accented Speech
L2-accented speech recognition has typically been studied with monolingual listeners or late L2-learners, but simultaneous bilinguals may have a different experience: their two phonologies offer flexibility in phonological-lexical mapping (Samuel and Larraza, 2015), which may be advantageous. On the other hand, the two languages cause greater lexical competition (Marian & Spivey, 2003), which may impede successful L2-accented speech recognition. The competition between a bilinguals’ two languages is the oft-cited explanation, for example, as to why bilinguals underperform monolinguals in native-accented speech-in-noise tasks (Rogers et al., 2006).
To investigate the effect of bilingualism on L2-accented speech recognition, the current studies compare monolingual and simultaneous bilingual listeners in three separate experiments. In the first study, both groups repeated sentences produced by speakers of Mandarin-accented English whose English proficiencies varied. In the second study, the stimuli were presented in varying levels and types of noise, and a native-accented speaker was included. In each of these first two studies, the sentences were semantically anomalous (i.e., nonsensical). In the third study, the stimuli were meaningful sentences, presented in a single noise condition, and spoken by either a native speaker or an L2-accented speaker.
Mixed effects models revealed differences in L2-accented speech recognition measures driven by listeners’ language backgrounds only in Experiments 2 and 3; in Experiment 1, performance between groups was statistically identical. Results in Experiments 2 and 3 also replicated the prior finding that bilinguals perform worse for native-accented speech in noise.
We propose that neither a flexible phonological-lexical mapping system nor increased lexical competition can alone sufficiently explain the deficit (relative to monolinguals) that simultaneous bilinguals exhibit when faced with L2-accented speech in real-world listening conditions. We discuss the possible implications of processing capacity and cognitive load, and suggest that these two factors are more likely to contribute to experimental outcomes. Future studies with pupillometry to explore these hypotheses are also discussed
Cost-effective and accurate interlaminar stress modeling of composite Kirchhoff plates via immersed isogeometric analysis and equilibrium
The interest for composites has constantly grown in recent years, especially in the aerospace and automotive industries, as they can be moulded in complex form and geometry, as well as exhibit enhanced engineering properties. Nevertheless, despite the accelerated diffusion of laminated composites, the design of these materials is often restrained by the lack of cost-effective modeling techniques. In fact, the existing numerical strategies allowing for cheap simulations of laminated structures usually fail to directly capture out-of-plane through-the-thickness stresses, which are typically responsible for failure modes such as delamination. In this context, a stress recovery approach based on equilibrium has been recently shown to be an efficient modeling strategy in the framework of isogeometric analysis. Since immersed approaches like the finite cell method have been proven to be a viable alternative to mesh-conforming discretization for dealing with complex/dirty geometries as well as trimmed surfaces, we herein propose to extend the stress recovery approach combining the finite cell method, isogeometric analysis and equilibrium to model the out-of-plane behavior of Kirchhoff laminated plates. Extensive numerical tests showcase the effectiveness of the proposed approach
Sparse-grids uncertainty quantification of part-scale additive manufacturing processes
The present paper aims at applying uncertainty quantification methodologies
to process simulations of powder bed fusion of metal. In particular, for a
part-scale thermomechanical model of an Inconel 625 super-alloy beam, we study
the uncertainties of three process parameters, namely the activation
temperature, the powder convection coefficient and the gas convection
coefficient. First, we perform a variance-based global sensitivity analysis to
study how each uncertain parameter contributes to the variability of the beam
displacements. The results allow us to conclude that the gas convection
coefficient has little impact and can therefore be fixed to a constant value
for subsequent studies. Then, we conduct an inverse uncertainty quantification
analysis, based on a Bayesian approach on synthetic displacements data, to
quantify the uncertainties of the two remaining parameters, namely the
activation temperature and the powder convection coefficient. Finally, we use
the results of the inverse uncertainty quantification analysis to perform a
data-informed forward uncertainty quantification analysis of the residual
strains. Crucially, we make use of surrogate models based on sparse grids to
keep to a minimum the computational burden of every step of the uncertainty
quantification analysis. The proposed uncertainty quantification workflow
allows us to substantially ease the typical trial-and-error approach used to
calibrate power bed fusion part-scale models, and to greatly reduce
uncertainties on the numerical prediction of the residual strains. In
particular, we demonstrate the possibility of using displacement measurements
to obtain a data-informed probability density function of the residual strains,
a quantity much more complex to measure than displacements
Additive manufacturing graded-material design based on phase-field and topology optimization
In the present work we introduce a novel graded-material design for additive manufacturing based on phase-field and topology optimization. The main novelty of this work comes from the introduction of an additional phase-field variable in the classical single-material phase-field topology optimization algorithm. This new variable is used to grade the material properties in a continuous fashion. Two different numerical examples are discussed, in both of them we perform sensitivity studies to asses the effects of different model parameters onto the resulting structure. From the presented results we can observe that the proposed algorithm adds additional freedom in the design, exploiting the higher flexibility coming from additive manufacturing technology
Graded-material Design based on Phase-field and Topology Optimization
In the present work we introduce a novel graded-material design based on
phase-field and topology optimization. The main novelty of this work comes from
the introduction of an additional phase-field variable in the classical
single-material phase-field topology optimization algorithm. This new variable
is used to grade the material properties in a continuous fashion. Two different
numerical examples are discussed, in both of them we perform sensitivity
studies to asses the effects of different model parameters onto the resulting
structure. From the presented results we can observe that the proposed
algorithm adds additional freedom in the design, exploiting the higher
flexibility coming from additive manufacturing technology
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