25 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
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
Structural multiscale topology optimization with stress constraint for additive manufacturing
In this paper a phase-field approach for structural topology optimization for a 3D-printing process which includes stress constraint and potentially multiple materials or multiscales is analyzed. First order necessary optimality conditions are rigorously derived and a numerical algorithm which implements the method is presented. A sensitivity study with respect to some parameters is conducted for a two-dimensional cantilever beam problem. Finally, a possible workflow to obtain a 3D-printed object from the numerical solutions is described and the final structure is printed using a fused deposition modeling (FDM) 3D printer
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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