To: Professor
from: David R Fajardo
Advanced Manufacturing Processes
10 October
Assignment #1
In this assignment I’m going to explain how simulation models can be used for each advanced
manufacturing process
1) Numerical-simulation based: Fused Filament Fabrication Process
Fuse Filament Fabrication is a part of additive manufacturing into this process is very important
highlight the heat transfer from the nozzle to fabricated part and from filament to filament, these
aspects are important to ensure a correct material adhesion. Another aspect into this process is how
the field temperature affects the mechanical properties of the manufactured part which is relevant
when the manufactured part form part of a mechanical system as set of pulleys and gears. When
the filament material is deposited in layers exist rapid cycles of heating and cooling generating
temperature gradients which means internal stress and therefore a bad internal connections quality
thereby heat transfer equations helps to make a process simulation starting by built energy
conservation equations and making assumptions to simplify calculations, then we must define time
range and also the mesh where our simulation will be showed. (Benoît Cosson, 2019)
The predefined input parameters as uncontrollable inputs:
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Transition temperature, Tg = 65 °C
melting temperature, Tm = 130–230 °C.
The controllable process parameters:
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Diameter of printing nozzle is 300 μm
The initial printing speed of nozzle is Vprint = 100 mm/s
Temperature of the printing nozzle is set at Tbuilt = 230 °C
The layer thickness c, is set at 300 μm
The filament diameter of 1.75 mm
The uncontrollable process variables:
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temperature of the unheated support is equal to the room temperature, Ts = Tair = 24 °C.
The measurable process outputs (i.e. quality measures):
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The radiation heat exchange by the nozzle during printing
2) Regression-based methods: Structure and mechanical properties of a FeCoCrNi
fabricated via selective laser melting
Selective laser melting has became popular for complex and high value components production
because avoid production cost and machining difficulty, this method is a rapid solidification
technology compared with traditional casting an higher yield strength and tensile strength is
achieved. The volumetric energy density (VED) determinate the mechanical properties of
FeCoCrNi fabricate via SLM, with this process simulation by Polynomial regression analysis we
will aim to enhance mechanical properties. (DanyangLinabLianyong, 2020)
The predefined input parameters as uncontrollable inputs:
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Cubes (6 × 6 × 6 mm) were printed
FeCoCrNi HEA powder
The controllable process parameters:
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Layer thickness (40 μm) and rotation angle of 67° between each layer
Laser power (50–400 W)
Hatch spacing (20–200 μm)
Scanning speed
Exposure duration (40–700 μs)
The uncontrollable process variables:
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Grain boundary strengthening mechanisms
The measurable process outputs (i.e. quality measures):
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The Archimedes principle was applied to measure the relative density of the samples using
a densimeter (ST-100 E)
Tensile tests
The microstructure analysis
scanning electron microscopy (SEM)
optical microscopy (OM)
transmission electron microscopy (TEM)
3) Metamodel-based methods: Powder bed fusion
Metamodel is a model of models, in powder bed fusion this method is used to provide rules, frames
and constrains to predefine class of problems, this method improve how parameters, limitations
and capabilities are communicated and it will enhance to reduce the variability.
The goal is to describe a model for communicating information at both non-technical and technical
levels. (Witherell, 2014)
The predefined input parameters as uncontrollable inputs:
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Material viscosity
Capillary force
Material Conductivity
Melting temperature
Powder density
The controllable process parameters:
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Laser mode (continuous wave or pulsed)
Laser Power
Scan speed
Hatch space
Thickness
The uncontrollable process variables:
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Field temperature
Chamber pressure
The measurable process outputs (i.e. quality measures):
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Surface roughness
Geometric dimension
Residual stress
Porosity
4) Surrogate models: Powder bed fusion
This model can be magnitude faster than physics model-based, Surrogate model are used to predict
the optimal process parameters. this model has helped to predict solidification morphology which
aims to optimize microstructure to meet functional requirements of surface roughness and
mechanical strength.
This model generates process maps to provide a visual representation of the influence between
responses of interest and critical process parameters.
To develop this model, we need an efficient sampling strategy to search the design space efficiently
and empirical data points can be included, then a surrogate model is created for each response of
interest, then the surrogate model is validated to determine its accuracy, and if necessary, if in the
future the model can be update to performer a predictive accuracy.
In this case the output of function Y at input x which can be expressed in the form of Y=f(x)+ε,
where ε is a normally distributed independent and identically distributed error term. (Zhang, 2022)
Bibliography
Benoît Cosson, A. C. (2019). Effect of the Nozzle Radiation on the Fused Filament Fabrication Process:
Three-Dimensional Numerical Simulations and Experimental Investigation. J. Heat Transfer, 8.
DanyangLinabLianyong. (2020). Structure and mechanical properties of a FeCoCrNi high-entropy alloy
fabricated via selective laser melting. Intermetallics.
Witherell, P. (2014). Toward Metamodels for Composable and Reusable Additive Manufacturing Process
Models . Journal of Manufacturing Science and Engineering , 9.
Zhang, Y. (2022). A data-driven framework to predict fused filament fabrication part properties using
surrogate models and multi-objective optimisation. The International Journal of Advanced
Manufacturing Technology volume.