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Process Optimization for
enhanced mechanical props. (4.2. Process Maps,…
- Process Optimization for
enhanced mechanical props.
Intro
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The optimized process params. can then be utilized
for effectively 'seeding' a thermally monitored,
feedback-controlled DLD process.
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The existing research efforts aimed at optimizing the mechanical props. of DLD parts are now summarized
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4.2. Process Maps
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Have been developed for predicting the steady-state melt pool size for any practical combination of DLD/LENS process variables [85, 140-142]
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Birnbaum et al. [141] demonstrated how the normalized melt pool length (L) varies as a function of normalized distance traveled by the laser (X) (fig 18)
Each curve represents a process map of the
corresponding melt pool temperature, Tm
The vertical gap between the 2 curves represent changes in the (normalized) melt pool length when the melt pool temperature changes due to various DLD dynamics
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The horizontal gaps indicate the (normalized) distance needed to be travelled by the laser to obtain constante melt pool length when the melt pool temprature changes
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These times may be useful for establishing
lower bounds of responde times for existing
DLD thermal feedback control systems
The approaches and results pertaining to process maps can be, in general, to determine
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:-1: However, the methodology of process maps was initially develop specifically for application to LENS, which is typically equipped w/ a 500W Nd: Yag laser (or others w/ similar power)
:-1: There is a limited fundamental understanding of how to apply deposition knowledge acquired from small-scale systems to analogous large-scale systems
e.g. Aeromet, which manufactures components for the aerospace industry and uses an 18 kW CO2 laser [141]
:-1: Whenever a new LBAM system is developed at a different size scale, engineers must perform a large # of experiments to characterize their process
Multiple maps w/ various scales have been developed for developed for predicting part features for the large scale process via extrapolation [141, 145]
Although the resulting prediction can be used
to provide a possible range for the optimal
process params. in large-scale process
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4.3. Data Driven Models
Although physics-based models such as process maps are essential for thoroughly understanding the underlying DLD processes, their development is extremely challenging due to the complexity associated w/ DLD
Some research efforts have circumvented this challenge by utilizing data-driven methods that directly model how the the process parameters affect the quality of final parts.
These methods include,
but are not limited to:
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Artificial Inteligence (AI), etc.
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DOE
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Kummailil et al. [29, 147] have applied a 2-level
fractional factorial design to determine the effects of
build params. on the deposition of Ti-6Al-4V
By analyzing the experimental data, a
power relationship was reported between
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In the same way, response surface
modeling [148,149] has been adopted to
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:-1: Limitations
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Therefore, when a new material or a new
process is used, extensive experiments have
to be conducted for process optimization
AI
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w/ a large training data set, AI algorithms usually provide
accurate estimations of parameter-feature relations
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e.g. Casalino and
Ludovico [151],
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:warning:Despite these successful studies, the application
of AI methods in literature is quite rare at large
This is because the key to successful application
of AI methods is enrormous training data that can
be used to estimate the process model
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Moreover, due to the proprietary
nature of DLD experiment data
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