The main goal when studying a novel powder material / substrate coating system is to optimize its application in terms of the resulting properties and the overall process efficiency. Several approaches for the cold spray process optimization have been used in the past based on DOE - Design of Experiments - methods offering a systematic exploration of the spraying parameters combinations domain. In this work an optimization approach based on a model based reinforcement learning method is presented. For the cold spray technology reinforcement learning is used to guide researchers in the selection of the proper spraying parameters combination that will maximize a single or several coating properties and the overall process efficiency. A case study is presented showing the optimization of Stainless Steel depositions where the optimization task converges after few spraying operations. As a result, the methodology offers a map that describes the powder material response to the most possible spraying parameters combinations. The implementation of iterative experimentation for cold spray coatings optimization can potentially influence the successful development of new powder material / substrate coating systems and support the standardization of the cold spray technology.

1. Towardscold spray
coatings
optimization via
artificial intelligence
H. Canales, S. Dosta, I.G. Cano
ITSC, May 7, 2018 - Orlando, USA 1

2. Summary
We cannot solve our
problems with the
same thinking we
used when we
created them.
Albert Einstein
Motivation
1. Introduction
2. Optimization methodology
3. Stainless steel optimization via AI
4. DEMO
5. Brief discussion
6. Conclusions
2

3. 2015 was a great year for
Artificial Intelligence (AI)
Computer
algorithms were able
to find optimal
strategies in highly
complicated
environments
without guidance
and using the same
intelligent agent
Motivation
Mnih, Volodymyr, et al. "Human-level control through deep
reinforcement learning." Nature 518.7540 (2015): 529.
Comparison of the DQN agent
with the best reinforcement
learning methods in the literature
A visualization of the learned value
function on the game Breakout

4. The optimization paradigm used
for this achievement was:
Reinforcement Learning
Reinforcement
learning is a
machine learning
technique inspired
in the analogy of
biological learning
where an intelligent
agent selects the
actions that he
should take to
maximize a reward
during his
interaction with an
environment.
The learning
process occurs
through a series of
episodes where
actions are taken in
order to change the
environment's state
Motivation
Sutton, Richard S., and Andrew G. Barto.
Reinforcement learning: An introduction.
Vol. 1. No. 1. Cambridge: MIT press, 1998.

5. But, Can computers help us to
avoid Cold Sprayer Nightmares?
We all come with an
optimized, functional
and good looking
coatings.
But many research
efforts are invested
behind those
results.
The path to those
successes is what
we call science
Motivation
5
Erosion Residual stress

6. How complex is the
CGS process?
Let’s say we want to
explore 4 levels of 7
cold spray
parameters: that will
take 16,384
experiments
For this reason in
our field many
developments have
been driven by
experts intuition and
coincidences
Motivation
6
400,000 Cold Spray parameters combinations:
Just few of them with near 100% deposition efficiency

7. The Cold Spray process:
Powder materials
deposition
technology at
processing
temperatures below
the melting point.
Great potential in the
coatings and
additive
manufacturing
industries.
1. Introduction
7
Supersonic
Nozzle
Gas
Heater
Powder
feed
Cold Spray equipment Aluminum coating on
stainless steel

8. Materials deposition theories and
window of deposition in Cold Spray:
The theory used to
describe the
particles critical
velocity and finally a
bonding criteria for
the cold spray
process usually
study a single or
several particles
impacts onto the
substrate and
represent an
important
contribution to
fundamental
research
1. Introduction
8
Most researchers agree that particles bonding is
related to adiabatic shear instabilities caused by
high strain rates during the particles deformation
To the best of our knowledge, three theories have been proposed to
describe the deposition phenomena in cold spray:
1. The impact / critical velocity ratio (ŋ) 1
2. The rebounce vs. adhesion energies 2 and 3
3. The particle's total energy per unit mass 4
1
H. Assadi, T. Schmidt, H. Richter, J.-O. Kliemann, K. Binder, F. Gartner, T. Klassen, and H. Kreye, “On parameter
selection in cold spraying," Journal of thermal spray technology, vol. 20, no. 6, pp. 1161{1176, 2011.
2
A. Papyrin, V. Kosarev, S. Klinkov, A. Alkhimov, and V. M. Fomin, Cold spray technology. Elsevier, 2006.
3
Wu, Jingwei, Hongyuan Fang, Sanghoon Yoon, HyungJun Kim, and Changhee Lee. "The rebound phenomenon in kinetic
spraying deposition." Scripta Materialia 54, no. 4 (2006): 665-669.
4
H. Assadi, H. Kreye, F. Gartner, and T. Klassen, “Cold spraying{a materials perspective," Acta Materialia, vol. 116, pp.
382{407, 2016.

9. But the truth is only reachable
through experimentation:
But sometimes, you
just did not choose
the right parameters
combination
between those
400,000 options
So you keep trying
and following trends
and may stop with a
sub-optimal solution
achieving i.e. 90%DE
1. Introduction
9
Titanium coating adhesive
delamination
Copper coating cohesive
delamination

10. Optimization is what we do when
developing a new coating-substrate system
Numbers are the
highest degree of
knowledge, it is
knowledge itself
Plato
1. Introduction
10
f(x, y) = zf(x) = y
max (i.e. %DE)
max (i.e. %DE)
max (i.e. %DE)
max (i.e. %DE)

11. The user is the
responsible for the
interaction with the
experimental
environment
An utility
approximator is
trained during the
interaction with the
experimental
environment
An optimizer find the
spraying parameters
that will result in the
maximum utility
The interaction with
the experimental
environment
happens across
episodes
2. Optimization
methodology
11
xe
: Spraying Parameters
i.e.: [Total Pressure, Stagnation
Temperature, Stand-off distance,
Nozzle, Pre-chamber, Substrate
preparation, Robot velocity, etc.]
Ue+1
: Objective utility
i.e.: Deposition efficiency, Adhesion
Strength, Porosity, Hardness, etc.
Ũ: Approximated Utility
we
: Utility approximator weights
The objective is not to predict
the objective utility Ue+1
but to
take advantage of Ũ gradient
information

12. Screening
experiments are
required to initialize
the utility
approximator
The initial
information is used
to build parameter
maps that show the
response tendencies
The goal is not make
an accurate utility
prediction of the
whole domain, but to
efficiently use the
gradient information
for the experimental
environment
exploration
3. Stainless steel
optimization via AI
12
(1)
(5)
(1)
(5)
(Delaminated)

13. Experimentation is
performed using the
parameters that will
result to a maximal
response according
to the utility
approximator
The method will tend
to propose
experimentation
either for maximum
response or where
there is few
information
3. Stainless steel
optimization via AI
13
(9)
(9)

14. The iterations can be
performed until the
goal is reached
3. Stainless steel
optimization via AI
14
(1)
(13)
(13)
(15)
(15)
(~ 97 %DE)The coating with the
best deposition
efficiency

15. The exploration -
exploitation trade-off
is a fundamental
dilemma whenever
you learn about the
world by trying
things out. The
dilemma is between
choosing what you
know and getting
something close to
what you expect
(“exploitation”) and
choosing something
you aren’t sure
about and possibly
learning more
(“exploration”)
3. Stainless steel
optimization via AI
15
We need to
balance
exploration and
exploitation of the
achieved
knowledge
MIT 6.S191 Lecture: Deep Reinforcement Learning

16. Exploration took us
to a better coating
densification
keeping a high
deposition efficiency
3. Stainless steel
optimization via AI
16
(15)
2x from initial fixed robot velocity
1/2x from initial fixed robot velocity
(Delaminated)
Best spraying parameters at
initial fixed robot velocity
Exploring the robot velocity
dimension
The %DE stayed at ~97%
except for the case with
lower robot velocity where
delamination occurred

17. These parameters let
us make additive
manufacturing with
stainless steel
(still remaining a full
characterization of
the obtained bulk)
3. Stainless steel
optimization via AI
17
+10 mm deposition thickness

18. Numbers are the
highest degree of
knowledge, it is
knowledge itself
Plato
4. DEMO
18
cptub.com
or
thermal-spray.ai-facture.com
Youtube: Link

19. Conclusions:
19
6. Conclusions
1. We can do data science to better understand the cold
spray process bringing order from chaos
2. Results are heuristics - not fundamental science
3. The optimization method is “equipment agnostic”,
only experimental data is needed to use it
4. There still many work to do in this direction as
multi-objective and qualitative optimization

20. Let’s get in touch for cooperation and bring the cold
spray technology to the next level
Twitter: @HcanS
20