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\begin{document}

\title{Controlling Mixed-Mode Fatigue Crack Growth using Deep Reinforcement
Learning}



\author[1]{Yuteng Jin}%
\author[1]{Siddharth Misra}%
\affil[1]{Texas A\&M University}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\begin{abstract}
Mechanical discontinuity embedded in a material plays an essential role
in determining the bulk mechanical, physical, and chemical properties.
This paper is a proof-of-concept development and deployment of a
reinforcement learning framework to control both the direction and rate
of the growth of fatigue crack. The reinforcement learning framework is
coupled with an OpenAI-Gym-based environment that implements the
mechanistic equations governing the fatigue crack growth. Learning agent
does not explicitly know about the underlying physics; nonetheless, the
learning agent can infer the control strategy by continuously
interacting the numerical environment. The Markov decision process,
which includes state, action and reward, is carefully designed to obtain
a good control policy. The deep deterministic policy gradient algorithm
is implemented for learning the continuous actions required to control
the fatigue crack growth. An adaptive reward function involving reward
shaping improves the training. The reward is mostly positive to
encourage the learning agent to keep accumulating the reward rather than
terminate early to avoid receiving high accumulated penalties. An
additional high reward is given to the learning agent when the crack tip
reaches close enough to the goal point within specific training
iterations to encourage the agent to reach the goal points as quickly as
possible rather than slowly approaching the goal point to accumulate the
positive reward. The reinforcement learning framework can successfully
control the fatigue crack propagation in a material despite the
complexity of the propagation pathway determined by multiple goal
points.%
\end{abstract}%



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