Investigation of reinforcement learning for shape optimization of profile extrusion dies

• URL: http://arxiv.org/abs/2212.12207v1
• Date: Fri, 23 Dec 2022 08:53:09 GMT
• Title: Investigation of reinforcement learning for shape optimization of profile extrusion dies
• Authors: Clemens Fricke and Daniel Wolff and Marco Kemmerling and Stefanie Elgeti
• Abstract summary: Reinforcement Learning (RL) is a learning-based optimization algorithm. RL is based on trial-and-error interactions of an agent with an environment. We investigate this approach by applying it to two 2D test cases.
• Score: 1.5293427903448022
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Profile extrusion is a continuous production process for manufacturing plastic profiles from molten polymer. Especially interesting is the design of the die, through which the melt is pressed to attain the desired shape. However, due to an inhomogeneous velocity distribution at the die exit or residual stresses inside the extrudate, the final shape of the manufactured part often deviates from the desired one. To avoid these deviations, the shape of the die can be computationally optimized, which has already been investigated in the literature using classical optimization approaches. A new approach in the field of shape optimization is the utilization of Reinforcement Learning (RL) as a learning-based optimization algorithm. RL is based on trial-and-error interactions of an agent with an environment. For each action, the agent is rewarded and informed about the subsequent state of the environment. While not necessarily superior to classical, e.g., gradient-based or evolutionary, optimization algorithms for one single problem, RL techniques are expected to perform especially well when similar optimization tasks are repeated since the agent learns a more general strategy for generating optimal shapes instead of concentrating on just one single problem. In this work, we investigate this approach by applying it to two 2D test cases. The flow-channel geometry can be modified by the RL agent using so-called Free-Form Deformation, a method where the computational mesh is embedded into a transformation spline, which is then manipulated based on the control-point positions. In particular, we investigate the impact of utilizing different agents on the training progress and the potential of wall time saving by utilizing multiple environments during training.

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