Can We Use Reinforcement Learning to Automate TPMS Structure Design?

The framework implements a Q-learning algorithm with discrete state-action spaces. The environment state is defined by five continuous parameters: TPMS type index, C-value (level-set constant controlling porosity), primary mixing coefficient α, secondary mixing coefficient β, and geometric scale factor. The action space consists of seven discrete operations that modify these parameters incrementally.

The reward function evaluates structural performance based on two primary metrics: (1) volume fraction deviation from the optimal range of 0.3-0.7 for structural applications, weighted at 60%, and (2) geometric complexity measured via standard deviation of the implicit surface function, weighted at 40%.

Видео Can We Use Reinforcement Learning to Automate TPMS Structure Design? канала Akshansh Mishra