Towards Non-defective Injection Molding: Reinforcement Learning with an Anomaly Detection–based Environment

Abstract

Injection molding is a widely used process in the plastics industry, yet its quality depends heavily on precise adjustment of interdependent process parameters, which has traditionally relied on trial-and-error by skilled engineers. Such reliance is inefficient, time-consuming, and difficult to scale, particularly as the number of controllable parameters increases. This study investigates reinforcement learning (RL) as a means of supporting process optimization in injection molding by constructing an environmental model that reflects the severe class imbalance and the requirement for continuous control. The environmental model for RL-based agents was built using the Injection Molding AI Dataset, where weight balancing was applied to classifiers and anomaly detection methods were separately employed to improve reliability under imbalanced conditions. On this basis, two RL-based agents, Deep Q-Network (DQN) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG), were designed and evaluated for their ability to optimize key process parameters such as injection time, filling time, and screw positions. The comparative analysis showed that while DQN offered advantages in terms of lightweight architecture, stable training behavior, and ease of implementation in discretized settings, MADDPG achieved greater effectiveness in continuous action spaces with multiple interdependent variables. Importantly, the MADDPG-based agent consistently identified non-defective production conditions in a single step, within the simulated environment, suggesting promising industrial applicability in future injection molding systems.