DRL-Based Injection Molding Process Parameter Optimization for Adaptive and Profitable Production

TL;DR

This paper introduces a deep reinforcement learning framework for real-time optimization of injection molding processes, balancing quality and profit amid dynamic conditions, and outperforming traditional methods in speed and adaptability.

Contribution

It presents a novel DRL-based approach that integrates profit and quality objectives, enabling real-time, adaptive process control in injection molding.

Findings

DRL models achieved up to 135x faster inference than genetic algorithms.

The framework maintained product quality while maximizing profit under varying conditions.

It demonstrated superior adaptability to seasonal and operational changes.

Abstract

Plastic injection molding remains essential to modern manufacturing. However, optimizing process parameters to balance product quality and profitability under dynamic environmental and economic conditions remains a persistent challenge. This study presents a novel deep reinforcement learning (DRL)-based framework for real-time process optimization in injection molding, integrating product quality and profitability into the control objective. A profit function was developed to reflect real-world manufacturing costs, incorporating resin, mold wear, and electricity prices, including time-of-use variations. Surrogate models were constructed to predict product quality and cycle time, enabling efficient offline training of DRL agents using soft actor-critic (SAC) and proximal policy optimization (PPO) algorithms. Experimental results demonstrate that the proposed DRL framework can dynamically adapt to seasonal and operational variations, consistently maintaining product quality while maximizing profit. Compared to traditional optimization methods such as genetic algorithms, the DRL models achieved comparable economic performance with up to 135x faster inference speeds, making them well-suited for real-time applications. The framework's scalability and adaptability highlight its potential as a foundation for intelligent, data-driven decision-making in modern manufacturing environments.