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

arXiv: https://arxiv.org/abs/2505.10988 · HTML: https://mlsanity.com/paper/2505.10988/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 9 of its 67 references that are in the map.
- First posted on arXiv 16 May 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning](https://mlsanity.com/paper/2102.03355/) (2021)
- [Multi-Objective Optimization of the Textile Manufacturing Process Using Deep-Q-Network Based Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2012.01101/) (2020)
- [Single and Multi-Objective Real-Time Optimisation of an Industrial Injection Moulding Process via a Bayesian Adaptive Design of Experiment Approach](https://mlsanity.com/paper/2402.12077/) (2024)

## Abstract (arXiv)

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.