Reinforcement Learning-based Automatic Tuning Method and Application of Injection Molding Process Parameters

Feng Zihao; Wan Huilong; Lin Jianghao; Ren Zhigang

doi:10.12052/gdutxb.240142

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Reinforcement Learning-based Automatic Tuning Method and Application of Injection Molding Process Parameters

更新时间：2026-05-27

- Reinforcement Learning-based Automatic Tuning Method and Application of Injection Molding Process Parameters
- Vol. 42, Issue 2, Pages: 59-69(2025)
- 作者机构：
  
  广东工业大学自动化学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.240142
  CLC： TP271
- Published：2025
- 稿件说明：
移动端阅览
冯子豪, 万会龙, 林江豪, 等. 基于强化学习的注塑工艺参数自动调优方法及应用[J]. 广东工业大学学报, 2025, 42(2): 59–69.

Feng Zihao, Wan Huilong, Lin Jianghao, et al. Reinforcement Learning-based Automatic Tuning Method and Application of Injection Molding Process Parameters[J]. 2025, 42(2): 59-69.
冯子豪, 万会龙, 林江豪, 等. 基于强化学习的注塑工艺参数自动调优方法及应用[J]. 广东工业大学学报, 2025, 42(2): 59–69. DOI： 10.12052/gdutxb.240142

Feng Zihao, Wan Huilong, Lin Jianghao, et al. Reinforcement Learning-based Automatic Tuning Method and Application of Injection Molding Process Parameters[J]. 2025, 42(2): 59-69. DOI： 10.12052/gdutxb.240142.

摘要

注塑工艺参数的优化在当代制造业中至关重要，它不仅影响产品质量，还决定了生产成本和效率。传统的人工调优方法依赖试错，导致生产周期延长和成本上升。为此，本文首次将强化学习(Reinforcement Learning

RL)技术应用于注塑工艺参数调优，提出了一种基于RL的新型注塑工艺参数自动调优算法，旨在通过智能化手段优化注塑过程。首先，本文将注塑工艺参数调优问题建模为顺序决策问题，并设计了一个定制化的马尔可夫决策过程模型。然后，提出了一种无模型的RL方法，基于Q-learning算法实现了注塑工艺参数的自动选择。与传统方法相比，该算法能在动态变化的生产环境中自动探索并优化工艺参数配置。最后，通过实验验证了所提方法的可行性和有效性，展示了较好的应用潜力。

Abstract

The optimization of injection molding process parameters is crucial in contemporary manufacturing

as it affects not only product quality but also production costs and efficiency. Traditional manual tuning methods rely on trial and error

leading to extended production cycles and increased costs. To address this

reinforcement learning (RL) technology is applied

for the first time

to the tuning of injection molding process parameters

proposing a novel automatic tuning algorithm based on RL for injection molding process parameters. The paper first models the injection molding process parameter tuning problem as a sequential decision-making problem and designs a customized Markov Decision Process model. Subsequently

a model-free RL method is proposed

implementing the automatic selection of injection molding process parameters based on the Q-learning algorithm. Compared with traditional methods

this algorithm can automatically explore and optimize process parameter configurations in dynamically changing production environments. Finally

the feasibility and effectiveness of the proposed method are experimentally validated

demonstrating significant application potential.

关键词

Keywords

references

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