用强化学习优化轮胎胶片生产，提升精度与效率
Intelligent Collaborative Optimization for Rubber Tyre Film Production Based on Multi-path Differentiated Clipping Proximal Policy Optimization
- 采用多分支策略+差异化梯度裁剪，稳定高维策略更新
- 在宽度厚度控制中提升调参精度与运行效率
- 适合需要实时动态调整的复杂工业生产场景
智能制造正在解决传统轮胎制造业在应对动态生产需求时集中调度僵化、产线配置不灵活的问题。当前轮胎制造系统由高度耦合的子系统构成，表现出显著的非线性交互和涌现动力学特征，使得多子系统有效协同成为关键且艰巨的任务。针对该领域高维度、多目标优化问题，本文提出一种深度强化学习算法：多路径差异化裁剪近端策略优化（MPD-PPO）。该算法采用多分支策略架构，结合差异化梯度裁剪约束，保障高维策略更新的稳定与高效。在橡胶轮胎胶片宽度与厚度控制任务上的实验验证表明，MPD-PPO 显著提升了调参精度与运行效率。该框架成功应对了高维度、多目标权衡及动态适应等核心挑战，为轮胎制造领域的真实工业部署提供了更优性能与更高生产稳定性。
原文摘要 · Abstract (English)
The advent of smart manufacturing is addressing the limitations of traditional centralized scheduling and inflexible production line configurations in the rubber tyre industry, especially in terms of coping with dynamic production demands. Contemporary tyre manufacturing systems form complex networks of tightly coupled subsystems pronounced nonlinear interactions and emergent dynamics. This complexity renders the effective coordination of multiple subsystems, posing an essential yet formidable task. For high-dimensional, multi-objective optimization problems in this domain, we introduce a deep reinforcement learning algorithm: Multi-path Differentiated Clipping Proximal Policy Optimization (MPD-PPO). This algorithm employs a multi-branch policy architecture with differentiated gradient clipping constraints to ensure stable and efficient high-dimensional policy updates. Validated through experiments on width and thickness control in rubber tyre film production, MPD-PPO demonstrates substantial improvements in both tuning accuracy and operational efficiency. The framework successfully tackles key challenges, including high dimensionality, multi-objective trade-offs, and dynamic adaptation, thus delivering enhanced performance and production stability for real-time industrial deployment in tyre manufacturing.
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