Intelligent Collaborative Optimization for Rubber Tyre Film Production Based on Multi-path Differentiated Clipping Proximal Policy Optimization

2025-11-15

AI Summary

To address the challenges of collaborative optimization in rubber tire thin-film production—stemming from strong inter-subsystem coupling, highly nonlinear interactions, and significant dynamic response delays—this paper proposes an intelligent collaborative optimization framework based on deep reinforcement learning. We innovatively design a multi-path differential clipping Proximal Policy Optimization (PPO) algorithm, integrating multi-branch policy networks with gradient-constrained optimization to simultaneously ensure policy convergence and substantially enhance dynamic adaptability in high-dimensional, complex systems. The method enables real-time, multi-objective coordinated control and stable regulation of critical process parameters, such as film width and thickness. Experimental and industrial deployment results demonstrate a 23.6% improvement in regulation accuracy and a 41.2% reduction in steady-state fluctuations compared to conventional approaches, while maintaining excellent real-time performance and robustness. This work establishes a practical, autonomous decision-making paradigm for intelligent tire manufacturing.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Optimizing high-dimensional multi-objective production in tyre manufacturing
• Addressing dynamic adaptation challenges in complex industrial subsystems
• Improving coordination of nonlinear interactions in tyre film production

Innovation

• Multi-branch policy architecture for stable policy updates
• Differentiated gradient clipping for high-dimensional optimization
• Deep reinforcement learning for dynamic industrial production control