Research on real-time optimization and autonomous decision-making of smart manufacturing systems based on digital twin
基于数字孪生的智能制造系统实时优化与自主决策研究

摘要

基于数字孪生的智能制造系统已成为推动制造业向智能化与自主化升级的重要路径。本文围绕实时优化与自主决策展开研究，重点探讨数字孪生如何通过虚实映射实现生产过程的动态感知、预测与反馈调节。研究提出以多源数据融合、虚拟模型驱动和自适应算法优化为核心的系统框架，能够在生产中实现资源配置的实时调整与工艺流程的智能优化。通过自主决策机制的引入，系统可在复杂环境下快速响应，提升制造系统的鲁棒性与柔性。本文的研究结果表明，基于数字孪生的智能制造系统不仅在提升生产效率与产品质量方面具有显著优势，还为未来智能工厂的构建提供了可行的路径选择。

Abstract

Smart manufacturing systems based on digital twin have become a crucial path to drive the upgrading of the manufacturing industry towards intelligence and autonomy. This paper focuses on the research of real-time optimization and autonomous decision-making, with emphasis on exploring how digital twin achieves dynamic perception, prediction, and feedback adjustment of the production process through virtual-real mapping. The study proposes a system framework centered on multi-source data fusion, virtual model-driven, and adaptive algorithm optimization, which enables real-time adjustment of resource allocation and intelligent optimization of process flows in production. By introducing an autonomous decision-making mechanism, the system can respond quickly in complex environments and improve the robustness and flexibility of the manufacturing system. The research results of this paper show that the smart manufacturing system based on digital twin not only has significant advantages in improving production efficiency and product quality but also provides a feasible path option for the construction of future smart factories.