数控机床数字工程应用及发展综述

A Review on Digital Engineering Applications and Development in CNC Machine Tools

  • 摘要: 数控机床作为现代制造业的核心装备,其数智化转型已成为推动制造业高质量发展的重要驱动力。围绕数字工程在数控机床设计、制造与运维全生命周期中的应用现状及发展趋势,重点剖析了数据挖掘、数字孪生、机器学习和生成式人工智能四大关键技术的协同逻辑、技术特征及工程实践。研究表明,数据挖掘技术推动了制造过程的数据驱动优化,实现了隐性经验的结构化沉淀;数字孪生通过虚实交互提供了高精度仿真环境,助力机床性能优化与智能运维;机器学习加速多物理场耦合代理建模与智能决策,生成式人工智能突破数据稀缺性瓶颈并驱动创新设计。当前研究在数据异质性融合、模型通用性及实时性等方面仍面临挑战,未来需构建“工艺-物理”混合驱动框架,强化多域融合的数智生态,以实现机床全生命周期的自主优化与智能化升级。

     

    Abstract: As the core equipment of modern manufacturing, the digital transformation of CNC machine tools has become a key driver for high-quality development of the manufacturing industry. This paper reviews the application status and development trends of digital engineering throughout the lifecycle of CNC machine tools, encompassing design, manufacturing, and operation and maintenance. It specifically focuses on the synergistic logic, technical characteristics, and engineering practices of four key technologies: data mining, digital twins, machine learning, and generative artificial intelligence. Research demonstrates that data mining technology facilitates data-driven optimization of manufacturing processes, enabling the structured accumulation of implicit experience. Digital twins provide a high-precision simulation environment through virtual-physical interaction, enhancing machine tool performance optimization and intelligent operation and maintenance. Machine learning accelerates multi-physics-coupled surrogate modeling and intelligent decision-making, while generative artificial intelligence overcomes the bottleneck of data scarcity and drives innovative design. Current research still faces challenges in heterogeneous data integration, model generality, and real-time performance. Future efforts should focus on constructing a "process-physics" hybrid-driven framework and strengthening a multi-domain-integrated digital ecosystem to achieve autonomous optimization and intelligent upgrades throughout the lifecycle of machine tools.

     

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