Authors

Abstract

With the acceleration of high-performance, green, and intelligent agricultural equipment, premature wear and failure of agricultural machinery tools became a key bottleneck that restricted the high-quality development of agricultural machinery and equipment. Digital twin technology provided innovative theoretical and technical support, which enabled the accurate prediction and evaluation of the wear performance of agricultural machinery tools under dynamic and complex working conditions. This paper explained the key elements of digital twin technology and summarized the development history of tool wear research, categorizing it into three stages: physical experiment-driven, numerical simulation, and digital twin integration. Additionally, it highlighted the progress made in agricultural machinery tools based on digital twin technology, particularly in data acquisition, modeling, and data-driven approaches. The paper also introduced a case study of a self-developed agricultural machinery tool wear performance test machine. However, it addressed the key challenges faced in the application of digital twin technology for monitoring agricultural machinery tool wear, including difficulties in data perception and fusion, insufficient accuracy in multi-physical field modeling, and inadequate real-time performance. Future research focused on developing accurate multi-physics field coupling models, optimizing data processing mechanisms, and creating intelligent analysis frameworks. Additionally, it aimed to promote low-cost and efficient digital twin solutions to enhance the intelligence level and feasibility of agricultural machinery tool wear monitoring.

Abstract in Chinese

随着农业装备高性能、绿色化与智能化的加速推进，农机刀具过早磨损失效成为制约农机装备高质量发展的关键瓶颈。数字孪生技术为在动态复杂工况下农机刀具的磨损性能的精准预测评估提供了创新理论与技术支撑。本文阐述了数字孪生技术的关键要素，并按物理实验驱动、数值模拟和数字孪生集成3个阶段，总结了刀具磨损研究的发展历程；归纳了基于数字孪生技术的农机刀具在数据采集、建模和数据驱动方面的进展；具体介绍了自研农机刀具磨损性能测试试验机的案例；从数据感知与融合困难、多物理场建模精度不足以及实时性不足等方面介绍了数字孪生技术在农机刀具磨损监测应用的关键挑战。未来的研究与应用应聚焦于开发精准的多物理场耦合模型、优化数据处理机制与智能分析框架，并推广低成本高效的数字孪生解决方案，以提升农机刀具磨损监测的智能化水平和可实施性。