基于Digital Twin的数控机床智能运行维护方法研究

This project is aimed to solve the intelligent running and maintenance decision of CNC machine tools based on the theory of Digital Twin, which facing the whole life-cycle and different applications of machine tools. With the method of multi-domain modeling and the strategy of self-configuration, self-optimization, self-updating of models, the construction method of Digital Twin model for CNC machine tool is built, and the self-configuration, self-optimization, self-updating of CNC machine tool models are achieved. Meanwhile, the requirements of facing different applications and different stages of life-cycle for models are satisfied. In order to provide effective data for different running and maintenance applications, the intelligent perception strategy of CNC machine tool Digital Twin is designed, which is based on studying the application oriented Digital Twin aware model and the supporting theories and key technologies of building the application aware module. The multi-objective optimization method of CNC machine tool's parameters and the intelligent maintenance strategy of machine tools are designed based on Digital Twin application-aware. Then, the intelligent running and maintenance decision of different application scenarios in different stages of CNC machine tools' life cycle can be realized. Through the researches mentioned above, the CNC machine tool Digital Twin design method for intelligent running and maintenance decision is proposed, which provides theoretical support for the intelligent running and maintenance decision of CNC machine tools.

本课题旨在使用Digital Twin理念解决面向全生命周期和不同应用场景的数控机床智能运行和维护问题。通过研究多领域建模、模型的自配置、自优化、自更新策略，形成了数控机床Digital Twin模型的构建方法，实现了数控机床模型针对复杂、时变、耦合状态的自优化、自配置和自更新，满足了不同应用场景、生命周期不同阶段的模型使用要求。通过研究面向应用场景的Digital Twin感知模型，智能感知模块构建的支撑理论和关键技术，设计了数控机床Digital Twin的智能感知策略，为不同运行维护场景提供有效数据。设计了基于Digital Twin场景感知的数控机床运行参数多目标优化方法和数控机床的智能维护策略，实现了针对数控机床生命周期中不同阶段不同应用场景的智能运行和维护。通过以上研究，形成了面向智能运行维护的数控机床Digital Twin设计方法，为数控机床的智能运行和维护提供了理论支撑。

数控机床是制造业的“工作母机”，其发展水平代表了国家核心竞争力。作为智能制造国家战略实施的基础装备，数控机床的智能性提升是智能制造成功实施的关键。智能运行优化和智能维护是数控机床智能性的关键。.本课题旨在使用数字孪生理念解决面向全生命周期和不同应用场景的数控机床智能运行和维护问题，主要研究内容为数控机床数字孪生模型的构建方法；数字孪生智能感知策略；基于数字孪生场景感知的运行参数多目标优化方法；基于数字孪生场景感知的数控机床智能维护策略。.在研究过程中，基于多领域统一建模语言Modelica建立了机/电/液/控耦合数控机床数字孪生模型，提出了面向具体应用场景的数控机床数字孪生模型自配置方法，建立了反映其全生命周期动态时变状态的时间一致性模型，实现了对运行优化和智能维护决策的模型支持。提出了面向数控加工多目标优化的场景感知方案，实现动态数控场景对优化方案的指导，同时提出了基于云-雾-边缘协同的数字孪生制造仿真过程动态扰动响应方案，实现了对设备运转过程中动态扰动的快速识别。进一步，在数字孪生智能感知策略研究基础上，提出了基于数字孪生的铣削参数动态多目标优化方法，实现了对数控机床铣削参数和数控加工工艺参数的准确、快速寻优。然后，基于以上数控机床数字孪生模型构建和场景感知研究，提出了数字孪生模型与数据融合的预测性维护方法，提高了数控机床预测性维护的准确度，同时，提出了基于数字孪生模型的数控机床关键部件故障特征生成与诊断方法和虚拟调试方法。最后，开发了数字孪生原型系统软件，初步具备全要素建模、高精度仿真、多领域求解和系统级验证的功能，助力复杂系统数字孪生的落地应用。.本课题的研究形成了面向智能运行维护的数控机床DT设计方法，为数控机床的智能运行和维护提供了理论支撑和应用指导。