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Identification of modal parameters of machine tools during milling

Identification of modal parameters of machine tools during milling
铣削过程中机床模态参数辨识
批准号:
449987778
负责人:
Professor Dr.-Ing. Martin Dix, since 5/2021
金额:
$0.0万
依托单位国家:
德国
项目类别:
Research Grants
财政年份:
2020
资助国家:
德国
项目状态:
已结题
起止时间:
2019-12-31 至 2023-12-31

项目摘要

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中文摘要
翻译
机床的振动限制了加工精度和生产率。它们对工具和机器部件的磨损有很大的影响。此外,它们通过噪音和地面振动影响环境。实验模态分析(EMA)是研究机床动态特性的一种成熟方法。由于EMA假设被调查对象的线性、倒数、时不变、因果和稳定行为,因此在进行EMA时,机床在静止状态下进行评估。因此,在加工期间发生的效应,如由于陀螺效应而改变的模式,由于不同的静态预载荷而导致的具有非线性行为的部件的刚度改变,由于工件、工件固定件、工具等的附加质量而导致的惯性质量的差异,以及包括过程阻尼在内的不同阻尼特性,都被忽略。由于在与真实的切削条件相关的操作点中捕获机床的动态行为,操作模态分析(OMA)提供了在测量分析期间考虑这种影响的可能性。在机床上执行OMA需要新的方法,用于通过切削过程的宽带激励,用于考虑机床的时变行为以及用于处理机床的操作负载(特别是切削过程)与OMA中假设的激励不同的事实。虽然宽带激励和随时间变化的行为已在自己的初步工作中得到解决,该项目的重点是两种新的方法的发展,用于治疗的激励,即集中在几个测量点时,模态参数被确定为在机床操作过程中捕获的振动信号。在此背景下,这两种方法将开发新的数学工具,研究激励特性(统计和确定性)以及干扰的影响,这两种新的方法将通过仿真模型和实验来开发。然后,在机床上的实验方法进行了验证,除了考虑到与机床的操作有关的影响,OMA在机床上的成功和可靠的应用可以显着有助于监测机床的状态和加工过程中使用的系统描述值,如模态参数。此外,OMA可以用于数字孪生的周期性更新,以反映机床的真实的结构机械特性。
英文摘要
Vibrations in machine tools limit the working accuracy and the productivity. They significantly contribute to wear of tools and machine parts. Furthermore, they affect the environment by noise and ground vibrations. The experimental modal analysis (EMA) represents an established method for the investigation of the dynamic behaviour of machine tools. As the EMA assumes linear, reciprocal, time-invariant, causal and stable behaviour of the investigated subject, machine tools are assessed at standstill, when conducting EMA. Therefore, the effects occurring during machining like changed modes due to gyroscopic effects, changing stiffness of parts with non-linear behaviour caused by the different static preloads, differences in inertial mass resulting from the additional masses of the work piece, the work piece fixation, the tool etc. as well as different damping properties inclusively process damping are ignored.In principal, the Operational Modal Analysis (OMA) offers a possibility for consideration of such effects during a measurement analysis due to capturing the dynamic behaviour of a machine tool in the operating point related to the real cutting conditions. Performing OMA on machine tools requires new approaches for broadband excitation by the cutting process, for consideration of the time-variant behaviour of a machine tool and for the treatment of the fact, that the operating load of a machine tool, particularly the cutting process, is distinct from the excitation being assumed in OMA. While the broadband excitation and the time-variant behaviour have been addressed within own preliminary work, this project focusses on the development of two new methods for treating an excitation, that is concentrated on a few measuring points when modal parameters are identified from vibration signals captured during operation of a machine tool. In this context, new mathematical tools for the two methods are going to be developed and the excitation character (statistic and deterministic) as well as the effect of disturbances are investigated.The two new methods are going to be developed by using simulation models and experiments. Afterwards, the methods are validated in experiments on a machine tool.Beside the consideration of the effects related to the operation of a machine tool, the successfully and reliably application of OMA in machine tools can significantly contribute to monitoring of machine tools condition and machining processes using values for system description like modal parameters. Moreover, the OMA can be used for a periodic updating of digital twins in order to reflect the real structural-mechanical properties of a machine tool.
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基于异构医学影像数据的深度挖掘技术及中枢神经系统重大疾病的精准预测
  • 批准号:
    61672236
  • 项目类别:
    面上项目
  • 资助金额:
    64.0万元
  • 批准年份:
    2016
  • 负责人:
    王骏
  • 依托单位: