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Precision digital twin of production machines for sustained high accuracy and traceable part conformity

Precision digital twin of production machines for sustained high accuracy and traceable part conformity
生产机器的精密数字孪生,可实现持续的高精度和可追溯的零件一致性
批准号:
RGPIN-2022-04092
负责人:
Mayer, René
金额:
$2.33万
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2022
资助国家:
加拿大
项目状态:
已结题
起止时间:
2022-01-01 至 2023-12-31

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中文摘要
翻译
在许多制造过程中,机器相对于工件移动工具。根据工艺的不同,例如减法或加法,工具可以是切割工具、激光束或沉积喷嘴。制造零件的几何和尺寸质量在很大程度上取决于这些机器的定位和路径跟踪的精度。定制产品和小批量生产增加了对高精度机器的需求。精度受到机器的内置几何形状的影响,它与标称不同,在运动轴之间和每个轴的运动中表现出不完美的对准。进一步的几何变化是由磨损、热效应、顺应性或缺乏刚度以及滞后引起的。制造好的零件需要大量的资源,也需要通过计量来验证它们的一致性。在过去的几十年里,人们一直致力于研究在无载荷条件下发生的准静态几何误差。这一建议是针对那些表现出非线性行为和/或违背刚体假设的现象,因此将不被假设适用。该研究项目旨在开发模型、技术和方法,通过不断向机器控制器提供直接轴指令修正或更新原位计算机数控(CNC)修正模型的参数,为机器提供持续更新的实时数字孪生体,以获得稳健和持续的机器精度。同时,这些模型可以通过未来的研究,发布已生产部件的可追溯的合格状态,从而减少生产后检验的需要。除了丰富的模型外,还将研究精益、健壮、快速和自动化的车间兼容方法,以使模型与其实际的物理对应物相匹配。数字孪生将同时使用基于物理的模型(用于洞察)和错误表示模型。使用激光干涉仪和测力仪等实验室设备进行的详细实验将为模型开发提供必要的知识。然后,将研究和测试使用在线传感器(如接触式和非接触式探头和惯性测量单元)原位校准模型的方法,以及无传感器方法。现代机器的cnc提供的信号将被充分利用于此目的。该程序将推进对机床非线性和非刚性误差源的理解。除了科学文化外,该项目还将为hqp提供制造计量、机器误差及其补偿方面的专业知识。通过技术转让和训练有素的hqp,加拿大工业界将比国际竞争对手更早获得新知识和技术。
英文摘要
In many manufacturing processes a machine moves a tool relative to a workpiece. Depending on the process, such as subtractive or additive, the tool can be, for example, a cutting tool, laser beam or deposition nozzle. The geometric and dimensional quality of manufactured parts depends largely on the accuracy of positioning and path following of these machines. Customised products and small batch production increase the demands for highly accurate machines. Accuracy is affected by the built geometry of the machine, which differs from nominal, exhibiting imperfections in the alignment between moving axes and in the motion of each axis. Further geometry changes are caused by wear and tear, thermal effects, compliance or lack of stiffness, and hysteresis. Much resources are needed to make good parts and also to verify their conformity by metrology. Over past decades, much effort has been dedicated to quasi-static geometric errors occurring under no load conditions. This proposal is directed at phenomenon that exhibit non-linear behaviour and/or defy the rigid body hypothesis, which as a result will not be assumed to apply. The research program aims at developing the models, technologies and methodologies to provide a continuously updated live digital twin of the machine to obtain robust and sustained machine accuracy by continuously feeding the machine controller with either direct axis command corrections or updated parameters for in-situ computer numerical controller (CNC) correction models. In parallel, such models could, through future research, issue a traceable conformity status of the produced part thus reducing the need for post-production inspection. In addition to rich models, lean, robust, fast and automated shop-floor compatible means will be researched to match the model to its actual physical counterpart. The digital twin will use both physics-based models, for insight, and error representation models. Detailed experiments using laboratory equipment such as laser interferometers and dynamometers will provide the essential knowledge for model development. Then, means to calibrate the models in-situ using online sensors such as contact and non-contact probes and inertial measuring units, but also sensorless approaches, will be researched and tested. Signals made available by modern machines' CNCs will be exploited fully for this purpose. The program will advance the understanding of machine tools non-linear and non-rigid error sources. In addition to a scientific culture, the program will provide HQPs with expertise in manufacturing metrology, machine errors and their compensation. Canadian industry will have early access to the new knowledge and technologies before their international competitors through technology transfer and through the trained HQPs.
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Self-aware and self-correcting machine tools for robust accuracy
  • 批准号:
    RGPIN-2016-06418
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.11万
  • 财政年份:
    2021
  • 负责人:
    Mayer, René
  • 依托单位:
Self-aware and self-correcting machine tools for robust accuracy
  • 批准号:
    RGPIN-2016-06418
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.11万
  • 财政年份:
    2020
  • 负责人:
    Mayer, René
  • 依托单位:
Self-aware and self-correcting machine tools for robust accuracy
  • 批准号:
    RGPIN-2016-06418
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.11万
  • 财政年份:
    2019
  • 负责人:
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  • 依托单位:
Self-aware and self-correcting machine tools for robust accuracy
  • 批准号:
    RGPIN-2016-06418
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.11万
  • 财政年份:
    2018
  • 负责人:
    Mayer, René
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