Development of a grey-box model to understand and predict wear of coated cutting tools during turning

开发灰盒模型以了解和预测车削过程中涂层切削刀具的磨损

• 批准号: 521380776
• 负责人: Professor Dr. Eberhard Kerscher
• 金额: --
• 依托单位: Lehrstuhl für Fertigungstechnik und Betriebsorganisation (FBK)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/521380776?language=en
• 关键词: Development; grey; box; model; understand

Today, the majority of cutting tools with a geometrically defined cutting edge are coated. Compared to uncoated tools, coated cutting tools enable longer tool lives, significant increases in performance and thus a more efficient chip removal. A reliable wear prognosis of coated tools, in particular of the transient failure behavior, is currently not possible. Therefore, in the research project, a deeper understanding of the stationary and transient system behavior of coated tools in high-performance machining is created. A reliable forecast of the onset of failure, the progress of wear and the remaining tool life should be made possible. For this purpose, experiments with coated cutting tools must be carried out, which characterize the initial condition of the cutting tools, record the wear behavior and thus create the database for the models. The deterministic model (white box) is then combined with a new data-driven model (black box) to a grey-box model. In this way, the purely deterministic non-describable temporal changes in the tool properties caused by wear and the end of the tool life can be described as a model. To create a database for understanding and predicting the tool wear, turning experiments are carried out, selected in situ measured variables are recorded and two- and three-dimensional wear parameters are continuously determined ex situ. In addition, a white box model (FEM machining simulation) is set up in order to determine the specific stress distribution in the cutting wedge, taking into account the respective state of wear. For a deeper understanding of the mechanical damage processes in the coating, suitable methods for coating characterization are applied and analogy experiments are carried out on the mechanical damage processes in the coating. Machine learning methods are used as part of a gray box model to predict the state of wear and possible coating failures. For this purpose, suitable measurement data features are processed using statistical data analysis, which are then implemented into the black box together with the data of the white box model. In the 2nd funding period, the grey-box model will be further refined by expanding the parameter space and the tribological elements. In this way, valid forecasts can be ensured. After the end of the second funding period, this enables knowledge-based qualification of coated tools for more efficient machining processes.

今天，大多数具有几何定义的切削刃的切削工具都是涂层的。与未涂层刀具相比，涂层刀具可以延长刀具寿命，显著提高性能，从而更有效地去除切屑。涂层工具的可靠磨损预测，特别是瞬时失效行为，目前是不可能的。因此，在本研究项目中，对高性能加工中涂层刀具的稳态和瞬态系统行为有了更深入的了解。应该能够对失效的开始、磨损的进展和剩余的刀具寿命作出可靠的预测。为此，必须对涂层刀具进行实验，以表征刀具的初始状态，记录磨损行为，从而为模型创建数据库。然后将确定性模型（白盒）与新的数据驱动模型（黑盒）组合成灰盒模型。这样，由磨损和刀具寿命结束引起的刀具性能的纯粹确定性的不可描述的时间变化可以用模型来描述。为了建立一个了解和预测刀具磨损的数据库，进行了车削实验，记录了选定的原位测量变量，并连续地确定了二维和三维磨损参数。此外，建立了白盒模型（有限元加工仿真），以确定在考虑各自磨损状态的切削楔中的比应力分布。为了更深入地了解涂层的力学损伤过程，采用了合适的涂层表征方法，并对涂层的力学损伤过程进行了类比实验。机器学习方法被用作灰盒模型的一部分，以预测磨损状态和可能的涂层失效。为此，使用统计数据分析对合适的测量数据特征进行处理，然后与白盒模型的数据一起实现到黑盒中。在第二个资助期内，灰盒模型将通过扩展参数空间和摩擦学元素进一步细化。通过这种方式，可以确保有效的预测。在第二个资助期结束后，这使得基于知识的涂层工具资格认证更有效的加工过程。