Development of a Greybox Model for the Prediction of the Performance of PVD Coated Carbide Tools

根据现有技术，PVD涂覆的工具的真实的、复杂的应用行为既不能令人满意地测量，也不能在模型中充分描述。为了能够对这些工具进行基于知识的鉴定，有必要获得有关故障开始、磨损进展和剩余工具寿命的知识。特别是，不连续的涂层故障不能建模，由于缺乏知识的基本关系。此外，由于磨损，涂层性能和所施加的热机械载荷都随着操作时间的增加而变化。拟议的研究项目解决了一方面的可变涂层性能和另一方面的可变过程相关的负载之间的动态关系，用于合金C45 N和42CrMo4+QT的外部纵向车削。从现有技术已知，涂层性质对工具的应用行为具有显著影响。还已知的是，涂层性质和热机械载荷应力由于工具磨损而显著改变。考虑到不同的属性和负载，可以更准确地预测切削刀具的性能，并有助于建立基本的理解，以负载特定的方式设计和选择刀具。为此，在研究项目开始时，刀具就涂层和涂层与碳化物基体之间的界面特性进行了广泛的表征。然后为随后的建模工作建立详细的数据基础。借助一种基于刨削试验台研究的新方法，结合显微运动学，可以首次绘制出刀具磨损过程中作用在切削刃上的载荷应力。这些载荷应力与随时间变化的热载荷一起作为FEM模型的输入变量，用于绘制切削楔中促进涂层失效的临界应力。通过使用人工智能方法，该白盒模型的预测能力和准确性得到了扩展。由于很难根据物理关系绘制变化的涂层特性并将其用于此类预测，因此采用基于涂层特性和残余应力的高分辨率分析的数据驱动方法。通过观察应力和涂层性能之间的关系，可以预测工具的进一步行为，并获得对潜在机制的新见解。

According to the current state of the art, the real, complex application behaviour of PVD-coated tools can neither be measured satisfactorily nor described sufficiently in models. In order to be able to conduct a knowledge-based qualification of these tools, it is necessary to gain knowledge about the start of failure, the progress of wear and the remaining tool life. In particular, discontinuous coating failure cannot be modelled due to a lack of knowledge of the fundamental relationships. In addition, both the coating properties and the thermomechanical loads applied change with increasing operating time as a result of wear. The proposed research project addresses the dynamic relationship between variable coating properties on the one hand and the variable process-related loads on the other hand for external longitudinal turning of the alloys C45N and 42CrMo4+QT. It is known from the state of the art that the coating properties have a significant influence on the application behaviour of the tools. It is also known that both the coating properties and the thermomechanical load stresses change significantly due to tool wear. Taking into account the varying properties and loads allows for a more accurate prediction of the performance of the cutting tools and helps to build up fundamental understanding to design and select tools in a load-specific way. For this purpose, the tools are extensively characterised at the beginning of the research project with regard to coating and interface properties between the coating and the carbide substrate. This is followed by the development of a detailed data basis for the subsequent modelling. With the help of a new method based on investigations on a planing test rig in combination with microkinematography, it is possible for the first time to map the load stresses acting on the cutting edge as tool wear progresses. Together with the thermal load, which also changes over time, these load stresses serve as input variables for an FEM model for mapping critical stresses in the cutting wedge that promote coating failure. The predictive capability and accuracy of this whitebox model is extended by the use of artificial intelligence methods. Since it is difficult to map the changed coating properties on the basis of physical relationships and use them for such a prediction, data-driven approaches based on high-resolution analyses of the coating properties and residual stresses are employed. By observing the relationship between stresses and coating properties, it is possible to predict the further behaviour of the tools and gain new insights into the underlying mechanisms.