Prediction of performance of a TiAlN coating in profile turning using a grey box approach (PreProCoat)

在PreProCoat项目中，正在开发和使用一种基于灰箱模型的方法，通过该方法，可以预测淬火和回火钢C45的轮廓切槽干加工过程中TiAlN/TiN刀具涂层的应用行为。在切槽轮廓的情况下，输出切屑和涂层之间的接触区域中的载荷条件改变，并且因此局部分布的热机械载荷谱改变。在实验加工研究中，首先制定出加工参数以及刀具性能与工艺过程、涂层条件和加工质量评价标准之间的相关性。在模拟试验中，接触区的各个区域的温度和切削力的局部分布方面进行了调查，并与加工参数。在涂层系统寿命结束之前，对测试进行统计学验证。过程间歇磨损测量以及下游涂层条件分析伴随着实验阶段。所获得的结果也将用于验证随后创建的FE切屑形成模型，该模型可用于通过数值手段补充和扩展实验研究。特别是，有限元模型使局部和时间推导的热机械应力集体在涂层中。摩擦和材料行为所需的分析子模型取自文献。对AlTiN/TiN涂层试样和C45材料样品的接触对进行摩擦力学分析，提供所需的特定摩擦参数。借助于损伤模型，涂层系统中的局部残余应力从热-机械载荷集合中识别，并且由此导出可能的涂层失效。利用FE应力模型的计算沿着涂层使用寿命迭代地进行，其中，在每个迭代步骤之前，基于分析条件确定来调整用于摩擦和材料行为的子模型以及工具的几何模型。由于纯分析方法可能导致模糊预测，因此采用AI/ML方法，通过该方法可以将实验获得的分析数据与相应的影响变量相关联。最后，在要创建的灰盒结构的基础上，完成所得到的数据驱动模型与已经实现的分析模型的耦合。

In the PreProCoat project, the development and use of an approach based on greybox models is being pursued, with which the application behavior of a TiAlN/TiN tool coating can be predicted for the case of the dry machining process of profile grooving in quenched and tempered steel C45. With the grooving profile, the load conditions in the contact zone between the outgoing chip and the coating change and thus the locally distributed thermo-mechanical load spectrum. In experimental machining investigations, first correlations between machining parameters as well as tool properties and criteria for the evaluation of the process course, the coating condition and the machining quality are worked out. In an analogy test, individual areas of the contact zone are investigated with regard to the local distribution of temperature and cutting forces and correlated with the machining parameters. The tests are statistically validated up to the end of life of the coating system. Process intermittent wear measurements as well as downstream coating condition analyses accompany the experimental phase. The results obtained will also serve to validate an FE chip formation model to be subsequently created, which can be used to supplement and extend the experimental investigations by numerical means. In particular, the FE model enables the local and temporal derivation of the thermo-mechanical stress collectives in the coating. The required analytical submodels for friction and material behavior are taken from the literature. Tribomechanical analyses on contact pairs of AlTiN/TiN-coated test specimens and C45 material samples provide the required specific friction parameters. With the aid of damage models, the local residual stresses in the coating system are identified from the thermo-mechanical load collectives, among other things, and a possible coating failure is derived from this. The calculations with the FE stress model are carried out iteratively along the coating service life, whereby the submodels for friction and material behavior as well as the geometry model of the tool are adjusted on the basis of the analytical condition determination before each iteration step. Since a purely analytical approach is likely to result in fuzzy prediction, AI/ML approaches are pursued with which experimentally obtained analysis data can be correlated with corresponding influencing variables. The coupling of the resulting data-driven models with the already implemented analytical models is finally done on the basis of a greybox structure to be created.