Extrapolative digital greybox models for describing and predicting the macroscopic system behavior of TiAlN-coated cutting tools

具有硬涂层的工具构成了当今使用的大多数切削工具。它们可保护基材免受磨料磨损，提高耐化学性并降低摩擦系数，从而延长车削和铣削操作期间的刀具寿命。存在用于估计刀具寿命的各种经验或基于物理的模型。由于这些涂层的性能取决于大量的制造参数，没有一致的模型来预测硬涂层的磨损行为。拟议的研究项目的目的是获得知识的摩擦学因果关系的涂层刀具的磨损行为与工件材料和工艺参数的相互作用下，考虑到特定的机械，化学结构和摩擦学性能的涂层和映射到一个模型中的知识。因此，应使用灰盒模型。这些模型依赖于人工神经网络（黑盒），该网络使用基于加工测试的过程参数、测量和元数据的训练数据。磨损率预测和剩余刀具寿命的物理和经验模型也被集成到这些模型中（白盒）。这使得模型，由于高实验工作的训练数据的范围有限，预测物理上有意义的解决方案。在加工测试中，通过在CNC车床的工作空间中集成力、温度、结构声和表面测量技术以及显微镜技术实现部分自动化，为同步建模创建不断增长的数据库。为了最大限度地减少实验工作量，从而减少材料和能源成本，在每种情况下，下一个实验参数都是基于模型的当前训练状态，使用主动学习来确定的。所记录的测量和元数据可采用多种格式，如图像数据、连续测量或离散测量点。它们被处理并与元数据一起存储在数字实验室书籍中。随后，对数据进行均匀化和简化，以获得用于训练神经网络的平衡数据集。对于最终验证，进行盲测试，切割参数和涂层未知的模型，以确认灰箱模型的内插和外推能力。

Tools with a hard coating make up the majority of cutting tools used today. They protect the substrate from abrasive wear, increase chemical resistance and reduce the coefficient of friction, resulting in increased tool life during turning and milling operations. Various empirical or physically based models exist for the estimation of tool life. Since the properties of these coatings depend on a large number of manufacturing parameters, no consistent models exist to predict the wear behavior of hard coatings. The objective of the proposed research project is to gain knowledge about the tribological cause-and-effect relationships of the wear behavior of coated cutting tools in interaction with the workpiece material and the process parameters under consideration of the particular mechanical, chemical-structural and tribological properties of the coating and to map this knowledge into a model. Therefor greybox models shall be used. These models are rely on artificial neural networks (blackbox) that use training data based on process parameters, measurements and metadata from machining tests. Physical and empirical models for wear rate prediciton and remaining tool life are additionally integrated into these models (whitebox). This allows the model, with the limited scope of training data due to the high experimental effort, to predict physically meaningful solutions. In machining tests, which are partially automated by integrating force, temperature, structure-borne sound and surface measurement techniques as well as microscopy in the workspace of a CNC lathe, a continuously growing database is created for the simultaneous model building. In order to minimize the experimental effort and thus the material and energy costs, the next experimental parameters are determined in each case on the basis of the current training state of the model using Active Learning. The recorded measurement and metadata are available in a wide variety of formats, such as image data, continuous measurement or discrete measurement points. They are processed and stored with their metadata in an digital lab book. Subsequently, the data is homogenized and reduced to obtain a balanced data set for training the neural networks. For final validation, blind tests, with cutting parameters and coating unknown to the model, are performed to confirm the interpolation and extrapolation capabilities of the greybox model.