Development of a Greybox model for wear prognosis of PVD coated carbide tools during high performance turning of steels

物理气相沉积(PVD)涂层刀具在切割过程中的连续涂层和刀具材料的退化导致了刀具的瞬时热机械应力集中。然而，目前开发的刀具磨损预测分析白盒模型是基于未涂层刀具的静态积分热机械载荷，从而没有考虑从线性到渐进磨损增加的过渡。数据驱动的黑盒模型不能表示物理交互作用，当涉及到可变边界条件时，它们的稳健性与不确定性相关。因此，刀具寿命的准确预测和基于知识的涂层刀具资格鉴定是不可能的。研究项目的主要目的是开发一个灰箱模型，用于准确预测PVD涂层硬质合金刀具在高性能车削钢过程中的刀具寿命和剩余使用寿命。为此，在该项目的第一阶段，提出了用于确定切割过程中的瞬时热机械应力的解析白盒模型与数据驱动的黑盒模型之间的耦合。此外，还将深入研究涂层性能对刀具磨损过程的影响。首先，在刀具上沉积TiAlCrSiN和TiAlCrSiON涂层，每个涂层具有两种不同的涂层厚度，并对涂层进行表征。随后，确定了随温度变化的弹塑性涂层性能和系统行为。涂层刀具用于C45钢和42CrMo4钢的干态粗车削加工。为了确定开发白盒模型所需的不同工艺参数下的热机械刀具载荷，计划进行模拟切割试验。现场获取过程状态变量并聚焦于刀具从线性到渐进磨损过渡区的切削试验将形成定量的刀具损伤分析的实验基础。最后，将开发和验证灰箱模型，以便能够在线预测刀具在切割过程中的剩余寿命。为此，白盒模型和关于涂层性能和刀具磨损过程的实验确定的数据将通过基于机器学习的黑盒模型进行耦合。在二期工程中，将进行精度、敏感性和误差分析，以进一步提高所开发的灰箱模型的预测精度和可转移性。在这个过程中，可以探索以前未发现的新兴数字化加工技术的研究领域。

The continuous coating and cutting material degeneration during the cutting process contribute to an instationary thermomechanical stress collective of the physical vapor deposition (PVD) coated tool. However, the currently developed analytical Whitebox models for tool wear prediction are based on a stationary integral thermomechanical loading of uncoated tools, whereby the transition from linear to progressive wear increase is not considered. Data-driven Blackbox models cannot represent the physical interactions and their robustness is associated with uncertainties when it comes to variable boundary conditions. Consequently, an accurate prediction of tool service life and knowledge-based qualification of coated tools for demanding machining processes is not possible.The main aim of the research project is the development of a Greybox model for an accurate prediction of tool life and remaining service life of PVD coated carbide tools during high-performance turning of steels. For this purpose, a coupling between analytical Whitebox models for determination of the instationary thermomechanical stresses in the cutting process with data-driven Blackbox models is proposed in the first phase of the project. Moreover, the influence of temperature dependent coating properties on tool wear progress will be thoroughly investigated. Firstly, TiAlCrSiN and TiAlCrSiON coatings, each with two different coating thicknesses, will be deposited on the cutting inserts and characterized. Subsequently, the temperature dependent elastic-plastic coating properties and the system behavior are to be determined. The coated tools will be used for rough turning of heat-treated C45 and 42CrMo4 steels under dry condition. In order to determine the thermomechanical tool load at different process parameters, required for development of the Whitebox model, analogy cutting tests are planned. Cutting tests with in situ acquisition of the process state variables and focus on linear to progressive tool wear transition zone will form the experimental basis for quantitative tool damage analysis. Finally, a Greybox model will be developed and validated to enable online remaining tool life prediction during the cutting process. For this purpose, the Whitebox model and experimentally determined data on coating properties and tool wear progress will be coupled by means of machine learning based Blackbox models. In the second phase of the project, accuracy, sensitivity and error analyses will be carried out to further increase the prediction accuracy as well as the transferability of the developed Greybox model. In the process, previously undiscovered research areas for the emerging digitalized machining technology can be explored.