Greybox-modeling the run-in behavior of coated tools in the milling process as dynamic load profile based on operando, in situ and ex situ analyses

基于操作、原位和非原位分析，灰盒将铣削过程中涂层刀具的磨合行为建模为动态载荷曲线

• 批准号: 521382051
• 负责人: Professor Dr.-Ing. Dirk Biermann
• 金额: --
• 依托单位: Lehrstuhl für Experimentelle Physik II
• 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/521382051?language=en
• 关键词: Greybox; modeling; run; behavior; coated

The dynamic stability of a milling process is determined by cutting parameters as well as by structural-mechanical properties of the milling tool. In the cutting process, the tribological load changes properties of the tool cutting edge resulting in a transient system behavior. Consequently, dynamically unstable process conditions may drastically increase the tool wear and have a detrimental effect on the production quality. A wear protection layer of, e.g., TiAlN or TiAlSiN has a wear-reducing effect. A fundamental and holistic understanding of the transient system behavior of coated milling tools and its influence on the dynamic process stability allows for an efficient process design. In the project, measured data of the steady-state and transient system behavior are used to reveal the chemical and mechanical wear initiation and development of coated milling tools with respect to the dynamic run-in behavior by means of a multi-scale greybox modeling. The process-oriented design of the cutting-edge preparation as well as the PVD coating using TiAlN and TiAlSiN thin films consider tool concepts established for milling processes. Parametrically broadband measurement techniques quantify the wear behavior of the coated tool cutting edges spatially and temporally resolved, depending on the load profile. The targeted greybox modeling is based on (i) data from operando and in situ measurements of the transient system behavior as well as ex situ analyses of the “before and after” state of the milling tool, which are evaluated using artificial neural networks, and (ii) known physical as well as material and production technology causalities. Due to the interdisciplinary collaboration at TU Dortmund University between Prof. Dr. Dirk Biermann, Dr. Jörg Debus and Prof. Dr. Wolfgang Tillmann, expertise from the fields of machining technology, laser spectroscopy and multiparametric surface analysis as well as thin film technology is incorporated into the greybox model realized by a common platform. In the process, the onset of failure, wear progress and the end of lifetime of TiAlN and TiAlSiN coated tools are identified and predicted with reasonable confidence. The enhanced understanding of the system behavior allows for developing fundamental strategies to increase the tool life, process stability and efficiency regarding high manufacturing quality and productivity. The interdisciplinary findings and model-based predictions are used for the resiliently optimized design of the milling processes.

铣削过程的动态稳定性取决于切削参数以及铣削刀具的结构-机械特性。在切削过程中，摩擦载荷改变了刀具切削刃的性能，导致系统的瞬态行为。因此，动态不稳定的工艺条件可能会急剧增加刀具磨损，并对生产质量产生不利影响。例如，TiAlN或TiAlSiN具有减少磨损的效果。对涂层铣刀的瞬态系统行为及其对动态过程稳定性的影响的基本和全面的理解允许有效的工艺设计。在该项目中，稳态和瞬态系统行为的测量数据被用来揭示涂层铣刀的化学和机械磨损的启动和发展相对于动态运行的行为，通过多尺度灰箱建模。尖端制备的工艺导向设计以及使用TiAlN和TiAlSiN薄膜的PVD涂层考虑了为铣削工艺建立的刀具概念。参数化宽带测量技术量化涂层刀具切削刃的磨损行为，空间和时间分辨，取决于负载分布。有针对性的灰箱建模基于（i）来自瞬态系统行为的操作和现场测量的数据以及铣削工具“前后”状态的非现场分析（使用人工神经网络进行评估），以及（ii）已知的物理以及材料和生产技术因果关系。由于多特蒙德大学Dirk Biermann教授、Jörg Debus博士和Wolfgang Tillmann教授之间的跨学科合作，来自加工技术、激光光谱学和多参数表面分析以及薄膜技术领域的专业知识被整合到通过通用平台实现的灰箱模型中。在这个过程中，故障的发生，磨损的进展和寿命的结束的TiAlN和TiAlSiN涂层工具的识别和预测具有合理的信心。对系统行为的深入了解有助于制定基本策略，以提高刀具寿命、工艺稳定性和效率，从而提高制造质量和生产率。跨学科的发现和基于模型的预测用于铣削工艺的弹性优化设计。