Development and validation of an acoustic emission based process monitoring technique for the milling of carbon fibre reinforced plastics

基于声发射的碳纤维增强塑料铣削过程监控技术的开发和验证

• 批准号: 420609123
• 负责人: Professor Dr.-Ing. Eckart Uhlmann
• 金额: --
• 依托单位: Fachgebiet Werkzeugmaschinen und Fertigungstechnik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/420609123?language=en
• 关键词: Development; validation; acoustic; emission; based

Due to their high specific modulus of elasticity, fibre-reinforced plastics (FRP) are used as a material for lightweight components in automotive and aerospace industries. A special challenge for the economic and process-reliable production of FRP components is the machining by means of cutting processes such as milling. This is due to the abrasive glass or carbon fibres, which inevitably lead to wear on the milling tool in the form of increasing cutting edge rounding. The machining with worn milling tool leads to increasing process forces and unwanted component damages such as layer delaminations, which can make cost-intensive component inspections and costly reworking necessary.The implementation of online detection of tool and component damage during machining therefore has the potential to drastically reduce the manufacturing costs and processing times of FRP components. For this reason, the aim of this research project is the development and evaluation of an acoustic emission based online process monitoring technique for milling of FRP. This provides information on the wear progress of the coated and uncoated tool as well as on unwanted component damage and reduces the need for costly component inspections.Due to the high attenuation of acoustic emission in FRP and material anisotropy, the use of an acoustic emission sensor mounted on the workpiece side is not suitable for robust process monitoring during milling. This problem is to be solved in this research project for the first time by a tool-sided sensor coupling. The reduction of the thus recorded datasets by means of feature extraction and pattern recognition will enable an online process monitoring, which reliably detects both the tool condition and the undesired component damage.

由于其高弹性模量，纤维增强的塑料（FRP）被用作汽车和航空航天工业中轻质组件的材料。 FRP组件的经济和过程可靠生产的一个特殊挑战是通过切割工艺等方法进行加工。这是由于磨料玻璃或碳纤维，这不可避免地会导致磨制工具的磨损形式，形式增加了最前沿的圆形。带有磨损工具的加工会导致工艺力和不必要的组件损坏，例如层分解，这可能会进行成本密集的组件检查并进行成本昂贵的重新处理。因此，加工过程中工具和组件损坏的实施有可能大大减少FRP组件的制造成本和处理时间。因此，该研究项目的目的是开发和评估用于FRP铣削的基于声学的在线过程监测技术。这提供了有关涂层和未涂层​​工具以及不需要的组件损坏的磨损进度的信息，并减少了对昂贵的组件检查的需求。在FRP和材料各向异性中声发射的高度减弱，使用在工件侧进行的声音传感器的使用不适合在铣削的过程中进行强制性监控。该问题将在该研究项目中首次通过工具侧传感器耦合解决。通过特征提取和模式识别的方式减少了这样记录的数据集，将实现在线过程监视，该过程可靠地检测到工具状况和不希望的组件损坏。