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

由于其高比弹性模量，纤维增强塑料（FRP）被用作汽车和航空航天工业中轻质部件的材料。对经济和工艺可靠的玻璃钢构件生产的一个特殊挑战是通过铣削等切割工艺进行加工。这是由于磨料玻璃或碳纤维，这不可避免地导致铣刀磨损的形式增加切削刃圆。使用磨损的铣刀进行加工会导致加工力增加和不必要的部件损坏，例如分层，这可能会导致成本高昂的部件检查和昂贵的返工。因此，在加工过程中对工具和部件损坏进行在线检测有可能大大降低FRP组件的制造成本和加工时间。因此，本研究项目的目的是开发和评估基于声发射的玻璃钢铣削在线过程监测技术。这提供了涂层和未涂层工具磨损过程的信息，以及不必要的部件损坏，减少了昂贵的部件检查的需要。由于声发射在玻璃钢中的高度衰减和材料的各向异性，使用安装在工件侧面的声发射传感器不适合铣削过程中的鲁棒过程监测。本课题首次通过工具侧传感器耦合解决了这一问题。通过特征提取和模式识别来减少记录的数据集，将实现在线过程监控，从而可靠地检测工具状态和不希望出现的部件损坏。

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.