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。经济且工艺可靠的FRP部件生产面临的一个特殊挑战是通过铣削等切削工艺进行机加工。这是由于研磨玻璃或碳纤维，这不可避免地导致磨损的形式增加切削刃磨圆的铣刀。使用磨损的铣削刀具进行加工会导致加工力增加和不必要的部件损坏（如层间分层），从而需要进行成本高昂的部件检查和昂贵的返工。因此，在加工过程中实施刀具和部件损坏的在线检测有可能大幅降低FRP部件的制造成本和加工时间。因此，本研究计画的目的是发展及评估一种以声发射为基础的玻璃钢铣削制程在线监测技术。这提供了涂层和未涂层刀具的磨损过程以及不必要的部件损坏的信息，并减少了昂贵的部件检测需求。由于FRP中声发射的高衰减和材料的各向异性，使用安装在工件侧的声发射传感器不适合在铣削过程中进行可靠的过程监控。这一问题将在本研究项目中首次通过工具侧传感器耦合来解决。通过特征提取和模式识别减少记录的数据集将实现在线过程监控，从而可靠地检测刀具状况和不希望的部件损坏。

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.