Intelligent sensor system for the disturbance variable invariant conditioning of residual stresses during machining of Ti-6Al-4V - Phase 2

在计划的研究项目中，Ti-6Al-4V在纵向车削过程中应设置可靠且独立于任何刀具磨损的残余应力状态。因此，实现了一种由声学传感器组成的传感器系统，该系统测量芯片分割频率作为刀具磨损状态的指标。刀具磨损作为扰动值通过控制以下过程变量来补偿：切削速度、进给量、前角和进给角。利用软传感器，结合基于有限元模拟的工艺知识对声信号进行处理，确定影响部件表层状态的热力学载荷谱。其目的是通过控制上述工艺变量，使部件在循环载荷下的性能保持在一贯的高水平，尽管磨损引起的工具几何形状改变。第二个项目阶段的第一个目标是改进过程模型性能，能够使用具有更复杂几何形状的工业切削工具对切屑形成过程进行建模。第二个目标是增强信号处理方法的实时处理和评估（软测量）。软传感器是可靠的，可以识别干扰噪声，如切屑碰撞，金属切削液和刀具进给和出给引起的噪声，并可以从软传感器相关的信号中分离出来。第三个目标是过程控制，以调节加工后的残余应力状态。通过人工智能支持的系统识别，实时识别软传感器的动态行为以及材料和刀具的初始状态。根据这些信息，利用工艺模型对工艺参数（切削速度和进给量）进行控制和调整，并借助压电致动器对前角和进给角进行控制和调整，以获得最终零件所需的残余应力。

In the planned research project the residual stress state of Ti-6Al-4V should be set reliable and independent from any tool wear during longitudinal turning. Therefore, a sensor system consisting of acoustic sensors, which measure the chip segmentation frequency as an indicator for the tool wear state, is implemented. The tool wear as disturbance value is compensated by controlling the following process variables: cutting speed, feed, rake angle and entering angle. By using a soft sensor, the acoustic signal in combination with a FE-simulation based process knowledge is processed and the thermo-mechanic load spectrum which effects the component’s surface layer states is determined. The aim is to keep the component behavior under cyclic loading on a consistently high level despite a wear-induced tool geometry alteration, by controlling the mentioned process variables.The first objective of the second project phase is the improvement of the process model performance being able to model the chip formation process using industrial cutting tools with more complex geometry. The second objective is the enhancing of the signal processing method to real-time processing and evaluation (soft sensor). The soft sensor is reliable and can identify interfering noises such as chip collisions, noises caused by metal cutting fluids and tool infeed and outfeed and can separate them from the signals relevant for the soft sensors. The third objective is the process control to regulate residual stress states after machining. By an AI-supported system identification the dynamic behavior of the soft sensor and the initial material and tool condition are identified in real time. With this information and using the process model the process parameters (cutting speed and feed) and, with the help of piezoelectric actuators, the rake and entering angles are controlled and adjusted to obtain the desired residual stresses in the final part.