Intelligent Machine Tool for Autonomic Process Optimization

加工过程中的自激振动是造成极限切削参数的主要原因。一般来说，切割参数的优化是由机器操作员根据他们的专业知识或通过确定稳定性叶状图来完成的。到目前为止，在加工过程中，只有切削速度和轴向进给参数是自动适应的。调整切割深度和宽度的潜力仍未得到开发。该项目的主要目标是研究一种适应自主过程优化的切割参数的算法。该算法通过传感器信号和不断扩展的知识库来评估加工过程中的工艺参数。机器集成应变计和加速度计用于检测颤振，从而确定最大材料去除率。针对新的测量概念，分析了不同的颤振检测方法。在此基础上，对刀具轨迹的自动分割进行了分析，实现了切削深度和宽度的并行自适应。因此，将常用的方法进行扩展，并使用过程参数（vc, vf, ae, ap）进行优化。此外，通过创建自主流程优化所需的决策规则将生成基础知识。因此，该项目是迈向自动化机床的一步。

Self-excited vibrations during machining are the main reason for limit cutting parameters. In general, an optimization of cutting parameters is performed by machine operators based on their know-how or by determining a stability lobe chart. Up to now, only the parameters cutting speed and axis feed are adapted automatically during the machining process. The potential of adapting depth and width of cut remains unexploited. Main goal of the project is to investigate an algorithm that adapts cutting parameters for an autonomic process optimization. The algorithm rates process parameters during machining by the means of sensor signals and a continuously expanding knowledge data-base. Machine integrated strain gauges and accelerometers serve to detect chatter and hence to determine the maximal material removal rate. Different chatter detection approaches are analyzed regarding the new measurement concept. Furthermore, the autonomous segmentation of tool path will be analyzed for a process parallel adaption of the depth and width of cut. Thus, the common approach will be extended and the process parameters (vc, vf, ae, ap) are used for optimization. Additionally, fundamental knowledge will be generated by creating decision rules required for the autonomic process optimization. Therefore, the project is a step forward towards autonomic machine tools.