Acoustic emission and force sensor fusion for monitoring the cutting process
Acoustic emission and force sensor fusion for monitoring the cutting process
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DOI：
10.1016/0020-7403(89)90025-8
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发表时间：
1989
影响因子：
7.3
通讯作者：
E. Emel;E. Kannatey-Asibu
中科院分区：
文献类型：
--
作者：
E. Emel;E. Kannatey-Asibu
声发射和力为基础的传感器融合系统，包括模式识别分析已被用来检测刀具破损，芯片的形式和刀具后刀面磨损的阈值水平在车削。当与合力归一化时，在切削，径向和进给方向上的力分量被发现是高度敏感的变量，如进给速度，材料硬度，刀具涂层和刀具磨损，切削深度，和速度在分数析因实验。一个三维的分析力模型进行了扩展，包括后刀面磨损的影响，以解释实验结果。随后，利用经验的双线性关系之间的加工变量和力，一个过滤器的设计，以消除变量的影响，使模式识别的刀具故障在不同的条件下是可行的。传感器融合的方法，包括测试系统的设计中使用的相同的数据时，使用AE和力信号表示94%的准确度传感刀具磨损单独，而仅使用AE检测芯片的形式和刀具破损分别表示99%和96%的准确度。
An acoustic emission and force-based sensor fusion system involving pattern recognition analysis has been used to detect tool breakage, chip form and a threshold level of tool flank wear in turning. When normalized with the resultant force, the force components in the cutting, radial and feed directions were found to be highly sensitive to variables such as feedrate, material hardness, tool coating and tool wear, depth of cut, and speed in fractional factorial experiments. A three-dimensional analytical force model was extended to include the effect of flank wear in order to interpret the experimental findings. Subsequently, using an empirical bilinear relationship between the machining variables and forces, a filter was designed to eliminate the variable effects such that pattern recognition of tool failure under varying conditions was feasible. Results of the sensor fusion approach involving testing the system with the same data used in designing it when using AE and force signals indicate a 94% accuracy for sensing tool wear alone, whereas using only AE for detecting chip form and tool breakage indicate a 99 and 96% accuracy respectively.