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首页> 外文期刊>Mechanical systems and signal processing >Control of deviations and prediction of surface roughness from micro machining of THz waveguides using acoustic emission signals
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Control of deviations and prediction of surface roughness from micro machining of THz waveguides using acoustic emission signals

机译：利用声发射信号控制太赫兹波导微加工的偏差并预测表面粗糙度

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By using acoustic emission (AE) it is possible to control deviations and surface quality during micro milling operations. The method of micro milling is used to manufacture a submillimetre waveguide where micro machining is employed to achieve the required superior finish and geometrical tolerances. Submillimetre waveguide technology is used in deep space signal retrieval where highest detection efficiencies are needed and therefore every possible signal loss in the receiver has to be avoided and stringent tolerances achieved. With a sub-standard surface finish the signals travelling along the waveguides dissipate away faster than with perfect surfaces where the residual roughness becomes comparable with the electromagnetic skin depth. Therefore, the higher the radio frequency the more critical this becomes. The method of time-frequency analysis (STFT) is used to transfer raw AE into more meaningful salient signal features (SF). This information was then correlated against the measured geometrical deviations and, the onset of catastrophic tool wear. Such deviations can be offset from different AE signals (different deviations from subsequent tests) and feedback for a final spring cut ensuring the geometrical accuracies are met. Geometrical differences can impact on the required transfer of AE signals (change in cut off frequencies and diminished SNR at the interface) and therefore errors have to be minimised to within 1 urn. Rules based on both Classification and Regression Trees (CART) and Neural Networks (NN) were used to implement a simulation displaying how such a control regime could be used as a real time controller, be it corrective measures (via spring cuts) over several initial machining passes or, with a micron cut introducing a level plain measure for allowing setup corrective measures (similar to a spirit level).
机译：通过使用声发射（AE），可以控制微铣削加工过程中的偏差和表面质量。微铣削的方法被用于制造亚毫米级的波导，其中采用微加工来实现所需的优异光洁度和几何公差。亚毫米级的波导技术用于需要最高检测效率的深空信号检索中，因此必须避免接收器中所有可能的信号丢失，并达到严格的容差。在表面质量不合格的情况下，沿着波导传播的信号比在完美表面上的消散速度更快，在完美表面上，残留粗糙度变得与电磁趋肤深度相当。因此，射频频率越高，它变得越关键。时频分析（STFT）方法用于将原始AE转换为更有意义的显着信号特征（SF）。然后将这些信息与测得的几何偏差以及灾难性工具磨损的发生相关联。可以从不同的AE信号（与后续测试的不同偏差）和最终弹簧切割的反馈中抵消此类偏差，以确保满足几何精度。几何差异会影响所需的AE信号传输（截止频率的变化和界面处SNR的降低），因此必须将误差最小化到1 um之内。使用基于分类树和回归树（CART）以及神经网络（NN）的规则来进行模拟，以显示如何将这种控制方式用作实时控制器，无论是通过多次初始调整（通过弹簧切割）加工通过，或者用微米级切割引入水平仪，以进行设置校正措施（类似于水平仪）。

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