Arriandiaga Ander, Portillo Eva, Sánchez Jose A, Cabanes Itziar, Pombo Iñigo
Department of Automatic Control and System Engineering, University of the Basque Country, C/Alameda Urquijo s/n, 48013 Bilbao, Spain.
Department of Mechanical Engineering, University of the Basque Country, C/Alameda Urquijo s/n, 48013 Bilbao, Spain.
Sensors (Basel). 2014 May 19;14(5):8756-78. doi: 10.3390/s140508756.
Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 μm). In the case of surface finish, the absolute error is well below Ra 1 μm (average value 0.32 μm). The present approach can be easily generalized to other grinding operations.
磨削是一种先进的加工工艺，用于制造航空航天、风力发电等高附加值领域中具有重要价值的复杂精密零件。由于磨床内部的工况极其恶劣，诸如零件表面光洁度或砂轮磨损等关键工艺变量难以通过简单且低成本的方式进行在线测量。本文提出了一种用于在线监测这些变量的虚拟传感器。该传感器基于人工神经网络（ANN）对诸如磨削等随机和非线性过程的建模能力；所选用的架构是层递归神经网络。该传感器利用待测量变量与砂轮主轴功耗之间的关系，而主轴功耗易于测量。文中介绍了一种传感器校准方法，并讨论了预期的误差水平。通过将传感器的结果与在工业磨床中进行的实际测量结果相比较，对新型传感器进行了验证。结果表明，该传感器在砂轮磨损和表面粗糙度方面均具有出色的估计性能。对于砂轮磨损，绝对误差在微米范围内（平均值为32μm）。对于表面光洁度，绝对误差远低于Ra 1μm（平均值为0.32μm）。目前的方法可以很容易地推广到其他磨削操作中。
