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中文摘要：
为解决深孔加工中表面粗糙度在线检测困难这一问题，提出一种基于BP神经网络的表面粗糙度在线辨识方法，并以BTA钻削为例，建立表面粗糙度BP神经网络在线辨识模型，并将其引入钻削加工领域。该模型能方便地预测钻削加工参数对加工表面粗糙度的影响，有助于准确认识已加工表面质量随切削参数的变化规律，为切削参数的优选和表面粗糙度的控制提供了依据。实验和仿真结果表明，基于BP神经网络模型能够很好地预测表面粗糙度，对提高加工表面粗糙度具有一定的指导意义。
英文摘要：
In order to conquer the difficulty of on-fine surface roughness measuring, the surface roughness identification method based on BP networks is put forward. As an example, the identifi- cation model of BTA drilling is built and introduced into the field of drilling. The model conveniently predicts the effects of drillingparameters on surface roughness of machined surface, which con- tributes to accurately understand the variation law of quality of ma- chined surface following drilling parameters and provides the foun- dation for properly selecting cutting parameters and controlling surface quality. The simulation and experimental results show that BP neural network can well predict the surface roughness and have a certain guiding significance to improve the surface roughness.
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