表面粗糙度模糊神经网络在线辨识模型

为解决零件加工中表面粗糙度在线检测困难这一问题,提出一种基于模糊神经网络的零件表面粗糙度在线辨识方法,并以外圆纵向磨削为例,建立表面粗糙度模糊神经网络在线辨识模型.首先研究前人建立的外圆纵向磨削零件表面粗糙度理论公式及经验公式,得出加工中的工件速度、砂轮速度、磨削深度和纵向进给量对零件表面粗糙度有直接影响,并进一步提出以在线测得的加工中工件与砂轮的速度比、磨削深度和纵向进给量作为零件表面粗糙度辨识模型的输入.由于加工过程极其复杂,无法建立加工中零件表面粗糙度与加工参数之间的精确数学模型,故将模糊神经网络引入建模过程中.同时,由于加工中零件表面粗糙度的对数与加工参数的对数存在线性关系,故模型中采用了T-S型模糊推理.此模型应用于实际磨削加工中,建模型精度可达97%,这进一步证明此在线辨识方法的可行性.

ON-LINE IDENTIFICATION MODEL OF SURFACE ROUGHNESS BASED ON FUZZY-NEURAL NETWORKS

In order to conquer the difficulty of on-line meas- uring workpiece surface roughness,the surface roughness iden- tification method based on fuzzy-neural networks is put forward. As an example,the identification model of external cylindrical during grinding is built.Through the deep study of theory for- mulae and experimental formulae of external cylindrical surface roughness during grinding built before,it is known that the workpiece velocity,grinding wheel velocity,grinding depth and table feed have significant effect on the roughness.Further,the above grinding parameters measured on-line as the inputs of the identification model is considered.Since the grinding process is too complicated to be built exact mathematic model,the fuzzy-neural networks is introduced to the identification model. T-S type fuzzy inference is adopted to obtain the roughness because there exists linear relation between the roughness loga- rithm and the above grinding parameters logarithm.The model is used in the practical grinding process,and the model accu- racy is 97%.This verifies the feasibility of the proposed method.