ON-LINE IDENTIFICATION MODEL

OF SURFACE ROUGHNESS BASED

ON FUZZY-NEURAL NETWORKS

LI Xiaomei¹ DING Ning¹ ZHU Xilin²

(1. College of Mechanical Engineering, University of Changchun, Changchun 130022;

 2. College of Mechanical Science and Engineering, University of Jilin, Changchun 130025 )

Abstract: In order to conquer the difficulty of on-line measuring workpiece surface roughness, the surface roughness identification 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 formulae 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 logarithm and the above grinding parameters logarithm. The model is used in the practical grinding process, and the model accuracy is 97%. This verifies the feasibility of the proposed method.

Key words: Surface roughness On-line identification Fuzzy-neural networks External cylindrical grinding

CLC No: TH166 TP18

吉林省科技发展计划资助项目(20020632). Received 20060607, received in revised form 20061127