ATS-FNN-based modeling and simulation for compensation prediction of FWP machining deformation

作者：Deng, Yao-Hua; Liu, Gui-Xiong^*

来源：Journal of South China University of Technology(Natural Science Edition), 2012, 40(3): 137-142.

DOI：10.3969/j.issn.1000-565X.2012.03.022

• Connection weights
• Deformation compensation
• Fuzzy membership function
• Machining deformation
• Model and simulation
• Real time requirement
• T-S fuzzy neural network
• Workpiece

In order to meet the real-time requirements of the prediction model for deformation compensation of flexible workpiece path (FWP) with complex factors, a ATS-FNN (Adaptive TS Fuzzy Neural Network)-based modeling method for the compensation prediction of FWP machining deformation is proposed. In this method, the adaptive fuzzy clustering method is employed to obtain the antecedent fuzzy membership functions and fuzzy rule fitness of TS-FNN from historical machining data, and the steepest descent method is used as the learning algorithm of the consequent network to quickly calculate the parameters of connection weights. Simulated results indicate that, as compared with the standard TS-FNN, the ATS-FNN reduces a modeling time of 52.34% and a mean square error of the predicted compensation respectively by 36.50% or 33.34% in x or y direction.

• 出版日期2012-3
• 单位华南理工大学