Research of FWP Process Deformation Compensation Forecasting on the Basis of TS-FNN
After analyzing the influencing factors of flexible workpiece path(FWP) process deformation, this article proposes the basic conception of process deformation intelligent forecasting and compensation, start from the process modeling method of Takagi-Sugeno fuzzy neural network, to modify the classic FNN model and construct the multiple input/output TS-FNN model for FWP process control; with LMS law and steepest descent method, antecedent network membership function parameter adjustment and descent network parameter study method of TS-FNN model is deduced; finally to carry on comprehensive simulation on TS-FNN model, the result shows the constructed model is better than BP neural network and RBF neural network for an order of magnitude on predication accuracy; in the quilting process of flexible objects, compensated by TS-FNN, the path processing obtains good approaching effect, testing result indicates that the position error scope of quilting is from 0.078 to 0.162(mm), the accuracy is higher than excellence grade of quilting which refers to national standard FZ/T81005-2006.
Pengcheng Wang, Liqun Ai, Yungang Li, Xiaoming Sang and Jinglong Bu
Y. H. Deng et al., "Research of FWP Process Deformation Compensation Forecasting on the Basis of TS-FNN", Advanced Materials Research, Vols. 295-297, pp. 2430-2437, 2011