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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.