Paper Title:
Research of FWP Process Deformation Compensation Forecasting on the Basis of TS-FNN
  Abstract

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.

  Info
Periodical
Advanced Materials Research (Volumes 295-297)
Chapter
Chapter 8: Manufacturing in a Competitive Environment
Edited by
Pengcheng Wang, Liqun Ai, Yungang Li, Xiaoming Sang and Jinglong Bu
Pages
2430-2437
DOI
10.4028/www.scientific.net/AMR.295-297.2430
Citation
Y. H. Deng, G. X. Liu, Q. F. Liao, L. Zeng, L. M. Wu, "Research of FWP Process Deformation Compensation Forecasting on the Basis of TS-FNN", Advanced Materials Research, Vols. 295-297, pp. 2430-2437, 2011
Online since
July 2011
Export
Price
$32.00
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