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.

A New Method Applied to the Multi-Parametric Nonlinear Compensations of Computer Numeric Control Bender

“…Curve Fitting of Line Angles. Following the above considerations, the asymmetrical sampling points [1,2,3,4,5,6,7,8,9,10,15,20,30,45,60,75,90] Surface Fitting of Arc Angles. Unlike lines, arcs are transformed into repetitive folded lines with small angles to handle.…”

“…2 Contrastive fitting surface of arc angles using MLS and DRMLS So the principle that the radius is large where the sampling point set is sparser is followed to choose sampling points. Because of the acceleration and deceleration of driver equipments, the rotating speed of rotary device is difficult to measure and also inconvenient to define [6]. Sake for convenience, this paper selects step pulse frequency of rotary motor instead.…”

Study and Testing of Processing Trajectory Measurement Method of Flexible Workpiece

“…Let ( 1 , 2 ) be the flexible workpiece trajectory processing image. 1 ( 1 , 2 ) reflects low-frequency components in both the horizontal direction ( 1 ) and vertical direction ( 2 ) after conducting the two-dimensional wavelet transform of processing image, 1 (1) ( 1 , 2 ) reflects the lowfrequency components in direction 1 and high-frequency components in direction 2 , and 1 (2) ( 1 , 2 ) reflects the high-frequency components in direction 1 and low-frequency components in direction 2 , 1 (3) ( 1 , 2 ) reflects high-frequency in both directions 1 , 2 [11]; represents the low-pass filter having the impulse response and represents the high-pass filter having the impulse response, according to Mallat algorithm, wavelet decomposition, or reconstruction of ( 1 , 2 ) is consisted of several levels high-pass filter and low-pass filter , , can be built by using finite length (FIR) filter as Figure 7 shows when the filter coefficients is known [12][13][14].…”

“…Flexible workpiece trajectory processing refers to the procedure of conducting all kinds of complicated image processing in workpiece which is consisted of multilayer soft material, and emerged some uneven pattern on the surface of workpiece [1,2]. Straightness, roundness, and primitives angle error geometrical are the important measurement parameters of machining profile of flexible workpiece trajectory processing which is an important indicator of evaluating the trajectory processing precision, also providing a basis for the processing feedback compensation control [3]. However, the edge and corner of the pattern of flexible workpiece processing trajectory are fuzzy and shaped; the extraction of processing image feature information (such as edges, corners and shapes) is a key issue in processing trajectory visual measure method.…”

Deformation Forecast of Flexible Material Process By Spline Finite Element Method and Application

“…In the paper [10], researchers predict the deformation according to the fabric's border and the compression to adaptively choose the finite element method, experiment demonstrates that the result of finite element equation suggested by the researchers is more effective than any other methods in the past. Reference [11][12][13][14][15][16] studied the modeling of flexible material deformation by intelligent methods such as neural networks, regression analysis. These methods have obtained better simulation effect.…”