Three-axis CNC machining feedrate scheduling based on the feedrate restricted interval identification with sliding arc tube

2018 ◽  
Vol 99 (1-4) ◽  
pp. 1047-1058
Author(s):  
Zhiwei Su ◽  
Huicheng Zhou ◽  
Pengcheng Hu ◽  
Wei Fan
Keyword(s):  
Cnc Machining ◽  
Feedrate Scheduling

Modeling and simulation of trajectory smoothing and feedrate scheduling for vibration-damping CNC machining

2020 ◽  
Vol 99 ◽  
pp. 102028 ◽  
Author(s):  
Wei Fan ◽  
Jianwei Ji ◽  
Pengyue Wu ◽  
Dingzhu Wu ◽  
Hua Chen
Keyword(s):  
Modeling And Simulation ◽  
Vibration Damping ◽  
Cnc Machining ◽  
Feedrate Scheduling

An optimized feedrate scheduling method for CNC machining with round-off error compensation

2018 ◽  
Vol 97 (5-8) ◽  
pp. 2369-2381 ◽  
Author(s):  
Hepeng Ni ◽  
Tianliang Hu ◽  
Chengrui Zhang ◽  
Shuai Ji ◽  
Qizhi Chen
Keyword(s):  
Error Compensation ◽  
Cnc Machining ◽  
Feedrate Scheduling ◽  
Scheduling Method

Feedrate scheduling and jerk control algorithm for high-speed CNC machining

2009 ◽  
Vol 17 (3) ◽  
pp. 216 ◽  
Author(s):  
Daping Wan ◽  
ShiLong Wang ◽  
CaiChao Zhu ◽  
Fanming Meng
Keyword(s):  
High Speed ◽  
Control Algorithm ◽  
Cnc Machining ◽  
Feedrate Scheduling

A G4 continuous B-spline transition algorithm for CNC machining with jerk-smooth feedrate scheduling along linear segments

2019 ◽  
Vol 115 ◽  
pp. 231-243 ◽  
Author(s):  
Yong Zhang ◽  
Mingyong Zhao ◽  
Peiqing Ye ◽  
Hui Zhang
Keyword(s):  
Cnc Machining ◽  
B Spline ◽  
Feedrate Scheduling

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

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