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Method for determining feedrate in multi-axis machine control systems using predictive control algorithms and artificial neural networks

Krystian Erwiński

Abstract

The dissertation concerns the problem of optimal feedrate determination in multi-axis machine control systems - especially CNC machine tools. Problem of trajectory generation in CNC machine tool control system is described. A novel solution of maximum feedrate determination is presented which ensures constriction of contour error to arbitrary levels. The algorithm utilizes a neural model of machine feed drives in order to estimate contour errors during machining. Using this information an optimization algorithm determines the optimum feedrate profile. The nonlinear optimization problem is solved utilizing the Particle Swarm Optimization algorithm extended with the Augmented Lagrangian method. The algoritm was implemented in a PC based CNC controller with Linux RTAI real-time operating system. Simulation and experimental results confirm that ralization time of the given motion trajectory was decreased without violating given contour error limit.
Record ID
WUT5275961194bc4c20bfcc32c1e6b26198
Diploma type
Doctor of Philosophy
Author
Krystian Erwiński Krystian Erwiński,, Undefined Affiliation
Title in Polish
Metoda doboru prędkości posuwu w układach sterowania numerycznego maszyn wieloosiowych z wykorzystaniem algorytmów sterowania predykcyjnego oraz sztucznych sieci neuronowych
Title in English
Method for determining feedrate in multi-axis machine control systems using predictive control algorithms and artificial neural networks
Language
(pl) Polish
Certifying Unit
Faculty of Electrical Engineering (FoEE)
Discipline
automation and robotics / (technology domain) / (technological sciences)
Status
Finished
Start date
17-06-2010
Defense Date
01-12-2014
Title date
17-12-2014
Supervisor
Pages
120
Keywords in English
CNC, feedrate optimization,contour error,neural network,particle swarmoptimization
Abstract in English
The dissertation concerns the problem of optimal feedrate determination in multi-axis machine control systems - especially CNC machine tools. Problem of trajectory generation in CNC machine tool control system is described. A novel solution of maximum feedrate determination is presented which ensures constriction of contour error to arbitrary levels. The algorithm utilizes a neural model of machine feed drives in order to estimate contour errors during machining. Using this information an optimization algorithm determines the optimum feedrate profile. The nonlinear optimization problem is solved utilizing the Particle Swarm Optimization algorithm extended with the Augmented Lagrangian method. The algoritm was implemented in a PC based CNC controller with Linux RTAI real-time operating system. Simulation and experimental results confirm that ralization time of the given motion trajectory was decreased without violating given contour error limit.
Thesis file
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Uniform Resource Identifier
https://repo.pw.edu.pl/info/phd/WUT5275961194bc4c20bfcc32c1e6b26198/
URN
urn:pw-repo:WUT5275961194bc4c20bfcc32c1e6b26198

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