Tool cutting force modeling in ball-end milling using multilevel perceptron

Povzetek

This paper uses the artificial neural networks (ANNs) approach to evolve an efficient model for estimation of cutting forces, based on a set of input cutting conditions. A neural network algorithms are developed for use as a direct modeling method, to predict forces for ball-end milling operation. Supervised neural networks are used to successfully estimate the cutting forces developed during end milling process. The training of the networks is preformed with experimental machining data. The predictive capability of using analytical and neural network approaches are compared using statistics, which showed that neural network predictions for three cutting force components were for 4% closer to the experimental measurements, compared to 11% using analytical method. Exhaustive experimentation is conduced to develop the model and to validate it. The milling experiments prove that this model can predict accurately the cutting forces in three Cartesian directions.The force model can be used for simulation purposes and for defining threshold values in cutting tool condition monitoring system.

Ključne besede

čelno frezanje;krogelno oblikovno frezalo;rezalne sile;modeliranje;nevronske mreže;umetna inteligenca;ball end milling;cutting forces;modelling;artificial intelligence;neural networks;

Podatki

Jezik:	Angleški jezik
Leto izida:	2004
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UM FS - Fakulteta za strojništvo
UDK:	621.914:004.89
COBISS:	8791062
ISSN:	0924-0136
Št. ogledov:	1842
Št. prenosov:	91
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Angleški jezik
Sekundarne ključne besede:	čelno frezanje;krogelno oblikovno frezalo;rezalne sile;modeliranje;nevronske mreže;umetna inteligenca;
URN:	URN:SI:UM:
Strani:	str. 268-275
Letnik:	ǂVol. ǂ153/154
Čas izdaje:	Nov. 2004
ID:	8718697