Modelling of end milling process by generalized regression networks

• Autorzy: Župerl U. , Čuš F. , Balič J.
• Konferencja: Automatyzacja produkcji. Nauka - Wiedza - Innowacje. Tom. I. Referaty plenarne i sesje. Wrocław, 11-12 grudnia 2003
• Języki publikacji: EN

Abstrakty

EN

Supervised neural network algorithms are developed for use as a direct modelling method, to predict forces for ball-end milling operation. The training of the networks is performed with experimental machining data This paper uses the regression neural networks approach to evolve an efficient model for estimation of cutting forces, based on a set of input cutting conditions. 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.

Słowa kluczowe

EN

machining; cutting forces; modelling; generalized regression networks

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Prace Naukowe Instytutu Technologii Maszyn i Automatyzacji Politechniki Wrocławskiej. Konferencje
• Rocznik: 2003
• Tom: Vol. 84, nr 41
• Strony: 337--344
• Opis fizyczny: bibliogr. 3 poz., rys.

Twórcy

autor

Župerl U.

autor

Čuš F.

autor

Balič J.

• University of Maribor, Faculty of Mechanical Engineering, Smetanova 17, 2000 Maribor, Slovenia