Konferencja
Automatyzacja produkcji. Nauka - Wiedza - Innowacje. Tom. I. Referaty plenarne i sesje. Wrocław, 11-12 grudnia 2003
Języki publikacji
Abstrakty
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
Czasopismo
Rocznik
Strony
337--344
Opis fizyczny
Bibliogr. 3 poz., rys.
Twórcy
autor
- University of Maribor, Faculty of Mechanical Engineering, Smetanova 17, 2000 Maribor, Slovenia
autor
- University of Maribor, Faculty of Mechanical Engineering, Smetanova 17, 2000 Maribor, Slovenia
autor
- University of Maribor, Faculty of Mechanical Engineering, Smetanova 17, 2000 Maribor, Slovenia
Bibliografia
- [1] CUS F., BALIC J., Selection of cutting conditions and tool flow in flexible manufacturing system, The international journal for manufacturing science & technology, Vol. 2, 2000, 101-106.
- [2] KOPAC J., Influence of cutting material and coating on tool quality and tool life, J. mater, process, technol, Vol. 78, 1/3, 1998, 95-103.
- [3] LEE T.S., A 3D Predictive Cutting-Force Model for End Milling of Parts Having Sculptured Surfaces, International Journal of Advanced Manufacturing Technology, Vol. 16, 2000, 773-783.
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS3-0011-0017