The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of MECHANICS
Transactions ID Number: 20-866
Full Name: David Samek
Position: Doctor (Researcher)
Age: ON
Sex: Male
Address: nam. T. G. Masaryka 5555, Zlin
Country: CZECH REPUBLIC
Tel: 576035157
Tel prefix: +420
Fax: 576035176
E-mail address: samek@ft.utb.cz
Other E-mails:
Title of the Paper: Prediction of grinding parameters for plastics by artificial neural networks
Authors as they appear in the Paper: David Samek, Ondrej Bilek, Jakub Cerny
Email addresses of all the authors: samek@ft.utb.cz, bilek@ft.utb.cz, j1cerny@ft.utb.cz
Number of paper pages: 12
Abstract: The grinding technology is widely used in the manufacturing of various materials. This technology process is driven by many input parameters that influence resulting product. This work is focused on an application of artificial neural network with radial basis function in modeling of polymer materials grinding. In this paper the two key parameters were selected – feed rate and depth of cut. The task of the artificial neural network based predictor is to provide resulting arithmetical mean roughness and maximum height of the profile parameter. Furthermore, the article presents extensive experimental measurements aimed to grinding of polypropylene, polyamide 6 filled with 30% of glass fibers, polytetrafluoro-ethylene and polycarbonate. All measurements results are statistically evaluated and presented in the figures.
Keywords: Artificial neural networks, Grinding, Prediction, Radial basis function
EXTENSION of the file: .doc
Special (Invited) Session: Prediction of technological parameters during polymer material grinding
Organizer of the Session: 655-205
How Did you learn about congress: Lubomir Macku macku@fai.utb.cz, Jakub Javorik javorik@ft.utb.cz
IP ADDRESS: 195.178.89.71
