Artificial neural network modelling of vibration in the milling of AZ91D alloy

• Autorzy: Zagórski I. , Kulisz M. , Semeniuk A. , Malec A.

Identyfikatory

DOI

10.12913/22998624/76546

• Języki publikacji: EN

Abstrakty

EN

The paper reports the results of artificial neural network modelling of vibration in a milling process of magnesium alloy AZ91D by a TiAlN-coated carbide tool. Vibrations in machining processes are regarded as an additional, absolute machinability index. The modelling was performed using the so-called “black box” model. The best fit was determined for the input and output data obtained from the machining process. The simulations were performed by the Statistica software using two types of neural networks: RBF (Radial Basis Function) and MLP (Multi-Layered Perceptron).

Słowa kluczowe

EN

magnesium alloys; high-speed dry milling; vibration; chatter in milling; simulation; artificial neural networks

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2017
• Tom: Vol. 11, no 3
• Strony: 261--269
• Opis fizyczny: Bibliogr. 25 poz., fig.

Twórcy

autor

Zagórski I.

• Department of Production Engineering, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland

autor

Kulisz M.

• Department of Organisation of Enterprises, Faculty of Management, Lublin University of Technology, Nadbystrzycka 38, 20-618 Lublin, Poland

autor

Semeniuk A.

• Student of Production Engineering, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland

autor

Malec A.

• Student of Production Engineering, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland

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Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)