2D Geometric Surface Structure ANN Modeling after Milling of the AZ91D Magnesium Alloy

• Autorzy: Kulisz Monika , Zagórski Ireneusz , Józwik Jerzy

Identyfikatory

DOI

10.12913/22998624/146765

• Języki publikacji: EN

Abstrakty

EN

The paper presents the results of modeling 2D surface roughness parameters in milling by means of an artificial neural network (ANN). The AZ91D magnesium alloy was used. A HSS milling cutter was employed in the research. The main aim of the study was to obtain the lowest possible surface roughness (good quality) using a commonly available HSS cutter. The results of the research work were presented in the form of bar charts, surface charts and graphs depicting the quality of artificial neural networks. The conducted research shows that it is possible to carry out the machining processes that enable obtaining an average surface quality (defined by roughness parameters Ra, Rz, RSm, Rsk). The Ra, Rz, RSm parameters increase along with the machining parameters (fz, ap), as expected. The Rsk parameter takes (in most cases) negative values, which may indicate a surface with more intense friction and indicative of flat-topped distribution. On the other hand, the results of modeling the selected parameters – Ra, Rz, RSm – with the use of artificial neural networks allow concluding that the obtained network models show satisfactory predictive ability (R = 0.99), and thus are an appropriate tool for the prediction of surface roughness parameters.

Słowa kluczowe

EN

magnesium alloys; milling; roughness; artificial neural networks; simulations

• 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: 2022
• Tom: Vol. 16, no 2
• Strony: 131--140
• Opis fizyczny: Bibliogr. 26 poz., fig., tab.

Twórcy

autor

Kulisz Monika

• Department of Organisation of Enterprise, Management Faculty, Lublin University of Technology

• https://orcid.org/0000-0002-8111-2316

autor

Zagórski Ireneusz

• Department of Production Engineering, Mechanical Engineering Faculty, Lublin University of Technology

• https://orcid.org/0000-0001-6016-7011

autor

Józwik Jerzy

• Department of Production Engineering, Mechanical Engineering Faculty, Lublin University of Technology

• https://orcid.org/0000-0002-8845-0764

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).