- Tytuł:
- Surface Hardness Prediction Model of Turning Duplex Stainless Steel under Different Cutting Variables
- Autorzy:
-
Abdulateef, Osamah Fadhil
Ali, Abduljabar H.
Al Kareem, Salah Sabeeh Abed - Powiązania:
- https://bibliotekanauki.pl/articles/2201899.pdf
- Data publikacji:
- 2023
- Wydawca:
- Stowarzyszenie Inżynierów i Techników Mechaników Polskich
- Tematy:
-
CNC turning
orthogonal array
ANN
signal-to-noise ratio - Opis:
- The quality of machine components surfaces plays an important impact on their functional performance. Product performance may be restricted by changes to surface integrity, which includes changes to roughness, hardness, and microstructure. In this research, the impact of cutting variables in CNC turning under the conventional cooling condition on surface hardness of Duplex Stainless Steel. Cutting variables under conventional cooling, including cutting speed, feed, and depth of cut, have been optimized utilizing Taguchi’s L9 orthogonal array designed with three stages of turning variables. The optimal variable stages and the degree of significance of the cutting variables, respectively, were determined utilizing the analysis of means (ANOM) and analysis of variance (ANOVA). Effectiveness tests with optimum stages of variables were done to prove the viability of optimization by utilizing Taguchi. It has been found that the maximum surface hardness is most strongly affected by the feed 71.29%, followed by the depth of cut 12.1%, and finally the cutting speed 11.61%.
- Źródło:
-
Advances in Science and Technology. Research Journal; 2023, 17, 1; 1--7
2299-8624 - Pojawia się w:
- Advances in Science and Technology. Research Journal
- Dostawca treści:
- Biblioteka Nauki
Artykuł