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

Identyfikatory

DOI

10.12913/22998624/157528

• Języki publikacji: EN

Abstrakty

EN

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%.

Słowa kluczowe

EN

CNC turning; orthogonal array; ANN; signal-to-noise ratio

• 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: 2023
• Tom: Vol. 17, no 1
• Strony: 1--7
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Abdulateef Osamah Fadhil

• Automated Manufacturing Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Al-Jadriayh, Baghdad, Iraq

• https://orcid.org/0000-0002-7417-5108

autor

Ali Abduljabar H.

• Biomedical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Al-Jadriayh, Baghdad, Iraq

autor

Al Kareem Salah Sabeeh Abed

• Automated Manufacturing Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Al-Jadriayh, Baghdad, Iraq

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).