Modeling and Optimization of Cutting Parameters When Turning EN-AW-1350 Aluminum Alloy
An experimental investigation is carried out to examine the effects of various cutting parameters on the response criteria when turning EN-AW-1350 aluminum alloy under dry cutting conditions. The experiments related to the analysis of the influence of turning parameters on the surface roughness ( Ra...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Zielona Góra
2022-06-01
|
| Series: | International Journal of Applied Mechanics and Engineering |
| Subjects: | |
| Online Access: | https://www.ijame-poland.com/Modeling-and-Optimization-of-Cutting-Parameters-When-Turning-EN-AW-1350-Aluminum,166713,0,2.html |
| _version_ | 1797750420930560000 |
|---|---|
| author | F. Khrouf H. Tebassi M.A. Yallese K. Chaoui A. Haddad |
| author_facet | F. Khrouf H. Tebassi M.A. Yallese K. Chaoui A. Haddad |
| author_sort | F. Khrouf |
| collection | DOAJ |
| description | An experimental investigation is carried out to examine the effects of various cutting parameters on the response criteria when turning EN-AW-1350 aluminum alloy under dry cutting conditions. The experiments related to the analysis of the influence of turning parameters on the surface roughness ( Ra ) and material removal rate ( MRR ) were carried out according to the Taguchi L27 orthogonal array ( 313 ) approach. The analysis of variance (ANOVA) was applied to characterizing the main elements affecting response parameters. Finally, the desirability function (DP) was applied for a bi-objective optimization of the machining parameters with the objective of achieving a better surface finish ( Ra ) and a higher productivity ( MRR ). The results showed that the cutting speed is the most dominant factor affecting Ra followed by the feed rate and the depth of cut. Moreover, the Artificial Neural Network (ANN) approach is found to be more reliable and accurate than its Response Surface methodology ( RSM ) counterpart in terms of predicting and detecting the non-linearity of the surface roughness and material removal rate mathematical models. ANN provided prediction models with a precision benefit of 8.21 % more than those determined by RSM . The latter is easier to use, and provides more information than ANN in terms of the impacts and contributions of the model terms. |
| first_indexed | 2024-03-12T16:33:28Z |
| format | Article |
| id | doaj.art-69d1dcc57e9b43c7b16063464393525c |
| institution | Directory Open Access Journal |
| issn | 1734-4492 2353-9003 |
| language | English |
| last_indexed | 2024-03-12T16:33:28Z |
| publishDate | 2022-06-01 |
| publisher | University of Zielona Góra |
| record_format | Article |
| series | International Journal of Applied Mechanics and Engineering |
| spelling | doaj.art-69d1dcc57e9b43c7b16063464393525c2023-08-08T15:04:49ZengUniversity of Zielona GóraInternational Journal of Applied Mechanics and Engineering1734-44922353-90032022-06-0127212414210.2478/ijame-2022-0024166713Modeling and Optimization of Cutting Parameters When Turning EN-AW-1350 Aluminum AlloyF. Khrouf0H. Tebassi1M.A. Yallese2K. Chaoui3A. Haddad4Laboratory of Mechanics, Chaabet-Ersas Campus, Mechanical Eng. Dept., Université Frères Mentouri, 25000, Constantine -1, AlgeriaMechanics and Structures Research Laboratory (LMS), Mechanical Eng. Dept., Université 8 Mai 1945 Guelma, BP 401, 24000, Guelma, AlgeriaMechanics and Structures Research Laboratory (LMS), Mechanical Eng. Dept., Université 8 Mai 1945 Guelma, BP 401, 24000, Guelma, AlgeriaMechanics of Materials and Industrial Maintenance Research Laboratory (LR3MI), Mechanical Eng. Dept., Badji Mokhtar University, PO Box 12, 23052, Annaba, AlgeriaApplied Mechanics for New Materials Laboratory (LMANM), Mechanical Eng. Dept., Université 8 Mai 1945 Guelma, BP 401, 24000, Guelma, AlgeriaAn experimental investigation is carried out to examine the effects of various cutting parameters on the response criteria when turning EN-AW-1350 aluminum alloy under dry cutting conditions. The experiments related to the analysis of the influence of turning parameters on the surface roughness ( Ra ) and material removal rate ( MRR ) were carried out according to the Taguchi L27 orthogonal array ( 313 ) approach. The analysis of variance (ANOVA) was applied to characterizing the main elements affecting response parameters. Finally, the desirability function (DP) was applied for a bi-objective optimization of the machining parameters with the objective of achieving a better surface finish ( Ra ) and a higher productivity ( MRR ). The results showed that the cutting speed is the most dominant factor affecting Ra followed by the feed rate and the depth of cut. Moreover, the Artificial Neural Network (ANN) approach is found to be more reliable and accurate than its Response Surface methodology ( RSM ) counterpart in terms of predicting and detecting the non-linearity of the surface roughness and material removal rate mathematical models. ANN provided prediction models with a precision benefit of 8.21 % more than those determined by RSM . The latter is easier to use, and provides more information than ANN in terms of the impacts and contributions of the model terms.https://www.ijame-poland.com/Modeling-and-Optimization-of-Cutting-Parameters-When-Turning-EN-AW-1350-Aluminum,166713,0,2.htmlanovaartificial neural networkschip shapeoptimizationresponse surface methodology |
| spellingShingle | F. Khrouf H. Tebassi M.A. Yallese K. Chaoui A. Haddad Modeling and Optimization of Cutting Parameters When Turning EN-AW-1350 Aluminum Alloy International Journal of Applied Mechanics and Engineering anova artificial neural networks chip shape optimization response surface methodology |
| title | Modeling and Optimization of Cutting Parameters When Turning EN-AW-1350 Aluminum Alloy |
| title_full | Modeling and Optimization of Cutting Parameters When Turning EN-AW-1350 Aluminum Alloy |
| title_fullStr | Modeling and Optimization of Cutting Parameters When Turning EN-AW-1350 Aluminum Alloy |
| title_full_unstemmed | Modeling and Optimization of Cutting Parameters When Turning EN-AW-1350 Aluminum Alloy |
| title_short | Modeling and Optimization of Cutting Parameters When Turning EN-AW-1350 Aluminum Alloy |
| title_sort | modeling and optimization of cutting parameters when turning en aw 1350 aluminum alloy |
| topic | anova artificial neural networks chip shape optimization response surface methodology |
| url | https://www.ijame-poland.com/Modeling-and-Optimization-of-Cutting-Parameters-When-Turning-EN-AW-1350-Aluminum,166713,0,2.html |
| work_keys_str_mv | AT fkhrouf modelingandoptimizationofcuttingparameterswhenturningenaw1350aluminumalloy AT htebassi modelingandoptimizationofcuttingparameterswhenturningenaw1350aluminumalloy AT mayallese modelingandoptimizationofcuttingparameterswhenturningenaw1350aluminumalloy AT kchaoui modelingandoptimizationofcuttingparameterswhenturningenaw1350aluminumalloy AT ahaddad modelingandoptimizationofcuttingparameterswhenturningenaw1350aluminumalloy |