Optimisation of machining parameters of train wheel for shrink-fit application by considering surface roughness and chip morphology parameters
The train wheel is one of the elements most exposed to static and dynamic loads during the transport. For this reason, it is of great importance for the safety of rail transportation that the wheel-axle assembly is carried out securely through the shrink-fit method. The surface roughness of the inne...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-10-01
|
| Series: | Engineering Science and Technology, an International Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098620304729 |
| _version_ | 1818261298537824256 |
|---|---|
| author | Mehmet Emin Akay Anil Ridvanogullari |
| author_facet | Mehmet Emin Akay Anil Ridvanogullari |
| author_sort | Mehmet Emin Akay |
| collection | DOAJ |
| description | The train wheel is one of the elements most exposed to static and dynamic loads during the transport. For this reason, it is of great importance for the safety of rail transportation that the wheel-axle assembly is carried out securely through the shrink-fit method. The surface roughness of the inner diameter of the wheel must be within 0.8–3.2 μm in order to provide the optimum shrink-fit. In this study, different depth-of-cut, feed rate and cutting speed parameters were considered in the turning process of ER8 class train wheel, and optimum machinability parameters were determined. In the experimental study, the Taguchi experimental design method, regression analysis and variance analysis (ANOVA) method were used. Experimental results were examined visually by using chip photographs and SEM images. According to the ANOVA results, it was determined that the most effective parameter is the feed rate with 93.78% on surface roughness in the turning of the train wheel. The SEM images derived from chips proved that the feed rate has strong correlation with surface roughness. Optimum machining parameters were determined as 1.5 mm depth of cut, 0.1 mm/rev feed rate and 250 rpm cutting speed. |
| first_indexed | 2024-12-12T18:45:00Z |
| format | Article |
| id | doaj.art-21c5d9dae17e4a499a5c87ded5370029 |
| institution | Directory Open Access Journal |
| issn | 2215-0986 |
| language | English |
| last_indexed | 2024-12-12T18:45:00Z |
| publishDate | 2020-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj.art-21c5d9dae17e4a499a5c87ded53700292022-12-22T00:15:32ZengElsevierEngineering Science and Technology, an International Journal2215-09862020-10-0123511941207Optimisation of machining parameters of train wheel for shrink-fit application by considering surface roughness and chip morphology parametersMehmet Emin Akay0Anil Ridvanogullari1Karabuk University, Engineering Faculty, Mechanical Engineering Department, Karabuk 78050, Turkey; Corresponding author.Mus Alparslan University, Technical Vocational School, Rail Systems Road Technology Program, Mus 49250, TurkeyThe train wheel is one of the elements most exposed to static and dynamic loads during the transport. For this reason, it is of great importance for the safety of rail transportation that the wheel-axle assembly is carried out securely through the shrink-fit method. The surface roughness of the inner diameter of the wheel must be within 0.8–3.2 μm in order to provide the optimum shrink-fit. In this study, different depth-of-cut, feed rate and cutting speed parameters were considered in the turning process of ER8 class train wheel, and optimum machinability parameters were determined. In the experimental study, the Taguchi experimental design method, regression analysis and variance analysis (ANOVA) method were used. Experimental results were examined visually by using chip photographs and SEM images. According to the ANOVA results, it was determined that the most effective parameter is the feed rate with 93.78% on surface roughness in the turning of the train wheel. The SEM images derived from chips proved that the feed rate has strong correlation with surface roughness. Optimum machining parameters were determined as 1.5 mm depth of cut, 0.1 mm/rev feed rate and 250 rpm cutting speed.http://www.sciencedirect.com/science/article/pii/S2215098620304729Train wheelTaguchi methodSurface roughnessANOVARegression analysis |
| spellingShingle | Mehmet Emin Akay Anil Ridvanogullari Optimisation of machining parameters of train wheel for shrink-fit application by considering surface roughness and chip morphology parameters Engineering Science and Technology, an International Journal Train wheel Taguchi method Surface roughness ANOVA Regression analysis |
| title | Optimisation of machining parameters of train wheel for shrink-fit application by considering surface roughness and chip morphology parameters |
| title_full | Optimisation of machining parameters of train wheel for shrink-fit application by considering surface roughness and chip morphology parameters |
| title_fullStr | Optimisation of machining parameters of train wheel for shrink-fit application by considering surface roughness and chip morphology parameters |
| title_full_unstemmed | Optimisation of machining parameters of train wheel for shrink-fit application by considering surface roughness and chip morphology parameters |
| title_short | Optimisation of machining parameters of train wheel for shrink-fit application by considering surface roughness and chip morphology parameters |
| title_sort | optimisation of machining parameters of train wheel for shrink fit application by considering surface roughness and chip morphology parameters |
| topic | Train wheel Taguchi method Surface roughness ANOVA Regression analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2215098620304729 |
| work_keys_str_mv | AT mehmeteminakay optimisationofmachiningparametersoftrainwheelforshrinkfitapplicationbyconsideringsurfaceroughnessandchipmorphologyparameters AT anilridvanogullari optimisationofmachiningparametersoftrainwheelforshrinkfitapplicationbyconsideringsurfaceroughnessandchipmorphologyparameters |