Optimisation of machining parameters of train wheel for shrink-fit application by considering surface roughness and chip morphology parameters
| Autor: | Anil Ridvanogullari, Mehmet Emin Akay |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Materials science
Computer Networks and Communications Depth of cut 020209 energy Machinability Mechanical engineering 02 engineering and technology Biomaterials Taguchi methods Surface roughness Machining 0202 electrical engineering electronic engineering information engineering Civil and Structural Engineering Fluid Flow and Transfer Processes ANOVA Mechanical Engineering 020208 electrical & electronic engineering Metals and Alloys Process (computing) Chip Electronic Optical and Magnetic Materials lcsh:TA1-2040 Hardware and Architecture Taguchi method lcsh:Engineering (General). Civil engineering (General) Regression analysis Train wheel Chip morphology |
| Zdroj: | Engineering Science and Technology, an International Journal, Vol 23, Iss 5, Pp 1194-1207 (2020) |
| ISSN: | 2215-0986 |
| DOI: | 10.1016/j.jestch.2020.06.013 |
| Popis: | 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 mu m 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. (c) 2020 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). KBU-BAP [KBU-BAP-17-YL-456] The authors would like to acknowledge the KBU-BAP office. This work was supported by KBU-BAP (Project ID number: KBU-BAP-17-YL-456). |
| Databáze: | OpenAIRE |
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