| Autor: |
TZOTZIS, Anastasios, KORLOS, Apostolos, VERMA, Rajesh Kumar, KYRATSIS, Panagiotis |
| Předmět: |
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| Zdroj: |
Academic Journal of Manufacturing Engineering; 2023, Vol. 21 Issue 4, p27-35, 9p |
| Abstrakt: |
Surface roughness is considered to be an index of a part's machined surface quality and thus it is widely used in the industry to evaluate both the machinability of a material and the performance of a cutting process. The current article, presents an investigation on the surface quality evaluation of 7075-T6 aluminium alloy (AA) slot milling with both up-milling and down-milling techniques, by utilizing carbide end mills. Moreover, the study includes the modelling procedure of the process, according to the artificial neural network (ANN) methodology. The study revealed that both cutting techniques performed equally, with a mean value of surface roughness close to 0.8422µm for up-milling and 0.8396µm for down-milling respectively. In addition, both up-milling and down-milling ANN models yielded predictions very close to the experimental results with relative error varying between -13.68% and 15.71%. Concluding, the mean effects plots were employed to visualize the effect of each one of the three cutting parameters (spindle speed, feed and cutting depth) that were applied to the experiments, on the arithmetic mean value of surface roughness (Ra). It was found that both feed and depth of cut act increasingly on the surface roughness, whereas any increase in spindle speed generates the opposite result. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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