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Modeling And Improvement of the Surface Roughness Model in Hole Turning Process 3x13 Stainless Steel by Using Johnson Transformation

Journal: International Journal of Mechanical and Production Engineering Research and Development (IJMPERD ) (Vol.10, No. 3)

Publication Date:

Authors : ; ;

Page : 12097-12110

Keywords : Hole Turning; 3X13 Steel; Surface Roughness; Modelling & Johnson Transformation;

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N this paper, a study was performed to improve the accuracy of the surface roughness model when hole turning the 3X13 steel by using response surface methodology (RSM) and Johnson transformation. This study was presented including three contents that were determination of the influence degree of cutting velocity, feed rate, depth of cut, and cutter nose radius on the surface roughness, building the regression model of the surface roughness by a quadratic model of above input parameters, and building the surface roughness model by using Johnson transformation. By experimental data and using analysis of variance (ANOVA), the influence of input parameters on surface roughness was investigated. Feed rate that was a factor has the most influence on the surface roughness, the influence of the cutter nose radius and cutting velocity on the surface roughness was smaller thanthe influence of feed rate on the that one. Cutting depth has a negligibleeffect on surface roughness. The interaction between the feed rate and the depth of cutting has the greatest effect on surface roughness, followed by the degree of interaction between the cutting velocity and the cutter nose radius. The interaction between other factors has a negligible influence on the surface roughness. Besides, the surface roughness model was improved to increase the accuracy by using Johnson transformation. These models have been verified and evaluated by comparison process between thepredicted and measured surface roughness. The model using the Johnson transformation was more accurate than the model using without the Johnson transformation. Johnson transformations can be applied to improve the accuracy of surface roughness prediction models in the hole turning processes.

Last modified: 2020-10-03 16:18:09