Improving the Accuracy of Surface Roughness Modeling when Milling 3x13 Steel
Received: 6 May 2022 | Revised: 21 May 2022 | Accepted: 22 May 2022 | Online: 7 August 2022
Corresponding author: N. V. Cuong
Abstract
In this study, a milling experiment was performed, with 3x13 steel selected as the experimental material along with TiAlN coated inserts. The Box-Behnken method was used to design the experimental matrix with a total of eighteen experiments. Cutting speed, feed amount, and depth of cut were selected as the input parameters. Three regression models of surface roughness have been established, one using the experimentally measured surface texture, one using the Johnson transform to convert the surface texture data, and one using Box-Cox transformation to convert the surface texture data. A comparison of the accuracy of the three models was performed. The results show that the model using the Box-Cox transformation has the highest accuracy, followed by the model using the Johnson transformation. In addition, the influence of cutting parameters on surface roughness is also discussed in detail.
Keywords:
Milling, 3X13 steel, surface roughness, regression model, Johnson transformation, , Box-Cox transformationDownloads
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