Improving accuracy of surface roughness model while turning 9XC steel using a Titanium Nitride-coated cutting tool with Johnson and Box-Cox transformation

Abstract

The surface roughness model for predicting surface roughness during machining is built in order to deal with time constraints of adjusting and testing. This study aims to achieve this purpose. The 9XC steel turning experiment is performed on a CNC lathe with the cutting tool is Titanium Nitride-coated. The input parameters selected for the test matrix include cutting velocity, feed rate, depth of cut and tool nose radius. The experiments were carried out based on Central Composite Design (CCD) with 29 trials. The analysis of results using Minitab software reveals that feed rate is the most influential parameter, while the others have a negligible impact on surface roughness. The response surface method (RSM) is applied for modeling surface roughness. Johnson and Box-cox transformations are also used to develop two new models of surface roughness. The comparison of predicted results from these three models with experimental results shows that the Box-Cox-based model has the highest accuracy, followed by the Johnson while the model not using these transformation is the least. Mean absolute error and mean square error of the RSM-based model are 17.264% and 2.712% respectively; while they are 10.373% and 1.280% in the Johnson-based and 10.208% and 1.284% when using the Box-Cox transformation.